report on the molecular investigations into the jet …...research and clinical interest in the...
TRANSCRIPT
�����
Report on the�
Molecular Investigations into the
Jet Fuel and solvent exposure in the
DeSeal/ReSeal programme
conducted at the
Mater Research Institute (UQ),
Brisbane.
��
����Professor FG Bowling��BSc, PhD, MBBS, FRCPA, FHGSA, MBA ��31st JULY 2014��
����������������� ������������������ �������������������������������������� ��
!"#$��%��&%'!�'!��
Preface
Executive Summary 10
Key findings 10
Discussion & Conclusions 15
Recommendations 18
Chapter 1. Background and Overview �of Research Plan
19
Overview of systems biology approach used
21
Historical background 28
Chapter 2. Study of JP8 fuel and deseal/reseal solvents in a cell toxicity model
30
Development of exposure model of cell studies
31
Determination of toxicity of individual components of JP8 and solvents used in deseal/reseal
41
Chapter 3. Study of exposed workers 59
Overview 60
Study plan and methods 61
Results of study of exposed workers 79
Appendices 84
1 Ethic approvals 85
2 Analysis of Health assessment 90
3 Analysis of gene expression 95
4 Analysis of regulatory genes 169
5 Analysis of mitochondrial DNA 248
6 Analysis of blood cell proteome 256
7 List of abbreviations 280
8 Glossary 281
9 List of references 282
Page 3
PREFACE
�� ��Confidentiality Statement�Property of Mater Research�Institute (UQ)
This document is confidential and may not be used, divulged, published or otherwise disclosed without the consent of the Commonwealth of Australia, Department of Defence. The Intellectual property from research in this study is the property of Mater Research Institute (UQ).
(ii) How to read this report For the lay/non-technical reader.
For the general background to the study and an overview of the study’s structure, read Chapters 1 and 2. Chapters 3 and 4 can be skipped: they contain specifics about the recruitment of subjects, determination of exposure, measures, and analyses. For the results given in Chapters 3 the introduction and discussion sections can be read in isolation for a quick synopsis of the results in non-technical language. Detailed analyses are given in the Chapter 4 Appendices. An overall discussion of the entire results (i.e. how different findings “cluster” together) is presented in the Executive Summary Chapter 1.
(iii) Cover Image Courtesy of Mater Health Services
Title: Research scientist in the laboratory, Mater Research Institute, Aubigny Place, Brisbane, Qld, 2014
�
�
Page 4
(iv) Project Team Leaders � �Professor Francis Bowling BSc, PhD, MBBS, FRCPA FHGSA, MBA.
Professor Bowling is the Principal Investigator on the Jet Fuel Exposure Syndrome Study. He is currently the Director of Biochemical Diseases at Mater Children’s Hospital and is a Professorial Fellow at the Mater Research Institute of the University of Queensland . Professor Bowling has an active research and clinical interest in the molecular basis of disease, particularly neurodegenerative diseases of children. He is a member of the Genomic Advisory Committee of the Royal Australasian College of Pathologists. He has developed a rigorous quality system for the validation of genomic technologies and monitoring of the diagnostic performance of clinical genomic laboratories. Professor Bowling is the Director of iMolecular Consulting, which offers expert opinion in molecular pathology.
�Associate Professor Nigel Waterhouse BSc(Hons), PhD.
After graduating from University College Dublin, Ireland in 1998, Associate Professor Waterhouse moved to the sunnier climes of Australia. With support from Dianne Watters and Martin Lavin at the Queensland Institute of Medical Research he completed his PhD on “Proteolysis in apoptosis”, and then moved to Doug Green’s laboratory at La Jolla Institute for Allergy & Immunology in San Diego to investigate how mitochondria regulate caspase activation in apoptosis. Nigel received a Peter Doherty Fellowship from the National Health and Medical Research Council (NHMRC) to return to Joe Trapani’s Laboratory at the Peter MacCallum Cancer Centre in Melbourne to investigate the role of mitochondria in cell death induced by cytotoxic lymphocytes; cells of the immune system that protect the body from disease by seeking out and killing cancer or virus infected cells. This work continued with the support of an RD Wright Fellowship and a Career Development Award from the NHMRC, and afforded him the opportunity to begin his own group with the support of Ricky Johnstone and Grant McArthur in the Cancer Therapeutics Program at the Peter MacCallum Cancer Centre. The Apoptosis
Page 5
����������������� ������������������ �������������������������������������� ��and Natural Toxicity Laboratory was dedicated to understanding cancer cell death by cytotoxic drugs and natural killer cells, how cancer cells avoid cell death, and how we can use therapeutic drugs to kill cancer cells by restoring cell-specific defects in death pathways.
This theme is continued in Associate Professor Waterhouse’s current research in the Apoptosis and Cytotoxicity Laboratory at the Mater Medical Research Institute which was started in 2009 with the support of an Australian Research Council Futures Fellowship and the Mater Foundation.
�Dr Mervyn Thomas BA DPhil�
Mervyn Thomas is the founder and a partner in Emphron Research Pty Ltd: a biostatistics and bioinformatics consultancy. He has an international private practice, working with US and Australian biotech companies. His technical interests are in massively multivariate statistics for biomarker and diagnostic discovery and development. Before founding Emphron Research he �led the biostatistics and bioinformatics research programme for �CSIRO Mathematical and Information sciences. His career has involved senior roles in private and public sector research organisations. � He is a member of the NHMRC research committee.�
�Professor Brett McDermott MD, FRANZCP.
Brett McDermott is an Australian medical graduate and trained in Psychiatry and Child Psychiatry in the UK and Sydney.� Current appointments are the Executive Director of the Mater Child and Youth Mental Health Service in Brisbane, Chair of the Queensland Child and Youth disaster response, Professorial Fellow at the Mater Medical Research Institute and Adjunct Professor at the Queensland University of Technology.� Professor McDermott is also a By-Fellow at Churchill College, Cambridge University; and a Director of the Australian national depression initiative, beyondblue. Recently, (2013) Professor McDermott was appointed Director of the new adolescent and young adult hospital at the Mater (MAYAC). Clinical and research areas of
Page 6
����������������� ������������������ ��������������������������������������
interest include children and adolescents with depression, post-traumatic mental health, and child and youth mental health service provision.
�Professor Deon Venter MBChB, PhD, MBA, FRCPA
Professor Venter is Director of Pathology, Mater Health Services; Professor of Pathology, School of Medicine, University of Queensland; Honorary Principal Research Fellow Mental Health Research Institute, Melbourne; Honorary Consultant Pathologist, Victorian Clinical Genetics Services Ltd, Melbourne; and Founder and Medical Advisor, Athlomics Pty Ltd.
He is a consultant anatomical and molecular pathologist who has driven the establishment of a genomic and proteomic diagnostics facility at Mater Pathology. Professor Venter has been directly responsible for the launch of several new gene-based diagnostics in Australian companies and reference laboratories. He has acted as a consultant to major pharmaceutical companies in addition to biotechnology companies based in Europe and the USA.
Professor Venter has served on major global bodies such as the World Health Organisation, where he helped to formulate strategies to implement genetic services in developing countries and the Expert Group of the International Olympic Committee (IOC) Medical Commission on the molecular basis of connective tissue and muscle injuries in sport. Professor Venter is an author of over 90 publications on cancer genetics and genetic-based diagnostics.
�
Page 7
����������������� ������������������ �������������������������������������� ��
Project Team Members The contribution of each of the following members of the research team is acknowledged:-
Avis McWhinney, B Med Lab Sci is the Supervising Scientist of the Metabolic Section, Department of Clinical Chemistry, Mater Pathology, Brisbane Australia and has over 25 years experience in this area and who developed and validated the HPLC, UPLC, LC-MS/MS and GCMS assays .
Elisa Jansen, PhD, Cell Biologist, Mater Research Institute.
Lisa Crowley, PhD, Cell Biologist, Mater Research Institute.
Melinda Christensen, PhD, Cell Biologist, Mater Research Institute.
Adrian Scott, PhD, is a molecular biologist at the Mater Research Institute who worked on development of the cell exposure model.
Gareth Price, ,PhD supervised the genomic studies at Mater Pathology.
Renee Gallagher, B. App Sci, is a cytogeneticist in Mater Pathology who performed the genetic studies using Array technology.
Tristan Wallis PhD is a biochemist at Mater Pathology who developed the plasma and cell proteomic analysis.
Megan Bowlay, PhD is a biochemist at Mater Pathology who performed plasma and cellular proteomic analysis by MALDI-TOF.
Brooke Marfell, PhD, is a cell biologist at Mater Research Institute, who undertook cell exposure studies of the toxicity of compounds and the mechanism of that toxicity.
Rachelle Warner, is a toxicologist in the Defence Department.
��
Page 8
����������������� ������������������ ��������������������������������������
�ACKNOWLEDGEMENTS �The project team also wish to acknowledge the support of :-
Professor Ian R. Gardner MBBS, MPH, FAFOEM, FACOEM, FRSM
Senior Physician in Occupational and Environmental Medicine, Defence Centre for Occupational Health.
Professor Mark Bowles PhD GradDipMgt
Chief Operating Officer, Mater Research Institute.
Ms Nicole Shiveley
Deputy Director, Research Management Office UQ
Representatives of the JFES Study Consultative Forum Committee:
Hon Arch Bevis - Chairman
Professor Ian R. Gardner - Senior Physician in Occupational and Environmental Medicine, Defence Centre for Occupational Health
Mr Max Chambers – DeSeal/ReSeal Support Group
Mr Geoff Curl – DeSeal/ReSeal Support Group
Mr David Janik – Department of Veterans Affairs
Wing Commander Ron Usher (Retd) – RAAF Association
Mr Viv Quinn – RSL Australia (Qld)
WOFF-AF Mark Pentreath – CDF Representative
Wing Commander Lance Halvorson (Retd) – RAAF Association
Commodore Michael Dowsett (Retd) – Defence Force Welfare Association
���
Page 9
����������������� ������������������ �������������������������������������� ��
EXECUTIVE SUMMARY �Overview
The main objective of this project was to investigate the toxicity of JP-8 fuel and the solvents used in the Deseal/Reseal programme using a systems biology approach. In the exposure environment (fuel tanks, aircraft hangers etc), workers were typically exposed either by inhalation of vapours or by absorption through the skin. There were occasionally reports of direct ingestion through the mouth. Health studies of exposed workers and other research reports show premature death for some individuals, an increased risk of unusual malignancies in internal organs such as small bowel, erectile dysfunction, and behavioural disturbances. These findings may manifest years after exposure suggesting changes to the cells and tissues not directly exposed to the fuel and solvents. Changes to the systems biology was investigated by proteomic and genomic studies.
Laboratory cell studies of DeSeal/ReSeal compounds
Development of Cell exposure model
Previous methods for studying cellular responses to JP8 and solvents involved direct addition of these compounds to cells in laboratory growth plates using other solvents such as Ethanol. These methods were considered to be inadequate because they did not recognise the role of circulating blood plasma in distributing these compounds to internal organs. The JFES project team developed a method of studying cells by exposing them to blood plasma, which they believe is a better model of the inhalation and skin exposure routes for distributing solvents to internal organs. This method has been published in a peer reviewed journal. (See Appendix 1)
Distribution of JP8 and DeSeal/ReSeal solvents
The studies of plasma exposed to JP8 and solvents showed that the compounds are not distributed by plasma in the same proportions as found in the fuel and solvent mixtures. This means that higher levels of some compounds are actually presented to cells and organs than those
Page 10
����������������� ������������������ ��������������������������������������
proportions in the fuel solvent mixtures. The study showed that the majority of the compounds are distributed by binding to plasma lipids rather than simply dissolved in the plasma water. This raises the possibility that individuals with higher bloods lipids may distribute more of the compounds to internal organs.
The effects of the JP8 and solvents on cells
The study then tested the effects of the JP8 and solvents on cells. The JP8 and solvents were tested as a mixture and individually. The key findings were:-
Plasma exposed to JP8 alone is directly toxic to cells
Plasma exposed to a mixture of JP8 combined with solvents has greater toxicity to cells with 40% cells showing changes before 4 hours, and 90% cells affected at 12 hours.
The following individual components were found to have the highest cellular toxicity:-
o Kerosene
o Benzene and butylbenzene
o All Alkanes including iso-octane, decane, dodecane, tetradecane and hexadecane
o Diegme
o N, N Dimethyl acetimide
o Naptha
o Thiophenol
�The solvents used in the Deseal/Reseal programme demonstrated either low cell toxicity or manifest toxicity to a lesser extent than the JP8 fuel components.
�
Page 11
����������������� ������������������ �������������������������������������� ��Effects on gene expression
Gene expression in cells was altered following exposure. Changes greater then 5 fold were considered significant. The genes altered are shown in Table (3). The function of these genes involved mostly cell survival/death, metabolism, cell cycle, DNA maintenance (housekeeping), and cell regulation. These genes have been implicated in pathological processes including cancer, neurodegeneration, and immune suppression.
Effects on proteins
Cellular proteins were altered after exposure. The changes to cellular proteins reflected the changes in gene expression involving cell survival/death, metabolism, cell division, and roles in cellular gene transcription/translation.
Cell Death
Cell death occurred by two mechanisms. A number of cells appeared more vulnerable with death occurring by disruption of cellular membranes and by lysis (bursting) of the cells. The more common mechanism of cell death was by apoptosis, which is a programmed response of cells to injury. Not all injured cells undergo complete apoptosis indicating persistence of injured cells. This may suggest a survival of injured cells with malignant potential. The cell culture methods could not determine the long term effects.
�
Page 12
����������������� ������������������ ��������������������������������������
Study of exposed workers
The study of exposed workers showed differences from the matched control group in health indices, and in some genomic studies. The changes were not as significant as those seen in the acute cell exposure model in the laboratory.
Rating of exposure
Because of the unavailability of accurate exposure data (degree and duration), a problem also encountered in other studies, the workers were classified into 3 groups.
1. Definite high exposure who worked inside the fuel tanks
2. Significant contact such as by dosing of skin or accidental ingestion
3. Minimal contact in the general area such as collection of rags or cleaning of the area.
Health Assessment Scores
The Health assessment scores showed exposed workers to have a lower health rating than controls. There did not appear to be a decrease in the health scores (dose response) related to the degree of exposure. Workers with mild exposure had the same decrease in their health scores as those with high exposure. This suggests that other factors beyond the Deseal/Reseal contact have decreased the health scores.
Genetic studies of blood cells from exposed workers
All studies were undertaken on plasma and white blood cells as these were the only tissues for which it was possible to obtain samples. The genetic studies of blood cells examined two types of changes in gene expression, the presence of chromosomal changes, and for appearance of mutations in the mitochondrial DNA. There were no chromosomal changes detected at a level of 50Kb using a high resolution SNP ARRAY.
There were no changes in the mitochondrial DNA mutation load between exposed workers and age matched controls (Mitochondrial DNA changes can accumulate with age).
Page 13
����������������� ������������������ �������������������������������������� ��There were no changes in the amount or type of protein coding mRNA expression, which is an index of cell activity. In disease states , these are usually tissue specific and may not appear in blood cells unless they are directly involved in the disease process.
There were small but significant and consistent changes in the expression of regulatory microRNAs that control activity of other genes. The regulatory functions of the altered genes have been linked to neurological changes and neurodegenerative disorders. It must be emphasised that interpretation of the function of regulatory genes is an evolving science with much uncertainty at present. The regulatory genes, which compose 98% of our genome, have a major role in human development, adaptation and response to disease. The function is only known for ~40% of these at present. Disease causing associations, with some early exceptions, are still unmapped.
Protein studies of plasma and blood cells
No significant changes were seen in the levels and types of protein expressed in the plasma and blood cells of exposed workers. A few small changes were seen consistently, but these did not reach a level that the researchers considered significant.
�
Page 14
����������������� ������������������ ��������������������������������������
Discussion and Conclusions
Confounders and sensitivity
����������������������
A dose response would have been expected but was not observed in the workers with different exposure histories. The unexpected similarity in the health scores and genomic studies within the exposed groups (low, medium, high) raises several hypotheses:-
���������
There are other factors independent of Deseal/Reseal exposure which could produce the changes seen. Confounders could include:-
o An ascertainment bias whereby only those workers affected by any exposure volunteered to participate in the study.
o An ascertainment bias whereby only those workers NOT affected by the exposure (i.e.. Survivors) volunteered to participate in the study.
o The workers were stratified by their exposure to Deseal/Reseal materials. The effects seen may NOT be due to the Deseal/Reseal materials but to some other experience of the workers. The cellular studies suggest that exposure to fuel alone could be responsible.
o It was not possible to examine other possible shared confounding events in the work careers or in the lifestyle of the personnel. (e.g.. other occupational exposure not related to Deseal/Reseal such as medications, substance abuse, nutrition)
o This study was conducted on individuals between 10 and 30 years after their exposure. If significant changes occurred at the time of exposure, normal cellular repair and selection mechanisms may have lessened the biological signal that could be observed in this study. The small but consistent changes observed suggest this possibility. Either the effect at the time was minimal but has persisted, or the effect was larger but has diminished over-time.
Page 15
����������������� ������������������ �������������������������������������� ��
o The cellular studies show that the compounds are mostly distributed by plasma lipids. The exposure to organs within the body would likely depend on the concentration of plasma lipids at the time of fuel exposure. Plasma lipids vary genetically between individuals, with lifestyle and alcohol intake, with composition of their diet, as well as the time after meals when the exposure occurred. The lack of a dose effect could be explained if workers in the lower exposure group had higher plasma lipids at the time of exposure. Individuals in the high exposure group worked within the fuel tanks and were selected because they were leaner and smaller, possibly protected to some extent by lower plasma lipids.
���
�
Page 16
����������������� ������������������ ��������������������������������������
Significance of findings
The cellular findings, supported by other recently published genomic studies, indicate a definite toxicity from JP-8 to exposed cells . The components of JP8 tested are commonly found in most (aviation) fuels. The results indicate that there is a need for concern about exposure to fuels in general.
The study was not designed to determine the degree of occupational exposure necessary to produce cellular changes. However, the results show that cells grown in a nutrient containing as little as 5% exposed plasma are affected. In the body, blood cells have 100% exposure to plasma while other organs will have less exposure depending on the net blood flow and cellular membrane barriers. Organs such as brain, liver and bowel have very high blood flow. Cellular membranes generally have greater permeability to substances dissolved in lipids.
The study was also not designed to determine the most toxic routes of exposure (inhalation, ingestion, skin contact), but did demonstrate that fuel components can be distributed to organs through blood plasma, i.e. organs such as brain or liver, not directly exposed in the contact, may undergo secondary exposure. The implication is that all body systems must be considered in assessing/monitoring the health of exposed workers.
While the changes seen many years after exposure were small, they were consistent. The changes are most apparent in gene regulation and had some association to the health problems (e.g., malignancy) identified in other studies.
There were no chromosomal changes or mutations linked to the exposure. The genes changes seen can be described as Epigenetic, which is a mechanism of cellular adaptation to some environmental influence. Epigenetic changes are less clearly linked (at the present knowledge) to disease. Epigenetic changes occur through a variety of cellular mechanisms and these were not investigated in this study. Some epigenetic changes can be transferred down through successive generations but currently have not been shown to cause birth defects or mutation in off-spring.
Page 17
����������������� ������������������ �������������������������������������� ��Recommendations
The cell results show a definite cellular toxicity from JP8 fuel. The components of the fuel exhibiting toxicity are common to most fuels. Consideration should be given to further studies of workers exposed to fuel of any type.
Newer genomic and bioinformatic technologies have been developed during the time of this study and have been employed in other studies of occupational fuel exposure. These technologies can be applied to other exposure risks (including PTSD) in defence (veteran) health risk assessment. An expert committee should be constituted to advise on research and clinical application of these technologies.
Plasma free DNA sequencing can now be used to assess (from blood samples), the cellular death associated with tumours , transplant rejection, miscarriage, and infections. Targeted RNA expression studies can reveal immediate changes in gene activity following fuel exposure. A study of workers with recent or past fuel exposure is recommended.
The best time to study cellular changes would be immediately after direct exposure. A protocol should be established for assessment of an exposed individual to include sample collection immediately after the exposure for quantification of plasma lipids, plasma fuel components, free DNA sequencing, and targeted RNA expression.
Exposed veterans should be reassured that while small and consistent changes were observed in this study, there were no changes detected known to have immediate or severe health consequences. The changes support the findings from other studies that there is a possible increased risk of developing health problems. As the changes observed are in gene regulation, it is also possible that healthy lifestyle changes may ameliorate the risk.
���
Page 18
������������������CHAPTER 1
�Background and Overview
of Research Plan �
Page 19
����������������� ������������������ ��������������������������������������
Fig. 1. Overview of systems biology and its components (centre of image, Wired Systems Biology, adapted from Chemical & Engineering News 81 [20) 2003). (methylomics), non-coding microRNA (miRNAomics) along with small interfering RNA (siRNA) and short hairpin RNA (shRNA) for RNA interference (RNAi), and histone modification. Epigenetic modifications play an essential role in regulating gene expression and biological and molecular functions in living cells, without altering the genome. Another tool central to systems biology is bioinformatics, the application of computational information technology to the field of molecular biology to understand how cells and cell systems work 6,7. Bioinformatics facilitates the analysis of complex biological data (toxicogenomic and epigenomic endpoints) and applies knowledge from annotated functions, pathways and networks to describe normal and perturbed biological states, also known as phenomics, the study of outcomes (phenotypic endpoints). Together, these omic technologies can each provide a “molecular signature” or “fingerprint” of chemical exposure, early effect or genetic susceptibility, which may enhance our understanding of gene–environment interactions. Thus, this approach has the potential to comprehensively define the mechanisms contributing to disease.
Page 20
��
1. INTRODUCTION 1.1 Overview
Systems biology is a recent and evolving interdisciplinary field that focuses on the systematic study of complex interactions in biological systems. Systems biology employs a multidisciplinary approach to study all components and interactions in the network of DNA (genes), RNA, proteins and biochemical reactions within a cell or organism. This new field utilises powerful tools that include toxicogenomics, epigenomics, bioinformatics, and phenomics, classical toxicological or phenotypic endpoints (Fig. 1). Toxicogenomics combines toxicology with molecular profiling technologies, including genomics (DNA), transcriptomics (mRNA), proteomics (proteins) and metabolomics (chemical metabolites) to elucidate molecular mechanisms involved in chemically induced toxicity. Chemically induced alterations in the transcriptome, proteome, and metabolome are analysed in the context of the stable, inherited genome, which is assessed by genomics. Toxicogenomic studies of human populations are crucial to understanding gene–environment interactions, and can provide the ability to develop novel biomarkers of exposure, early effect, and susceptibility (genome). Epigenomics is the study of epigenetic elements, including DNA methylation, but also histone modification. Alkylation of histone proteins has been shown following hydrocarbon exposure.
���1.2 Application of systems biology in studies of benzene and hydrocarbon toxicity
The systems biology approach has been informative in another incident of benzene exposure involving chemical plant workers. Toxicogenomic studies, including genome-wide analyses of susceptibility genes (genomics), gene expression (transcriptomics), protein expression (proteomics), and epigenetic modifications (epigenomics), of human populations exposed to benzene were used to understanding gene–environment interactions, providing the ability to develop biomarkers of exposure, early effect and susceptibility.
Page 21
����������������� ������������������ �������������������������������������� ��Comprehensive analysis of toxicogenomic and epigenomic profiles by bioinformatics in the context of phenotypic endpoints, helped define the mechanisms by which benzene causes leukaemia. A systems biology approach was applied to a molecular epidemiology study of workers exposed to benzene. Hematotoxicity, defined as a significant decrease in almost all blood cell counts, was identified as a phenotypic effect of benzene that occurred even below 1ppm benzene exposure. A significant decrease in the formation of progenitor colonies arising from bone marrow stem cells with increasing benzene exposure, showed that progenitor cells are more sensitive to the effects of benzene than mature blood cells, likely leading to the observed hematotoxicity. Analysis of transcriptomics by microarray in the peripheral blood mononuclear cells of exposed workers, identified genes and pathways (apoptosis, immune response, and inflammatory response) altered at high (>10 ppm) and low (<1 ppm) benzene levels. Serum proteomics by SELDI-TOF-MS revealed proteins consistently down-regulated in exposed workers. Preliminary epigenomics data showed effects of benzene on the DNA methylation of specific genes.
Benzene, a ubiquitous chemical, is an established cause of acute myeloid leukaemia (AML), myelodysplastic syndromes (MDS), and probably lymphocytic leukemias and non-Hodgkin lymphoma (NHL) in humans. Benzene-induced toxicity in blood forming systems has been known for more than a century. In 1982, the International Agency for Research on Cancer (IARC) stated, “There is sufficient evidence that benzene is carcinogenic to man”, and when a new IARC classification system was established in 1987, benzene was immediately placed in the Group 1 human carcinogen category.. Potential mechanisms of benzene toxicity have been investigated primarily in the following areas: (1) benzene metabolism in the liver (CYP2E1, etc.) and transportation to the bone marrow for secondary metabolism (MPO, NQO1) ; (2) oxidative stress from reactive oxygen species generated by redox cycling ; (3) chromosome alterations including translocations, deletions, and aneuploidy; (4) protein damage to tubulin, histone proteins, topoisomerase II, etc. and (5) immune system dysfunction (TNF-_, INF-_, AhR, etc.).
Page 22
����������������� ������������������ �������������������������������������� ��Benzene induces chromosomal alterations similar to those found in therapy-related MDS and AML (t-MDS/AML), and in de novo leukaemia. Distinct chromosome effects arise following exposure to alkylating agents (5q-/-5 or 7q-/-7 and associated genetic abnormalities) and topoisomerase II inhibitors (recurrent balanced translocations or inversions) used in chemotherapy treatment. Exposure to benzene or its metabolites has been associated with loss and long (q) arm deletion of chromosomes 5 and 7 and translocations involving t(21q), further suggesting that benzene induces leukaemia through multiple different mechanisms. Studies to date have provided evidence for multiple potential mechanisms using approaches that rely on limited research tools that analyse only one or a few, a priori selected genes, pathways or metabolites at a time. A systems biology approach is necessary to interrogate all potential mechanisms by which benzene and other hydrocarbon exposures contribute to disease.
�1.3 Gene expression profiling by transcriptomics
Transcriptomic studies are useful in determining the impact of environmental or occupational exposure to chemicals on the transcriptome, the set of all mRNA transcripts expressed within a cell. To better understand the risks of benzene in humans, the peripheral blood mononuclear cell (PBMC) transcriptomes from occupationally exposed workers in China were examined by microarray (Affymetrix). Analysis of six exposed-control pairs revealed differential expression in 29 genes in the exposed individuals, compared to the controls. Four genes, CXCL16, ZNF331, JUN, and PF4, were shown to be potential biomarkers of early response to benzene exposure as they were confirmed by quantitative-polymerase chain reaction (q-PCR). A later study of eight exposed-control pairs confirmed these results, using two different microarray platforms (Affymetrix and Illumina) to identify global gene expression changes. The differential expression of 2692 genes and 1828 genes was found by Affymetrix and Illumina, respectively, and the 4 genes, CXCL16, ZNF331, JUN, and PF4, were among the most significantly altered, validating the findings from the earlier Forrest et al. study.This study
Page 23
����������������� ������������������ �������������������������������������� ��additionally identified biological pathways that were associated with high benzene exposure, including genes involved in apoptosis and lipid metabolism. The effects of exposure to high levels of benzene are well documented compared to low-level exposure, the latter being more challenging due to confounders. More recently, in an expanded study of 125 factory workers, low-dose benzene exposure (<1 ppm, n = 59) can also cause widespread subtle, yet highly significant, perturbation of gene expression in PBMC. The study was designed with sufficient power to detect robust expression changes, accounting for technical variability as well as age, gender and other confounders. Microarray analysis revealed significant dysregulation of more than 2500 genes by low-dose benzene exposure, over 70 of which had differential expression ratios exceeding 1.5.
Several of the detected genes exhibited significantly altered expression only at low levels of benzene exposure, and are thus potential biomarkers of low-dose exposure. The findings show that even low levels of occupational benzene exposure cause a significant perturbation of expression of genes involved in immune and inflammatory responses. These techniques will be employed in this study.
��
Page 24
����������������� ������������������ �������������������������������������� ��
1.4 Proteomic biomarkers of benzene and hydrocarbons
Another important toxicogenomic tool, proteomics, can be used to measure alterations in the proteome (e.g., protein levels, post-translational modifications) associated with exogenous chemical exposure. Effects on the blood proteome may reflect effects at distal body sites. As with transcriptomics, proteomics can be used to discover biomarkers of exposure and early effect, as well as increase our understanding of the mechanisms underlying disease. The impact of benzene on the human serum proteome in exposed factory workers and controls was used to obtain insight into the mechanism of action of benzene. Serum samples were fractionated and proteins were bound to surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF- MS) chips. Three proteins (4.1, 7.7, and 9.3 kDa) were consistently down-regulated in the exposed (n = 10) compared to the control (n = 10) individuals in two separate sets of study subjects (40 subjects total). All proteins were highly inversely correlated with individual estimates of benzene exposure. The 7.7- and 9.3-kDa proteins were identified as platelet factor 4 (PF4), also down-regulated at the gene expression level described above, and connective tissue activating peptide CTAP-III, respectively, both platelet-derived CXC chemokines. Thus, reduced protein levels of PF4 or CTAP-III are potential biomarkers of the early biologic effects of benzene.
Our proteomic studies could identify further biomarkers of JP8 exposure, and elucidate the mechanisms underlying toxicity and associated disease. A similar mass spectrometry approach using MALDI-TOF and LC-Electrospray will be employed in this study.
�
Page 25
����������������� ������������������ �������������������������������������� ��1.5 Epigenomics in pilot benzene studies
Gene expression and ultimately protein expression is regulated at the epigenetic level by processes including DNA methylation, histone modification and miRNA (microRNA) expression. The epigenome, while stable through cell division and even in some cases reproduction, can be reprogrammed by nutritional, chemical, and physical factors. Further, epigenetic modifications represent more stable biomarkers and fingerprints of exposure than altered gene or protein expression. Thus, the study of effects on the epigenome is crucial to understanding mechanisms of toxin action. While epigenetics refers to the study of individual or specific gene activity, epigenomics focuses on global analyses of epigenetic changes across the entire genome.
A recent study reported that hyper-methylation in p15 and genome-wide hypo-methylation assessed by LINE-1 (Long Interspersed Nuclear Element-1) were associated with very low benzene exposures (<22 ppb), in healthy subjects including petrol station attendants and traffic police officers, although the corresponding effects on methylation were very low. To determine whether epigenetics plays a role in the hematotoxicity of benzene, recent pilot epigenomic studies including DNA methylation and miRNA expression arrays identified changes in the blood of workers occupationally exposed to benzene.
Hematotoxicity is a phenotypic outcome of benzene exposure. Although benzene was known to have toxic effects on the hematopoietic system (hematotoxicity) at high, occupational doses for over a century, the degree of hematotoxicity at low levels of exposure was largely unknown. Recently, a study of 250 workers exposed to varying levels of benzene and 140 unexposed controls in Tianjin, China, during which benzene and other chemical exposure levels were monitored repeatedly for up to 12 months, was conducted. Air, urine and blood samples were collected and complete blood counts (CBC) analysed. In comparison with the non-exposed controls (n = 140), a significant decrease was observed in almost all blood cell counts, such as white blood cells (WBC), granulocytes, lymphocytes and platelets, in
Page 26
����������������� ������������������ �������������������������������������� ��workers exposed to benzene (n = 250), even at exposures below 1ppm (n = 109), the current occupational standard in the U.S. Additionally, lymphocyte subset analysis showed significant, dose-dependent, decreases in CD4+-T cells, CD4+/CD8+ ratio, and B cells at <1ppm benzene exposures. These findings, based on the differentiated blood cell counts, provide evidence of bone marrow toxicity in workers exposed to benzene at or below 1ppm. Because all types of WBC counts were suppressed, it was suspected that the number or functionality of hematopoietic stem and/or progenitor cells generated in the bone marrow had been reduced by benzene.
To test this hypothesis, progenitor cells circulating in peripheral blood were examined to see the effects of benzene on different types of progenitor cell colony formation (CFU-GM, BFU-E, CFU-GEMM). The results showed highly significant dose-dependent decreases in colony formation from all three types of progenitor cells, especially when compared to the corresponding decreased levels of differentiated WBC and granulocytes27. This suggests that early myeloid progenitor cells are more sensitive than mature cells to the effects of benzene, and clarifies the role of benzene in reduced blood cell counts. Overall, these haematological effects could reflect events in the bone marrow that may be associated with adverse health effects in the future.
Having established that hematotoxicity, specifically effects myeloid progenitor cells, as a phenotypic anchor of benzene toxicity, the molecular mechanisms underlying these effects, through the comprehensive systems biology approach should be undertaken to include the JP8 fuel and solvents in Deseal/Reseal exposure. The MRI laboratory has special expertise in Dendritic Cell analysis, prime mediators of haematological response.
�
Page 27
��1.6 Historical Background
Purchased from the United States of America in the 1970s, the F-111 strategic strike aircraft does not have a dedicated fuel bladder, with fuel instead occupying the empty spaces between other metal structures. Over time, the fuel-tight sealant between mating and internal structures degraded and it was necessary to carry out periodic seal repair programs. In most cases, this involved firstly removing the original sealant (desealing) and then replacing it with new sealant (resealing), with one program spraying new sealant over the existing defective sealant. In Australia, four formal F-111 fuel tank Deseal/Reseal (DSRS) programs were implemented over more than two decades (1975-1999), each involving different processes and the use of approximately 60 hazardous substances, mainly organic solvents. Workers on the DSRS programs frequently spent extended periods of time inside the fuel tanks in conditions that were cramped and inadequately ventilated. Throughout this period concerns had been raised about the health of DSRS workers, culminating in a Board of Inquiry appointed by the Chief of Air Force in July 2000. Concurrently it was decided to conduct an epidemiological study of the health outcomes of F-111 DSRS workers. Between 2001 and 2004, a comprehensive, large-scale epidemiological investigation was conducted on behalf of the Australian Department of Defence into the health of all personnel involved in DSRS activities in Australia since the programs began.
Three reports from the epidemiological studies were published:
The neurological study showed there was a strong and consistent increase in self-reported cognitive problems among the exposed, and small but significant exposure-specific differences on objective tests of cognitive functioning. The findings are consistent with the development of adverse neuropsychiatric changes related to occupational exposure to solvents.
A further study of mental health showed there was high agreement between self-reported mood disturbance and objective tests. The exposed group was more likely to self-report previous diagnoses of depression/anxiety, had higher use of antidepressant medications, and had increased risk of
Page 28
����������������� ������������������ �������������������������������������� ��diagnosis of depression/anxiety. Results were consistently strong against both comparison groups, with the exposed more likely to have mental distress and social dysfunction when compared with the Australian population. The study showed there is robust evidence for an association between F-111 DSRS exposure and impaired mental health.
A study of cancer incidence and mortality in aircraft maintenance workers showed cancer incidence was higher in the exposed group, with marginally significant increases of 40-50% (cancer incidence rate ratio range 1.45-1.62). Exposed group mortality was significantly lower than both comparison groups, likely due to survivor bias in the exposed group (mortality rate ratio range 0.33-0.44). The study showed that, on the balance of probabilities, there is an increased risk of cancer associated with participation in F-111 DSRS activities.
Page 29
����������
Chapter 2� �
Study of JP8 fuel and Deseal/Reseal solvents in a cell toxicity model
���
Page 30
������������������� ������� ������������������������� ������ ��������������������
����Development of exposure model for cell studies
Exposure to jet propulsion-8 (JP-8) aviation fuel and chemicals used in aircraft maintenance have been linked to a variety of pathologies that may occur as a consequence of their systemic effect on cells in the body. Using in vitro culture models to understand these effects poses a significant problem because JP-8 is largely insoluble in aqueous media that is used to culture cells. Previous studies have attempted to overcome this by dissolving the JP-8 in ethanol but this approach is not physiologically robust because ethanol is likely to deliver components of JP-8 that may not contribute to adverse health effects in vivo. Components of JP-8 that act systemically are likely to be distributed to organs by plasma regardless of the route of exposure. The components and relative concentrations of the components of JP8 are listed in the Table 1. We therefore investigated whether human plasma could be used as a physiologically relevant carrier for JP-8 to test its effects on cells in vitro. JP-8 did not dissolve in human plasma but human plasma that had been mixed with JP-8 was toxic to cultured cells in a dose dependent manner. This effect was specific because serum free media, water or serum albumin mixed with JP-8 was not toxic when added to cells. The method we have developed and published which exposes human plasma to JP-8 and solvents represents a new more physiologically relevant strategy for delivery of JP-8 to cells in vitro that will facilitate research into understanding the effects of occupational exposure.
The physical properties of JP-8 such as water insolubility and volatility are a significant challenge to in vitro toxicological studies to investigate the effects of occupational or environmental exposure.
���
Page 31
������������������� ������� ������������������������� ������ ������������������
Figure 2 - JP-8 solubility in human plasma. JP-8 was added to human plasma in glass tubes at the volumes indicated and mixed overnight at 37°C on a slowly rotating wheel. The resulting mixture was centrifuged (4,000 x g for 10 min) at room temp. An expanded image of the interface in the 5 ml JP-8/ 5 ml plasma mixture is also shown.
In vivo models are available to investigate the effects of JP-8 exposure but these models are limited in their capacity to uncover the molecular mechanisms of the direct effect of JP-8 exposure on cells. Alternatively, in vitro toxicological studies can be used to investigate toxicity mechanisms and inform in vivo studies where necessary. We have developed an in vitro exposure model for cell cultures using human plasma as a carrier for JP-8 that is physiologically relevant because all routes of in vivo exposure (dermal,
Table 1. Molecular compounds and relative concentrations forming the JP8 fuel mixTable 1. Molecular compounds and relative concentrations forming the JP8 fuel mix
Page 32
������������������� ������� ������������������������� ������ ��������������������respiratory or oral) would result in the toxic component(s) of JP-8 carried and distributed in plasma.
We initially found that JP-8 was toxic when added directly to cells in culture (Figure 3).
�
�
Figure 3. U937 cells were cultured in 24 well plates and treated with JP-8 added directly to culture media A) Cells treated with JP-8 were imaged after 24 h or B) the cells in adjacent wells were assayed by propidium iodide uptake at 24 hrs using flow cytometry. C) Images of the lid of a 24 well plate of U937 cells treated with different concentrations of JP-8.
�However this strategy was unsatisfactory because we were unable to add low enough volumes of JP-8 to cells to obtain a toxicity profile. Further, the majority of JP-8 floated on the top of the media, making it difficult to
Page 33
������������������� ������� ������������������������� ������ ��������������������ascertain the level of JP-8 that entered the media. We also observed that cells in wells adjacent to the treated cultures also died .
Volatile components of JP-8 may therefore transfer from well to well and adversely affect results obtained from experiments performed in this way. The lids of culture dishes became opaque if JP-8 was added directly to the cultured cells and this interaction of JP-8 with the plastic may have an indirect, non-physiological, effect on the viability of cells in vitro. Finally, strong smelling vapours were released into the cell incubator when JP-8 was added directly to cultured cells, which posed potential risk to the investigator. Direct addition of JP-8 to cultured cells was therefore not deal for investigating the effects of JP-8 on cells.
�In the current study we demonstrated that human plasma exposed to JP-8 can be used to deliver toxic components of JP-8 to cells in culture.
Method for preparing exposed plasma ��
�
Page 34
������������������� ������� ������������������������� ������ ���������������������Figure 4 - Preparation and toxicity of JP-8-exposed plasma (JEP). JP-8 was mixed with an equal volume of human plasma followed by constant rotation overnight at 37°C. A) Serial centrifugations using the conditions indicated in the flow chart were used to isolate and clarify the plasma layer. B) The general appearance, turbidity of the mixture and presence of microscopic droplets was determined by imaging and flow cytometry at each step in the process. C) The toxicity of JP-8/plasma mixture following the 4,000 x g and each 14,000 x g centrifugation steps was determined by flow cytometric measurement of propidium iodide uptake in treated U937 cells at 24 h. Data presented is the average +/- SEM of three independent experiments.
The method we developed used glass tubes to mix the JP-8 solvents with plasma to overcome any interaction of the JP-8 solvents with the mixing vessel. The exposed plasma we produced dissolved completely when added to the culture medium and did not adversely affect viability of cells in neighbouring wells (not shown). The exposed plasma did not visibly affect the culture plastic when added to the cultured cells and did not emit strong smelling odours in the culture incubator.
Our method reduces method-related issues that may have affected previous studies where JP-8 was either added directly to cells in culture medium or delivered by non-physiological carriers (e.g ethanol), thereby presenting a more relevant, safer, quantifiable and reliable method of delivery. The data presented using this method is also consistent with previous research that showed components of JP-8 mixed with cell culture media were toxic to keratinocyte cultures and a haematopoietic cell line despite the insoluble nature of JP-8 in serum free culture media. Previous studies have employed ethanol as a delivery vehicle for JP-8. These studies found that JP-8 was toxic to cells at concentrations as low as 0.001% v/v. We also observed that JP-8 dissolved in ethanol was highly toxic to U937 cells with an LC50 of approximately 0.03% v/v, which was significantly higher than the LC50 for JEP (1.5%). This suggests that previous studies which used ethanol to deliver JP-8 may have over-estimated the toxicity of JP-8 by up to 15 fold.
It is important to note that the exact concentrations of JP-8 used in each study cannot be compared directly because of the manner in which the preparations were made (direct solubility vs exposure). This study also cannot be used to determine the concentration of JP-8 that is likely to be toxic in vivo or how much JP-8 is likely to enter the body because the
Page 35
������������������� ������� ������������������������� ������ ��������������������transport and delivery mechanisms through skin,. respiratory epithelium and gut mucosa are different. The different volatility of each of the components of JP-8 and chemicals used in aircraft maintenance may also lead to differences in exposure of cells. However it is likely the alveolar membrane is capable of delivering high levels of these chemicals into plasma given chemical similarities between JP-8 and chemicals used in aircraft maintenance with certain inhaled anaesthetic agents. It is interesting to speculate why JEP is toxic to cells.
In our study, the cells were treated with JEP in medium containing 10% FBS and L-glutamine, which contains all of the factors required for cell growth in culture. This suggests that the toxic effect of JEP is due to components of JP-8 that are transferred into human plasma rather than the effect of JP-8 stripping essential growth factors/nutrients from the plasma. This is supported by our biochemical comparisons which showed that the plasma layer from the JP-8/plasma mixtures did not differ significantly from control plasma in any plasma component detected.
� �Figure 5. Showing cell death over time following exposure to different concentrations of plasma which had been saturated with JP8.
Page 36
������������������� ������� ������������������������� ������ ��������������������
Differences between fuel components and solvents
The individual components of the JP8 and solvents were tested for preparation in the plasma model. Significant differences were found in the miscibility with plasma. These differences related to the polarity of the compound and the hydrophillic tendency to mix (dissolve) in plasma lipids.
This finding is significant in that it points to differences in the extent to which compounds would be taken up by plasma and distributed to internal organs.
�
Figure 6. Interaction between DeSeal/ReSeal Compounds and human plasma. Some were completely mixable while others formed “soaps” with plasma lipids.
Mass Spectrometry Analysis of Plasma Binding
The exposed plasma was also subjected to analysis by mass spectrometry to quantify the extent to which individual compounds were bound to plasma and the mechanism by which they are transported in plasma .
Page 37
������������������� ������� ������������������������� ������ ��������������������Numerous hydrocarbons, naphthalene derivatives (and several unidentifiable peaks) were extracted from the JEP plasma and identified by mass spectrometry. However attempts at producing calibration data for titrated amounts of JP-8 components for absolute quantification were unsuccessful, possibly because the compounds were distributed in plasma proteins, lipoprotein complexes, and in lipids. However, for compounds identified and quantitated, the relative proportions of JP8 compounds bound to plasma components did not match the concentration distribution in the fuel. An example is shown in Figure 5 with significant enhancement of the relative quantity of napthalene in plasma compared to the quantity in JP8.
�
�
Figure 7. Comparison of the proportion of Napthalene in fuel compared to the proportion carried in human plasma showing a relative increase in the Napthalene carried and distributed to cells and tissues.
Investigation of other plasma components
In this study, we also investigated other potential physiological carriers of JP-8 such as commercial human serum, foetal bovine serum, and Albumin. In all cases we found that toxicity was reduced compared to exposed whole plasma. It is interesting to speculate on the compositional differences of these substances which may influence their capacity to transfer toxic components of JP-8 to cells. Serum is derived from plasma that has had clotting factors removed and perhaps these factors contribute to its reduced
Page 38
������������������� ������� ������������������������� ������ ��������������������capacity for toxic components of JP-8. Serum is often routinely collected in a fasting state, and lipid levels are proven to be lower. It is also possible that serum from different donors will have different capacity for these components or that serum that is stored for extended periods has reduced capacity for these compounds. Such differences may make some individuals more susceptible to JP-8 exposure than others.
This may also explain the differences in our results using human plasma and foetal bovine serum, which are different in their lipid and protein composition. Albumin is known to play a significant role in plasma transport. We used the human blood product Albumex 20 (200g/L albumin) to investigate whether the albumin in human plasma was a carrier for toxic JP-8 components but this was not the case as no cytotoxicity was observed in U937 cells treated with the Albumex 20/JP-8 mixture.
Therefore, other plasma components are responsible for the solubility of components of JP-8 of which lipids are an obvious candidate for further investigation as there are similarities between alkanes and plasma long chain fatty acids. Lipoprotein absorption could be considered as a possible transport mechanism of JP-8 in human plasma. Ultimately, using human plasma in preference to other sources will allow further investigations to determine how and why JP-8 affects cells and whether any of these effects can account for the pathology.
Page 39
������������������� ������� ������������������������� ������ ��������������������
�
Figure 8. Cytotoxicity of JP-8 mixed with commercial human serum, foetal bovine serum or human albumin (Albumex 20). U937 cells were treated with commercial human serum (CHS), foetal bovine serum (FBS) or Albumex 20 (20% human albumin) that had been mixed with JP-8 and clarified using the same method as for human plasma. Cell death was measured at 24 h by propidium iodide uptake using flow cytometry. Data is the average +/- SEM of three independent experiments observed in personnel exposed to JP-8.
Response of various cell types to treatment with JP8 exposed plasma
The toxicity model was also tested against a variety of cultured cell types.
The cultured cell lines chosen were NK cells which are derivative of CD lymphocytes, REH cells which are a precursor B-cell, YT cells which are a transformed T cell, and RPMI which are a plasma cell myeloma derivative.
All cell types were susceptible.
Page 40
������������������� ������� ������������������������� ������ ��������������������
�
Figure 9. - JEP displays cytotoxicity for various human haematopoietic cells. Cultured cells were treated with various concentrations of JEP and cell death was determined by propidium iodide uptake at 24h using flow cytometry. Data is the average +/- SEM of three independent experiments.
Finally, it must be noted that toxicity is only one potential outcome of JP-8 exposure that could lead to disease. Our study provides a physiological model for studying various cellular effects of JP-8, including toxicity, which may help uncover the contribution of JP-8 exposure to a variety of pathologies and also raises the possibility of tracking and limiting exposure to these compounds in the future.
.
Determination of toxicity of individual components of JP8 and DeSeal/ReSeal solvents �Background to the cytotoxicity assays.
This model (above) was then used in identification of toxic components of JP-8, which may have affected the exposed workers. Exposed plasma
Page 41
������������������� ������� ������������������������� ������ ��������������������preparations were made for each of the components of JP8 and solvents used in the DeSeal/ReSeal.
Overview of cell responses to injury
A cell initiates intracellular apoptotic signalling in response to a stress, which may bring about cell suicide. The binding of nuclear receptors by glucocorticoids, heat, radiation, nutrient deprivation,viral infection,hypoxia and increased intracellular calcium concentration, for example, by damage to the membrane, can all trigger the release of intracellular apoptotic signals by a damaged cell. A number of cellular components, such as poly ADP ribose polymerase, may also help regulate apoptosis.
Before the actual process of cell death is precipitated by enzymes, apoptotic signals must cause regulatory proteins to initiate the apoptosis pathway. This step allows apoptotic signals to cause cell death, or the process to be stopped, should the cell no longer need to die. Several proteins are involved, but two main methods of regulation have been identified: targeting mitochondria functionality, or directly transducing the signal via adaptor proteins to the apoptotic mechanisms. Another extrinsic pathway for initiation identified in several toxin studies is an increase in calcium concentration within a cell caused by drug activity, which also can cause apoptosis via a calcium binding protease calpain.
Mitochondrial regulation
The mitochondria are essential to multicellular life. Without them, a cell ceases to respire aerobically and quickly dies. This fact forms the basis for some apoptotic pathways. Apoptotic proteins that target mitochondria affect them in different ways. They may cause mitochondrial swelling through the formation of membrane pores, or they may increase the permeability of the mitochondrial membrane and cause apoptotic effectors to leak out.These are very closely related to intrinsic pathway, and tumors arise more frequently through intrinsic pathway than the extrinsic pathway because of sensitivity.There is also a growing body of evidence indicating that nitric
Page 42
������������������� ������� ������������������������� ������ ��������������������oxide is able to induce apoptosis by helping to dissipate the membrane potential of mitochondria and therefore make it more permeable.
Mitochondrial proteins known as SMACs (small mitochondria-derived activator of caspases) are released into the cytosol following an increase in permeability. SMAC binds to inhibitor of apoptosis proteins (IAPs) and deactivates them, preventing the IAPs from arresting the apoptotic process and therefore allowing apoptosis to proceed. IAP also normally suppresses the activity of a group of cysteine proteases called caspases, which carry out the degradation of the cell, therefore the actual degradation enzymes can be seen to be indirectly regulated by mitochondrial permeability.
Cytochrome c is also released from mitochondria due to formation of a channel, the mitochondrial apoptosis-induced channel (MAC), in the outer mitochondrial membrane, and serves a regulatory function as it precedes morphological change associated with apoptosis. Once cytochrome c is released it binds with Apoptotic protease activating factor - 1 (Apaf-1) and ATP, which then bind to pro-caspase-9 to create a protein complex known as an apoptosome. The apoptosome cleaves the pro-caspase to its active form of caspase-9, which in turn activates the effector caspase-3.
Page 43
������������������� ������� ������������������������� ������ ��������������������MAC (not to be confused with the Membrane Attack Complex formed by complement activation, also commonly denoted as MAC), also called "Mitochondrial Outer Membrane Permeabilization Pore" is regulated by various proteins, such as those encoded by the mammalian Bcl-2 family of anti-apoptopic genes, the homologs of the ced-9 gene found in C.
elegans.Bcl-2 proteins are able to promote or inhibit apoptosis by direct action on MAC/MOMPP. Bax and/or Bak form the pore, while Bcl-2, Bcl-xL or Mcl-1 inhibit its formation. Two theories of the direct initiation of apoptotic mechanisms in mammals have been suggested: the TNF-induced (tumour necrosis factor) model and the Fas-Fas ligand-mediated model, both involving receptors of the TNF receptor (TNFR) family coupled to extrinsic signals.
TNF Path
TNF is a cytokine produced mainly by activated macrophages, and is the major extrinsic mediator of apoptosis. Most cells in the human body have two receptors for TNF: TNF-R1 and TNF-R2. The binding of TNF to TNF-R1 has been shown to initiate the pathway that leads to caspase activation via the intermediate membrane proteins TNF receptor-associated death domain (TRADD) and Fas-associated death domain protein (FADD). cIAP1/2 can inhibit TNF-α signaling by binding to TRAF2. FLIP inhibits the activation of caspase-8. Binding of this receptor can also indirectly lead to the activation of transcription factors involved in cell survival and inflammatory responses. However, signalling through TNF-R1 might also induce apoptosis in a Caspase-independent manner. The link between TNF and apoptosis shows why an abnormal production of TNF plays a fundamental role in several human diseases, especially in autoimmune diseases.
Fas Path
Page 44
������������������� ������� ������������������������� ������ ��������������������The Fas receptor - First apoptosis signal(Fas) (also known as Apo-1 or CD95) binds the Fas ligand (FasL), a transmembrane protein part of the TNF family.[23] The interaction between Fas and FasL results in the formation of the death-inducing signaling complex (DISC), which contains the FADD, caspase-8 and caspase-10. In some types of cells (type I), processed caspase-8 directly activates other members of the caspase family, and triggers the execution of apoptosis of the cell. In other types of cells (type II), the Fas-DISC starts a feedback loop that spirals into increasing release of proapoptotic factors from mitochondria and the amplified activation of caspase-8.
Common components
Following TNF-R1 and Fas activation in mammalian cells a balance between proapoptotic (BAX, BID, BAK, or BAD) and anti-apoptotic (Bcl-Xl and Bcl-2) members of the Bcl-2 family is established. This balance is the proportion of proapoptotic homodimers that form in the outer-membrane of the mitochondrion. The proapoptotic homodimers are required to make the mitochondrial membrane permeable for the release of caspase activators such as cytochrome c and SMAC. Control of proapoptotic proteins under normal cell conditions of nonapoptotic cells is incompletely understood, but in general, Bax or Bak are activated by the activation of BH3-only proteins, part of the Bcl-2 family.
Caspases
Caspases play the central role in the transduction of DR apoptotic signals. Caspases are proteins that are highly conserved, cysteine-dependent aspartate-specific proteases. There are two types of caspases: initiator caspases, caspase and effector caspases, caspase. The activation of initiator caspases requires binding to specific oligomeric adaptor protein. Effector caspases are then activated by these active initiator caspases through proteolytic cleavage. The active effector caspases then proteolytically degrade a host of intracellular proteins to carry out the cell death program.
Page 45
������������������� ������� ������������������������� ������ ��������������������Caspase-independent apoptotic pathway
There also exists a caspase-independent apoptotic pathway that is mediated by AIF (apoptosis-inducing factor).
Execution
Many pathways and signals lead to apoptosis, but there is only one mechanism that actually causes the death of a cell. After a cell receives stimulus, it undergoes organised degradation of cellular organelles by activated proteolytic caspases. A cell undergoing apoptosis shows a characteristic morphology:-
Cell shrinkage and rounding are shown because of the breakdown of the proteinaceous cytoskeleton by caspases.
The cytoplasm appears dense, and the organelles appear tightly packed.
Chromatin undergoes condensation into compact patches against the nuclear envelope (also known as the perinuclear envelope) in a process known as pyknosis, a hallmark of apoptosis.
The nuclear envelope becomes discontinuous and the DNA inside it is fragmented in a process referred to as karyorrhexis. The nucleus breaks into several discrete chromatin bodies or nucleosomal units due to the degradation of DNA.
The cell membrane shows irregular buds known as blebs.
The cell breaks apart into several vesicles called apoptotic bodies, which are then phagocytosed.
Apoptosis progresses quickly and its products are quickly removed, making it difficult to detect or visualise. During karyorrhexis, endonuclease activation leaves short DNA fragments, regularly spaced in size.
Cells growing in culture medium were exposed to increasing concentrations of the individual compounds. The cellular response was monitored using two different markers of cell death.
Page 46
������������������� ������� ������������������������� ������ ��������������������
The Annexin V assay
In molecular biology, an annexin A5 affinity assay is a test to quantify the number of cells undergoing apoptosis. The assay uses the protein annexin A5 to tag apoptotic and dead cells, and the numbers are then counted using either flow cytometry or a fluorescence microscope.
The annexin a5 protein binds to apoptotic cells in a calcium-dependent manner using phosphatidylserine-containing membrane surfaces that are usually present only on the inner leaflet of the membrane.
Apoptosis is a form of programmed cell death that is used by the body to remove unwanted, damaged, or senescent cells from tissues. Removal of apoptotic cells is carried out via phagocytosis by white blood cells such as macrophages and dendritic cells. Phagocytic white blood cells recognise apoptotic cells by their exposure of negatively charged phospholipids (phosphatidylserine) on the cell surface.In normal cells, the negative phospholipids reside on the inner side of the cellular membrane and the outer surface of the membrane is occupied by phospholipids, which do not have a charge. After a cell has entered apoptosis, the negatively charged phospholipids are transported to the outer cell surface by a hypothetical protein known as scramblase. Phagocytic white blood cells express a receptor that can detect the negatively charged phospholipids. After detection the apoptotic cells are removed.
Page 47
������������������� ������� ������������������������� ������ ��������������������Detection of cell death with annexin A5
Healthy individual apoptotic cells are rapidly removed by phagocytes. However, in pathological processes, the removal of apoptotic cells may be delayed or even absent. Dying cells in tissue can be detected with annexin A5. Labeling of annexin A5 with fluorescent or radioactive molecules makes it possible to detect binding of labeled annexin A5 to the cell surface of apoptotic cells. After binding to the phospholipid surface, annexin A5 assembles into a trimeric cluster. This trimer consists of three annexin A5 molecules that are bound to each other via non-covalent protein-protein interactions. The formation of annexin A5 trimers results in the formation of a two-dimensional crystal lattice on the phospholipid membrane. This clustering of annexin A5 on the membrane greatly increases the intensity of annexin A5 when labeled with a fluorescent or radioactive probe. Two-dimensional crystal formation is believed to cause internalisation of annexin A5 through a novel process of endocytosis if it occurs on cells that are in the early phase of executing cell death. Internalisation amplifies additionally the intensity of the annexin A5 stained cell.
Detection of cell membrane damage with Propidium iodide
Propidium iodide (PI) staining based on cell membrane damage is a sensitive and quantitative method to test the cytotoxicity of typical lipophilic compounds. Cell membrane damage that resulted in increased PI uptake may be due to variations in the phospholipid and protein content of the membrane, which are affected by interactions between the lipophilic compounds and components of the cell membrane.
����������
Page 48
������������������� ������� ������������������������� ������ ��������������������
�Results of cytotoxicity assays for JP8 components and Deseal/Reseal solvents
The relative toxicity of individual components when exposed to cell was classified according to the amount required to produce a cell response. A highly toxic response was defined by cell death when exposed plasma was mixed in cell culture medium at <5%. Medium toxicity was defined as toxic when delivered at between 5-10%. Low toxicity was defined as no appreciable toxicity when delivered at >10%.
�
�Table 2. Relative toxicity of compounds of JP8 and Deseal/Reseal solvents
Compound Relative Toxicity
Acetone Low
2 Butanone Low
Antifoam A Low
Ethanol Low
Toluene Low
Xylene Medium
Ethyl Acetate Low
Propylene Glycol Monomethyl Ether Medium
2-Propanol Medium
DiEGME High
Kerosene High
Napthalene Medium
N,N Dimethyl acetimide High
Sodium Hydroxide High
Naptha High
Thiophenol High
Page 49
������������������� ������� ������������������������� ������ ��������������������Cell Survival
The cell survival on exposure to individual components are shown in Figures below:-
�
�
Ethyl acetate
0 2 4 6 8 100
20
40
60
80
100PI positiveAnnexin V positive
Percentage Compound-infused Plasma in Media
Perc
enta
ge c
ells
pos
itive
Figure 11. Propylene glycol with medium toxicity
Propylene glycol monomethyl ether
0 2 4 6 8 100
20
40
60
80
100PI positiveAnnexin V positive
Percentage Compound-infused Plasma in Media
Perc
enta
ge c
ells
pos
itive
Figure 10.Ethyl acetate did not demonstrate toxicity
Page 50
������������������� ������� ������������������������� ������ ��������������������
�
Figure 12. Propanolol demonstrated a different mechanism of cell injury with the apototic response mostly through the caspase pathway.
��
� Figure 13. N,N - dimethyl acetamide showed high toxicity
�
2-Propanol
0 2 4 6 8 100
20
40
60
80
100PI positiveAnnexin V positive
Percentage Compound-infused Plasma in Media
Perc
enta
ge c
ells
pos
itive
N,N-dimethyl acetimide
0 2 4 6 8 100
20
40
60
80
100PI positiveAnnexin V positive
Percentage Compound-infused Plasma in Media
Perc
enta
ge c
ells
pos
itive
Page 51
������������������� ������� ������������������������� ������ ��������������������
� Figure 14. Sodium hydroxide demonstrated high toxicity
����
� Figure 15. Naptha was highly toxic
��
Sodium hydroxide
0 2 4 6 8 100
20
40
60
80
100PI positiveAnnexin V positive
Percentage Compound-infused Plasma in Media
Perc
enta
ge c
ells
pos
itive
Naphtha
0 2 4 6 8 100
20
40
60
80
100PI positiveAnnexin V positive
Percentage Compound-infused Plasma in Media
Perc
enta
ge c
ells
pos
itive
Page 52
������������������� ������� ������������������������� ������ ��������������������
� Figure 16. Thiphenol was highly toxic
��Conclusions from cell exposure studies.
The compounds Kerosene, Naptha, Thiopenol, N,N Dimethyl acetimide demonstrated significant cytotoxicity when added to the cell culture media. Kerosene is a (variable) mixture of alkanes ranging in size from octane to hexadecane. The data are summarised as a kerosene mixture as each individual alkane demonstrated similar response. The cell death markers Annexin V and PI suggest that the cell death mostly occurred by disruption to the cell membrane initiating an apoptotic response. Earlier work by this group which implicated mitochondrial involvement in jet fuel toxicity through proteomic involvement may be explained by the role of mitochondria in the response of the injured cell through apoptosis.
�
Thiophenol
0 2 4 6 8 100
20
40
60
80
100PI positiveAnnexin V positive
Percentage Compound-infused Plasma in Media
Perc
enta
ge c
ells
pos
itive
Page 53
������������������� ������� ������������������������� ������ ���������������������Effects of exposure of cell gene expression
The effects of exposure to JP8 components and Deseal/Reseal solvents on cell biology were investigated changes to cell gene expression and to cell proteins. Results are summarised in the tables below:-
Table 3. Genes that were that had a five fold change in expression
��
Genes with expression changed >5 fold Function of gene
Upregulated
SUM01 Regulates sumoylation. BRCA1 is modified by SUM01 in response to genotoxic stress
ZNF417 Regulator of transcription
NOP16 Up-regulated in breast cancer. Regulates apoptosis via p581PK
TMEM167A Transcriptional repressor. Elicits spontaneous immune responses in cancer patients
RLN3 Regulation of endocrine and autocrine/paracrine hormones. Member of the insulin genesuperfamily
Downregulated
PRY Similar to tyrosine phosphatase, unknown function
NEDD8 Regulates degradation of DNA damage response proteins following ubiquitination
STAP1 Regulates TEC protein kinase activity, which regulates cytokine receptor activity
OR4D1 Olfactory receptor
DNM1L Dynamin GTPase. Establishes mitochondrial morphology. Mouse knockout has decreased neuritis and defective synapse formation
GK Glycerol kinase. Involved in lipid metabolism
Page 54
������������������� ������� ������������������������� ������ ��������������������Table 4. The functions of groups of genes altered by exposure
�������������
Cell Survival/Death NOP16 DYNLL1 FAM58A
Cell Metabolism ISCA2 ENSA RLN3 GK TPTE
Cell Structure DNM1L SMU1 HIST1H2BJ
Cell cycle/DNA damage SUM01 SSX NEDD8 FAM58A
Cell Transcription/Translation ZNF417 ZNF675 NEDD8 RPS4Y2 EIF2S2 RPL4
Page 55
������������������� ������� ������������������������� ������ ��������������������Table 5. Physiological role of clusters of genes involved in the response
��Table 6. Clusters of genes with known pathological associations identified in the cell response following exposure.
Haematological STAP1
Breast NOP16
Olfactory TASR20OR4D1OR5AU1OR4N1OR7C2
Metabolism ISCA2ENSARNL3GKTPTE
Cell Based Immunity SSX2DYNLL1SLP1CD1B
Neuronal DYNLL1SYPDNM1L
Cancer NOP16
SSX2
IMP3
Sumo1
Immune suppression SSX2
DYNLL1
SLP1
STAP1
Page 56
������������������� ������� ������������������������� ������ ��������������������
��Table 7. Proteins most altered in cells treated with JP-8/Serum
������
Gene housekeeping Sumo1
SSX2
EIF2S2
RPL4
FAM58
NEDD8
ZNF417/675
RPS4Y2
Protein Function
Up-regulated40S Ribosomal proteinProgrammed cell death protein 514-3-3 gammaComplement component 1QGalactin 1ZNF469
RNA processingMay be involved in cell deathSignal transductionAntibody responsesBinds complex carbohydratesTranscription/translation
Down-regulatedATP synthase subunit delta�CD44Nucleosomal assembly protein 1Progesterone receptor component 1Acidic leucine rich nuclear – Phosphoprotein 32
Similar to tyrosine phosphatase, unknown functionReceptor for hyaluronic acidChromatin formationProgesterone receptorSignal transduction
Page 57
������������������� ������� ������������������������� ������ ��������������������Table 8. Cell biology functions affected based on proteins most altered by JP-8/Serum
�Table 9. Pathological systems affected based on proteins most altered by JP-8/Serum
��
Cell Survival/Death
Cell Metabolism
Cell Structure
Cell cycle/DNA damage
Cell Transcription/Translation
Pathology Gene clusters identified
Cancer 14-3-3Galectin 1Mastermind like protein 2Bcl-9
Myopathy Galectin 1Superoxide dismutaseTransporter of the inner membraneCytochrome oxidase subunit 5A
Cell Based Immunity C1Q
Page 58
����������������
CHAPTER 3�Study of Exposed Workers
�
Page 59
������������������� ������� ������������������������� ������ ��������������������
3. Study of Exposed Workers 3.1 Overview
Genetic damage occurring as a result of jet fuel toxicity
The onset of pathology many years after exposure that was not detected shortly after exposure raises the possibility of epigenetic changes induced by the exposure as well at mitochondrial or nuclear gene mutations.
Genomic imprinting is an epigenetic process which can be studied using microarray investigations to assess gene expression profiles and methylation status (using two different microarray approaches). This process involves DNA methylation and histone modifications that result in alterations to gene expression. Such patterns of gene expression occur without any fundamental mutations to the DNA sequence pertaining to the gene of interest. That is, the imprint is via alteration to the gene control rather than the sequence. This is a dynamic process which can be erased and than re-established.
Mitochondrial DNA damage
In two case study investigations based on post-mortem data from Air Force maintenance staff that had worked on the Deseal/Reseal project, it was revealed that each carried a mutation in the mitochondrial DNA. Mitochondria are akin to mini power plants within a cell that supply cellular energy through the production of adenosine tri-phosphate (ATP). Mitochondria also control the programmed death of injured cells through a process called apoptosis. The novelty of this organelle is that it has its own genome that allows scientists to investigate changes in mitochondrial genes as well as nuclear genes. The two individuals who were the subject of this pilot investigation had succumbed rapidly to severe neurodegenerative disease with a temporal relationship to jet fuel and solvent exposure on the Deseal/Reseal project. Taken together, it suggests that there is a link between jet fuel/ solvent exposure and increased prevalence of morbidity and mortality in individuals with inherited mitochondrial defects.
Further research demonstrated that the levels of a protein referred to as TOM40 (important to mitochondrial repair and replication), were significantly
Page 60
������������������� ������� ������������������������� ������ ��������������������elevated in affected Air Force veterans that had worked in the Deseal/Reseal project. Such results indicate that the mitochondria had been exposed to a severe degree of sustained insult. Using a proteomic approach capable of assessing changes in a large number of biomarkers, it was observed that five proteins were consistently increased in all experimental samples.9 Although not conclusive, there is sound reasoning and justification based on pre-clinical research and pilot data to investigate further in a large genomic and proteomic project using sophisticated bioinformatics methods, to definitively identify the relationship between the severity of jet fuel/solvent exposure and presentation of clinical phenotypes.
3.2 Study Plan
This study was conducted by Mater Research Institute (UQ) in collaboration with the Omics Laboratory within Mater Pathology over three years. Air Force veterans who worked on the De-seal/Re-seal project between 1973 and 1999 and/ or had direct or indirect exposure to F111 jet fuel were recruited. In addition, healthy controls matched for age and gender were recruited from within the general community.
An assessment of health outcomes was undertaken using a standardised General Health Assessment score. Study participants and their substitute decision maker (if applicable), were asked to attend a single visit for the purpose of blood collection at a local centre.
At the screening/study visit, Air Force veterans and healthy controls were enrolled into this trial if they had fulfilled all of the inclusion criteria and none of the exclusion criteria, and informed consent was complete. Clinical data was ascertained for the presence of any of the following conditions: Haematological malignancy, myelodysplasia, leukaemia, lymphoma, anaemia, immune deficiency, myopathy, cardiomyopathy, dementia, pychoses, any non-haematological neoplasia (benign and malignant, recurrent infection, bleeding disorders. This data was obtained both by interview of the subject.
Page 61
������������������� ������� ������������������������� ������ ��������������������Exposure data was obtained for each individual noting the duties which they undertook and the duration. Individuals were assigned to one of 3 groups based on the exposure history. These groups relate to dose of exposure and NOT to the Tier structure. If it was not possible to classify the exposure dose, then individuals were assumed to have been exposed to at the higher level. (This essentially would minimise the significance of any changes seen
supporting the Null Hypothesis).
1. Definite high exposure by those who worked inside the fuel tanks.
2. Significant contact such as by dosing of skin, accidental ingestion, or mixing of the sealant
3. Lesser contact in the general area such as collection of rags or cleaning of the area.
A unique identification number assigned to participants at the clinic visit was recorded in the study database.
Primary endpoint assessment were determined by changes in blood count, genomic (gene expression and copy number) and proteomic variables using resources at the Mater Pathology Omics Laboratory. All serum, EDTA blood and PAXgene samples sent to this laboratory contained the participant’s unique identification number, the participant’s date of birth, and collection date. Analysis was performed using Affymetrix GeneChip analysis and LC MALDI TOF mass spectrometry. Masked results of genomic and proteomic laboratory reports were sent directly to the Principal Investigator and the Bioinformatition on completion of the test. Additional secondary endpoints were evaluated using outcomes from bioinformatic analysis of the genomic and proteomic results in-conjunction with medical, mental health and neurocognitive outcomes to establish risk determinants for prediction of future genotypic, phenotypic and clinical pathology.
Exploratory objectives were evaluated by toxicology and changes in signalling responses to biological stimuli determined from experimental procedures carried out at the MRI(UQ). Experimental data and respective analysis were collated and provided to the principal investigator. Human cell
Page 62
������������������� ������� ������������������������� ������ ��������������������lines were exposed to individual chemical mixtures and then subjected to genomic and proteomic analyses to identify individual agents or combination of agents able to produce cellular dysfunction.
Health Assessment Scores
General Health Questionnaire (GHQ30)
The purpose of the GHQ30 is to screen for medical disorders and non-psychotic psychiatric disorders. It is suitable for candidates 18+ years of age. The GHQ 30 provides a rapid and accurate assessment of general psychiatric status. It is a self-administered questionnaire that focuses on two major areas: the inability to carry out normal functions and the appearance of distress – to assess well-being in a person.
An individual's responses to Goldberg's 30-item General Health Questionnaire are usually represented as a single score which provides a measure of the number of psychiatric symptoms reported. No account is taken of the nature of the symptoms. Factor analyses of the GHQ-30 indicate an impressive degree of consistency of the factor structure, and the identification of five distinct factors corresponding to anxiety, feelings of incompetence, depression, difficulty in coping, and social dysfunction.
Molecular Investigations and Data Collection
�����������
A single SST tube was collected in order to conduct LC-MALDI TOF and LC-Electrospray MS investigations into the serum proteome. Additionally, the cellular proteome of leucocytes (derived from EDTA blood) was evaluated by 2 different approaches: LC-MALDI and LC-Electrospray mass spectroscopy. The mass spectroscopy platforms at the Omics laboratory include the Bruker Ultraflex III currently and MicroTOF Q-II.
���������
The aim of the genomics component of the JFESS was to use capture a comprehensive profile of the genome and transcriptome from a readily available tissue from men operating in the Deseal / Reseal programs and
Page 63
������������������� ������� ������������������������� ������ ��������������������aged-matched non fuel and solvent exposed male controls. Peripheral blood mononucleated cells (PBMCs) were chosen for the ease of collection and as one of the hypothesised tissues to have been exposed to any potential toxins during the Deseal / Reseal programs. To establish not only a comprehensive, robust and reproducible profile for each participant that can be compared between individuals the nucleic acid content of the collected PBMCs requires temporal fixation. This is achieved using collection tubes spiked with fixation solution specific for the fixation of DNA or RNA. The methodology chosen for extraction of nucleic acid can influence the range of DNA fragments or the relative ratio of RNA species isolated from a sample. One way to minimise variation in a dataset is to consistently perform extractions using the same protocol under the same conditions, accordingly extractions were conducted on an automated robot using magnetic bead purification to produce high quality nucleic acid samples.
The nuclear chromosome content encodes for almost the entire transcribed content within a cell, with an extremely minor but mitochondrial specific fraction coming from the mitochondrial chromosome. The regulation of gene expression can be achieved by a myriad of reversible and irreversible means including; gene deletion, gene duplication, gene silencing, expression of transcription factors, modification of DNA backbone (methylation, acetylation, phosphorylation and ubiquitination), histone modification and micro RNA interactions. To understand how an individual may respond to an external agent, an understanding of their capacity to express genes would give a basal prediction for their response, this can be ascertained by assessment of the number of gene deletions and duplications they carry. DNA samples were assayed by the Cytoscan HD (High Density) GeneChip® to describe how intact each participant’s genome was, if any genome contained regions of DNA loss or duplication. The Cytoscan assay provides copy number information and homozygous / heterozygous status on the nuclear genome. The mitochondrial genome was also assayed and by virtue of its smaller size at 16569 basepairs it was possible to sequence the entire
Page 64
������������������� ������� ������������������������� ������ ��������������������genome to determine the sequence of every expressed mitochondrial gene and control regions.
�The transcribed content of a cell is contained within the RNA population, itself a mixture of ribosomal, transfer, messenger and micro RNAs. The more dynamic populations of RNA are the messenger (mRNA) and micro (miRNA) RNAs and these were assayed to determine the current state of gene expression within the PMBCs. The Human Transcriptome 2.0 GeneChip® covers 67,528 genes, both coding and non-coding as well as probes spanning splice junctions to allow for the determination of differential exon usage. The miRNA 2.0 GeneChip® covers 1,105 pre-miRNA and their mature counterparts as well as 2,334 other mixed snoRNA, CDBox RNA, H/ACA Box RNA and scaRNAs. It is possible from the combination of these datasets to describe the genes and pathways active in the participants PBMCs and determine any differences compared to the age-matched non fuel and solvent exposed controls.
��������������
DNA was isolated from PaxgeneDNA blood tubes using the QiaSymphony® robot (Qiagen) which uses magnetic bead purification to yield high-molecular, high quality DNA. PaxgeneDNA tubes were stored at -70oC from 24 hours post-collection till 24 hours prior to extraction. Tubes were thawed at room temperature for 24 hours followed by extraction of 200μl of whole blood using protocol Blood200v6 and eluted into 50μl Qiagen Elution Buffer. DNA was quantified by UV absorbance on a NanoDrop 8000 UV-Vis Spectrophotometer (Thermo Scientific) and stored at 4oC till required.
���������������
RNA was isolated from PaxgeneRNA blood tubes using the QiaSymphony® robot (Qiagen). PaxgeneRNA tubes were stored at -70oC from 24 hours post-collection till 24 hours prior to extraction, where tubes were thawed at room temperature followed by centrifugation and washing of the PBMC pellet
Page 65
������������������� ������� ������������������������� ������ ��������������������twice in PBS. Cell pellets where then treated by protocol Paxgene_RNA_v5 and eluted into 50μl Qiagen Elution Buffer. RNA was quantified by NanoDrop 8000 UV-Vis Spectrophotometer (Thermo Scientific) and stored at -70oC till required.
�����������������
All total RNA samples were assessed for integrity using the Experion RNA StdSens Analysis Kit (Bio-Rad). Each sample was heated at 70oC for 2 minutes prior to rapid chilling on a 4oC cooling chamber (Diversified Biotech). One microliter of each sample was assayed alongside the supplied RNA ladder in the presence of loading buffer containing a marker allowing for normalisation of RNA migration time. A RNA Quality Index (RQI) score is generated for each sample from the Experion Data Analysis Tools, version 3.2. The RQI score ranges from 0 -10 and for this study only samples with an RQI above 5.7 were assayed further, median RQI was 8.8 +/- 0.7.
�������������� ���������!�"#$�����!�
Total RNA was prepared for analysis on the Human Transcriptome Array 2.0 using the WT Expression Kit and the WT Terminal Labelling Kits from Ambion and Affymetrix respectively (ThermoFisher and Affymetrix). Manufactures instructions were followed with 100ng RNA used as input for all samples. In brief, RNA was used as a template for reverse transcription to create an RNA-DNA hybrid by incubation with First Stand Buffer/Enzyme mix in the presence of a primer containing a T7 promoter sequence, for 1 hour at 25°C, then 1 hour at 42°C, then at least 2 minutes at 4°C. Second Stand cDNA synthesis was performed, in the presence of RNase H, using DNA Polymerase I incubated at 1 hour at 16°C, 10 minutes at 65°C, then at least 2 minutes at 4°C. Next, cRNA is produced using T7 RNA Polymerase incubated at 40°C for 16 hours before purification of the cRNA and normalisation of sample yield to 455ng/μl in 22μl (10μg total). The cRNA is reverse transcribed in the presence of random primers and a nucleotide mix
Page 66
������������������� ������� ������������������������� ������ ��������������������where dTTP is underrepresented relative to the other three dNTPs, with the difference in ratio supplied by dUTP. Firstly cRNA is incubated with random primers only for 5 minutes at 70°C, 5 minutes at 25°C then at least 2 minutes at 4°C before addition of the 2nd-Cycle Master Mix and incubation for 10 minutes at 25°C, 90 minutes at 42°C, 10 minutes at 70°C, then at least 2 minutes at 4 °C. Next the cRNA is hydrolysed by RNaseH, to leave single stranded cDNA, by incubation for 45 minutes at 37°C, 5 minutes at 95°C then at least 2 minutes at 4°C before magnetic bead purification and quantification. Samples yielding 5.5μg cDNA were fragmented using a combination of uracil-DNA glycosylase and apurinic/apyrimidinic (AP) endonuclease by incubation for 1 hour at 37°C, 2 minutes at 93°C then at least 2 minutes at 4°C before end labelling using terminal deoxynucleotidyl transferase and DNA Labelling Reagent by incubation for 1 hour at 37°C, 10 minutes at 70°C then at least 2 minutes at 4°C. The labelled cDNA sample was prepared for hybridisation to the HTA 2.0 Array (Affymetrix, California, USA) by addition of 3.7μl Control Oligonucleotide B2 (3nM), 11μl 20X Eukaryotic Hybridization Controls, 110μl 2X Hybridization Mix, 15.4μl DMSO and 19.9μl nuclease-free water (GeneChip Eukaryotic Hyb Control Kit and GeneChip Hyb, Wash and Stain Kit (Affymetrix)). The hybridisation solution was incubated at 99oC for 5 minutes followed by 45oC for 5 minutes before 200μl was injected onto the array. Arrays were incubated for 16 hours at 45°C and 60 rpm in a Hybridization Oven 645 (Affymetrix) before washing and staining on a Fluidics Station 450 using protocol FS450_001 and reagents from the GeneChip Hyb, Wash and Stain Kit (Affymetrix). Arrays were scanned on a GeneChip® Scanner 3000 7G and CEL files generated automatically by the Affymetrix Command Console Software, version 3.2.
����������"#$�����!�
Total RNA was prepared for microRNA analysis using the FlashTag® Biotin HSR RNA Labelling Kit for Affymetrix® GeneChip® miRNA Arrays from Genisphere (Genisphere). Manufactures instructions were followed with 100ng RNA used as input for all samples. Samples were incubated with ATP
Page 67
������������������� ������� ������������������������� ������ ��������������������and Poly A Polymerase for 15 minutes at 37oC to add a polyA tail to the 3’ end of all RNA before addition of the proprietary biotinalyted 3DNA® dendrimer by ligation using T4 DNA Ligase by incubation for 30 minutes at 25oC. The 23.5μl of labelled RNA was prepared for hybridisation to the miRNA 2.0 Array (Affymetrix) by addition of 15μl 27.5% Formamide (FlashTag kit), 50μl 2X Hybridization Mix and 10μl DMSO, both from GeneChip Hyb, Wash and Stain Kit (Affymetrix), 5μl 20X Eukaryotic Hybridization Controls and 1.7μl 3nM Control Oligonucleotide B2, both from the GeneChip Eukaryotic Hyb Control Kit (Affymetrix). The hybridisation cocktail was incubated at 99oC for 5 minutes followed by 45oC for 5 minutes before 100μl was injected onto the array. Arrays were incubated for 16 hours at 48°C and 60 rpm in a Hybridization Oven 645 (Affymetrix) before washing and staining on a Fluidics Station 450 using protocol FS450_003 and reagents from the GeneChip Hyb, Wash and Stain Kit (Affymetrix). Arrays were scanned on a GeneChip® Scanner 3000 7G and CEL files generated automatically by the Affymetrix Command Console Software, version 3.2.
� !��������������!�
Genomic DNA was prepared for the Cytoscan HD Array using the CytoScan® HD Array Kit and Reagent Kit Bundle (Affymetrix) following manufactures instructions. In brief, 250ng of DNA at 50ng/μl was digested with NspI for 2 hours at 37oC then 20 minutes at 65oC to heat inactivate the restriction enzyme. Next, the NspI Adaptor sequence was attached to the digested DNA by incubation with T4 DNA Ligase at 16oC for 2 hours followed by 20 minutes at 70oC to heat inactivate the enzyme. The adaptor linked DNA fragments were diluted 1:4 and 10μl was amplified by PCR in four separate 100μl reactions containing: 39.5μl nuclease-free water (Affymetrix), 10μl 10X TITANIUM Taq PCR Buffer, 20μl GC Melt, 14μl dNTP Mixture (2.5mM each), 4.5μl PCR Primer 002 (Affymetrix) and 2.0μl 50X TITANIUM Taq DNA Polymerase (all reagents supplied by Clontech other than specified). The PCR was performed on an ABI 9700 with Gold Block in MAX mode with pre-incubation at 94°C for 3 minutes followed by 30 cycles of 94°C for 30
Page 68
������������������� ������� ������������������������� ������ ��������������������seconds, 60°C for 45 seconds, 68°C for 15 seconds finishing with 68°C for 7 minutes and hold at 4°C. The four PCR reactions were pooled and DNA isolated using supplied magnetic beads and eluted into 52μl of Elution Buffer from which 47μl was recovered for quantification and fragmentation. Only samples that yielded >3ug/μl as measured on a Nanodrop 2000 were subsequently processed. PCR samples were fragmented with 0.05U Fragmentation Reagent, 37°C for 35 minutes, 95°C for 15 minutes and held at 4°C before end labelling using terminal deoxynucleotidyl transferase and DNA Labelling Reagent by incubation at 37oC for 4 hours, heat inactivation at 95 °C for 15 minutes and hold at 4°C. The hybridisation solution was prepared using 70.5μl labelled DNA and 165μl Hyb Buffer Part 1, 15μl Hyb Buffer Part 2, 7.0μl Hyb Buffer Part 3, 1.0μl Hyb Buffer Part 4 and 2.0μl Oligo Control Reagent 0100 before incubation at 95oC for 10 minutes and held at 49oC prior to loading of 200μl onto the Cytoscan HD Array. Arrays were incubated for 16 hours at 50°C and 60 rpm in a Hybridization Oven 645 (Affymetrix) before washing and staining on a Fluidics Station 450 using protocol CytoScanHD_Array_450 and reagents from the CytoScan® HD Array Kit and Reagent Kit (Affymetrix). Arrays were scanned on a GeneChip® Scanner 3000 7G and CEL files generated automatically by the Affymetrix Command Console Software, version 3.2.
�����%��&���������������������'�
The mitochondrial genome was first enriched by PCR from the total DNA sample then sequenced by massively parallel sequencing technology. The TaKaRa Long Range PCR kit (version 2.1,Clontech) was used to amplify the mitochondrial genome (mtDNA) in three separate PCR reactions producing amplicons of 3968, 5513 and 7814bp. Primer pairs used were: Mito1–Forward (ACA TAG CAC ATT ACA GTC AAA TCC CTT CTC GTC CC), Mito1-Reverse (TGA GAT TGT TTG GGC TAC TGC TCG CAG TGC), Mito2-Forward (TAC TCA ATC CTC TGA TCA GGG TGA GCA TCA AAC TC), Mito2-Reverse (GCT TGG ATT AAG GCG ACA GCG ATT TCT AGG ATA GT), Mito3-Forward (TCA TTT TTA TTG CCA CAA CTA ACC TCC TCG GAC TC) and Mito3-
Page 69
������������������� ������� ������������������������� ������ ��������������������Reverse (CGT GAT GTC TTA TTT AAG GGG AAC GTG TGG GCT AT). PCR reactions were performed in 25μl containing 50ng DNA, 14.75μl nuclease free water, 2.5μl 10X Buffer (containing 25mM MgCl2), 4μl dNTPs (2.5mM each), 1μl 10μM primer mix and 0.25μl TaKaRa LA Taq™ (5U/ul), with PCR cycled as pre-incubation at 94°C for 2 minutes followed by 30 cycles of 94°C for 15 seconds, 68°C for 8 minutes finishing with 68°C for 13 minutes and hold at 12°C. Amplicon yield was assessed by QuBit assay (Life Technologies) and equimolar amounts of the three PCRs pooled and purified using QIAquick PCR purification columns (Qiagen) and yield quantified on a Nanodrop 2000. Each pooled sample was then prepared for sequencing on a MiSeq instrument. Firstly samples were simultaneously tagged and fragmented using the Nextera XT transposome by incubation at 55°C for 5 minutes then held at 10°C till reaction termination by addition of neutralisation buffer. Next a limited cycle PCR was performed in the presence of Index 1 and Index 2 sequences, selected in combination such that up to 96 samples could be individually identified by their unique Index 1 and Index 2 sequence combination. PCR was performed using the Nextera PCR Master Mix under the following conditions; pre-incubation at 72°C for 3 minutes, 95°C for 30 seconds followed by 12 cycles of 95°C for 10 seconds, 55°C for 30 seconds, 72°C for 30 seconds finishing with 72°C for 5 minutes and hold at 10°C. PCRs were purified using AMPure XP magnetic beads, normalised using Library Normalisation Beads and all samples were pooled into a single tube ready for sequencing. Sequencing was performed according to manufacturer’s instructions, using 600μl of pooled library and a 600 cycle version 3 kit (Illumina). MiSeq control software binned each sample according to the Index 1 / Index 2 pairing, produced paired forward and reverse FASTQ files that were used as input into Genomics Workbench v6.5 (CLC-Bio, Qiagen) where sequence variants were identified by mapping to NC_012920 genome (Homo sapiens mitochondrion, complete genome 16,569 bp circular DNA) using a Probabilistic Variant model to call variants and generated VCF v4.1 files.
Page 70
������������������� ������� ������������������������� ������ ��������������������Ranking of exposure
A modified exposure index based on the SHOAMP study was employed but simplified into 3 levels (High, Moderate, Low) . DSRS exposure was confirmed via a self-administered Exposure Questionnaire specifically developed for this study, with respondents indicating the specific DSRS program(s) they had been involved in , the tasks they performed, and for how long.
Data Quality Assurance
��������&���'���&������'��
Clinical and experimental laboratory data was managed according to the standard procedures of the MRI(UQ). Copies of the completed CRFs were entered into the study database. Procedures for the treatment of missing, unused and spurious data was addressed in the detailed Statistical Analysis Plan (SAP).
Efficacy and Safety Variables
������!���& �����
Change in genomic and proteomic profiles between jet fuel/solvent exposed veterans and healthy controls.
�����&��!���& �����
Mortality and morbidity risk prediction based on genotypic, phenotypic and clinical outcomes measures.
Relationship between exposure indices and genomic/proteomic properties.
Statistical Considerations
��� �����(���������������
Statistical analyses have been developed to assess the hypothesis that genomic and proteomic profiles will be different when a group of Air Force Veterans who were exposed to jet fuel/solvents are compared with a healthy (age and gender-matched) control group.
Page 71
������������������� ������� ������������������������� ������ ��������������������Sample sizes were set up to allow 90% power to detect a 20% difference between gene and protein expression profiles, with an alpha value of 0.05, based on data from a similar study.
������������%�&��
Statistical analyses were undertaken to assess the hypothesis that genomic and proteomic pathology will be different when a group of Air Force Veterans who were exposed to jet fuel/solvents are compared with a healthy (age and gender-matched) control group. Sample sizes were calculated to allow 90% power to detect a 20% difference between gene and protein expression profiles, with an alpha value of 0.05, based on observation from a similar study.
The statistical bioinformatic analyses was conducted by Emphron Informatics, and in consultation with the Principal Investigator and the Steering Committee. Statistical consultants were responsible for identifying and justifying the most appropriate statistical approach and producing a statistical report at study completion.
Analyses utilised an intent-to-analyse population and include all subjects with available data. No substitutions were made for missing data. Each data set, unless otherwise defined, was indexed on the participant initials and the unique identifying number.
Chi-square tests were performed on untransformed and on log-transformed data to demonstrate independence between genomic and proteomic outcomes for the healthy control group and the jet fuel/solvent (Air Force Veteran) exposed group. For the log transformed data, a 95% CI was given for the ratio of any two means. For the untransformed data, study group data was compared using the 95% CI to ascertain the difference between means.
Multivariate logistic regression analyses were conducted to determine relationships between molecular investigations, clinical data, and the exposure data in the jet fuel/solvent exposed group. It is possible that genetically determined susceptibility in a subset of individuals may be
Page 72
������������������� ������� ������������������������� ������ ��������������������identified. The effect may be independent of dose. The power of this observation cannot be determined until the data is obtained. An Odds Ratio will be presented to predict the risk of future genotypic, phenotypic and clinical pathology in the jet fuel/solvent exposed group.
Analyses utilised an intent-to-analyse population and included all subjects with available data. No substitutions were made for missing data. The analyses were defined in a detailed Statistical Analysis Plan (SAP) and the details of all analyses, listings and tabulations were defined. Any deviations from the SAP were clearly identified and justified in the final report.
All statistical analyses were performed using the statistical package, R. Each data set, unless otherwise defined were indexed on the participant initials and the unique identifying number.
The Intent-to-Analyse (ITA) Population comprises all participants who have either proteomic or genomic analyses conducted. All primary endpoints were analysed using the ITA population.
The Per-Protocol (PP) Population comprises all participants who have complete genomic and proteomic studies conducted for the purposes of this study. The Per-Protocol population were used for sensitivity analyses.
Where information was not available for data fields, it was marked as “Unknown” or “Not applicable”. All case report forms (CRF) were legible and only referred to the participant’s unique identifying number/code. The unique identifying number was documented in the CRF for the purpose of recording clinical data.
Bioinformatic Analysis of Proteomic and Genomic Outcomes
The quality of all RNA extracts were assessed prior to processing on gene chips. Gene expression estimates were developed using the RMA algorithm, applied to exon microarray data. XMAP annotation data were used to generate both gene level and exon level expression estimates. All chip data were screened using a variety of chip quality metrics (including but not limited to spike in concentrations, within-chip spatial correlations, and between chip correlations.
Page 73
������������������� ������� ������������������������� ������ ��������������������Proteomic analysis utilised proprietary software from Bruker (ProteinScape 1.3), capable of normalising spectra across multiple specimens and between runs; identifying statistically differentially expressed protein peaks in hundreds of samples; and subsequently identifying the nature of these individual proteins. Correlation of genes with proteins / peptides were greatly facilitated by our approach of running exon microarrays (rather than 3’UTR-based arrays), and utilised the algorithms described by Bitton et al, 2008.
Exploratory analysis of gene expression and proteomic data were based on principal components plots and hierarchical clustering.
In the absence of stratification, Chi-square tests were performed on untransformed and on log-transformed data to demonstrate independence between genomic and proteomic outcomes for the healthy control group and the jet fuel/solvent (Air Force Veteran) exposed group. In the presence of stratification, a Mantel Haenszel procedure was adopted.
All p values from association tests were adjusted to maintain a 5% false discovery rate.
A Hypergeometric Ontology enrichment analysis was used to identify molecular functions, biological processes, cellular components and pathways that are over-represented in the list of differentially expressed markers.
Multivariate logistic regression analyses were conducted to determine relationships between molecular investigations and clinical data in the jet fuel/solvent exposed group. Parsimonious models were fitted using stochastic variable elimination techniques based on a Laplacian prior. These techniques provide for computationally efficient selection from amongst massively multivariate putative predictors. Permutation tests of the overall association were used, and classification success will be assessed using multiple realisations of a k-fold cross validation scheme. In order to minimise selection bias, Cross validation was external to the marker selection process. An Odds Ratio was presented to predict the risk of future genotypic, phenotypic and clinical pathology in the jet fuel/solvent exposed group.
Page 74
������������������� ������� ������������������������� ������ ��������������������Review of Descriptive Medical Data
Full blood counts, and the Health assessment scores were summarised by level of exposure. Dichotomous variables were summarised by total number of observations, number and percentage of subjects with the characteristic. Continuous variables were summarised by mean, SD, median, minimum and maximum. Clinical laboratory values were presented by exposure group. Tables of selected laboratory parameters were provided where appropriate. Detailed subject listings of all laboratory data collected during the study were presented. Laboratory values outside normal limits were identified in the subject data listings with flags for high and low values.
�Study population and recruitment
546 participants from a phenotype and exposure database (SHOAMP) that had already been created for the purpose of a previous study investigating the relationship between exposure to jet fuel/solvents in the Deseal/Reseal project and health outcomes responded to a request to participate in this study. This list was categorised by location and amount of exposure to jet fuel. 196 current and ex-Air Force personnel who worked either indirectly or directly on the Deseal/Reseal project between 1973 and 1999 and/ or had direct or indirect exposure to F111 jet fuel were approached to take part in the study in October 2012. 6 participants withdrew from the study, 10 could not be collected and 2 participants died during the recruitment phase of the study. 178 participants were given the opportunity to provide blood samples and complete health questionnaires between November 2012 and June 2013. In addition, 77 healthy controls that were matched for age and gender were also recruited from within the general community or current and ex-Air Force personnel were tested between June and December 2013.
�
Page 75
������������������� ������� ������������������������� ������ ��������������������Table 10. Study participants
�Control population
In addition, 84 healthy controls that were matched for age and gender from within the general community or current and ex-Air Force personnel consented to be tested between June and December 2013. 7 controls withdrew from the study.
Table 11. Study controls
��
Jet Fuel Exposure
Participants selected
Number
Participants
Participation rate
High Exposure
96 84 87.5%
Moderate Exposure
50 45 90%
Low Exposure
50 46 92%
TOTAL 196 175 89.3%
Jet Fuel Exposure
Participants approached
Number
Participants
Participation rate
None 84 77 91.7%
Page 76
������������������� ������� ������������������������� ������ ��������������������Age matched controls There were no statistical differences in the age distributions of controls or exposure groups. A Kruskal Wallis rank sum test (a non-parametric 1 way analysis of variance) obtained the result:-�Kruskal-Wallis chi-squared = 4.2433, df = 3, p-value = 0.2364
Figure 17. Comparison of age distribution of exposed workers and controls
Page 77
������������������� ������� ������������������������� ������ ��������������������
� Figure 18. Comparison of age distribution by type of exposure (1 – High, 2 – Medium, 3 – Low) and Controls (4)
�Ethics Ethical approval for the project was obtained from Mater Health Services, the Australian Department of Defence and the Department of Veterans Affairs. No ethical issues or complaints arose with participants or controls during the study.
���
�
�
Page 78
������������������� ������� ������������������������� ������ ��������������������3.3 Results of study of exposed workers
The study of exposed workers showed differences from the matched control group in health indices, and in some genomic studies. Details are presented below.
Health Assessment Scores
The Health assessment scores showed exposed workers to have a lower health rating than controls. There did not appear to be a decrease in the health scores (dose response) related to the degree of exposure. Workers with mild exposure had the same decrease in their health scores as those with high exposure.
Bioinformatic analysis of Genomic and Proteomic data and Health scores
Detailed test results and statistical analysis of the genomic, proteomic, and health assessments are include in Appendices 3-7.
The genetic studies of blood cells examined two types of changes in gene expression, and the presence of chromosomal changes and for mutations the mitochondrial DNA.
There were no chromosomal changes detected at a level of 50Kb using a high resolution SNP ARRAY
There was no change in the mitochondrial DNA mutation load between exposed workers and age matched controls. Mitochondrial DNA changes can accumulate with age.
There were no changes in the amount or type of protein coding mRNA expression, which is an index of cell activity. In disease states , these are usually tissue specific and may not appear in blood cells unless they are directly involved in the disease process.
There were small but significant and consistent changes in the expression of regulatory microRNAs that control activity of other genes. The regulatory functions of the altered genes have been linked to neurological changes and neurodegenerative disorders. It must be emphasised that interpretation of the
Page 79
������������������� ������� ������������������������� ������ ��������������������function of regulatory genes is an evolving science with much uncertainty at present. (The regulatory genes, which compose 98% of our genome, have a major role in human development, adaptation and response to disease. The function is only known for ~40% of these at present. Disease causing associations, with some early exceptions, are still unmapped).
�����������&�����)� ��������&�*���&�������
No significant changes were seen in the levels and types of protein expressed in the plasma and blood cells of exposed workers. A few small changes were seen consistently, but these did not reach a level that the researchers considered significant.
�
Page 80
������������������� ������� ������������������������� ������ ��������������������3.4 Discussion and Conclusions
Confounders and sensitivity
��+��)�&������� �����
A dose response would have been expected but was not observed in the workers with different exposure histories. The unexpected similarity in the health scores and genomic studies within the exposed groups (low, medium, high) raises several hypotheses:-
%�� ����*����!��)����)���&������
Confounders could include:-
An ascertainment bias whereby only those workers affected by any exposure volunteered to participate in the study.
An ascertainment bias whereby only those workers NOT affected by the exposure (ie. Survivors) volunteered to participate in the study.
The workers were stratified by their exposure to Deseal/Reseal materials. The effects seen may NOT be due to the Deseal/Reseal materials but to some other experience of the workers. The cellular studies suggest that exposure to fuel alone could be responsible.
It was not possible to examine other possible shared confounding events in the work careers or in the lifestyle of the personal. (eg. other occupational exposure not related to Deseal/Reseal such as medications, substance abuse, nutrition)
This study was conducted on individuals between 10 and 30 years after their exposure. If significant changes occurred at the time of exposure, normal cellular repair and selection mechanisms may have lessened the biological signal that could be observed in this study. The small but consistent changes observed suggest this possibility. Either the effect at the time was minimal but has persisted, or the effect was larger but has diminished overtime.
Page 81
������������������� ������� ������������������������� ������ ��������������������The cellular studies show that the compounds are mostly distributed by plasma lipids. The exposure to organs within the body would likely depend on the concentration of plasma lipids at the time of fuel exposure. Plasma lipids vary genetically between individuals, with lifestyle and alcohol intake, with composition of their diet, as well as the time after meals when the exposure occurred. The lack of a dose effect could be explained if workers in the lower exposure group had higher plasma lipids at the time of exposure. Individuals in the high exposure group worked within the fuel tanks and were selected because they were leaner and smaller, possibly protected to some extent by lower plasma lipids.
3.5 Significance of findings
The cellular findings, supported by other recently published genomic studies, indicate a definite toxicity from JP-8 to exposed cells. The components of JP8 tested are commonly found in most (aviation) fuels. The results indicate that there is a need for concern about exposure to fuels in general.
The study was not designed to determine the degree of occupational exposure necessary to produce cellular changes. However, the results show that cells grown in a nutrient containing as little as 5% exposed plasma are affected. In the body, blood cells have 100% exposure to plasma while other organs will have less exposure depending on the net blood flow and cellular membrane barriers. Organs such as brain, liver and bowel have very high blood flow. Cellular membranes generally have greater permeability to substances dissolved in lipids.
The study was also not designed to determine the most toxic routes of exposure (inhalation, ingestion, skin contact), but did demonstrate that fuel components can be distributed to organs through blood plasma, ie organs such as brain or liver, not directly exposed in the contact, may undergo secondary exposure. The implication is that all body systems must be considered in assessing/monitoring the health of exposed workers.
�
Page 82
������������������� ������� ������������������������� ������ ��������������������While the changes seen many years after exposure were small they were consistent within the group. The changes are most apparent in gene regulation and had some association to the health problems (eg, malignancy) identified in other studies. There were no chromosomal changes or mutations linked to the exposure. The genes changes seen can be described as Epigenetic, which is a mechanism of cellular adaptation to some environmental influence. Epigenetic changes are less clearly linked at the present time to disease. Epigenetic changes occur through a variety of cellular mechanisms and these were not investigated in this study. Some epigenetic changes can be transferred down through successive generations but to date have not been shown to cause birth defects or mutation in off-spring.
�
Page 83
�������
CHAPTER 4�Appendices, References, Glossary�
��
Page 84
������������������� ������� ������������������������� ������ ���������������������Appendix 1 Ethical Statements
Ethics
Ethics approval was obtained from all Institutions involved in the study :-
Mater Health Services Human Research Ethics Committee (Ethics Approval MHS
HREC.pdf)
Australian Defence Human Research Ethics Committee�, Directorate of Health Research Co-ordination, Joint Health CommandDepartment of Defence ) ADHREC Approval 12 Nov 2012.pdf }
DVA Human Research Ethics Committee, Australian Government,
Department of Veterans’ Affairs ( E010-027 - DVA Human Research Ethics Committee.pdf }
�
Page 85
Page 86
Page 87
Page 88
1
J O I N T H E A L T H C O M M A N D
ADHREC, CP3-6-036, Campbell Park Offices, PO Box 7911, Canberra BC ACT 2610
2010/1145828ADHREC/OUT/2011/R13148748
Prof Frank Bowling Prof Deon Venter A/Prof Waterhouse Miss Rachelle Warner Dear Researchers
AUSTRALIAN DEFENCE HUMAN RESEARCH ETHICS COMMITTEE (ADHREC) COMMENTS ON PROTOCOL 605-10 - MOLECULAR INVESTIGATIONS INTO THE BASIS OF DISORDERS MANIFESTED FOLLOWING DESEAL/RESEAL AND CLINICAL RISK PREDICTION ASSOCIATED WITH JET FUEL EXPOSURE SYNDROME.
1. Thank you for submitting your research modification for consideration by ADHREC. Your modification request was considered by the committee out of session.
2. ADHREC has approved your modification noting: a. The new questionnaire issue was clarified. Some members felt it may not be as
effective as the previously proposed questionnaire, but that it is a methodological, rather than an ethical issue, and the researchers’ justification for the change was satisfactory.
b. The consent form has now been adequately modified to communicate the destruction of the blood samples to participants.
3. Should you have any questions in relation to this matter please contact the ADHREC Secretariat. Contact details are provided below.
Yours sincerely,
Sarah Blackledge SecretaryADHREC
Tel (02) 6266 3807 Fax (02) 6266 2697 E-mail: [email protected]
12 November 2012
Page 89
������������������� ������� ������������������������� ������ ��������������������Appendix 2 Results of health assessments
��
Page 90
Jet Fuel and Solvents ExposureProject
Health Questionnaire
Emphron Informatics Pty Ltd www.emphron.com
Report Number: 2012.020
Version Number: 1.0
Client: Jet Fuel and Solvents Project
Date: July 25, 2014
Author: Mervyn Thomas
Email: [email protected]
Signature: ..................................................
Page 91
Jet Fuel and Solvents Health Questionnaire
1 Introduction
1.1 Background and Objectives
The Jet Fuel and Solvents Exposure study has been set up to investigate physiological
and other differences between ex Air Force employees who were exposed to aviation
fuel during their service. Subjects were stratified into four exposure groups, where
Group 4 is a control group (representing no known exposure) and Group 1 is the highest
level of exposure.
Data are available from several genomic and proteomic modalities, including: cyto-
genetics data, High Throughput mRNA gene expression data, micro RNA expression
data, serum analysis by LCMS and full blood counts. This report is concerned with
the analysis of a health questionnaire scale. The scale is generated as a sum of 30
items: each measured on a discrete scale. Higher values of the score represent poorer
psychological states.
1.2 Available Data
Mitochondrial DNA variant data are available from 245 subjects. 84 of these subjects
were in the high exposure group; 44 in the medium exposure group; 41 in the low
exposure group and 76 in the control group.
2 Methods
A box and whisker plots[1] was generated, showing the difference in health scores be-
tween exposure groups. Between group differences were investigated using an analysis
of variance. All pair-wise group comparisons were performed, using t tests based on
the pooled estimate of residual variance from the analysis of variance
3 Results
The distribution of health scores by exposure group is shown in Figure 1. There is
no suggestion of any differences between the exposure groups, but a clear difference
between the exposure groups and the control group.
Commercial In Confidence 1
Page 92
Jet Fuel and Solvents Health Questionnaire
●
●
20
40
60
80
High Exposure Medium Exposure Low Exposure ControlExposure Group
Hea
lth S
core
Psycho Social Health Score Distribution by Exposure Group
Figure 1: Distribution of Health Scores By Exposure Group
Table ?? shows the t statistics for the pair-wise comparisons between exposure groups.
The control group is statistically significantly different from every other group. There
are no statistically significant differences between the exposed groups.
Exposure
High Medium Low Control
High Exposure 0.00 1.18 1.32 -6.92
Medium Exposure -1.18 0.00 0.15 -6.95
Low Exposure -1.18 0.00 0.00 -6.96
Control 6.92 6.95 6.96 0.00
Table 1: t Statistics for Pairwise Comparisons Between Exposure Groups.The critical
value for t at the 5% level is 1.97. The control group is statistically significantly differ-
ent from every other group. There are no statistically significant differences between
the exposed groups.
Commercial In Confidence 2
Page 93
Jet Fuel and Solvents Health Questionnaire
4 Discussion
There are clear statistically significant differences between the control group, and each
of the exposed groups. The control group score is lower than the exposed groups:
demonstrating that psychological health in the control groups is better than in the ex-
posed groups.
There are demographic differences between the groups, and this result does not neces-
sarily imply a causative relationship.
References
[1] J. M. Chambers, W. S. Cleveland, B. Kleiner, and P. Tukey. Graphical Methodsfor Data Analysis. Wadswoth and Brooks / Cole, 1983.
Commercial In Confidence 3
Page 94
������������������� ������� ������������������������� ������ ��������������������
Appendix 3 Report from Emphron on Bioinformatic analysis of gene expression array data from leucocytes of exposed workers and controls.
���������������
ANALYSIS of HTA 1����Emphron Informatics Pty Ltd Author: Mervyn Thomas Date: 11 June 2014 �
Page 95
Jet Fuel and Solvents ExposureProject
Analysis of High Throughput Arrays – HTA2.0
Emphron Informatics Pty Ltd www.emphron.com
Report Number: 2012.020
Version Number: 1.0
Client: Jet Fuel & Solvents Project
Date: July 27, 2014
Author: Mervyn Thomas
Email: [email protected]
Signature: ..................................................
Page 96
Jet Fuel & Solvents HTA 2 Arrays
Contents
1 Introduction 8
1.1 Background and Objectives . . . . . . . . . . . . . . . . . . . . . . . 8
1.2 Available Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Pre Processing of Gene Chip Data 9
3 Exploratory Analysis of Gene Expression 12
4 Differential Expression 16
4.1 Between Group Differences . . . . . . . . . . . . . . . . . . . . . . . 16
4.2 Relationship With Health Score . . . . . . . . . . . . . . . . . . . . 59
5 Ontology Enrichment 60
6 Conclusions 71
List of Tables
1 Samples By Exposure Group and Date Scanned . . . . . . . . . . . . 8
2 Statistically Significant Differences in Gene Expression by Contrast;
Results are ranked by absolute log2 fold change. Gene names and
gene symbols are as defined by the HUGO Gene Nomenclature Com-
mittee www.genenames.org. Entrez is the NCBI Entrez code for
the gene http://www.ncbi.nlm.nih.gov/gene/. Note some
gene symbols are associated with multiple probe sets, and therefore
have multiple entries in this table. . . . . . . . . . . . . . . . . . . . 17
2 Statistically Significant Differences in Gene Expression by Contrast;
Results are ranked by absolute log2 fold change. Gene names and
gene symbols are as defined by the HUGO Gene Nomenclature Com-
mittee www.genenames.org. Entrez is the NCBI Entrez code for
the gene http://www.ncbi.nlm.nih.gov/gene/. Note some
gene symbols are associated with multiple probe sets, and therefore
have multiple entries in this table. . . . . . . . . . . . . . . . . . . . 18
Commercial In Confidence 1
Page 97
Jet Fuel & Solvents HTA 2 Arrays
2 Statistically Significant Differences in Gene Expression by Contrast;
Results are ranked by absolute log2 fold change. Gene names and
gene symbols are as defined by the HUGO Gene Nomenclature Com-
mittee www.genenames.org. Entrez is the NCBI Entrez code for
the gene http://www.ncbi.nlm.nih.gov/gene/. Note some
gene symbols are associated with multiple probe sets, and therefore
have multiple entries in this table. . . . . . . . . . . . . . . . . . . . 19
2 Statistically Significant Differences in Gene Expression by Contrast;
Results are ranked by absolute log2 fold change. Gene names and
gene symbols are as defined by the HUGO Gene Nomenclature Com-
mittee www.genenames.org. Entrez is the NCBI Entrez code for
the gene http://www.ncbi.nlm.nih.gov/gene/. Note some
gene symbols are associated with multiple probe sets, and therefore
have multiple entries in this table. . . . . . . . . . . . . . . . . . . . 20
2 Statistically Significant Differences in Gene Expression by Contrast;
Results are ranked by absolute log2 fold change. Gene names and
gene symbols are as defined by the HUGO Gene Nomenclature Com-
mittee www.genenames.org. Entrez is the NCBI Entrez code for
the gene http://www.ncbi.nlm.nih.gov/gene/. Note some
gene symbols are associated with multiple probe sets, and therefore
have multiple entries in this table. . . . . . . . . . . . . . . . . . . . 21
2 Statistically Significant Differences in Gene Expression by Contrast;
Results are ranked by absolute log2 fold change. Gene names and
gene symbols are as defined by the HUGO Gene Nomenclature Com-
mittee www.genenames.org. Entrez is the NCBI Entrez code for
the gene http://www.ncbi.nlm.nih.gov/gene/. Note some
gene symbols are associated with multiple probe sets, and therefore
have multiple entries in this table. . . . . . . . . . . . . . . . . . . . 22
2 Statistically Significant Differences in Gene Expression by Contrast;
Results are ranked by absolute log2 fold change. Gene names and
gene symbols are as defined by the HUGO Gene Nomenclature Com-
mittee www.genenames.org. Entrez is the NCBI Entrez code for
the gene http://www.ncbi.nlm.nih.gov/gene/. Note some
gene symbols are associated with multiple probe sets, and therefore
have multiple entries in this table. . . . . . . . . . . . . . . . . . . . 23
2 Statistically Significant Differences in Gene Expression by Contrast;
Results are ranked by absolute log2 fold change. Gene names and
gene symbols are as defined by the HUGO Gene Nomenclature Com-
mittee www.genenames.org. Entrez is the NCBI Entrez code for
the gene http://www.ncbi.nlm.nih.gov/gene/. Note some
gene symbols are associated with multiple probe sets, and therefore
have multiple entries in this table. . . . . . . . . . . . . . . . . . . . 24
Commercial In Confidence 2
Page 98
Jet Fuel & Solvents HTA 2 Arrays
2 Statistically Significant Differences in Gene Expression by Contrast;
Results are ranked by absolute log2 fold change. Gene names and
gene symbols are as defined by the HUGO Gene Nomenclature Com-
mittee www.genenames.org. Entrez is the NCBI Entrez code for
the gene http://www.ncbi.nlm.nih.gov/gene/. Note some
gene symbols are associated with multiple probe sets, and therefore
have multiple entries in this table. . . . . . . . . . . . . . . . . . . . 25
2 Statistically Significant Differences in Gene Expression by Contrast;
Results are ranked by absolute log2 fold change. Gene names and
gene symbols are as defined by the HUGO Gene Nomenclature Com-
mittee www.genenames.org. Entrez is the NCBI Entrez code for
the gene http://www.ncbi.nlm.nih.gov/gene/. Note some
gene symbols are associated with multiple probe sets, and therefore
have multiple entries in this table. . . . . . . . . . . . . . . . . . . . 26
2 Statistically Significant Differences in Gene Expression by Contrast;
Results are ranked by absolute log2 fold change. Gene names and
gene symbols are as defined by the HUGO Gene Nomenclature Com-
mittee www.genenames.org. Entrez is the NCBI Entrez code for
the gene http://www.ncbi.nlm.nih.gov/gene/. Note some
gene symbols are associated with multiple probe sets, and therefore
have multiple entries in this table. . . . . . . . . . . . . . . . . . . . 27
2 Statistically Significant Differences in Gene Expression by Contrast;
Results are ranked by absolute log2 fold change. Gene names and
gene symbols are as defined by the HUGO Gene Nomenclature Com-
mittee www.genenames.org. Entrez is the NCBI Entrez code for
the gene http://www.ncbi.nlm.nih.gov/gene/. Note some
gene symbols are associated with multiple probe sets, and therefore
have multiple entries in this table. . . . . . . . . . . . . . . . . . . . 28
3 log2 Fold Change of mRNA Species By Comparison. Gene is the
HUGO Gene Nomenclature Committee www.genenames.orgName
for the gene. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3 log2 Fold Change of mRNA Species By Comparison. Gene is the
HUGO Gene Nomenclature Committee www.genenames.orgName
for the gene. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3 log2 Fold Change of mRNA Species By Comparison. Gene is the
HUGO Gene Nomenclature Committee www.genenames.orgName
for the gene. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3 log2 Fold Change of mRNA Species By Comparison. Gene is the
HUGO Gene Nomenclature Committee www.genenames.orgName
for the gene. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3 log2 Fold Change of mRNA Species By Comparison. Gene is the
HUGO Gene Nomenclature Committee www.genenames.orgName
for the gene. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Commercial In Confidence 3
Page 99
Jet Fuel & Solvents HTA 2 Arrays
3 log2 Fold Change of mRNA Species By Comparison. Gene is the
HUGO Gene Nomenclature Committee www.genenames.orgName
for the gene. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3 log2 Fold Change of mRNA Species By Comparison. Gene is the
HUGO Gene Nomenclature Committee www.genenames.orgName
for the gene. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3 log2 Fold Change of mRNA Species By Comparison. Gene is the
HUGO Gene Nomenclature Committee www.genenames.orgName
for the gene. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4 Status in the full data set (all dates scanned) vs Status in the Reduced
Data Sets (only chips scanned on or after 16thOctober 2010) . . . . . 46
5 Statistically Significant Differences in Gene Expression by Contrast for
data scanned on or after 16thOctober 2013 ; Results are ranked by ab-
solute log2 fold change. Gene names and gene symbols are as defined
by the HUGO Gene Nomenclature Committee www.genenames.org. Entrez is the NCBI Entrez code for the gene http://www.ncbi.nlm.nih.gov/gene/. Note some gene symbols are asso-
ciated with multiple probe sets, and therefore have multiple entries in
this table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5 Statistically Significant Differences in Gene Expression by Contrast for
data scanned on or after 16thOctober 2013 ; Results are ranked by ab-
solute log2 fold change. Gene names and gene symbols are as defined
by the HUGO Gene Nomenclature Committee www.genenames.org. Entrez is the NCBI Entrez code for the gene http://www.ncbi.nlm.nih.gov/gene/. Note some gene symbols are asso-
ciated with multiple probe sets, and therefore have multiple entries in
this table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5 Statistically Significant Differences in Gene Expression by Contrast for
data scanned on or after 16thOctober 2013 ; Results are ranked by ab-
solute log2 fold change. Gene names and gene symbols are as defined
by the HUGO Gene Nomenclature Committee www.genenames.org. Entrez is the NCBI Entrez code for the gene http://www.ncbi.nlm.nih.gov/gene/. Note some gene symbols are asso-
ciated with multiple probe sets, and therefore have multiple entries in
this table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5 Statistically Significant Differences in Gene Expression by Contrast for
data scanned on or after 16thOctober 2013 ; Results are ranked by ab-
solute log2 fold change. Gene names and gene symbols are as defined
by the HUGO Gene Nomenclature Committee www.genenames.org. Entrez is the NCBI Entrez code for the gene http://www.ncbi.nlm.nih.gov/gene/. Note some gene symbols are asso-
ciated with multiple probe sets, and therefore have multiple entries in
this table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Commercial In Confidence 4
Page 100
Jet Fuel & Solvents HTA 2 Arrays
5 Statistically Significant Differences in Gene Expression by Contrast for
data scanned on or after 16thOctober 2013 ; Results are ranked by ab-
solute log2 fold change. Gene names and gene symbols are as defined
by the HUGO Gene Nomenclature Committee www.genenames.org. Entrez is the NCBI Entrez code for the gene http://www.ncbi.nlm.nih.gov/gene/. Note some gene symbols are asso-
ciated with multiple probe sets, and therefore have multiple entries in
this table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
6 Gene Ontology Enrichment for Genes which are Over Expressed in
Group 4 vs Group 1. The ‘GO ID’ is the unique identifier in the Gene
Ontology Consortium ontology database, and may be searched via the
AMI GO interface http://amigo.geneontology.org/amigo.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
7 Gene Ontology Enrichment for for Genes which are Under Expressed
in Group 4 vs Group 1. The ‘GO ID’ is the unique identifier in the Gene
Ontology Consortium ontology database, and may be searched via the
AMI GO interface http://amigo.geneontology.org/amigo. 64
7 Gene Ontology Enrichment for Genes which are Under Expressed in
Group 4 vs Group 1. The ‘GO ID’ is the unique identifier in the Gene
Ontology Consortium ontology database, and may be searched via the
AMI GO interface http://amigo.geneontology.org/amigo.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
8 Gene Ontology Enrichment for for Genes which are Over Expressed
in Group 4 vs Group 1 for chips scanned on or after October 16th2013.
The ‘GO ID’ is the unique identifier in the Gene Ontology Consortium
ontology database, and may be searched via the AMI GO interface
http://amigo.geneontology.org/amigo. . . . . . . . . . 67
8 Gene Ontology Enrichment for Genes which are Over Expressed in
Group 4 vs Group 1, for chips scanned on or after October 16th2013.
The ‘GO ID’ is the unique identifier in the Gene Ontology Consortium
ontology database, and may be searched via the AMI GO interface
http://amigo.geneontology.org/amigo. . . . . . . . . . 68
9 Gene Ontology Enrichment for Genes which are Under Expressed in
Group 4 vs Group 1. The ‘GO ID’ is the unique identifier in the Gene
Ontology Consortium ontology database, and may be searched via the
AMI GO interface http://amigo.geneontology.org/amigo.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
List of Figures
1 Exponentially Weighted Moving Average (EWMA) Control Chart for
Mean Absolute Deviation of Residuals . . . . . . . . . . . . . . . . . 10
Commercial In Confidence 5
Page 101
Jet Fuel & Solvents HTA 2 Arrays
2 Distribution of Mean Absolute Deviation of Residuals . . . . . . . . . 10
3 EWMA Chart for Relative Log Expression . . . . . . . . . . . . . . . 11
4 Distribution of Relative Log Expression . . . . . . . . . . . . . . . . 12
5 Scree Plot For Principal Components Showing Percent Variance Ex-
plained By the First 10 Components. . . . . . . . . . . . . . . . . . . 13
6 Disposition of Each Gene Chip[ in the Space of the First Two Principal
Components. Exposure Groups are Colour Coded. . . . . . . . . . . . 14
7 Disposition of Each Gene Chip in the Space of the Third and Fourth
Principal Components. Exposure Groups are Colour Coded. . . . . . 14
8 Cluster Analysis based on Complete Average and Single Linkage . . . 15
9 Group Differences for Gene Symbol UBAP2L : ubiquitin associated
protein 2-like . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
10 Group Differences for Gene Symbol FBXO42 : F-box protein 42 . . . 39
11 Group Differences for Gene Symbol UBE2Q1 : ubiquitin-conjugating
enzyme E2Q family member 1 . . . . . . . . . . . . . . . . . . . . . 39
12 Group Differences for Gene Symbol NRBP1 : nuclear receptor binding
protein 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
13 Group Differences for Gene Symbol KLHL23 : kelch-like family mem-
ber 23 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
14 Group Differences for Gene Symbol VHL : von Hippel-Lindau tumor
suppressor, E3 ubiquitin protein ligase . . . . . . . . . . . . . . . . . 41
15 Group Differences for Gene Symbol EPGN : epithelial mitogen . . . 41
16 Group Differences for Gene Symbol PPP1R10 : protein phosphatase
1, regulatory subunit 10 . . . . . . . . . . . . . . . . . . . . . . . . . 42
17 Group Differences for Gene Symbol ZDHHC4 : zinc finger, DHHC-
type containing 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
18 Group Differences for Gene Symbol ZPBP : zona pellucida binding
protein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
19 Group Differences for Gene Symbol GLE1 : GLE1 RNA export mediator 43
20 Group Differences for Gene Symbol CCNK : cyclin K . . . . . . . . 44
21 Group Differences for Gene Symbol C15orf61 : chromosome 15 open
reading frame 61 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
22 Group Differences for Gene Symbol ASPHD2 : aspartate beta-hydroxylase
domain containing 2 . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Commercial In Confidence 6
Page 102
Jet Fuel & Solvents HTA 2 Arrays
23 Group Differences for Gene Symbol SNORD94 : small nucleolar RNA,
C/D box 94 In Reduced Data Set. . . . . . . . . . . . . . . . . . . . . 52
24 Group Differences for Gene Symbol SCARNA6 : small Cajal body-
specific RNA 6 In Reduced Data Set. . . . . . . . . . . . . . . . . . . 53
25 Group Differences for Gene Symbol SNORD32B : small nucleolar
RNA, C/D box 32B In Reduced Data Set. . . . . . . . . . . . . . . . 53
26 Group Differences for Gene Symbol HIST1H4D : histone cluster 1,
H4d In Reduced Data Set. . . . . . . . . . . . . . . . . . . . . . . . 54
27 Group Differences for Gene Symbol HIST1H3F : histone cluster 1,
H3f In Reduced Data Set. . . . . . . . . . . . . . . . . . . . . . . . . 54
28 Group Differences for Gene Symbol SNORA5A : small nucleolar RNA,
H/ACA box 5A In Reduced Data Set. . . . . . . . . . . . . . . . . . 55
29 Group Differences for Gene Symbol SNORA5C : small nucleolar RNA,
H/ACA box 5C In Reduced Data Set. . . . . . . . . . . . . . . . . . 55
30 Group Differences for Gene Symbol SCARNA9L : small Cajal body-
specific RNA 9-like In Reduced Data Set. . . . . . . . . . . . . . . . 56
31 Group Differences for Gene Symbol SCARNA9 : small Cajal body-
specific RNA 9 In Reduced Data Set. . . . . . . . . . . . . . . . . . . 56
32 Group Differences for Gene Symbol SNORD116-25 : small nucleolar
RNA, C/D box 116-25 In Reduced Data Set. . . . . . . . . . . . . . . 57
33 Group Differences for Gene Symbol SNORD116-26 : small nucleolar
RNA, C/D box 116-26 In Reduced Data Set. . . . . . . . . . . . . . . 57
34 Group Differences for Gene Symbol SNORD116-27 : small nucleolar
RNA, C/D box 116-27 In Reduced Data Set. . . . . . . . . . . . . . . 58
35 Group Differences for Gene Symbol RNY4P8 : RNA, Ro-associated
Y4 pseudogene 8 In Reduced Data Set. . . . . . . . . . . . . . . . . . 58
36 Group Differences for Gene Symbol MIR3687 : microRNA 3687 In
Reduced Data Set. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
37 Gene Expression for TTTY20 (testis-specific transcript, Y-linked 20 –
non-protein coding) vs Health Score . . . . . . . . . . . . . . . . . . 60
Commercial In Confidence 7
Page 103
Jet Fuel & Solvents HTA 2 Arrays
1 Introduction
1.1 Background and Objectives
The Jet Fuel and Solvents Exposure study has been set up to investigate physiological
and other differences between ex Air Force employees who were exposed to aviation
fuel during their service. Subjects were stratified into four exposure groups, where
Group 4 is a control group (representing no known exposure) and Group 1 is the highest
level of exposure.
Data are also available from other genomic modalities (including cytogentics data and
micro RNA data) and from proteomic sources. Only the results of gene expression
from blood samples, estimated using the Affymetrix HTA2.0 chip are described in this
report.
Each subject was also asked to complete a health questionnaire (Ref), and data from
the psycho social scale of the questionnaire were investigated in conjunction with the
gene expression.
1.2 Available Data
HTA gene chip data were available from 225 subjects. The break down of these sub-
jects by exposure group and date at which the chips were scanned is shown in Table 1.
We note that all group 4 chips were scanned on or after 16thOctober 2013. The com-
parison of Group 4 with the other groups is therefore partially confounded with date of
scanning. This issue is addressed in the Methods section below.
Date Exposure Group
Scanned 1 2 3 4
2013-03-12 2 2 1 0
2013-07-19 1 1 1 0
2013-08-01 9 5 8 0
2013-08-02 9 4 3 0
2013-08-05 0 0 2 0
2013-08-07 0 1 0 0
2013-10-04 6 0 3 0
2013-10-11 11 3 9 0
2013-10-16 3 4 4 20
2013-11-06 14 5 1 8
2014-01-10 7 2 2 21
2014-03-19 8 4 4 11
2014-03-26 7 8 5 6
Table 1: Samples By Exposure Group and Date Scanned
Commercial In Confidence 8
Page 104
Jet Fuel & Solvents HTA 2 Arrays
2 Pre Processing of Gene Chip Data
Raw gene chip data were processed to give gene level estimates of expression using the
RMA algorithm[6], as implemented in the Affymetrix Power Tools Suite[8]. Standard
quality metrics were generated as recommended by Affymetrix[1].
Two of those quality metrics were selected for further examination, based on the au-
thor’s experience of their performance on large data sets. The metrics considered were:
all+probesets mad residual mean The mean of the absolute deviation of the resid-
uals from the median. The RMA algorithm produces a model for probe level
responses, based on the particular probe and the probe set to which it belongs.
Residuals from this model represent departures from the model prediction for
the given probe on the given chip. The mean absolute deviation for a chip pro-
vides a measure of how well the particular chip fits the RMA model: which was
developed across all chips. A high mean absolute deviation of the residuals from
the median suggests quality problems for the chip concerned.
all probesets rle mean The mean absolute relative log expression (RLE) for all the
probesets on a given chip. This metric calculates the log2 ratio of the intensity for
each probeset to the median intensity for that probeset over all chips. The mean
log2 ratio is calculated. This measure will vary with true biological variability
between samples, but since in any one data set most genes will be unchanged,
this metric is generally low for high quality data.
Chips were sorted by date and time of scanning, and exponentially weighted moving
average control (EWMA) charts[10, 11] were generated for each metric. The EWMA
chart is a powerful method of identifying when a process moved out of control. It is not
intended to identify individual extreme values; the EWMA is more sensitive to runs of
extreme values. It generally lags the onset of extreme values by a few observations.
Histograms were constructed for each metric, and robust Huber estimates[5] of the
mean and standard deviation were used to generate mean ± 3 standard deviation ranges.
Figure 1 shows the EWMA plot for mean absolute deviation of residuals. There is
a period towards the end of the sequence in which the process appears to be out of
control. Figure 2 shows a histogram and kernel density estimate[12] of the distribution
of this metric. The distribution is relatively well behaved, with a small number of
extreme values. These extreme values occur during g the period highlighted on the
EWMA chart.
Inspection of the data revealed that these extreme values were for five RNA samples
obtained by manual extraction. The samples were dropped from down stream analysis.
Commercial In Confidence 9
Page 105
Jet Fuel & Solvents HTA 2 Arrays
EWMA Chartfor Relative log Expression
Group
Gro
up S
umm
ary
Sta
tistic
s
1 13 27 41 55 69 83 97 113 131 149 167 185 203
0.30
0.35
0.40
0.45
●●
●●
●
●
●
●
●
●
●
●●●
●●
●
●
●●
●
●●
●●
●●
●
●
●
●
●●
●
●
●
●
●
●●●●
●
●
●
●
●
●●
●
●
●
●
●●●
●●●
●
●●
●
●●
●
●
●
●●
●
●
●●
●●●●●
●
●
●●●●●
●●●
●●
●●
●
●
●
●
●●●●●●●●●●●
●
●●●
●●
●
●●
●
●●
●●
●
●
●●
●●
●●●●●●
●●
●
●●
●
●
●●
●
●
●●●
●●
●●
●●●●●●
●
●
●●●
●●●●
●●●●
●
●●
●
●
●●
●●
●
●
●
●●
●
●
●
●
●
●
●
●●●●
●
●●
●●
●
●
●
●●
●●
●●●●●●●
●
●
●●
●
LCL
UCL
●
●
●●
●●● ●
Number of groups = 220Center = 0.3514737StdDev = 0.03331009
Smoothing parameter = 0.2Control limits at 3*sigmaNo. of points beyond limits = 8
Figure 1: Exponentially Weighted Moving Average (EWMA) Control Chart for Mean
Absolute Deviation of Residuals
Distribution of Mean MAD of Residuals
Mean MAD
Den
sity
0.30 0.35 0.40 0.45
05
1015
Figure 2: Distribution of Mean Absolute Deviation of Residuals
Commercial In Confidence 10
Page 106
Jet Fuel & Solvents HTA 2 Arrays
Figure 3 shows the EWMA control chart for the relative log expression metric. Again,
there is a group of samples towards the end of the period in which the process appears
to be out of control. The distribution of the metric is shown in Figure 4. Again, the five
most extreme values correspond to the RNA samples obtained using manual extraction.
EWMA Chartfor Relative log Expression
Group
Gro
up S
umm
ary
Sta
tistic
s
1 13 27 41 55 69 83 97 113 131 149 167 185 203
0.10
0.15
0.20
0.25
●
●
●●●
●●
●
●
●●●●
●●
●●
●●●
●●●●●●●●●●●
●
●
●
●
●
●
●●●●●
●
●●
●
●●
●
●
●
●
●
●●●
●●
●
●
●●
●●●
●
●
●●
●
●●
●
●●●
●
●
●●
●●
●
●
●●
●●●●●
●●
●
●●●●●●
●
●
●●
●●●●
●●●●
●
●
●
●●
●●
●
●●●
●●
●
●●
●●
●
●
●●
●●●●●
●
●
●
●●●●●
●●●●
●
●●●●●●
●●
●●
●●●●●
●
●●●●●●●
●●
●
●
●●
●
●
●
●●
●
●●
●
●
●
●
●
●●
●
●●●
●
●
●
●●●●●
●
●●●●
●
●
●
●
●
●●
LCL
UCL●●
●
●
● ●●
●●●●●
●
●●
●
●●●
Number of groups = 220Center = 0.1469189StdDev = 0.02437711
Smoothing parameter = 0.2Control limits at 3*sigmaNo. of points beyond limits = 19
Figure 3: EWMA Chart for Relative Log Expression
Commercial In Confidence 11
Page 107
Jet Fuel & Solvents HTA 2 Arrays
Distribution of Mean Relative Log Expression
Mean RLE
Den
sity
0.10 0.15 0.20 0.25
05
1015
Figure 4: Distribution of Relative Log Expression
The quality metrics calculated for these data were very satisfactory. A small number of
extreme values were associated with documented differences in chip processing. These
chips have been excluded from further analysis.
Probe sets which had low overall expression (median across all chips log2 expression
< 2.4) or low overall variability (median absolute deviation from median across all
chips log2 expression < 0.064) were filtered out of all further analysis analysis.
3 Exploratory Analysis of Gene Expression
Principal components[7] of the gene expression matrix (represented as samples X
genes) were calculated. The scree plot is shown in Figure 5. This plot shows the
percentage of total variance explained fir the first 10 components. The plot shows a
rapid decline in variance for the first four components, followed by a long tail. The
first four components account for a total of 44% of the total variance.
Commercial In Confidence 12
Page 108
Jet Fuel & Solvents HTA 2 Arrays
0
5
10
15
20
2.5 5.0 7.5 10.0Component
% V
aria
nce
Figure 5: Scree Plot For Principal Components Showing Percent Variance Explained
By the First 10 Components.
Figure 6 shows the disposition of data for each chip in the space of the first two prin-
cipal components. Exposure group is colour coded. The plot reveals no evidence of
structure, and no suggestion that the first two principal components are associated with
exposure to jet fuel and solvents. Figure 7 shows similar results for components 3 and
4. Again there is no suggestion of any relationship with exposure group. The implica-
tion is that if there are differences between the Jet Fuel and Solvent exposure groups,
they are not a major source of chip to chip variation.
Commercial In Confidence 13
Page 109
Jet Fuel & Solvents HTA 2 Arrays
●●
●
●
●
●
●
●
●
● ●
●●
●●
●
●
●
●●●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●●
● ●
●
●
●
●
●
● ●●●
●●
●
●
●●●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●●
●●
●
●
●
●
●
●
●● ●●
●
●
●●
●
●
●●
●
●
●●
●
●
●
● ●●
●
●
●
●
●
●●●
●●
●●●
●
●● ●
●
●
●
●●
●
●●
●
●●
●
●
●●
●
●
●
●
●
●
●●
●
●
● ●
●
●
●
●●
●●
●
●
●
●
●
●●
●
●
●
●
● ●●
●●
●●
●
●
●●
●
●
●
●
●●
●
●
●
●
●●
● ●
●
●●
●●
●●
●
●
●
−25
0
25
−40 −20 0 20Component 1
Com
pone
nt 2 Group
●●●●
1
2
3
4
Observations By Exposure In Space of First Two Principal Components
Figure 6: Disposition of Each Gene Chip[ in the Space of the First Two Principal
Components. Exposure Groups are Colour Coded.
●
●
●●
●
●
●
●
●●
●
●
●●
●●
●●
●
●
●●
●
●
●
●●
●●
●
●
●
●●
●
●
●●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●●
●
●
●
●
●●
●
●
●
●●
●
●●●
●●●
●
●
●
●
●
●
●●
●
●●
●
●
●●
●
●
●●
●
●●
●
● ●
●
●
●
●
●
●
●
●
●
●●
●
●
●
● ●
●●
●
●
●
●
● ●
●
●●●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●●●
●
●●
●
●
●
●
●●
●
●
●●
●
●
●●
●
●
●
●
●●
●●●
●
●
●●
●
●●●
●
●
●
●●
●
●
●
●
●
●●●
●
●
●
●
●●
●●
●
●●
●
●
●
●
●
●
●
●
−20
−10
0
10
20
−20 −10 0 10 20Component 3
Com
pone
nt 4 Group
●●●●
1
2
3
4
Observations By Exposure In Space of Principal Components 3 and 4
Figure 7: Disposition of Each Gene Chip in the Space of the Third and Fourth Principal
Components. Exposure Groups are Colour Coded.
Commercial In Confidence 14
Page 110
Jet Fuel & Solvents HTA 2 Arrays
The gene expression data were clustered using three hierarchical clustering algorithms[2]:
complete, average and single linkage[4]. Cluster plots are often used to over-interpret
data. Apparent cluster structure is frequently illusory. Cluster solutions should only be
interpreted when they are markedly consistent across different algorithms.
Figure 8 shows cluster solutions using all three algorithms. Single linkage produces
chained clusters, in which each step in the hierarchical aggregation adds one more
observation to an existing large cluster. Complete linkage produces nicely balanced
solutions suggesting the presence of discrete groups. Unfortunately, this aesthetic pref-
erence for complete linkage does not reflect real structure in the data. The shape of the
solution is markedly different between algorithms, strongly suggesting the absence of
any true cluster structure.
JF40
53JF
4057
JF40
69 JF10
05JF
2064
JF10
60JF
1075
JF30
55JF
1089
JF20
55JF
1007
JF20
58JF
3058
JF10
27JF
4018
JF11
05JF
2079
JF30
04JF
1062
JF40
82JF
4039
JF40
44JF
3023
JF10
94JF
1066
JF30
43 JF20
32JF
2074
JF40
07JF
4015
JF30
24JF
2036
JF40
71JF
3020
JF10
40JF
1046 JF
2069
JF10
37JF
1050
JF30
17JF
2038
JF30
33JF
1048
JF10
73JF
4075
JF30
28JF
2068
JF30
22JF
4043
JF40
42JF
4076
JF10
72JF
1053
JF30
27 JF20
50JF
1006
JF40
54JF
2049
JF20
56JF
1092
JF10
84JF
2089 JF30
37JF
1086
JF20
14JF
4065
JF20
13JF
4072
JF20
33JF
4062
JF40
78 JF3
063
JF10
93JF
4036
JF20
47JF
4029 JF
1091
JF10
65JF
1056
JF30
38JF
2024
JF40
12JF
4011
JF10
76JF
1079
JF10
34JF
4080
JF40
77JF
4083
JF10
14JF
3056
JF40
27JF
1025
JF40
66 JF10
09JF
2065
JF40
04JF
1003
JF40
05JF
4013
JF40
25JF
1035
JF20
40JF
4081
JF10
29JF
1052
JF40
45JF
1081
JF30
66 JF40
52JF
3047
JF10
80JF
4050
JF30
46JF
1097
JF30
57JF
3002
JF30
61JF
1082
JF20
27JF
1004
JF20
42JF
1100
JF10
08JF
3054 J
F102
0JF
1085
JF40
58JF
3003
JF40
74JF
1090
JF30
60JF
4064
JF40
79JF
1023
JF40
32JF
1012
JF10
21JF
3015
JF30
44JF
3045
JF30
07JF
4055
JF20
35JF
1036
JF10
69JF
1063
JF10
88JF
4033
JF10
59JF
4040
JF10
22JF
2041
JF40
01JF
4056
JF10
02JF
1067
JF40
34JF
2051
JF40
84 JF10
24JF
3036
JF10
58JF
1045
JF10
47JF
2080
JF30
16JF
1098
JF30
59JF
1095
JF40
48JF
1026
JF30
40JF
2030
JF20
67JF
3064
JF10
99JF
3051
JF20
45JF
1051
JF40
63JF
3041
JF10
13JF
1057
JF20
57JF
1087
JF20
81JF
4010
JF40
31JF
4002
JF40
20JF
4028
JF20
25JF
2031
JF10
83JF
2003
JF20
48JF
3035
JF40
08JF
1031
JF40
14JF
4038
JF10
32JF
2078
JF40
06JF
3019
JF40
03JF
4024
JF10
43JF
3032
JF30
52JF
4009
JF10
70JF
2059 JF
1044
JF40
67JF
4068
JF10
28JF
1042
JF20
392030
4050
6070
8090
HTA Arrays Complete Linkage Clusters
Sample
Hei
ght
JF20
56JF
4053
JF40
57JF
4069
JF10
92JF
1060
JF10
05JF
2064
JF20
50JF
1072
JF20
68JF
1053
JF30
27JF
3028
JF30
22JF
3058
JF10
03JF
4012
JF10
95JF
4048
JF40
11JF
1076
JF10
79JF
1014
JF10
34JF
3056 JF
2024
JF10
09JF
1025
JF40
66JF
1007
JF10
75JF
1084
JF20
89JF
1086
JF20
14JF
4054
JF20
49JF
2038
JF30
33JF
1048
JF10
73JF
4075
JF20
32JF
2074
JF40
07JF
4015
JF40
24JF
1043
JF30
32JF
2069
JF10
37JF
1050
JF30
17JF
3023
JF11
05JF
1062
JF40
82JF
2058
JF30
55JF
1089
JF20
55JF
3020
JF10
40JF
1046
JF20
79JF
1066
JF30
43JF
4043
JF40
39JF
4044
JF40
42JF
4076
JF30
37JF
3024
JF20
36JF
1006
JF40
71JF
1027
JF40
18 JF10
58JF
1045
JF10
47JF
1024
JF30
36JF
4056
JF10
02JF
4034
JF20
51JF
4084
JF10
65JF
1056
JF30
38 JF1
085
JF10
91JF
1028
JF10
42JF
2039 JF
1026
JF20
48JF
3040
JF20
30 JF10
44JF
2059
JF10
70JF
3052
JF40
09JF
1020
JF20
80JF
1082
JF40
52JF
3063
JF10
90JF
3060
JF40
45JF
1081
JF30
66 JF30
47JF
1080
JF40
50JF
1067
JF10
12JF
1021
JF30
15JF
3044
JF30
45JF
4067
JF40
68JF
4065
JF10
93JF
2013
JF40
72 JF40
79JF
2033
JF40
62JF
4078
JF40
36JF
2047
JF40
29 JF40
25JF
4064
JF40
81JF
3041
JF10
13JF
1057
JF20
57 JF2
045
JF30
35JF
4008
JF10
51JF
4063 JF
2081
JF40
10JF
4032
JF30
04JF
1023
JF40
33JF
1059
JF40
40JF
1031
JF10
94JF
3016
JF10
98JF
3059
JF10
32JF
2078
JF40
06JF
3019
JF40
03JF
2067
JF30
64JF
1099
JF30
51JF
1035
JF20
40JF
1022
JF20
41JF
4001 JF
2003
JF10
63JF
1088
JF10
36JF
1069
JF20
35JF
3007
JF40
55JF
4027
JF40
14JF
4038
JF40
31JF
1087
JF10
83JF
4004
JF40
02JF
4020
JF40
28JF
2025
JF20
31 JF20
27JF
4005
JF40
13JF
3046
JF40
58JF
3003
JF40
74 JF20
65JF
4080
JF40
77JF
4083
JF10
97JF
3057
JF30
02JF
3061
JF10
29JF
1052
JF11
00JF
1008
JF30
54JF
1004
JF20
42
2030
4050
60
HTA Arrays Average Linkage Clusters
Sample
Hei
ght
JF20
56JF
3058
JF30
28JF
1092
JF30
22JF
2050
JF40
18JF
1005
JF40
12JF
1006
JF40
56JF
1003
JF10
72JF
1058
JF10
84JF
2089
JF10
14JF
2064
JF20
68JF
2024
JF30
56JF
3036
JF10
86JF
1027
JF10
09JF
1053
JF40
54JF
2049
JF30
27JF
1044
JF10
91JF
2014
JF10
75JF
1085
JF10
60JF
1034
JF40
52JF
1002
JF40
11JF
1076
JF10
79 JF40
81JF
4071
JF10
45JF
4043
JF30
37JF
2051
JF30
24JF
2059
JF30
47JF
3038
JF10
90JF
2065
JF10
47JF
3040
JF30
46JF
1067
JF30
23JF
1025
JF10
20JF
2080
JF30
60JF
4064
JF10
82JF
1065
JF10
26JF
2047
JF20
69JF
3063
JF20
32JF
1056
JF20
48JF
2036
JF40
50JF
4010
JF40
66JF
1080
JF20
81JF
4058
JF10
29JF
2030
JF40
31JF
4084
JF40
48JF
4036
JF10
52JF
4025
JF40
67JF
4068
JF11
05JF
4029
JF10
07JF
1004
JF20
42JF
4065
JF20
74JF
4045
JF40
79JF
4082
JF20
39JF
1012
JF40
34JF
2027
JF10
62JF
1037
JF30
44JF
1070
JF40
74JF
2033
JF10
21JF
1100
JF10
08JF
3054
JF10
42JF
3051
JF20
40JF
3015
JF40
27JF
1083
JF10
81JF
3066
JF20
58JF
4039
JF40
44JF
4042
JF40
76 JF30
45JF
3003
JF10
50JF
3017
JF40
80JF
4077
JF40
83 JF10
63JF
4062
JF40
78JF
4006
JF10
95JF
2045
JF40
24JF
1066
JF30
43JF
3052
JF40
09JF
4014
JF40
38JF
4004
JF40
72JF
4002
JF40
20JF
1099
JF10
43JF
3032 J
F400
7JF
4015
JF10
97JF
3057
JF40
32JF
2079
JF10
87JF
2067
JF10
31JF
4028
JF40
05JF
4013
JF30
41JF
1022
JF30
02JF
3061
JF10
88JF
2003
JF30
35JF
2041
JF40
01JF
3020
JF40
08JF
1051
JF40
63JF
1093
JF30
04JF
1040
JF10
46JF
3055
JF10
89JF
2055
JF30
07JF
4055
JF30
19JF
1023
JF20
35JF
4033
JF10
32JF
1059
JF40
40JF
1035
JF20
13JF
2025
JF20
31JF
1094
JF30
16JF
3064
JF10
98JF
3059
JF10
13JF
2078
JF10
36JF
1069
JF40
03JF
1057
JF20
57JF
4075
JF10
28JF
1048
JF10
73JF
1024
JF20
38JF
3033
JF40
53JF
4057
JF40
69
2025
3035
4045
50
HTA Arrays Single Linkage Clusters
Sample
Hei
ght
Figure 8: Cluster Analysis based on Complete Average and Single Linkage
The absence of any obvious structure in these exploratory analyses does not demon-
strate that there are no differences between the groups. Rather it suggests that any
differences are small relative to the inherent within group variation.
Commercial In Confidence 15
Page 111
Jet Fuel & Solvents HTA 2 Arrays
4 Differential Expression
4.1 Between Group Differences
Between group differences were evaluated with a linear model, and empirical Bayes
moderated t tests[9] based on linear model parameter estimates. These were imple-
mented using Smyth’s limma methodology[13]. The following between group com-
parisons were performed:
1. Group 1 (Control) vs group 2 (Low exposure);
2. Group 1 (Control) vs group 3 (Moderate exposure);
3. Group 1 (Control) vs group 4 (High Exposure);
Date at which chips were scanned was included as a covariate (in an attempt to control
for differences reflecting aging of reagents).
Individual p values for each gene were adjusted to maintain a false discovery rate of 5%
using the Benjamini Hochberg procedure[15]. This procedure was adopted separately
for each between groups comparison.
The probe sets were ranked in decreasing order of absolute log2 fold change, and are
shown in Table 2.
Commercial In Confidence 16
Page 112
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Hig
hv
sC
on
tro
l1
51
23
0K
LH
L2
3kel
ch-l
ike
fam
ily
mem
ber
23
0.0
53
.25
4.2
60
.02
Hig
hv
sC
on
tro
l8
81
2C
CN
Kcy
clin
K-0
.06
8.8
6-3
.75
0.0
5
Hig
hv
sC
on
tro
l1
10
55
ZP
BP
zon
ap
ellu
cid
ab
ind
ing
pro
tein
0.0
63
.30
4.1
00
.03
Hig
hv
sC
on
tro
l7
42
8V
HL
vo
nH
ipp
el-L
ind
autu
mo
rsu
pp
ress
or,
E3
ubiq
uit
inpro
tein
ligas
e
0.0
78
.70
4.3
10
.02
Hig
hv
sC
on
tro
l2
55
32
4E
PG
Nep
ith
elia
lm
ito
gen
0.0
72
.98
3.7
50
.05
Hig
hv
sC
on
tro
l5
51
4P
PP
1R
10
pro
tein
ph
osp
hat
ase
1,
reg
ula
tory
subu
nit
10
-0.0
78
.97
-3.9
60
.03
Hig
hv
sC
on
tro
l5
51
4P
PP
1R
10
pro
tein
ph
osp
hat
ase
1,
reg
ula
tory
subu
nit
10
-0.0
78
.97
-3.9
60
.03
Hig
hv
sC
on
tro
l5
51
4P
PP
1R
10
pro
tein
ph
osp
hat
ase
1,
reg
ula
tory
subu
nit
10
-0.0
78
.97
-3.9
60
.03
Hig
hv
sC
on
tro
l5
51
4P
PP
1R
10
pro
tein
ph
osp
hat
ase
1,
reg
ula
tory
subu
nit
10
-0.0
78
.97
-3.9
60
.03
Hig
hv
sC
on
tro
l5
51
4P
PP
1R
10
pro
tein
ph
osp
hat
ase
1,
reg
ula
tory
subu
nit
10
-0.0
78
.97
-3.9
60
.03
Hig
hvs
Contr
ol
55585
UB
E2Q
1ubiq
uit
in-c
onju
gat
ing
enzy
me
E2Q
fam
ily
mem
ber
1
-0.0
78
.94
-4.4
90
.02
Hig
hv
sC
on
tro
l5
51
4P
PP
1R
10
pro
tein
ph
osp
hat
ase
1,
reg
ula
tory
subu
nit
10
-0.0
78
.96
-4.0
00
.03
Hig
hv
sC
on
tro
l5
51
4P
PP
1R
10
pro
tein
ph
osp
hat
ase
1,
reg
ula
tory
subu
nit
10
-0.0
78
.86
-4.0
10
.03
Hig
hv
sC
on
tro
l2
73
3G
LE
1G
LE
1R
NA
exp
ort
med
iato
r-0
.07
8.4
9-4
.49
0.0
2
Hig
hv
sC
on
tro
l5
44
55
FB
XO
42
F-b
ox
pro
tein
42
-0.0
88
.12
-4.2
00
.03
Hig
hv
sC
on
tro
l9
89
8U
BA
P2
Lu
biq
uit
inas
soci
ated
pro
tein
2-l
ike
-0.0
88
.70
-3.7
40
.05
Hig
hv
sC
on
tro
l2
99
59
NR
BP
1n
ucl
ear
rece
pto
rb
ind
ing
pro
tein
1-0
.08
8.9
0-4
.70
0.0
1
Hig
hvs
Contr
ol
57168
AS
PH
D2
aspar
tate
bet
a-hydro
xyla
sedom
ain
conta
in-
ing
2
0.0
87
.81
4.3
20
.02
Hig
hv
sC
on
tro
l1
45
85
3C
15
orf
61
chro
mo
som
e1
5o
pen
read
ing
fram
e6
10
.08
6.4
23
.85
0.0
4
Hig
hv
sC
on
tro
l5
51
46
ZD
HH
C4
zin
cfi
ng
er,
DH
HC
-ty
pe
con
tain
ing
40
.08
7.6
94
.78
0.0
1
Hig
hv
sC
on
tro
l9
35
2T
XN
L1
thio
red
ox
in-l
ike
10
.08
8.0
73
.95
0.0
3
Hig
hvs
Contr
ol
55170
PR
MT
6pro
tein
argin
ine
met
hylt
ransf
eras
e6
0.0
96.9
43.9
00.0
4
Hig
hv
sC
on
tro
l1
00
28
87
30
PAN
3-A
S1
PAN
3an
tise
nse
RN
A1
0.0
97
.48
3.7
70
.05
Tab
le2
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
t;R
esu
lts
are
ran
ked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.E
ntr
ezis
the
NC
BI
En
trez
cod
efo
rth
eg
ene
http://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 17
Pag
e 11
3
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Hig
hv
sC
on
tro
l5
52
52
AS
XL
2ad
dit
ion
alse
xco
mb
sli
ke
2(D
roso
ph
ila)
-0.0
98
.94
-4.1
60
.03
Hig
hv
sC
on
tro
l2
13
0E
WS
R1
EW
SR
NA
-bin
din
gp
rote
in1
-0.0
98
.37
-3.9
50
.03
Hig
hv
sC
on
tro
l2
14
5E
ZH
1en
han
cer
of
zest
eh
om
olo
g1
(Dro
sop
hil
a)-0
.09
9.4
9-4
.09
0.0
3
Hig
hv
sC
on
tro
l1
04
82
NX
F1
nu
clea
rR
NA
exp
ort
fact
or
1-0
.09
9.2
6-4
.11
0.0
3
Hig
hv
sC
on
tro
l1
61
14
5T
ME
M2
29
Btr
ansm
emb
ran
ep
rote
in2
29
B0
.09
10
.74
3.8
00
.04
Hig
hv
sC
on
tro
l8
35
93
RA
SS
F5
Ras
asso
ciat
ion
(Ral
GD
S/A
F-6
)d
om
ain
fam
-
ily
mem
ber
5
-0.0
91
0.2
3-3
.90
0.0
4
Hig
hv
sC
on
tro
l8
84
1H
DA
C3
his
ton
ed
eace
tyla
se3
-0.0
98
.00
-3.8
60
.04
Hig
hvs
Contr
ol
50628
GE
MIN
4gem
(nucl
ear
org
anel
le)
asso
ciat
edpro
tein
40.0
96.8
03.9
00.0
4
Hig
hv
sC
on
tro
l5
54
6P
RC
Cp
apil
lary
ren
alce
llca
rcin
om
a(t
ran
slo
cati
on
-
asso
ciat
ed)
-0.0
97
.80
-4.8
90
.01
Hig
hv
sC
on
tro
l1
00
87
38
48
RN
7S
KP
4R
NA
,7
SK
smal
ln
ucl
ear
pse
ud
og
ene
40
.09
6.8
53
.86
0.0
4
Hig
hvs
Contr
ol
54856
GO
N4L
gon-4
-lik
e(C
.el
egan
s)-0
.09
8.0
3-3
.76
0.0
5
Hig
hv
sC
on
tro
l1
00
87
38
50
RN
7S
KP
6R
NA
,7
SK
smal
ln
ucl
ear
pse
ud
og
ene
60
.09
5.0
74
.81
0.0
1
Hig
hvs
Contr
ol
29089
UB
E2T
ubiq
uit
in-c
onju
gat
ing
enzy
me
E2T
(puta
tive)
0.0
96.2
83.9
00.0
4
Hig
hvs
Contr
ol
4802
NF
YC
nucl
ear
tran
scri
pti
on
fact
or
Y,gam
ma
-0.0
98.3
4-4
.50
0.0
2
Hig
hv
sC
on
tro
l9
79
8IS
T1
incr
ease
dso
diu
mto
lera
nce
1h
om
olo
g
(yea
st)
-0.0
99
.47
-4.3
00
.02
Hig
hv
sC
on
tro
l1
00
87
38
47
RN
7S
KP
3R
NA
,7
SK
smal
ln
ucl
ear
pse
ud
og
ene
30
.10
9.2
03
.92
0.0
4
Hig
hv
sC
on
tro
l3
43
50
5N
BP
F7
neu
rob
last
om
ab
reak
po
int
fam
ily,
mem
ber
7-0
.10
5.3
8-3
.98
0.0
3
Hig
hv
sC
on
tro
l1
10
16
AT
F7
acti
vat
ing
tran
scri
pti
on
fact
or
7-0
.10
7.9
0-4
.23
0.0
2
Hig
hv
sC
on
tro
l6
93
14
0M
IR5
55
mic
roR
NA
55
5-0
.10
9.2
2-3
.95
0.0
3
Hig
hv
sC
on
tro
l6
60
1S
MA
RC
C2
SW
I/S
NF
rela
ted
,m
atri
xas
soci
ated
,ac
tin
dep
end
ent
reg
ula
tor
of
chro
mat
in,
sub
fam
ily
c,m
emb
er2
-0.1
08
.86
-4.1
80
.03
Hig
hv
sC
on
tro
l1
26
07
4S
WS
AP
1S
WIM
-ty
pe
zin
cfi
ng
er7
asso
ciat
edp
rote
in1
0.1
06
.70
3.9
00
.04
Hig
hv
sC
on
tro
l1
00
87
38
73
RN
7S
KP
10
RN
A,
7S
Ksm
all
nu
clea
rp
seu
do
gen
e1
00
.10
6.6
44
.13
0.0
3
Tab
le2
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
t;R
esu
lts
are
ran
ked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.E
ntr
ezis
the
NC
BI
En
trez
cod
efo
rth
eg
ene
http://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 18
Pag
e 11
4
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Hig
hv
sC
on
tro
l9
81
5G
IT2
Gp
rote
in-c
ou
ple
dre
cep
tor
kin
ase
inte
ract
ing
Arf
GA
P2
-0.1
01
0.3
9-3
.75
0.0
5
Hig
hv
sC
on
tro
l2
91
07
NX
T1
NT
F2
-lik
eex
po
rtfa
cto
r1
0.1
06
.85
4.3
20
.02
Hig
hv
sC
on
tro
l1
46
43
4Z
NF
59
7zi
nc
fin
ger
pro
tein
59
70
.10
8.2
03
.89
0.0
4
Hig
hv
sC
on
tro
l2
34
71
TR
AM
1tr
ansl
oca
tio
nas
soci
ated
mem
bra
ne
pro
tein
10
.10
9.3
24
.08
0.0
3
Hig
hv
sC
on
tro
l8
02
1N
UP
21
4n
ucl
eop
ori
n2
14
kD
a-0
.10
8.9
1-4
.21
0.0
3
Hig
hv
sC
on
tro
l1
00
42
30
18
MIR
31
56
-3m
icro
RN
A3
15
6-3
0.1
04
.50
3.8
80
.04
Hig
hv
sC
on
tro
l1
00
61
62
63
MIR
44
85
mic
roR
NA
44
85
0.1
01
0.7
34
.11
0.0
3
Hig
hv
sC
on
tro
l5
13
35
NG
RN
neu
gri
n,
neu
rite
ou
tgro
wth
asso
ciat
ed0
.10
7.4
14
.09
0.0
3
Hig
hv
sC
on
tro
l3
00
11
SH
3K
BP
1S
H3
-do
mai
nk
inas
eb
ind
ing
pro
tein
1-0
.10
9.0
3-4
.08
0.0
3
Hig
hv
sC
on
tro
l2
86
82
7T
RIM
59
trip
arti
tem
oti
fco
nta
inin
g5
90
.10
6.9
03
.74
0.0
5
Hig
hv
sC
on
tro
l1
00
29
41
45
LO
C1
00
29
41
45
un
char
acte
rize
dL
OC
10
02
94
14
50
.10
7.2
73
.91
0.0
4
Hig
hv
sC
on
tro
l9
37
7C
OX
5A
cyto
chro
me
co
xid
ase
subu
nit
Va
0.1
18
.07
3.7
40
.05
Hig
hv
sC
on
tro
l8
65
5D
YN
LL
1d
yn
ein
,li
gh
tch
ain
,L
C8
-ty
pe
10
.11
7.4
24
.58
0.0
2
Hig
hv
sC
on
tro
l1
00
29
41
45
LO
C1
00
29
41
45
un
char
acte
rize
dL
OC
10
02
94
14
50
.11
6.6
53
.85
0.0
4
Hig
hv
sC
on
tro
l9
41
07
TM
EM
20
3tr
ansm
emb
ran
ep
rote
in2
03
0.1
18
.10
3.7
40
.05
Hig
hv
sC
on
tro
l6
41
37
3N
RO
Nn
on
-pro
tein
cod
ing
RN
A,
rep
ress
or
of
NFA
T0
.11
4.7
13
.77
0.0
5
Hig
hv
sC
on
tro
l2
33
39
VP
S3
9vac
uo
lar
pro
tein
sort
ing
39
ho
mo
log
(S.ce
re-
vis
iae)
-0.1
18
.93
-3.8
50
.04
Hig
hvs
Contr
ol
9682
KD
M4A
lysi
ne
(K)-
spec
ific
dem
ethyla
se4A
-0.1
17.6
7-4
.00
0.0
3
Hig
hv
sC
on
tro
l5
12
27
PIG
Pp
ho
sph
atid
yli
no
sito
lg
lyca
nan
cho
rb
iosy
n-
thes
is,
clas
sP
0.1
16
.99
4.3
90
.02
Hig
hv
sC
on
tro
l1
65
9D
HX
8D
EA
H(A
sp-G
lu-A
la-H
is)
bo
xp
oly
pep
tid
e8
-0.1
18
.44
-3.8
90
.04
Hig
hvs
Contr
ol
2720
GL
B1
gal
acto
sidas
e,bet
a1
-0.1
28.3
0-4
.33
0.0
2
Hig
hv
sC
on
tro
l7
95
95
SA
P1
30
Sin
3A
-ass
oci
ated
pro
tein
,1
30
kD
a-0
.12
8.9
2-4
.91
0.0
1
Hig
hvs
Contr
ol
4717
ND
UF
C1
NA
DH
deh
ydro
gen
ase
(ubiq
uin
one)
1,
sub-
com
ple
xu
nk
now
n,
1,
6k
Da
0.1
26
.79
5.6
90
.00
Tab
le2
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
t;R
esu
lts
are
ran
ked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.E
ntr
ezis
the
NC
BI
En
trez
cod
efo
rth
eg
ene
http://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 19
Pag
e 11
5
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Hig
hv
sC
on
tro
l5
13
17
PH
F2
1A
PH
Dfi
ng
erp
rote
in2
1A
-0.1
29
.72
-3.7
50
.05
Hig
hv
sC
on
tro
l1
00
12
80
55
SM
AR
CA
5-A
S1
SM
AR
CA
5an
tise
nse
RN
A1
0.1
26
.78
3.7
70
.05
Hig
hv
sC
on
tro
l2
33
89
ME
D1
3L
med
iato
rco
mp
lex
subu
nit
13
-lik
e-0
.12
10
.69
-4.0
90
.03
Hig
hvs
Contr
ol
23352
UB
R4
ubiq
uit
inpro
tein
ligas
eE
3co
mponen
tn-
reco
gn
in4
-0.1
28
.91
-3.9
30
.04
Hig
hvs
Contr
ol
7298
TY
MS
thym
idyla
tesy
nth
etas
e0.1
26.1
64.1
00.0
3
Hig
hv
sC
on
tro
l6
39
16
EL
MO
2en
gu
lfm
ent
and
cell
mo
tili
ty2
-0.1
28
.07
-4.4
40
.02
Hig
hv
sC
on
tro
l1
00
87
38
51
RN
7S
KP
7R
NA
,7
SK
smal
ln
ucl
ear
pse
ud
og
ene
70
.13
6.6
74
.79
0.0
1
Hig
hv
sC
on
tro
l8
72
9G
BF
1g
olg
ib
refe
ldin
Are
sist
antg
uan
ine
nu
cleo
tid
e
exch
ang
efa
cto
r1
-0.1
38
.36
-4.5
80
.02
Hig
hv
sC
on
tro
l2
99
37
NE
NF
neu
des
inn
euro
tro
ph
icfa
cto
r0
.13
8.2
23
.74
0.0
5
Hig
hv
sC
on
tro
l2
31
26
PO
GZ
po
go
tran
spo
sab
leel
emen
tw
ith
ZN
Fd
om
ain
-0.1
38
.54
-4.4
50
.02
Hig
hv
sC
on
tro
l1
79
4D
OC
K2
ded
icat
or
of
cyto
kin
esis
2-0
.13
10
.23
-4.0
40
.03
Hig
hv
sC
on
tro
l1
00
13
21
01
HE
RC
2P
7h
ect
do
mai
nan
dR
LD
2p
seu
do
gen
e7
-0.1
37
.12
-3.8
30
.04
Hig
hv
sC
on
tro
l2
19
7FA
UF
inkel
-Bis
kis
-Rei
lly
mu
rin
esa
rco
ma
vir
us
(FB
R-M
uS
V)
ub
iqu
ito
usl
yex
pre
ssed
0.1
37
.74
4.3
20
.02
Hig
hv
sC
on
tro
l1
00
13
24
06
NB
PF
10
neu
rob
last
om
ab
reak
po
intfa
mil
y,m
emb
er1
0-0
.13
11
.80
-3.8
80
.04
Hig
hv
sC
on
tro
l1
43
1C
Sci
trat
esy
nth
ase
-0.1
39
.25
-3.9
10
.04
Hig
hv
sC
on
tro
l2
33
85
NC
ST
Nn
icas
trin
-0.1
38
.55
-3.9
30
.04
Hig
hvs
Contr
ol
25840
ME
TT
L7A
met
hylt
ransf
eras
eli
ke
7A
0.1
38.2
54.2
20.0
3
Hig
hv
sC
on
tro
l8
17
04
DO
CK
8d
edic
ato
ro
fcy
tok
ines
is8
-0.1
31
0.5
9-3
.95
0.0
3
Hig
hv
sC
on
tro
l3
71
6JA
K1
Jan
us
kin
ase
1-0
.14
10
.67
-4.4
70
.02
Hig
hv
sC
on
tro
l9
61
1N
CO
R1
nu
clea
rre
cep
tor
core
pre
sso
r1
-0.1
49
.24
-4.0
40
.03
Hig
hv
sC
on
tro
l5
53
53
LA
PT
M4
Bly
soso
mal
pro
tein
tran
smem
bra
ne
4b
eta
0.1
46
.70
3.9
10
.04
Hig
hv
sC
on
tro
l1
00
13
24
06
NB
PF
10
neu
rob
last
om
ab
reak
po
intfa
mil
y,m
emb
er1
0-0
.14
11
.60
-4.0
30
.03
Hig
hv
sC
on
tro
l9
84
4E
LM
O1
eng
ulf
men
tan
dce
llm
oti
lity
1-0
.14
8.8
6-4
.58
0.0
2
Hig
hv
sC
on
tro
l2
58
32
NB
PF
14
neu
rob
last
om
ab
reak
po
intfa
mil
y,m
emb
er1
4-0
.14
11
.44
-3.9
30
.04
Tab
le2
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
t;R
esu
lts
are
ran
ked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.E
ntr
ezis
the
NC
BI
En
trez
cod
efo
rth
eg
ene
http://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 20
Pag
e 11
6
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Hig
hv
sC
on
tro
l5
56
72
NB
PF
1n
euro
bla
sto
ma
bre
akp
oin
tfa
mil
y,m
emb
er1
-0.1
48
.95
-3.7
30
.05
Hig
hv
sC
on
tro
l1
00
30
21
95
MIR
32
0C
2m
icro
RN
A3
20
c-2
0.1
53
.66
4.5
10
.02
Hig
hv
sC
on
tro
l5
51
87
VP
S1
3D
vac
uo
lar
pro
tein
sort
ing
13
ho
mo
log
D(S
.
cere
vis
iae)
-0.1
58
.19
-3.7
90
.05
Hig
hvs
Contr
ol
2730
GC
LM
glu
tam
ate-
cyst
eine
ligas
e,m
odifi
ersu
bunit
0.1
57.0
13.9
80.0
3
Hig
hv
sC
on
tro
l1
00
87
38
71
RN
U5
A-6
PR
NA
,U
5A
smal
ln
ucl
ear
6,
pse
ud
og
ene
0.1
53
.06
4.2
70
.02
Hig
hv
sC
on
tro
l6
45
25
1C
BX
1P
1ch
rom
ob
ox
ho
mo
log
1p
seu
do
gen
e1
-0.1
57
.70
-3.9
90
.03
Hig
hv
sC
on
tro
l5
74
45
2M
IR4
94
mic
roR
NA
49
40
.16
5.7
04
.11
0.0
3
Hig
hv
sC
on
tro
l1
16
54
0M
RP
L5
3m
ito
cho
nd
rial
rib
oso
mal
pro
tein
L5
30
.16
8.1
24
.53
0.0
2
Hig
hv
sC
on
tro
l4
00
81
8N
BP
F9
neu
rob
last
om
ab
reak
po
int
fam
ily,
mem
ber
9-0
.17
10
.57
-3.9
80
.03
Hig
hv
sC
on
tro
l3
88
67
7N
OT
CH
2N
Ln
otc
h2
N-t
erm
inal
like
-0.1
71
0.6
6-4
.37
0.0
2
Hig
hv
sC
on
tro
l1
00
12
90
34
LO
C1
00
12
90
34
un
char
acte
rize
dL
OC
10
01
29
03
4-0
.17
9.5
0-3
.81
0.0
4
Hig
hv
sC
on
tro
l4
00
81
8N
BP
F9
neu
rob
last
om
ab
reak
po
int
fam
ily,
mem
ber
9-0
.17
9.9
7-4
.07
0.0
3
Hig
hv
sC
on
tro
l4
01
10
5F
LJ4
23
93
un
char
acte
rize
dL
OC
40
11
05
-0.2
06
.56
-4.0
70
.03
Hig
hv
sC
on
tro
l6
62
8S
NR
PB
smal
ln
ucl
ear
rib
on
ucl
eop
rote
inp
oly
pep
tid
es
Ban
dB
1
-0.2
07
.97
-6.0
40
.00
Hig
hv
sC
on
tro
l6
93
20
8M
IR6
23
mic
roR
NA
62
3-0
.20
7.3
2-4
.14
0.0
3
Hig
hv
sC
on
tro
l8
36
6H
IST
1H
4B
his
ton
ecl
ust
er1
,H
4b
0.2
15
.64
4.3
20
.02
Hig
hv
sC
on
tro
l6
77
81
9S
NO
RA
37
smal
ln
ucl
eola
rR
NA
,H
/AC
Ab
ox
37
0.2
29
.81
3.8
20
.04
Hig
hv
sC
on
tro
l1
00
61
61
51
MIR
44
80
mic
roR
NA
44
80
-0.2
36
.87
-3.9
50
.03
Hig
hv
sC
on
tro
l6
92
22
5S
NO
RD
94
smal
ln
ucl
eola
rR
NA
,C
/Db
ox
94
0.2
31
0.0
53
.73
0.0
5
Hig
hv
sC
on
tro
l1
00
15
16
87
RN
U6
AT
AC
4P
RN
A,
U6
atac
smal
ln
ucl
ear
4,
pse
ud
og
ene
0.2
85
.02
3.9
70
.03
Hig
hv
sC
on
tro
l1
00
13
22
88
TE
KT
4P
2te
kti
n4
pse
ud
og
ene
20
.29
8.3
03
.81
0.0
4
Hig
hv
sC
on
tro
l6
93
19
7M
IR6
12
mic
roR
NA
61
2-0
.30
10
.27
-4.5
30
.02
Hig
hv
sC
on
tro
l1
00
15
16
88
RN
U6
AT
AC
5P
RN
A,
U6
atac
smal
ln
ucl
ear
5,
pse
ud
og
ene
0.4
06
.11
3.7
90
.05
Hig
hv
sC
on
tro
l1
00
15
16
86
RN
U6
AT
AC
3P
RN
A,
U6
atac
smal
ln
ucl
ear
3,
pse
ud
og
ene
0.4
45
.21
3.9
50
.03
Hig
hv
sC
on
tro
l1
00
15
16
85
RN
U6
AT
AC
2P
RN
A,
U6
atac
smal
ln
ucl
ear
2,
pse
ud
og
ene
0.4
47
.52
4.5
00
.02
Tab
le2
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
t;R
esu
lts
are
ran
ked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.E
ntr
ezis
the
NC
BI
En
trez
cod
efo
rth
eg
ene
http://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 21
Pag
e 11
7
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Hig
hv
sC
on
tro
l1
00
15
16
84
RN
U6
AT
AC
RN
A,
U6
atac
smal
ln
ucl
ear
(U1
2-d
epen
den
t
spli
cin
g)
0.5
88
.81
4.1
90
.03
Low
vs
Co
ntr
ol
51
07
2M
EM
O1
med
iato
ro
fce
llm
oti
lity
10
.05
8.2
93
.89
0.0
4
Low
vs
Co
ntr
ol
14
26
86
AS
B1
4an
ky
rin
rep
eat
and
SO
CS
bo
xco
nta
inin
g1
40
.06
3.6
13
.93
0.0
3
Low
vs
Co
ntr
ol
15
12
30
KL
HL
23
kel
ch-l
ike
fam
ily
mem
ber
23
0.0
63
.25
4.5
00
.02
Low
vs
Co
ntr
ol
12
69
69
SL
C4
4A
3so
lute
carr
ier
fam
ily
44
,m
emb
er3
0.0
84
.47
4.1
80
.02
Low
vs
Co
ntr
ol
55
14
PP
P1
R1
0p
rote
inp
ho
sph
atas
e1
,re
gu
lato
rysu
bu
nit
10
-0.0
88
.97
-3.7
20
.05
Low
vs
Co
ntr
ol
55
14
PP
P1
R1
0p
rote
inp
ho
sph
atas
e1
,re
gu
lato
rysu
bu
nit
10
-0.0
88
.97
-3.7
20
.05
Low
vs
Co
ntr
ol
55
14
PP
P1
R1
0p
rote
inp
ho
sph
atas
e1
,re
gu
lato
rysu
bu
nit
10
-0.0
88
.97
-3.7
20
.05
Low
vs
Co
ntr
ol
55
14
PP
P1
R1
0p
rote
inp
ho
sph
atas
e1
,re
gu
lato
rysu
bu
nit
10
-0.0
88
.97
-3.7
20
.05
Low
vs
Co
ntr
ol
55
14
PP
P1
R1
0p
rote
inp
ho
sph
atas
e1
,re
gu
lato
rysu
bu
nit
10
-0.0
88
.97
-3.7
20
.05
Low
vs
Co
ntr
ol
55
14
PP
P1
R1
0p
rote
inp
ho
sph
atas
e1
,re
gu
lato
rysu
bu
nit
10
-0.0
88
.96
-3.7
10
.05
Low
vs
Co
ntr
ol
10
01
29
38
5C
9o
rf9
2ch
rom
oso
me
9o
pen
read
ing
fram
e9
20
.08
4.8
93
.93
0.0
3
Low
vs
Co
ntr
ol
64
76
8IP
PK
ino
sito
l1
,3,4
,5,6
-pen
tak
isp
ho
sph
ate
2-k
inas
e-0
.08
7.0
3-4
.01
0.0
3
Low
vs
Co
ntr
ol
11
41
34
SL
C2
A1
3so
lute
carr
ier
fam
ily
2(f
acil
itat
edg
luco
se
tran
spo
rter
),m
emb
er1
3
0.0
86
.44
3.8
10
.04
Low
vs
Co
ntr
ol
55
14
PP
P1
R1
0p
rote
inp
ho
sph
atas
e1
,re
gu
lato
rysu
bu
nit
10
-0.0
88
.86
-3.8
20
.04
Low
vs
Co
ntr
ol
79
14
7F
KR
Pfu
ku
tin
rela
ted
pro
tein
0.0
87
.12
3.7
00
.05
Low
vs
Co
ntr
ol
55
46
PR
CC
pap
illa
ryre
nal
cell
carc
ino
ma
(tra
nsl
oca
tio
n-
asso
ciat
ed)
-0.0
97
.80
-3.9
30
.03
Low
vs
Co
ntr
ol
12
49
23
SG
K4
94
un
char
acte
rize
dse
rin
e/th
reo
nin
e-p
rote
ink
i-
nas
eS
gK
49
4
-0.0
96
.44
-4.1
90
.02
Low
vs
Co
ntr
ol
10
08
74
21
5A
TP
13
A4
-AS
1A
TP
13
A4
anti
sen
seR
NA
10
.09
3.4
43
.86
0.0
4
Low
vs
Co
ntr
ol
92
03
ZM
YM
3zi
nc
fin
ger
,M
YM
-ty
pe
3-0
.09
7.0
2-3
.78
0.0
5
Low
vs
Co
ntr
ol
22
98
5A
CIN
1ap
op
toti
cch
rom
atin
con
den
sati
on
ind
uce
r1
-0.0
98
.75
-4.0
30
.03
Low
vs
Co
ntr
ol
21
99
02
TM
EM
13
6tr
ansm
emb
ran
ep
rote
in1
36
0.0
94
.63
3.7
80
.05
Low
vs
Co
ntr
ol
55
14
6Z
DH
HC
4zi
nc
fin
ger
,D
HH
C-t
yp
eco
nta
inin
g4
0.1
07
.69
4.7
00
.02
Tab
le2
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
t;R
esu
lts
are
ran
ked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.E
ntr
ezis
the
NC
BI
En
trez
cod
efo
rth
eg
ene
http://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 22
Pag
e 11
8
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Low
vs
Co
ntr
ol
21
45
EZ
H1
enh
ance
ro
fze
ste
ho
mo
log
1(D
roso
ph
ila)
-0.1
09
.49
-3.7
80
.05
Low
vs
Co
ntr
ol
16
31
26
EID
2E
P3
00
inte
ract
ing
inh
ibit
or
of
dif
fere
nti
atio
n
2
0.1
07
.94
3.7
10
.05
Low
vs
Co
ntr
ol
84
26
0T
CH
Ptr
ich
op
lein
,ker
atin
fila
men
tb
ind
ing
-0.1
07
.45
-4.1
20
.02
Low
vs
Co
ntr
ol
86
55
DY
NL
L1
dy
nei
n,
lig
ht
chai
n,
LC
8-t
yp
e1
0.1
07
.42
3.7
40
.05
Low
vs
Co
ntr
ol
55
25
2A
SX
L2
add
itio
nal
sex
com
bs
like
2(D
roso
ph
ila)
-0.1
08
.94
-4.1
10
.02
Low
vs
Co
ntr
ol
16
11
45
TM
EM
22
9B
tran
smem
bra
ne
pro
tein
22
9B
0.1
01
0.7
43
.72
0.0
5
Low
vs
Co
ntr
ol
96
81
DE
PD
C5
DE
Pd
om
ain
con
tain
ing
5-0
.11
7.8
7-3
.79
0.0
5
Low
vs
Contr
ol
50628
GE
MIN
4gem
(nucl
ear
org
anel
le)
asso
ciat
edpro
tein
40.1
16.8
03.7
50.0
5
Low
vs
Co
ntr
ol
22
30
82
ZN
RF
2zi
nc
and
rin
gfi
ng
er2
0.1
18
.63
4.5
00
.02
Low
vs
Co
ntr
ol
29
10
7N
XT
1N
TF
2-l
ike
exp
ort
fact
or
10
.11
6.8
53
.88
0.0
4
Low
vs
Contr
ol
4717
ND
UF
C1
NA
DH
deh
ydro
gen
ase
(ubiq
uin
one)
1,
sub-
com
ple
xu
nk
now
n,
1,
6k
Da
0.1
16
.79
4.4
10
.02
Low
vs
Co
ntr
ol
66
12
SU
MO
3sm
all
ub
iqu
itin
-lik
em
od
ifier
30
.11
8.2
54
.18
0.0
2
Low
vs
Co
ntr
ol
26
24
8O
R2
K2
olf
acto
ryre
cep
tor,
fam
ily
2,
sub
fam
ily
K,
mem
ber
2
0.1
12
.71
3.8
10
.04
Low
vs
Co
ntr
ol
84
53
9M
CH
R2
mel
anin
-co
nce
ntr
atin
gh
orm
on
ere
cep
tor
20
.11
4.5
13
.91
0.0
4
Low
vs
Co
ntr
ol
12
83
67
OR
10
X1
olf
acto
ryre
cep
tor,
fam
ily
10
,su
bfa
mil
yX
,
mem
ber
1
0.1
12
.53
4.2
40
.02
Low
vs
Co
ntr
ol
40
81
87
SP
INK
14
seri
ne
pep
tid
ase
inh
ibit
or,
Kaz
alty
pe
14
(pu
-
tati
ve)
0.1
13
.04
3.7
30
.05
Low
vs
Co
ntr
ol
69
31
40
MIR
55
5m
icro
RN
A5
55
-0.1
19
.22
-3.7
90
.05
Low
vs
Co
ntr
ol
62
93
VP
S5
2vac
uo
lar
pro
tein
sort
ing
52
ho
mo
log
(S.ce
re-
vis
iae)
-0.1
17
.55
-3.7
00
.05
Low
vs
Co
ntr
ol
62
93
VP
S5
2vac
uo
lar
pro
tein
sort
ing
52
ho
mo
log
(S.ce
re-
vis
iae)
-0.1
17
.55
-3.7
00
.05
Low
vs
Co
ntr
ol
64
70
42
GO
LG
A6
L1
0g
olg
inA
6fa
mil
y-l
ike
10
-0.1
16
.74
-3.9
40
.03
Tab
le2
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
t;R
esu
lts
are
ran
ked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.E
ntr
ezis
the
NC
BI
En
trez
cod
efo
rth
eg
ene
http://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 23
Pag
e 11
9
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Low
vs
Co
ntr
ol
10
05
06
71
0L
OC
10
05
06
71
0en
do
gen
ou
sB
orn
avir
us-
like
nu
cleo
pro
tein
2
pse
ud
og
ene
0.1
18
.00
4.3
60
.02
Low
vs
Co
ntr
ol
62
93
VP
S5
2vac
uo
lar
pro
tein
sort
ing
52
ho
mo
log
(S.ce
re-
vis
iae)
-0.1
17
.55
-3.7
60
.05
Low
vs
Co
ntr
ol
62
93
VP
S5
2vac
uo
lar
pro
tein
sort
ing
52
ho
mo
log
(S.ce
re-
vis
iae)
-0.1
17
.55
-3.7
60
.05
Low
vs
Co
ntr
ol
62
93
VP
S5
2vac
uo
lar
pro
tein
sort
ing
52
ho
mo
log
(S.ce
re-
vis
iae)
-0.1
17
.55
-3.7
70
.05
Low
vs
Co
ntr
ol
56
85
0G
RIP
AP
1G
RIP
1as
soci
ated
pro
tein
1-0
.11
7.6
4-4
.00
0.0
3
Low
vs
Co
ntr
ol
10
08
73
84
5R
N7
SK
P1
RN
A,
7S
Ksm
all
nu
clea
rp
seu
do
gen
e1
0.1
16
.76
4.0
50
.03
Low
vs
Contr
ol
51504
TR
MT
112
tRN
Am
ethylt
ransf
eras
e11-2
hom
olo
g(S
.
cere
vis
iae)
0.1
29
.31
3.7
60
.05
Low
vs
Co
ntr
ol
80
21
NU
P2
14
nu
cleo
po
rin
21
4k
Da
-0.1
28
.91
-4.1
10
.02
Low
vs
Co
ntr
ol
11
01
6A
TF
7ac
tivat
ing
tran
scri
pti
on
fact
or
7-0
.12
7.9
0-4
.37
0.0
2
Low
vs
Co
ntr
ol
36
60
IRF
2in
terf
ero
nre
gu
lato
ryfa
cto
r2
-0.1
29
.80
-4.0
40
.03
Low
vs
Co
ntr
ol
14
64
34
ZN
F5
97
zin
cfi
ng
erp
rote
in5
97
0.1
28
.20
3.9
20
.04
Low
vs
Co
ntr
ol
79
59
5S
AP
13
0S
in3
A-a
sso
ciat
edp
rote
in,
13
0k
Da
-0.1
28
.92
-4.3
40
.02
Low
vs
Co
ntr
ol
23
11
2T
NR
C6
Btr
inu
cleo
tid
ere
pea
tco
nta
inin
g6
B-0
.12
9.2
6-4
.03
0.0
3
Low
vs
Contr
ol
92483
LD
HA
L6B
lact
ate
deh
ydro
gen
ase
A-l
ike
6B
0.1
22.7
84.6
30.0
2
Low
vs
Co
ntr
ol
66
01
SM
AR
CC
2S
WI/
SN
Fre
late
d,
mat
rix
asso
ciat
ed,
acti
n
dep
end
ent
reg
ula
tor
of
chro
mat
in,
sub
fam
ily
c,m
emb
er2
-0.1
28
.86
-4.4
50
.02
Low
vs
Co
ntr
ol
21
30
EW
SR
1E
WS
RN
A-b
ind
ing
pro
tein
1-0
.12
8.3
7-4
.62
0.0
2
Low
vs
Co
ntr
ol
10
03
80
27
1R
NY
5P
4R
NA
,R
o-a
sso
ciat
edY
5p
seu
do
gen
e4
0.1
24
.22
3.9
70
.03
Low
vs
Co
ntr
ol
51
33
5N
GR
Nn
eug
rin
,n
euri
teo
utg
row
thas
soci
ated
0.1
27
.41
4.1
50
.02
Low
vs
Co
ntr
ol
11
61
38
KL
HD
C3
kel
chd
om
ain
con
tain
ing
30
.13
8.4
84
.32
0.0
2
Low
vs
Co
ntr
ol
34
35
05
NB
PF
7n
euro
bla
sto
ma
bre
akp
oin
tfa
mil
y,m
emb
er7
-0.1
35
.38
-4.4
00
.02
Tab
le2
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
t;R
esu
lts
are
ran
ked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.E
ntr
ezis
the
NC
BI
En
trez
cod
efo
rth
eg
ene
http://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 24
Pag
e 12
0
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Low
vs
Co
ntr
ol
44
02
95
GO
LG
A6
L9
go
lgin
A6
fam
ily
-lik
e9
-0.1
36
.64
-4.1
50
.02
Low
vs
Co
ntr
ol
13
16
16
TM
EM
42
tran
smem
bra
ne
pro
tein
42
0.1
36
.70
3.7
80
.05
Low
vs
Co
ntr
ol
10
02
94
14
5L
OC
10
02
94
14
5u
nch
arac
teri
zed
LO
C1
00
29
41
45
0.1
37
.27
4.0
10
.03
Low
vs
Co
ntr
ol
51
22
7P
IGP
ph
osp
hat
idy
lin
osi
tol
gly
can
anch
or
bio
syn
-
thes
is,
clas
sP
0.1
36
.99
4.3
40
.02
Low
vs
Co
ntr
ol
92
40
PN
MA
1p
aran
eop
last
icM
aan
tig
en1
0.1
36
.96
3.7
60
.05
Low
vs
Co
ntr
ol
23
47
1T
RA
M1
tran
slo
cati
on
asso
ciat
edm
emb
ran
ep
rote
in1
0.1
39
.32
4.4
60
.02
Low
vs
Co
ntr
ol
55
26
6T
ME
M1
9tr
ansm
emb
ran
ep
rote
in1
90
.13
7.8
53
.75
0.0
5
Low
vs
Contr
ol
54856
GO
N4L
gon-4
-lik
e(C
.el
egan
s)-0
.13
8.0
3-4
.51
0.0
2
Low
vs
Co
ntr
ol
44
02
95
GO
LG
A6
L9
go
lgin
A6
fam
ily
-lik
e9
-0.1
36
.29
-4.0
50
.03
Low
vs
Co
ntr
ol
28
00
GO
LG
A1
go
lgin
A1
-0.1
47
.34
-4.8
00
.02
Low
vs
Co
ntr
ol
94
10
7T
ME
M2
03
tran
smem
bra
ne
pro
tein
20
30
.14
8.1
03
.95
0.0
3
Low
vs
Contr
ol
9682
KD
M4A
lysi
ne
(K)-
spec
ific
dem
ethyla
se4A
-0.1
47.6
7-4
.24
0.0
2
Low
vs
Co
ntr
ol
54
40
7S
LC
38
A2
solu
teca
rrie
rfa
mil
y3
8,
mem
ber
20
.14
10
.57
4.4
70
.02
Low
vs
Co
ntr
ol
10
03
02
23
7M
IR1
28
1m
icro
RN
A1
28
1-0
.14
9.7
9-4
.03
0.0
3
Low
vs
Co
ntr
ol
23
12
6P
OG
Zp
og
otr
ansp
osa
ble
elem
ent
wit
hZ
NF
do
mai
n-0
.14
8.5
4-4
.06
0.0
3
Low
vs
Co
ntr
ol
23
33
9V
PS
39
vac
uo
lar
pro
tein
sort
ing
39
ho
mo
log
(S.ce
re-
vis
iae)
-0.1
48
.93
-4.1
50
.02
Low
vs
Co
ntr
ol
16
59
DH
X8
DE
AH
(Asp
-Glu
-Ala
-His
)b
ox
po
lyp
epti
de
8-0
.14
8.4
4-4
.03
0.0
3
Low
vs
Co
ntr
ol
11
17
6B
AZ
2A
bro
mo
do
mai
nad
jace
ntto
zin
cfi
ng
erd
om
ain
,
2A
-0.1
49
.22
-3.8
40
.04
Low
vs
Co
ntr
ol
98
69
SE
TD
B1
SE
Td
om
ain
,b
ifu
rcat
ed1
-0.1
47
.65
-4.0
00
.03
Low
vs
Co
ntr
ol
23
21
5P
RR
C2
Cp
roli
ne-
rich
coil
ed-c
oil
2C
-0.1
41
0.2
2-3
.80
0.0
5
Low
vs
Co
ntr
ol
98
44
EL
MO
1en
gu
lfm
ent
and
cell
mo
tili
ty1
-0.1
58
.86
-3.9
80
.03
Low
vs
Contr
ol
23352
UB
R4
ubiq
uit
inpro
tein
ligas
eE
3co
mponen
tn-
reco
gn
in4
-0.1
58
.91
-4.0
90
.03
Low
vs
Co
ntr
ol
10
02
94
14
5L
OC
10
02
94
14
5u
nch
arac
teri
zed
LO
C1
00
29
41
45
0.1
57
.21
3.9
00
.04
Tab
le2
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
t;R
esu
lts
are
ran
ked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.E
ntr
ezis
the
NC
BI
En
trez
cod
efo
rth
eg
ene
http://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 25
Pag
e 12
1
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Low
vs
Co
ntr
ol
10
02
94
14
5L
OC
10
02
94
14
5u
nch
arac
teri
zed
LO
C1
00
29
41
45
0.1
57
.21
3.9
00
.04
Low
vs
Co
ntr
ol
10
02
94
14
5L
OC
10
02
94
14
5u
nch
arac
teri
zed
LO
C1
00
29
41
45
0.1
57
.21
3.9
00
.04
Low
vs
Co
ntr
ol
42
59
MG
ST
3m
icro
som
alg
luta
thio
ne
S-t
ran
sfer
ase
30
.15
6.4
63
.90
0.0
4
Low
vs
Co
ntr
ol
81
70
4D
OC
K8
ded
icat
or
of
cyto
kin
esis
8-0
.15
10
.59
-3.7
60
.05
Low
vs
Co
ntr
ol
10
02
94
14
5L
OC
10
02
94
14
5u
nch
arac
teri
zed
LO
C1
00
29
41
45
0.1
56
.65
4.6
30
.02
Low
vs
Co
ntr
ol
87
29
GB
F1
go
lgib
refe
ldin
Are
sist
antg
uan
ine
nu
cleo
tid
e
exch
ang
efa
cto
r1
-0.1
68
.36
-4.7
40
.02
Low
vs
Co
ntr
ol
55
42
1C
17
orf
85
chro
mo
som
e1
7o
pen
read
ing
fram
e8
5-0
.16
8.3
9-3
.90
0.0
4
Low
vs
Co
ntr
ol
17
94
DO
CK
2d
edic
ato
ro
fcy
tok
ines
is2
-0.1
61
0.2
3-4
.15
0.0
2
Low
vs
Co
ntr
ol
10
02
94
14
5L
OC
10
02
94
14
5u
nch
arac
teri
zed
LO
C1
00
29
41
45
0.1
67
.19
4.4
70
.02
Low
vs
Co
ntr
ol
10
02
94
14
5L
OC
10
02
94
14
5u
nch
arac
teri
zed
LO
C1
00
29
41
45
0.1
66
.86
4.4
30
.02
Low
vs
Co
ntr
ol
10
02
94
14
5L
OC
10
02
94
14
5u
nch
arac
teri
zed
LO
C1
00
29
41
45
0.1
66
.86
4.4
30
.02
Low
vs
Co
ntr
ol
23
42
0N
OM
O1
NO
DA
Lm
od
ula
tor
1-0
.17
9.6
9-4
.18
0.0
2
Low
vs
Co
ntr
ol
96
11
NC
OR
1n
ucl
ear
rece
pto
rco
rep
ress
or
1-0
.17
9.2
4-4
.15
0.0
2
Low
vs
Co
ntr
ol
82
43
SM
C1
Ast
ruct
ura
lm
ain
ten
ance
of
chro
mo
som
es1
A-0
.17
8.2
6-3
.83
0.0
4
Low
vs
Co
ntr
ol
55
67
2N
BP
F1
neu
rob
last
om
ab
reak
po
int
fam
ily,
mem
ber
1-0
.17
8.9
5-3
.71
0.0
5
Low
vs
Co
ntr
ol
10
01
32
40
6N
BP
F1
0n
euro
bla
sto
ma
bre
akp
oin
tfa
mil
y,m
emb
er1
0-0
.17
11
.80
-4.2
30
.02
Low
vs
Co
ntr
ol
10
06
16
49
8M
IR3
78
Em
icro
RN
A3
78
e-0
.17
9.1
5-4
.21
0.0
2
Low
vs
Co
ntr
ol
28
38
20
NO
MO
2N
OD
AL
mo
du
lato
r2
-0.1
79
.68
-4.2
20
.02
Low
vs
Co
ntr
ol
40
80
50
NO
MO
3N
OD
AL
mo
du
lato
r3
-0.1
89
.69
-4.2
50
.02
Low
vs
Co
ntr
ol
68
30
SU
PT
6H
sup
pre
sso
ro
fT
y6
ho
mo
log
(S.
cere
vis
iae)
-0.1
88
.82
-4.3
70
.02
Low
vs
Co
ntr
ol
25
83
2N
BP
F1
4n
euro
bla
sto
ma
bre
akp
oin
tfa
mil
y,m
emb
er1
4-0
.18
11
.44
-4.0
70
.03
Low
vs
Co
ntr
ol
14
72
26
UP
F3
AP
1U
PF
3A
pse
ud
og
ene
1-0
.18
7.2
0-4
.28
0.0
2
Low
vs
Co
ntr
ol
66
28
SN
RP
Bsm
all
nu
clea
rri
bo
nu
cleo
pro
tein
po
lyp
epti
des
Ban
dB
1
-0.1
87
.97
-4.4
60
.02
Low
vs
Co
ntr
ol
10
08
73
80
0R
NY
4P
22
RN
A,
Ro
-ass
oci
ated
Y4
pse
ud
og
ene
22
0.1
84
.60
3.8
50
.04
Tab
le2
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
t;R
esu
lts
are
ran
ked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.E
ntr
ezis
the
NC
BI
En
trez
cod
efo
rth
eg
ene
http://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 26
Pag
e 12
2
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Low
vs
Co
ntr
ol
26
49
7O
R1
0D
3o
lfac
tory
rece
pto
r,fa
mil
y1
0,
sub
fam
ily
D,
mem
ber
3(n
on
-fu
nct
ion
al)
0.1
86
.35
4.2
90
.02
Low
vs
Co
ntr
ol
10
01
32
40
6N
BP
F1
0n
euro
bla
sto
ma
bre
akp
oin
tfa
mil
y,m
emb
er1
0-0
.18
11
.60
-4.3
50
.02
Low
vs
Co
ntr
ol
65
29
66
SN
OR
D1
0sm
all
nu
cleo
lar
RN
A,
C/D
bo
x1
00
.19
9.0
73
.72
0.0
5
Low
vs
Co
ntr
ol
38
86
77
NO
TC
H2
NL
no
tch
2N
-ter
min
alli
ke
-0.1
91
0.6
6-4
.14
0.0
2
Low
vs
Co
ntr
ol
40
08
18
NB
PF
9n
euro
bla
sto
ma
bre
akp
oin
tfa
mil
y,m
emb
er9
-0.1
91
0.5
7-3
.89
0.0
4
Low
vs
Co
ntr
ol
10
06
16
31
9M
IR4
49
3m
icro
RN
A4
49
30
.20
4.3
04
.38
0.0
2
Low
vs
Co
ntr
ol
40
08
18
NB
PF
9n
euro
bla
sto
ma
bre
akp
oin
tfa
mil
y,m
emb
er9
-0.2
09
.97
-3.9
20
.04
Low
vs
Co
ntr
ol
31
91
03
SN
OR
D8
smal
ln
ucl
eola
rR
NA
,C
/Db
ox
80
.21
7.0
74
.03
0.0
3
Low
vs
Co
ntr
ol
76
75
65
SN
OR
D1
13
-5sm
all
nu
cleo
lar
RN
A,
C/D
bo
x1
13
-50
.22
3.9
83
.98
0.0
3
Low
vs
Co
ntr
ol
40
11
05
FL
J42
39
3u
nch
arac
teri
zed
LO
C4
01
10
5-0
.22
6.5
6-3
.77
0.0
5
Low
vs
Co
ntr
ol
69
31
89
MIR
60
4m
icro
RN
A6
04
-0.2
29
.46
-3.7
50
.05
Low
vs
Co
ntr
ol
13
15
83
FA
M4
3A
fam
ily
wit
hse
qu
ence
sim
ilar
ity
43
,m
emb
er
A
0.2
37
.92
5.0
20
.02
Low
vs
Co
ntr
ol
67
78
42
SN
OR
A7
6sm
all
nu
cleo
lar
RN
A,
H/A
CA
bo
x7
60
.25
6.8
73
.91
0.0
4
Low
vs
Co
ntr
ol
69
32
08
MIR
62
3m
icro
RN
A6
23
-0.2
67
.32
-4.3
20
.02
Low
vs
Co
ntr
ol
69
22
25
SN
OR
D9
4sm
all
nu
cleo
lar
RN
A,
C/D
bo
x9
40
.29
10
.05
3.8
30
.04
Low
vs
Co
ntr
ol
10
01
32
68
6L
OC
10
01
32
68
6u
nch
arac
teri
zed
LO
C1
00
13
26
86
-0.3
25
.03
-4.7
60
.02
Low
vs
Co
ntr
ol
10
06
16
15
1M
IR4
48
0m
icro
RN
A4
48
0-0
.33
6.8
7-4
.71
0.0
2
Low
vs
Co
ntr
ol
69
31
97
MIR
61
2m
icro
RN
A6
12
-0.3
61
0.2
7-4
.53
0.0
2
Low
vs
Co
ntr
ol
10
01
51
68
7R
NU
6A
TA
C4
PR
NA
,U
6at
acsm
all
nu
clea
r4
,p
seu
do
gen
e0
.37
5.0
24
.29
0.0
2
Low
vs
Co
ntr
ol
10
01
51
68
8R
NU
6A
TA
C5
PR
NA
,U
6at
acsm
all
nu
clea
r5
,p
seu
do
gen
e0
.49
6.1
13
.82
0.0
4
Low
vs
Co
ntr
ol
10
01
51
68
5R
NU
6A
TA
C2
PR
NA
,U
6at
acsm
all
nu
clea
r2
,p
seu
do
gen
e0
.50
7.5
24
.25
0.0
2
Low
vs
Co
ntr
ol
10
01
51
68
4R
NU
6A
TA
CR
NA
,U
6at
acsm
all
nu
clea
r(U
12
-dep
end
ent
spli
cin
g)
0.6
88
.81
4.1
10
.02
Med
ium
vs
Co
ntr
ol
84
22
4N
BP
F3
neu
rob
last
om
ab
reak
po
int
fam
ily,
mem
ber
3-0
.14
7.6
8-4
.63
0.0
5
Med
ium
vs
Co
ntr
ol
38
86
77
NO
TC
H2
NL
no
tch
2N
-ter
min
alli
ke
-0.2
11
0.6
6-4
.74
0.0
5
Tab
le2
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
t;R
esu
lts
are
ran
ked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.E
ntr
ezis
the
NC
BI
En
trez
cod
efo
rth
eg
ene
http://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 27
Pag
e 12
3
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Med
ium
vs
Co
ntr
ol
40
08
18
NB
PF
9n
euro
bla
sto
ma
bre
akp
oin
tfa
mil
y,m
emb
er9
-0.2
39
.97
-4.5
80
.05
Med
ium
vs
Co
ntr
ol
10
01
51
68
7R
NU
6A
TA
C4
PR
NA
,U
6at
acsm
all
nu
clea
r4
,p
seu
do
gen
e0
.42
5.0
24
.92
0.0
4
Tab
le2
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
t;R
esu
lts
are
ran
ked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.E
ntr
ezis
the
NC
BI
En
trez
cod
efo
rth
eg
ene
http://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 28
Pag
e 12
4
Jet Fuel & Solvents HTA 2 Arrays
We note that despite the statistically significant results, the log fold changes are very
small. The largest effect represents a 1.5 fold reduction in gene expression. many acute
conditions will show gene expression changes of the order of 8 to 32 fold.
Statistically significant fold changes are compared across contrasts for each gene in
Table 3. In general, the signs of changes are the same, though there is little evidence
of a dose response. That is the difference between the high dose and control is not
generally greater than the difference beween the low dose and the control.
Commercial In Confidence 29
Page 125
Jet Fuel & Solvents HTA 2 Arrays
Gen
eN
ame
Low
vs
Med
ium
vs
Hig
hv
s
IDC
on
tro
lC
on
tro
lC
on
tro
l
SM
AR
CA
5-A
S1
SM
AR
CA
5an
tise
nse
RN
A1
0.1
18
LO
C1
00
12
90
34
un
char
acte
rize
dL
OC
10
01
29
03
4-0
.16
9
C9
orf
92
chro
mo
som
e9
op
enre
adin
gfr
ame
92
0.0
82
HE
RC
2P
7h
ect
do
mai
nan
dR
LD
2p
seu
do
gen
e7
-0.1
30
TE
KT
4P
2te
kti
n4
pse
ud
og
ene
20
.29
1
NB
PF
10
neu
rob
last
om
ab
reak
po
intfa
mil
y,m
emb
er1
0-0
.13
6-0
.17
7
LO
C1
00
13
26
86
un
char
acte
rize
dL
OC
10
01
32
68
6-0
.31
6
RN
U6
AT
AC
RN
A,
U6
atac
smal
ln
ucl
ear
(U1
2-d
epen
den
t
spli
cin
g)
0.5
79
0.6
81
RN
U6
AT
AC
2P
RN
A,
U6
atac
smal
ln
ucl
ear
2,
pse
ud
og
ene
0.4
38
0.4
95
RN
U6
AT
AC
3P
RN
A,
U6
atac
smal
ln
ucl
ear
3,
pse
ud
og
ene
0.4
37
RN
U6
AT
AC
4P
RN
A,
U6
atac
smal
ln
ucl
ear
4,
pse
ud
og
ene
0.2
83
0.4
16
0.3
67
RN
U6
AT
AC
5P
RN
A,
U6
atac
smal
ln
ucl
ear
5,
pse
ud
og
ene
0.4
04
0.4
89
PAN
3-A
S1
PAN
3an
tise
nse
RN
A1
0.0
88
LO
C1
00
29
41
45
un
char
acte
rize
dL
OC
10
02
94
14
50
.10
60
.15
2
MIR
32
0C
2m
icro
RN
A3
20
c-2
0.1
47
MIR
12
81
mic
roR
NA
12
81
-0.1
39
RN
Y5
P4
RN
A,
Ro
-ass
oci
ated
Y5
pse
ud
og
ene
40
.12
4
MIR
31
56
-3m
icro
RN
A3
15
6-3
0.1
01
LO
C1
00
50
67
10
end
og
eno
us
Bo
rnav
iru
s-li
ke
nu
cleo
pro
tein
2
pse
ud
og
ene
0.1
12
MIR
44
80
mic
roR
NA
44
80
-0.2
33
-0.3
34
MIR
44
85
mic
roR
NA
44
85
0.1
01
MIR
44
93
mic
roR
NA
44
93
0.1
99
MIR
37
8E
mic
roR
NA
37
8e
-0.1
73
Tab
le3
:lo
g2
Fo
ldC
han
ge
of
mR
NA
Sp
ecie
sB
yC
om
par
iso
n.
Gen
eis
the
HU
GO
Gen
eN
om
encl
atu
reC
om
mit
teewww.genenames.org
Nam
e
for
the
gen
e.
Commercial In Confidence 30
Pag
e 12
6
Jet Fuel & Solvents HTA 2 Arrays
Gen
eN
ame
Low
vs
Med
ium
vs
Hig
hv
s
IDC
on
tro
lC
on
tro
lC
on
tro
l
RN
Y4
P2
2R
NA
,R
o-a
sso
ciat
edY
4p
seu
do
gen
e2
20
.18
1
RN
7S
KP
1R
NA
,7
SK
smal
ln
ucl
ear
pse
ud
og
ene
10
.11
4
RN
7S
KP
3R
NA
,7
SK
smal
ln
ucl
ear
pse
ud
og
ene
30
.09
6
RN
7S
KP
4R
NA
,7
SK
smal
ln
ucl
ear
pse
ud
og
ene
40
.09
2
RN
7S
KP
6R
NA
,7
SK
smal
ln
ucl
ear
pse
ud
og
ene
60
.09
2
RN
7S
KP
7R
NA
,7
SK
smal
ln
ucl
ear
pse
ud
og
ene
70
.12
5
RN
U5
A-6
PR
NA
,U
5A
smal
ln
ucl
ear
6,
pse
ud
og
ene
0.1
52
RN
7S
KP
10
RN
A,
7S
Ksm
all
nu
clea
rp
seu
do
gen
e1
00
.09
8
AT
P1
3A
4-A
S1
AT
P1
3A
4an
tise
nse
RN
A1
0.0
91
NX
F1
nu
clea
rR
NA
exp
ort
fact
or
1-0
.08
9
AT
F7
acti
vat
ing
tran
scri
pti
on
fact
or
7-0
.09
6-0
.11
9
ZP
BP
zon
ap
ellu
cid
ab
ind
ing
pro
tein
0.0
59
BA
Z2
Ab
rom
od
om
ain
adja
cen
tto
zin
cfi
ng
erd
om
ain
,
2A
-0.1
41
SL
C2
A1
3so
lute
carr
ier
fam
ily
2(f
acil
itat
edg
luco
se
tran
spo
rter
),m
emb
er1
3
0.0
84
KL
HD
C3
kel
chd
om
ain
con
tain
ing
30
.12
5
MR
PL
53
mit
och
on
dri
alri
bo
som
alp
rote
inL
53
0.1
65
SG
K4
94
un
char
acte
rize
dse
rin
e/th
reo
nin
e-p
rote
ink
i-
nas
eS
gK
49
4
-0.0
89
SW
SA
P1
SW
IM-t
yp
ezi
nc
fin
ger
7as
soci
ated
pro
tein
10
.09
7
SL
C4
4A
3so
lute
carr
ier
fam
ily
44
,m
emb
er3
0.0
79
OR
10
X1
olf
acto
ryre
cep
tor,
fam
ily
10
,su
bfa
mil
yX
,
mem
ber
1
0.1
10
FA
M4
3A
fam
ily
wit
hse
qu
ence
sim
ilar
ity
43
,m
emb
er
A
0.2
33
Tab
le3
:lo
g2
Fo
ldC
han
ge
of
mR
NA
Sp
ecie
sB
yC
om
par
iso
n.
Gen
eis
the
HU
GO
Gen
eN
om
encl
atu
reC
om
mit
teewww.genenames.org
Nam
e
for
the
gen
e.
Commercial In Confidence 31
Pag
e 12
7
Jet Fuel & Solvents HTA 2 Arrays
Gen
eN
ame
Low
vs
Med
ium
vs
Hig
hv
s
IDC
on
tro
lC
on
tro
lC
on
tro
l
TM
EM
42
tran
smem
bra
ne
pro
tein
42
0.1
28
AS
B1
4an
ky
rin
rep
eat
and
SO
CS
bo
xco
nta
inin
g1
40
.06
0
CS
citr
ate
syn
thas
e-0
.13
3
C1
5o
rf6
1ch
rom
oso
me
15
op
enre
adin
gfr
ame
61
0.0
79
ZN
F5
97
zin
cfi
ng
erp
rote
in5
97
0.1
01
0.1
21
UP
F3
AP
1U
PF
3A
pse
ud
og
ene
1-0
.17
7
KL
HL
23
kel
ch-l
ike
fam
ily
mem
ber
23
0.0
51
0.0
64
TM
EM
22
9B
tran
smem
bra
ne
pro
tein
22
9B
0.0
89
0.1
05
EID
2E
P3
00
inte
ract
ing
inh
ibit
or
of
dif
fere
nti
atio
n
2
0.1
02
DH
X8
DE
AH
(Asp
-Glu
-Ala
-His
)b
ox
po
lyp
epti
de
8-0
.11
4-0
.14
1
DO
CK
2d
edic
ato
ro
fcy
tok
ines
is2
-0.1
30
-0.1
60
EW
SR
1E
WS
RN
A-b
ind
ing
pro
tein
1-0
.08
9-0
.12
4
EZ
H1
enh
ance
ro
fze
ste
ho
mo
log
1(D
roso
ph
ila)
-0.0
89
-0.0
99
FA
UF
inkel
-Bis
kis
-Rei
lly
mu
rin
esa
rco
ma
vir
us
(FB
R-M
uS
V)
ub
iqu
ito
usl
yex
pre
ssed
0.1
31
TM
EM
13
6tr
ansm
emb
ran
ep
rote
in1
36
0.0
95
ZN
RF
2zi
nc
and
rin
gfi
ng
er2
0.1
06
AC
IN1
apo
pto
tic
chro
mat
inco
nd
ensa
tio
nin
du
cer
1-0
.09
3
TN
RC
6B
trin
ucl
eoti
de
rep
eat
con
tain
ing
6B
-0.1
24
PO
GZ
po
go
tran
spo
sab
leel
emen
tw
ith
ZN
Fd
om
ain
-0.1
28
-0.1
40
PR
RC
2C
pro
lin
e-ri
chco
iled
-co
il2
C-0
.14
3
VP
S3
9vac
uo
lar
pro
tein
sort
ing
39
ho
mo
log
(S.ce
re-
vis
iae)
-0.1
09
-0.1
41
UB
R4
ubiq
uit
inpro
tein
ligas
eE
3co
mponen
tn-
reco
gn
in4
-0.1
19
-0.1
49
Tab
le3
:lo
g2
Fo
ldC
han
ge
of
mR
NA
Sp
ecie
sB
yC
om
par
iso
n.
Gen
eis
the
HU
GO
Gen
eN
om
encl
atu
reC
om
mit
teewww.genenames.org
Nam
e
for
the
gen
e.
Commercial In Confidence 32
Pag
e 12
8
Jet Fuel & Solvents HTA 2 Arrays
Gen
eN
ame
Low
vs
Med
ium
vs
Hig
hv
s
IDC
on
tro
lC
on
tro
lC
on
tro
l
NC
ST
Nn
icas
trin
-0.1
33
ME
D1
3L
med
iato
rco
mp
lex
subu
nit
13
-lik
e-0
.11
9
NO
MO
1N
OD
AL
mo
du
lato
r1
-0.1
67
TR
AM
1tr
ansl
oca
tio
nas
soci
ated
mem
bra
ne
pro
tein
10
.10
10
.13
2
EP
GN
epit
hel
ial
mit
og
en0
.07
0
NB
PF
14
neu
rob
last
om
ab
reak
po
intfa
mil
y,m
emb
er1
4-0
.14
2-0
.17
6
ME
TT
L7A
met
hylt
ransf
eras
eli
ke
7A
0.1
34
OR
2K
2o
lfac
tory
rece
pto
r,fa
mil
y2
,su
bfa
mil
yK
,
mem
ber
2
0.1
09
OR
10
D3
olf
acto
ryre
cep
tor,
fam
ily
10
,su
bfa
mil
yD
,
mem
ber
3(n
on
-fu
nct
ion
al)
0.1
82
GL
B1
gal
acto
sidas
e,bet
a1
-0.1
16
GC
LM
glu
tam
ate-
cyst
eine
ligas
e,m
odifi
ersu
bunit
0.1
48
GL
E1
GL
E1
RN
Aex
po
rtm
edia
tor
-0.0
74
GO
LG
A1
go
lgin
A1
-0.1
36
NO
MO
2N
OD
AL
mo
du
lato
r2
-0.1
73
TR
IM5
9tr
ipar
tite
mo
tif
con
tain
ing
59
0.1
04
UB
E2T
ubiq
uit
in-c
onju
gat
ing
enzy
me
E2T
(puta
tive)
0.0
93
NX
T1
NT
F2
-lik
eex
po
rtfa
cto
r1
0.1
00
0.1
07
NE
NF
neu
des
inn
euro
tro
ph
icfa
cto
r0
.12
7
NR
BP
1n
ucl
ear
rece
pto
rb
ind
ing
pro
tein
1-0
.07
6
SH
3K
BP
1S
H3
-do
mai
nk
inas
eb
ind
ing
pro
tein
1-0
.10
3
SN
OR
D8
smal
ln
ucl
eola
rR
NA
,C
/Db
ox
80
.20
7
NB
PF
7n
euro
bla
sto
ma
bre
akp
oin
tfa
mil
y,m
emb
er7
-0.0
96
-0.1
27
IRF
2in
terf
ero
nre
gu
lato
ryfa
cto
r2
-0.1
21
JAK
1Ja
nu
sk
inas
e1
-0.1
36
Tab
le3
:lo
g2
Fo
ldC
han
ge
of
mR
NA
Sp
ecie
sB
yC
om
par
iso
n.
Gen
eis
the
HU
GO
Gen
eN
om
encl
atu
reC
om
mit
teewww.genenames.org
Nam
e
for
the
gen
e.
Commercial In Confidence 33
Pag
e 12
9
Jet Fuel & Solvents HTA 2 Arrays
Gen
eN
ame
Low
vs
Med
ium
vs
Hig
hv
s
IDC
on
tro
lC
on
tro
lC
on
tro
l
NO
TC
H2
NL
no
tch
2N
-ter
min
alli
ke
-0.1
67
-0.2
14
-0.1
89
NB
PF
9n
euro
bla
sto
ma
bre
akp
oin
tfa
mil
y,m
emb
er9
-0.1
70
-0.2
33
-0.1
98
FL
J42
39
3u
nch
arac
teri
zed
LO
C4
01
10
5-0
.19
5-0
.21
7
NO
MO
3N
OD
AL
mo
du
lato
r3
-0.1
76
SP
INK
14
seri
ne
pep
tid
ase
inh
ibit
or,
Kaz
alty
pe
14
(pu
-
tati
ve)
0.1
10
MG
ST
3m
icro
som
alg
luta
thio
ne
S-t
ran
sfer
ase
30
.15
3
GO
LG
A6
L9
go
lgin
A6
fam
ily
-lik
e9
-0.1
30
ND
UF
C1
NA
DH
deh
ydro
gen
ase
(ubiq
uin
one)
1,
sub-
com
ple
xu
nk
now
n,
1,
6k
Da
0.1
17
0.1
08
NF
YC
nucl
ear
tran
scri
pti
on
fact
or
Y,gam
ma
-0.0
95
GE
MIN
4gem
(nucl
ear
org
anel
le)
asso
ciat
edpro
tein
40.0
92
0.1
05
ME
MO
1m
edia
tor
of
cell
mo
tili
ty1
0.0
53
PIG
Pp
ho
sph
atid
yli
no
sito
lg
lyca
nan
cho
rb
iosy
n-
thes
is,
clas
sP
0.1
09
0.1
30
PH
F2
1A
PH
Dfi
ng
erp
rote
in2
1A
-0.1
18
NG
RN
neu
gri
n,
neu
rite
ou
tgro
wth
asso
ciat
ed0
.10
30
.12
5
TR
MT
112
tRN
Am
ethylt
ransf
eras
e11-2
hom
olo
g(S
.
cere
vis
iae)
0.1
18
SL
C3
8A
2so
lute
carr
ier
fam
ily
38
,m
emb
er2
0.1
39
FB
XO
42
F-b
ox
pro
tein
42
-0.0
75
GO
N4L
gon-4
-lik
e(C
.el
egan
s)-0
.092
-0.1
32
PP
P1
R1
0p
rote
inp
ho
sph
atas
e1
,re
gu
lato
rysu
bu
nit
10
-0.0
72
-0.0
81
ZD
HH
C4
zin
cfi
ng
er,
DH
HC
-ty
pe
con
tain
ing
40
.08
30
.09
8
PR
MT
6pro
tein
argin
ine
met
hylt
ransf
eras
e6
0.0
86
Tab
le3
:lo
g2
Fo
ldC
han
ge
of
mR
NA
Sp
ecie
sB
yC
om
par
iso
n.
Gen
eis
the
HU
GO
Gen
eN
om
encl
atu
reC
om
mit
teewww.genenames.org
Nam
e
for
the
gen
e.
Commercial In Confidence 34
Pag
e 13
0
Jet Fuel & Solvents HTA 2 Arrays
Gen
eN
ame
Low
vs
Med
ium
vs
Hig
hv
s
IDC
on
tro
lC
on
tro
lC
on
tro
l
VP
S1
3D
vac
uo
lar
pro
tein
sort
ing
13
ho
mo
log
D(S
.
cere
vis
iae)
-0.1
47
AS
XL
2ad
dit
ion
alse
xco
mb
sli
ke
2(D
roso
ph
ila)
-0.0
88
-0.1
05
TM
EM
19
tran
smem
bra
ne
pro
tein
19
0.1
32
LA
PT
M4
Bly
soso
mal
pro
tein
tran
smem
bra
ne
4b
eta
0.1
38
C1
7o
rf8
5ch
rom
oso
me
17
op
enre
adin
gfr
ame
85
-0.1
58
PR
CC
pap
illa
ryre
nal
cell
carc
ino
ma
(tra
nsl
oca
tio
n-
asso
ciat
ed)
-0.0
92
-0.0
89
UB
E2Q
1ubiq
uit
in-c
onju
gat
ing
enzy
me
E2Q
fam
ily
mem
ber
1
-0.0
72
NB
PF
1n
euro
bla
sto
ma
bre
akp
oin
tfa
mil
y,m
emb
er1
-0.1
43
-0.1
71
GR
IPA
P1
GR
IP1
asso
ciat
edp
rote
in1
-0.1
14
AS
PH
D2
aspar
tate
bet
a-hydro
xyla
sedom
ain
conta
in-
ing
2
0.0
78
MIR
49
4m
icro
RN
A4
94
0.1
60
VP
S5
2vac
uo
lar
pro
tein
sort
ing
52
ho
mo
log
(S.ce
re-
vis
iae)
-0.1
13
EL
MO
2en
gu
lfm
ent
and
cell
mo
tili
ty2
-0.1
25
NR
ON
no
n-p
rote
inco
din
gR
NA
,re
pre
sso
ro
fN
FA
T0
.10
9
CB
X1
P1
chro
mo
bo
xh
om
olo
g1
pse
ud
og
ene
1-0
.15
4
GO
LG
A6
L1
0g
olg
inA
6fa
mil
y-l
ike
10
-0.1
12
IPP
Kin
osi
tol
1,3
,4,5
,6-p
enta
kis
ph
osp
hat
e2
-kin
ase
-0.0
83
SN
OR
D1
0sm
all
nu
cleo
lar
RN
A,
C/D
bo
x1
00
.18
9
SM
AR
CC
2S
WI/
SN
Fre
late
d,
mat
rix
asso
ciat
ed,
acti
n
dep
end
ent
reg
ula
tor
of
chro
mat
in,
sub
fam
ily
c,m
emb
er2
-0.0
97
-0.1
24
Tab
le3
:lo
g2
Fo
ldC
han
ge
of
mR
NA
Sp
ecie
sB
yC
om
par
iso
n.
Gen
eis
the
HU
GO
Gen
eN
om
encl
atu
reC
om
mit
teewww.genenames.org
Nam
e
for
the
gen
e.
Commercial In Confidence 35
Pag
e 13
1
Jet Fuel & Solvents HTA 2 Arrays
Gen
eN
ame
Low
vs
Med
ium
vs
Hig
hv
s
IDC
on
tro
lC
on
tro
lC
on
tro
l
SU
MO
3sm
all
ub
iqu
itin
-lik
em
od
ifier
30
.10
8
SN
RP
Bsm
all
nu
clea
rri
bo
nu
cleo
pro
tein
po
lyp
epti
des
Ban
dB
1
-0.2
02
-0.1
79
SN
OR
A3
7sm
all
nu
cleo
lar
RN
A,
H/A
CA
bo
x3
70
.22
3
SN
OR
A7
6sm
all
nu
cleo
lar
RN
A,
H/A
CA
bo
x7
60
.24
6
SU
PT
6H
sup
pre
sso
ro
fT
y6
ho
mo
log
(S.
cere
vis
iae)
-0.1
76
SN
OR
D9
4sm
all
nu
cleo
lar
RN
A,
C/D
bo
x9
40
.23
40
.28
8
MIR
55
5m
icro
RN
A5
55
-0.0
97
-0.1
11
MIR
60
4m
icro
RN
A6
04
-0.2
23
MIR
61
2m
icro
RN
A6
12
-0.3
00
-0.3
60
MIR
62
3m
icro
RN
A6
23
-0.2
05
-0.2
56
TY
MS
thym
idyla
tesy
nth
etas
e0.1
22
VH
Lvo
nH
ipp
el-L
ind
autu
mo
rsu
pp
ress
or,
E3
ubiq
uit
inpro
tein
ligas
e
0.0
69
SN
OR
D1
13
-5sm
all
nu
cleo
lar
RN
A,
C/D
bo
x1
13
-50
.21
6
FK
RP
fuk
uti
nre
late
dp
rote
in0
.08
5
SA
P1
30
Sin
3A
-ass
oci
ated
pro
tein
,1
30
kD
a-0
.11
6-0
.12
3
NU
P2
14
nu
cleo
po
rin
21
4k
Da
-0.1
01
-0.1
18
DO
CK
8d
edic
ato
ro
fcy
tok
ines
is8
-0.1
35
-0.1
54
SM
C1
Ast
ruct
ura
lm
ain
ten
ance
of
chro
mo
som
es1
A-0
.17
0
RA
SS
F5
Ras
asso
ciat
ion
(Ral
GD
S/A
F-6
)d
om
ain
fam
-
ily
mem
ber
5
-0.0
91
HIS
T1
H4
Bh
isto
ne
clu
ster
1,
H4
b0
.21
1
NB
PF
3n
euro
bla
sto
ma
bre
akp
oin
tfa
mil
y,m
emb
er3
-0.1
43
TC
HP
tric
ho
ple
in,
ker
atin
fila
men
tb
ind
ing
-0.1
04
MC
HR
2m
elan
in-c
on
cen
trat
ing
ho
rmo
ne
rece
pto
r2
0.1
10
Tab
le3
:lo
g2
Fo
ldC
han
ge
of
mR
NA
Sp
ecie
sB
yC
om
par
iso
n.
Gen
eis
the
HU
GO
Gen
eN
om
encl
atu
reC
om
mit
teewww.genenames.org
Nam
e
for
the
gen
e.
Commercial In Confidence 36
Pag
e 13
2
Jet Fuel & Solvents HTA 2 Arrays
Gen
eN
ame
Low
vs
Med
ium
vs
Hig
hv
s
IDC
on
tro
lC
on
tro
lC
on
tro
l
DY
NL
L1
dy
nei
n,
lig
ht
chai
n,
LC
8-t
yp
e1
0.1
07
0.1
04
GB
F1
go
lgib
refe
ldin
Are
sist
antg
uan
ine
nu
cleo
tid
e
exch
ang
efa
cto
r1
-0.1
26
-0.1
56
CC
NK
cycl
inK
-0.0
55
HD
AC
3h
isto
ne
dea
cety
lase
3-0
.09
1
ZM
YM
3zi
nc
fin
ger
,M
YM
-ty
pe
3-0
.09
1
PN
MA
1p
aran
eop
last
icM
aan
tig
en1
0.1
31
LD
HA
L6B
lact
ate
deh
ydro
gen
ase
A-l
ike
6B
0.1
24
TX
NL
1th
iore
do
xin
-lik
e1
0.0
84
CO
X5
Acy
toch
rom
ec
ox
idas
esu
bu
nit
Va
0.1
05
TM
EM
20
3tr
ansm
emb
ran
ep
rote
in2
03
0.1
08
0.1
36
NC
OR
1n
ucl
ear
rece
pto
rco
rep
ress
or
1-0
.13
8-0
.17
0
DE
PD
C5
DE
Pd
om
ain
con
tain
ing
5-0
.10
5
KD
M4A
lysi
ne
(K)-
spec
ific
dem
ethyla
se4A
-0.1
09
-0.1
39
IST
1in
crea
sed
sod
ium
tole
ran
ce1
ho
mo
log
(yea
st)
-0.0
95
GIT
2G
pro
tein
-co
up
led
rece
pto
rk
inas
ein
tera
ctin
g
Arf
GA
P2
-0.0
99
EL
MO
1en
gu
lfm
ent
and
cell
mo
tili
ty1
-0.1
42
-0.1
47
SE
TD
B1
SE
Td
om
ain
,b
ifu
rcat
ed1
-0.1
43
UB
AP
2L
ub
iqu
itin
asso
ciat
edp
rote
in2
-lik
e-0
.07
6
Tab
le3
:lo
g2
Fo
ldC
han
ge
of
mR
NA
Sp
ecie
sB
yC
om
par
iso
n.
Gen
eis
the
HU
GO
Gen
eN
om
encl
atu
reC
om
mit
teewww.genenames.org
Nam
e
for
the
gen
e.
Commercial In Confidence 37
Pag
e 13
3
Jet Fuel & Solvents HTA 2 Arrays
Expression was plotted against exposure group for the genes associated with the 20
probe sets showing the largest fold changes for the comparison of the high exposure
group with the controls. These plots are shown in Figures ?? to ??. As expected, the
differences are small, and largely confined to exposure group 4.
●
●
●
●
●
●
TC01001267.hg.1
8.25
8.50
8.75
9.00
1 2 3 4Group
valu
e
Gene: ubiquitin associated protein 2−like
Figure 9: Group Differences for Gene Symbol UBAP2L : ubiquitin associated protein
2-like
Commercial In Confidence 38
Page 134
Jet Fuel & Solvents HTA 2 Arrays
●●●
●
●
●
●
TC01002262.hg.1
7.8
8.0
8.2
8.4
1 2 3 4Group
valu
e
Gene: F−box protein 42
Figure 10: Group Differences for Gene Symbol FBXO42 : F-box protein 42
●
●
●
TC01003293.hg.1
8.6
8.8
9.0
9.2
1 2 3 4Group
valu
e
Gene: ubiquitin−conjugating enzyme E2Q family member 1
Figure 11: Group Differences for Gene Symbol UBE2Q1 : ubiquitin-conjugating en-
zyme E2Q family member 1
Commercial In Confidence 39
Page 135
Jet Fuel & Solvents HTA 2 Arrays
●●
●
●
●
●
●
●
●
TC02000174.hg.1
8.6
8.8
9.0
9.2
1 2 3 4Group
valu
e
Gene: nuclear receptor binding protein 1
Figure 12: Group Differences for Gene Symbol NRBP1 : nuclear receptor binding
protein 1
●
●
TC02005002.hg.1
3.1
3.2
3.3
3.4
1 2 3 4Group
valu
e
Gene: kelch−like family member 23
Figure 13: Group Differences for Gene Symbol KLHL23 : kelch-like family member
23
Commercial In Confidence 40
Page 136
Jet Fuel & Solvents HTA 2 Arrays
●
●●
●
●
●
●
TC03000055.hg.1
8.2
8.4
8.6
8.8
9.0
1 2 3 4Group
valu
e
Gene: von Hippel−Lindau tumor suppressor, E3 ubiquitin protein ligase
Figure 14: Group Differences for Gene Symbol VHL : von Hippel-Lindau tumor sup-
pressor, E3 ubiquitin protein ligase
●
●
●
TC04000413.hg.1
2.8
3.0
3.2
1 2 3 4Group
valu
e
Gene: epithelial mitogen
Figure 15: Group Differences for Gene Symbol EPGN : epithelial mitogen
Commercial In Confidence 41
Page 137
Jet Fuel & Solvents HTA 2 Arrays
●
●
●
●
●
●●●
●●
●
●
●●
●
●
●
●●●
●●
●●
●
●
●
●
●●●
●●
●●
●
●
●
●
●●●
●●
●
●
●
●
●
●
●
●●●
●●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●●●
●●
●●
TC06001502.hg.1 TC6_cox_hap2000137.hg.1 TC6_dbb_hap3000126.hg.1
TC6_mann_hap4000115.hg.1 TC6_mcf_hap5000116.hg.1 TC6_qbl_hap6000127.hg.1
TC6_ssto_hap7000118.hg.1
8.50
8.75
9.00
9.25
8.50
8.75
9.00
9.25
8.50
8.75
9.00
9.25
1 2 3 4Group
valu
e
Gene: protein phosphatase 1, regulatory subunit 10
Figure 16: Group Differences for Gene Symbol PPP1R10 : protein phosphatase 1,
regulatory subunit 10
●
●●
●
●
●
●
●
TC07000067.hg.1
7.6
7.8
8.0
1 2 3 4Group
valu
e
Gene: zinc finger, DHHC−type containing 4
Figure 17: Group Differences for Gene Symbol ZDHHC4 : zinc finger, DHHC-type
containing 4
Commercial In Confidence 42
Page 138
Jet Fuel & Solvents HTA 2 Arrays
●
●
●
TC07001368.hg.1
3.1
3.2
3.3
3.4
3.5
1 2 3 4Group
valu
e
Gene: zona pellucida binding protein
Figure 18: Group Differences for Gene Symbol ZPBP : zona pellucida binding protein
●
●
●
●
●
●●
●
●●
TC09000693.hg.1
8.2
8.3
8.4
8.5
8.6
8.7
1 2 3 4Group
valu
e
Gene: GLE1 RNA export mediator
Figure 19: Group Differences for Gene Symbol GLE1 : GLE1 RNA export mediator
Commercial In Confidence 43
Page 139
Jet Fuel & Solvents HTA 2 Arrays
●
●
●
●
●
● ●
●●
●
●
TC14000622.hg.1
8.6
8.7
8.8
8.9
9.0
9.1
1 2 3 4Group
valu
e
Gene: cyclin K
Figure 20: Group Differences for Gene Symbol CCNK : cyclin K
●
●
●
●
TC15000624.hg.1
6.25
6.50
6.75
1 2 3 4Group
valu
e
Gene: chromosome 15 open reading frame 61
Figure 21: Group Differences for Gene Symbol C15orf61 : chromosome 15 open
reading frame 61
Commercial In Confidence 44
Page 140
Jet Fuel & Solvents HTA 2 Arrays
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
TC22000169.hg.1
7.4
7.6
7.8
8.0
1 2 3 4Group
valu
e
Gene: aspartate beta−hydroxylase domain containing 2
Figure 22: Group Differences for Gene Symbol ASPHD2 : aspartate beta-hydroxylase
domain containing 2
Since date scanned is partially confounded with exposure group, the analysis was re-
peated using only those chips scanned on or after 16thOctober 2013: the earliest date on
which exposure group 4 samples were scanned. There were no statistically significant
gene expression differences other than for group 1 (control) compared with Group 4
(high exposure). 98 probe sets were statistically significant for the reduced data set.
Commercial In Confidence 45
Page 141
Jet Fuel & Solvents HTA 2 Arrays
Each gene was recorded as being statistically significant or not in the full data set, and
in the reduced data set. Status in the reduced data set was then cross-tabulated with
status in the reduced data set. The results are shown in Table 4. This comparison is
made at the level of the gene (Entrez gene ID) rather than at the level of the probesets;
some Entrez IDs have more than one probeset.
Full Reduced Data Set
Data Set FALSE TRUE
FALSE 22091 75
TRUE 154 17
Table 4: Status in the full data set (all dates scanned) vs Status in the Reduced Data
Sets (only chips scanned on or after 16thOctober 2010)
Although there is obviously a strong relationship between the two outcomes (Fisher’s
exact test p value ¡ 10−16), there are 229 genes which are significant in only one of
the two data sets. The majority of these divergent results are significant only in the full
data set, and not when the early scan dates are excluded. The top 20 probe sets for the
reduced data set are shown in Table 5. Again the log fold changes are very small. The
maximum effect is a reduction of 1.4 fold.
Commercial In Confidence 46
Page 142
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Hig
hv
sC
on
tro
l1
00
03
34
38
SN
OR
D1
16
-26
smal
ln
ucl
eola
rR
NA
,C
/Db
ox
11
6-2
60
.47
7.6
64
.24
0.0
3
Hig
hv
sC
on
tro
l1
00
03
34
36
SN
OR
D1
16
-25
smal
ln
ucl
eola
rR
NA
,C
/Db
ox
11
6-2
50
.42
8.0
54
.00
0.0
4
Hig
hv
sC
on
tro
l1
00
03
34
39
SN
OR
D1
16
-27
smal
ln
ucl
eola
rR
NA
,C
/Db
ox
11
6-2
70
.39
7.2
94
.03
0.0
4
Hig
hv
sC
on
tro
l6
77
77
2S
CA
RN
A6
smal
lC
ajal
bo
dy
-sp
ecifi
cR
NA
60
.34
10
.34
4.2
70
.03
Hig
hv
sC
on
tro
l6
92
22
5S
NO
RD
94
smal
ln
ucl
eola
rR
NA
,C
/Db
ox
94
0.3
21
0.0
84
.08
0.0
4
Hig
hv
sC
on
tro
l6
77
79
6S
NO
RA
5C
smal
ln
ucl
eola
rR
NA
,H
/AC
Ab
ox
5C
0.3
09
.14
4.8
20
.01
Hig
hv
sC
on
tro
l1
00
50
08
15
MIR
36
87
mic
roR
NA
36
87
0.3
08
.49
4.2
00
.03
Hig
hv
sC
on
tro
l1
00
15
82
62
SC
AR
NA
9L
smal
lC
ajal
bo
dy
-sp
ecifi
cR
NA
9-l
ike
0.2
95
.56
4.7
90
.01
Hig
hv
sC
on
tro
l6
19
38
3S
CA
RN
A9
smal
lC
ajal
bo
dy
-sp
ecifi
cR
NA
90
.28
9.2
23
.89
0.0
5
Hig
hv
sC
on
tro
l6
92
09
2S
NO
RD
32
Bsm
all
nu
cleo
lar
RN
A,
C/D
bo
x3
2B
0.2
76
.45
4.9
10
.01
Hig
hv
sC
on
tro
l6
92
09
2S
NO
RD
32
Bsm
all
nu
cleo
lar
RN
A,
C/D
bo
x3
2B
0.2
76
.45
4.9
10
.01
Hig
hv
sC
on
tro
l6
92
09
2S
NO
RD
32
Bsm
all
nu
cleo
lar
RN
A,
C/D
bo
x3
2B
0.2
76
.45
4.9
10
.01
Hig
hv
sC
on
tro
l6
92
09
2S
NO
RD
32
Bsm
all
nu
cleo
lar
RN
A,
C/D
bo
x3
2B
0.2
76
.45
4.9
10
.01
Hig
hv
sC
on
tro
l6
92
09
2S
NO
RD
32
Bsm
all
nu
cleo
lar
RN
A,
C/D
bo
x3
2B
0.2
76
.45
4.9
10
.01
Hig
hv
sC
on
tro
l6
92
09
2S
NO
RD
32
Bsm
all
nu
cleo
lar
RN
A,
C/D
bo
x3
2B
0.2
76
.45
4.9
10
.01
Hig
hv
sC
on
tro
l1
00
37
92
95
RN
Y4
P8
RN
A,
Ro
-ass
oci
ated
Y4
pse
ud
og
ene
80
.26
6.4
13
.88
0.0
5
Hig
hv
sC
on
tro
l8
96
8H
IST
1H
3F
his
ton
ecl
ust
er1
,H
3f
0.2
69
.93
3.9
20
.05
Hig
hv
sC
on
tro
l6
92
09
2S
NO
RD
32
Bsm
all
nu
cleo
lar
RN
A,
C/D
bo
x3
2B
0.2
56
.44
4.6
80
.01
Hig
hv
sC
on
tro
l6
54
31
9S
NO
RA
5A
smal
ln
ucl
eola
rR
NA
,H
/AC
Ab
ox
5A
0.2
58
.81
4.2
90
.03
Hig
hv
sC
on
tro
l8
36
0H
IST
1H
4D
his
ton
ecl
ust
er1
,H
4d
0.2
58
.50
4.0
80
.04
Hig
hv
sC
on
tro
l8
33
9H
IST
1H
2B
Gh
isto
ne
clu
ster
1,
H2
bg
0.2
46
.79
4.0
30
.04
Hig
hv
sC
on
tro
l8
36
6H
IST
1H
4B
his
ton
ecl
ust
er1
,H
4b
0.2
35
.65
4.0
10
.04
Hig
hv
sC
on
tro
l6
77
84
5S
NO
RA
79
smal
ln
ucl
eola
rR
NA
,H
/AC
Ab
ox
79
0.2
26
.46
4.0
20
.04
Hig
hv
sC
on
tro
l1
00
50
08
45
MIR
64
2B
mic
roR
NA
64
2b
0.2
05
.75
3.9
40
.05
Hig
hv
sC
on
tro
l6
87
5T
AF
4B
TA
F4
bR
NA
po
lym
eras
eII
,T
AT
Ab
ox
bin
d-
ing
pro
tein
(TB
P)-
asso
ciat
edfa
cto
r,1
05
kD
a
0.2
07
.40
3.8
40
.05
Tab
le5
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
tfo
rd
ata
scan
ned
on
or
afte
r1
6th
Oct
ob
er2
01
3;
Res
ult
sar
era
nked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.
En
trez
isth
eN
CB
IE
ntr
ezco
de
for
the
gen
ehttp://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 47
Pag
e 14
3
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Hig
hv
sC
on
tro
l1
16
54
0M
RP
L5
3m
ito
cho
nd
rial
rib
oso
mal
pro
tein
L5
30
.20
8.1
34
.73
0.0
1
Hig
hv
sC
on
tro
l8
34
6H
IST
1H
2B
Ih
isto
ne
clu
ster
1,
H2
bi
0.1
95
.49
3.8
50
.05
Hig
hv
sC
on
tro
l8
96
9H
IST
1H
2A
Gh
isto
ne
clu
ster
1,
H2
ag0
.19
8.7
44
.59
0.0
1
Hig
hv
sC
on
tro
l1
00
87
38
22
RN
Y4
P2
8R
NA
,R
o-a
sso
ciat
edY
4p
seu
do
gen
e2
80
.19
5.7
84
.57
0.0
1
Hig
hv
sC
on
tro
l6
62
8S
NR
PB
smal
ln
ucl
ear
rib
on
ucl
eop
rote
inp
oly
pep
tid
es
Ban
dB
1
-0.1
88
.03
-4.4
00
.03
Hig
hv
sC
on
tro
l2
68
23
RN
U1
2-2
PR
NA
,U
12
smal
ln
ucl
ear
2,
pse
ud
og
ene
0.1
86
.67
4.0
60
.04
Hig
hv
sC
on
tro
l1
00
61
63
60
MIR
24
67
mic
roR
NA
24
67
-0.1
75
.31
-3.9
20
.05
Hig
hv
sC
on
tro
l2
19
7FA
UF
inkel
-Bis
kis
-Rei
lly
mu
rin
esa
rco
ma
vir
us
(FB
R-M
uS
V)
ub
iqu
ito
usl
yex
pre
ssed
0.1
77
.74
4.8
00
.01
Hig
hv
sC
on
tro
l9
93
7D
CL
RE
1A
DN
Acr
oss
-lin
kre
pai
r1
A0
.17
6.0
83
.90
0.0
5
Hig
hv
sC
on
tro
l1
00
86
26
70
RN
Y3
P7
RN
A,
Ro
-ass
oci
ated
Y3
pse
ud
og
ene
70
.17
4.0
54
.21
0.0
3
Hig
hv
sC
on
tro
l1
00
61
63
55
MIR
45
31
mic
roR
NA
45
31
-0.1
67
.59
-4.1
10
.04
Hig
hv
sC
on
tro
l2
67
47
NU
FIP
1n
ucl
ear
frag
ile
Xm
enta
lre
tard
atio
np
rote
in
inte
ract
ing
pro
tein
1
0.1
66
.31
4.0
80
.04
Hig
hv
sC
on
tro
l5
37
AT
P6
AP
1A
TP
ase,
H+
tran
spo
rtin
g,
lyso
som
alac
ces-
sory
pro
tein
1
-0.1
69
.04
-3.9
10
.05
Hig
hvs
Contr
ol
4717
ND
UF
C1
NA
DH
deh
ydro
gen
ase
(ubiq
uin
one)
1,
sub-
com
ple
xu
nk
now
n,
1,
6k
Da
0.1
56
.79
6.0
50
.00
Hig
hv
sC
on
tro
l8
33
8H
IST
2H
2A
Ch
isto
ne
clu
ster
2,
H2
ac0
.15
10
.34
3.9
30
.05
Hig
hv
sC
on
tro
l7
67
5Z
NF
12
1zi
nc
fin
ger
pro
tein
12
10
.15
8.3
74
.24
0.0
3
Hig
hv
sC
on
tro
l2
28
69
ZN
F5
10
zin
cfi
ng
erp
rote
in5
10
0.1
56
.94
4.1
00
.04
Hig
hv
sC
on
tro
l9
53
2B
AG
2B
CL
2-a
sso
ciat
edat
han
og
ene
20
.15
6.7
43
.86
0.0
5
Hig
hv
sC
on
tro
l8
43
16
NA
A3
8N
(alp
ha)
-ace
tylt
ran
sfer
ase
38
,N
atC
aux
il-
iary
subu
nit
0.1
57
.58
3.8
30
.05
Hig
hv
sC
on
tro
l5
51
56
AR
MC
1ar
mad
illo
rep
eat
con
tain
ing
10
.15
7.8
04
.28
0.0
3
Hig
hv
sC
on
tro
l2
85
23
7C
3o
rf3
8ch
rom
oso
me
3o
pen
read
ing
fram
e3
80
.15
8.1
74
.13
0.0
4
Tab
le5
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
tfo
rd
ata
scan
ned
on
or
afte
r1
6th
Oct
ob
er2
01
3;
Res
ult
sar
era
nked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.
En
trez
isth
eN
CB
IE
ntr
ezco
de
for
the
gen
ehttp://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 48
Pag
e 14
4
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Hig
hvs
Contr
ol
7112
TM
PO
thym
opoie
tin
0.1
58.7
54.5
60.0
2
Hig
hv
sC
on
tro
l6
07
1R
NU
6V
RN
A,U
6sm
alln
ucl
ear
var
ian
tse
qu
ence
wit
h
SN
RP
Ep
seu
do
gen
ese
qu
ence
0.1
47
.62
3.8
40
.05
Hig
hv
sC
on
tro
l1
28
82
2C
ST
9cy
stat
in9
(tes
tati
n)
-0.1
45
.80
-4.9
60
.01
Hig
hv
sC
on
tro
l2
37
88
MT
CH
2m
ito
cho
nd
rial
carr
ier
20
.14
8.9
34
.10
0.0
4
Hig
hv
sC
on
tro
l5
28
3P
IGH
ph
osp
hat
idy
lin
osi
tol
gly
can
anch
or
bio
syn
-
thes
is,
clas
sH
0.1
47
.00
4.1
60
.03
Hig
hv
sC
on
tro
l1
03
80
BP
NT
13
’(2
’),
5’-
bis
ph
osp
hat
en
ucl
eoti
das
e1
0.1
47
.20
3.9
20
.05
Hig
hv
sC
on
tro
l1
05
75
CC
T4
chap
ero
nin
con
tain
ing
TC
P1
,su
bu
nit
4
(del
ta)
0.1
48
.43
3.8
40
.05
Hig
hv
sC
on
tro
l3
71
6JA
K1
Jan
us
kin
ase
1-0
.13
10
.69
-3.9
80
.04
Hig
hv
sC
on
tro
l1
00
28
79
48
GM
14
0u
nch
arac
teri
zed
LO
C1
00
28
79
48
-0.1
33
.90
-4.0
00
.04
Hig
hv
sC
on
tro
l3
53
13
9L
CE
2A
late
corn
ified
envel
op
e2
A-0
.13
6.2
4-4
.11
0.0
4
Hig
hv
sC
on
tro
l9
41
2M
ED
21
med
iato
rco
mp
lex
subu
nit
21
0.1
39
.40
3.8
40
.05
Hig
hv
sC
on
tro
l1
00
42
28
95
MIR
42
94
mic
roR
NA
42
94
-0.1
36
.91
-3.9
40
.05
Hig
hv
sC
on
tro
l9
40
39
ZN
F1
01
zin
cfi
ng
erp
rote
in1
01
0.1
39
.00
3.8
20
.05
Hig
hv
sC
on
tro
l7
80
5L
AP
TM
5ly
soso
mal
pro
tein
tran
smem
bra
ne
5-0
.12
12
.48
-3.9
30
.05
Hig
hv
sC
on
tro
l1
12
97
0K
TI1
2K
TI1
2h
om
olo
g,
chro
mat
inas
soci
ated
(S.
cere
vis
iae)
0.1
27
.51
4.0
10
.04
Hig
hvs
Contr
ol
2720
GL
B1
gal
acto
sidas
e,bet
a1
-0.1
28.3
4-3
.91
0.0
5
Hig
hv
sC
on
tro
l2
97
1G
TF
3A
gen
eral
tran
scri
pti
on
fact
or
IIIA
0.1
28
.80
4.0
30
.04
Hig
hvs
Contr
ol
29089
UB
E2T
ubiq
uit
in-c
onju
gat
ing
enzy
me
E2T
(puta
tive)
0.1
26.2
84.4
90.0
2
Hig
hv
sC
on
tro
l1
49
42
0P
DIK
1L
PD
LIM
1in
tera
ctin
gk
inas
e1
like
0.1
27
.01
4.1
70
.03
Hig
hv
sC
on
tro
l5
51
75
KL
HL
11
kel
ch-l
ike
fam
ily
mem
ber
11
0.1
26
.85
4.2
50
.03
Hig
hv
sC
on
tro
l8
00
67
DC
AF
17
DD
B1
and
CU
L4
asso
ciat
edfa
cto
r1
70
.12
8.0
34
.02
0.0
4
Hig
hv
sC
on
tro
l6
93
12
3M
IR4
49
Bm
icro
RN
A4
49
b-0
.12
7.4
5-3
.86
0.0
5
Hig
hv
sC
on
tro
l7
59
6Z
NF
45
zin
cfi
ng
erp
rote
in4
50
.11
7.1
33
.86
0.0
5
Tab
le5
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
tfo
rd
ata
scan
ned
on
or
afte
r1
6th
Oct
ob
er2
01
3;
Res
ult
sar
era
nked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.
En
trez
isth
eN
CB
IE
ntr
ezco
de
for
the
gen
ehttp://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 49
Pag
e 14
5
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Hig
hv
sC
on
tro
l1
00
61
62
16
MIR
47
97
mic
roR
NA
47
97
0.1
12
.64
4.1
60
.03
Hig
hv
sC
on
tro
l5
12
63
MR
PL
30
mit
och
on
dri
alri
bo
som
alp
rote
inL
30
0.1
18
.30
4.1
90
.03
Hig
hv
sC
on
tro
l4
00
12
3L
INC
00
54
8lo
ng
inte
rgen
icn
on
-pro
tein
cod
ing
RN
A5
48
-0.1
14
.70
-4.3
60
.03
Hig
hvs
Contr
ol
116236
AB
HD
15
abhydro
lase
dom
ain
conta
inin
g15
0.1
17.7
23.8
20.0
5
Hig
hv
sC
on
tro
l1
50
27
4H
SC
BH
scB
mit
och
on
dri
alir
on
-su
lfu
rcl
ust
erco
-
chap
ero
ne
0.1
16
.95
4.8
80
.01
Hig
hv
sC
on
tro
l1
00
28
87
30
PAN
3-A
S1
PAN
3an
tise
nse
RN
A1
0.1
17
.50
4.2
30
.03
Hig
hv
sC
on
tro
l7
00
9T
MB
IM6
tran
smem
bra
ne
BA
Xin
hib
ito
rm
oti
fco
nta
in-
ing
6
-0.1
11
1.5
3-4
.18
0.0
3
Hig
hv
sC
on
tro
l1
00
42
30
18
MIR
31
56
-3m
icro
RN
A3
15
6-3
0.1
14
.50
3.8
50
.05
Hig
hv
sC
on
tro
l5
43
3P
OL
R2
Dp
oly
mer
ase
(RN
A)
II(D
NA
dir
ecte
d)
po
lyp
epti
de
D
0.1
17
.05
3.8
30
.05
Hig
hv
sC
on
tro
l6
48
41
GN
PN
AT
1g
luco
sam
ine-
ph
osp
hat
eN
-ace
tylt
ran
sfer
ase
1
0.1
17
.32
4.2
70
.03
Hig
hv
sC
on
tro
l4
00
74
3L
OC
40
07
43
un
char
acte
rize
dL
OC
40
07
43
-0.1
06
.33
-3.8
30
.05
Hig
hvs
Contr
ol
91942
ND
UFA
F2
NA
DH
deh
ydro
gen
ase
(ubiq
uin
one)
com
ple
x
I,as
sem
bly
fact
or
2
0.1
06
.89
3.8
30
.05
Hig
hv
sC
on
tro
l8
11
91
OR
5G
5P
olf
acto
ryre
cep
tor,
fam
ily
5,
sub
fam
ily
G,
mem
ber
5p
seu
do
gen
e
0.1
03
.39
3.9
70
.04
Hig
hv
sC
on
tro
l9
79
8IS
T1
incr
ease
dso
diu
mto
lera
nce
1h
om
olo
g
(yea
st)
-0.1
09
.50
-4.1
00
.04
Hig
hv
sC
on
tro
l6
04
5R
NF
2ri
ng
fin
ger
pro
tein
20
.10
8.0
94
.00
0.0
4
Hig
hvs
Contr
ol
4802
NF
YC
nucl
ear
tran
scri
pti
on
fact
or
Y,gam
ma
-0.1
08.3
6-3
.91
0.0
5
Hig
hv
sC
on
tro
l5
13
4P
DC
D2
pro
gra
mm
edce
lld
eath
20
.09
7.8
93
.96
0.0
5
Hig
hv
sC
on
tro
l2
83
51
8K
CN
RG
po
tass
ium
chan
nel
reg
ula
tor
0.0
98
.24
4.3
00
.03
Hig
hv
sC
on
tro
l6
98
8T
CT
AT
-cel
lle
ukem
iatr
ansl
oca
tio
nal
tere
d0
.09
7.5
93
.95
0.0
5
Tab
le5
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
tfo
rd
ata
scan
ned
on
or
afte
r1
6th
Oct
ob
er2
01
3;
Res
ult
sar
era
nked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.
En
trez
isth
eN
CB
IE
ntr
ezco
de
for
the
gen
ehttp://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 50
Pag
e 14
6
Jet Fuel & Solvents HTA 2 Arrays
Co
mp
aris
on
En
trez
Gen
eS
ym
bo
lG
ene
Nam
elo
gF
CA
veE
xp
rt
adj.
P.V
al
Hig
hv
sC
on
tro
l1
14
13
4S
LC
2A
13
solu
teca
rrie
rfa
mil
y2
(fac
ilit
ated
glu
cose
tran
spo
rter
),m
emb
er1
3
0.0
96
.46
4.1
50
.03
Hig
hv
sC
on
tro
l7
42
8V
HL
vo
nH
ipp
el-L
ind
autu
mo
rsu
pp
ress
or,
E3
ubiq
uit
inpro
tein
ligas
e
0.0
98
.70
4.3
90
.03
Hig
hv
sC
on
tro
l1
37
69
5T
ME
M6
8tr
ansm
emb
ran
ep
rote
in6
80
.09
6.2
93
.88
0.0
5
Hig
hv
sC
on
tro
l6
11
9R
PA3
rep
lica
tio
np
rote
inA
3,
14
kD
a0
.09
6.2
24
.57
0.0
1
Hig
hv
sC
on
tro
l1
00
10
14
67
ZS
CA
N3
0zi
nc
fin
ger
and
SC
AN
do
mai
nco
nta
inin
g3
00
.08
6.3
03
.88
0.0
5
Hig
hv
sC
on
tro
l9
12
5R
QC
D1
RC
D1
req
uir
edfo
rce
lld
iffe
ren
tiat
ion
1h
o-
mo
log
(S.
po
mb
e)
0.0
88
.76
3.8
50
.05
Hig
hv
sC
on
tro
l2
55
32
4E
PG
Nep
ith
elia
lm
ito
gen
0.0
82
.97
3.9
30
.05
Hig
hv
sC
on
tro
l5
51
46
ZD
HH
C4
zin
cfi
ng
er,
DH
HC
-ty
pe
con
tain
ing
40
.08
7.6
93
.83
0.0
5
Hig
hv
sC
on
tro
l5
72
26
LY
RM
2L
YR
mo
tif
con
tain
ing
20
.07
6.0
03
.95
0.0
5
Hig
hv
sC
on
tro
l8
41
40
FA
M1
61
Afa
mil
yw
ith
seq
uen
cesi
mil
arit
y1
61
,m
emb
er
A
0.0
64
.27
3.9
10
.05
Tab
le5
:S
tati
stic
ally
Sig
nifi
can
tD
iffe
ren
ces
inG
ene
Ex
pre
ssio
nb
yC
on
tras
tfo
rd
ata
scan
ned
on
or
afte
r1
6th
Oct
ob
er2
01
3;
Res
ult
sar
era
nked
by
abso
lute
log2
fold
chan
ge.
Gen
en
ames
and
gen
esy
mb
ols
are
asd
efin
edb
yth
eH
UG
OG
ene
No
men
clat
ure
Co
mm
itte
ewww.genenames.org
.
En
trez
isth
eN
CB
IE
ntr
ezco
de
for
the
gen
ehttp://www.ncbi.nlm.nih.gov/gene/
.N
ote
som
eg
ene
sym
bo
lsar
eas
soci
ated
wit
hm
ult
iple
pro
be
sets
,an
dth
eref
ore
hav
em
ult
iple
entr
ies
inth
ista
ble
.
Commercial In Confidence 51
Pag
e 14
7
Jet Fuel & Solvents HTA 2 Arrays
Gene expression in the reduced data set was plotted against exposure group for genes
associated with the 20 most differential probe sets. These plots are shown in Figures
?? to ??. Again, differences are small and mostly confined to the high exposure group.
The results are, however, more variable because of the reduced sample size.
●
●
●
●
●
TC02000521.hg.1
8
9
10
11
1 2 3 4Group
valu
e
Gene: small nucleolar RNA, C/D box 94
Figure 23: Group Differences for Gene Symbol SNORD94 : small nucleolar RNA,
C/D box 94 In Reduced Data Set.
Commercial In Confidence 52
Page 148
Jet Fuel & Solvents HTA 2 Arrays
●
●
●
TC02001417.hg.1
9.0
9.5
10.0
10.5
11.0
11.5
1 2 3 4Group
valu
e
Gene: small Cajal body−specific RNA 6
Figure 24: Group Differences for Gene Symbol SCARNA6 : small Cajal body-specific
RNA 6 In Reduced Data Set.
●
●
●
●
●●●
●
●
●
●
●●●
●
●
●
●
●●●
●
●
●
●
●●●
●
●
●
●
●●●
●
●
●
●
●●●
●
●
●
●
●●●
TC06000320.hg.1 TC6_apd_hap1000013.hg.1 TC6_cox_hap2000017.hg.1
TC6_dbb_hap3000013.hg.1 TC6_mann_hap4000014.hg.1 TC6_mcf_hap5000012.hg.1
TC6_qbl_hap6000012.hg.1
5.5
6.0
6.5
7.0
7.5
5.5
6.0
6.5
7.0
7.5
5.5
6.0
6.5
7.0
7.5
1 2 3 4Group
valu
e
Gene: small nucleolar RNA, C/D box 32B
Figure 25: Group Differences for Gene Symbol SNORD32B : small nucleolar RNA,
C/D box 32B In Reduced Data Set.
Commercial In Confidence 53
Page 149
Jet Fuel & Solvents HTA 2 Arrays
●
●
●
TC06001348.hg.1
7.5
8.0
8.5
9.0
1 2 3 4Group
valu
e
Gene: histone cluster 1, H4d
Figure 26: Group Differences for Gene Symbol HIST1H4D : histone cluster 1, H4d In
Reduced Data Set.
●
●
●
●
TC06001353.hg.1 TC06001427.hg.1
7
8
9
10
11
1 2 3 4 1 2 3 4Group
valu
e
Gene: histone cluster 1, H3f
Figure 27: Group Differences for Gene Symbol HIST1H3F : histone cluster 1, H3f In
Reduced Data Set.
Commercial In Confidence 54
Page 150
Jet Fuel & Solvents HTA 2 Arrays
●
●
●
●●
●●
●●
●
●
TC07001352.hg.1
8.0
8.5
9.0
9.5
10.0
1 2 3 4Group
valu
e
Gene: small nucleolar RNA, H/ACA box 5A
Figure 28: Group Differences for Gene Symbol SNORA5A : small nucleolar RNA,
H/ACA box 5A In Reduced Data Set.
●
●
TC07001353.hg.1
8.5
9.0
9.5
10.0
10.5
1 2 3 4Group
valu
e
Gene: small nucleolar RNA, H/ACA box 5C
Figure 29: Group Differences for Gene Symbol SNORA5C : small nucleolar RNA,
H/ACA box 5C In Reduced Data Set.
Commercial In Confidence 55
Page 151
Jet Fuel & Solvents HTA 2 Arrays
●
●
●
●
TC0X000910.hg.1
4.5
5.0
5.5
6.0
1 2 3 4Group
valu
e
Gene: small Cajal body−specific RNA 9−like
Figure 30: Group Differences for Gene Symbol SCARNA9L : small Cajal body-
specific RNA 9-like In Reduced Data Set.
●●
●
●
●
●
●
TC11000910.hg.1
7.5
8.0
8.5
9.0
9.5
10.0
1 2 3 4Group
valu
e
Gene: small Cajal body−specific RNA 9
Figure 31: Group Differences for Gene Symbol SCARNA9 : small Cajal body-specific
RNA 9 In Reduced Data Set.
Commercial In Confidence 56
Page 152
Jet Fuel & Solvents HTA 2 Arrays
●
●●
●
●
●
●
●
●
●●
●●
TC15000068.hg.1
5
6
7
8
9
1 2 3 4Group
valu
e
Gene: small nucleolar RNA, C/D box 116−25
Figure 32: Group Differences for Gene Symbol SNORD116-25 : small nucleolar RNA,
C/D box 116-25 In Reduced Data Set.
●
●●
●
●
●
●
●
●
●
●
●
●
TC15000069.hg.1
5
6
7
8
9
1 2 3 4Group
valu
e
Gene: small nucleolar RNA, C/D box 116−26
Figure 33: Group Differences for Gene Symbol SNORD116-26 : small nucleolar RNA,
C/D box 116-26 In Reduced Data Set.
Commercial In Confidence 57
Page 153
Jet Fuel & Solvents HTA 2 Arrays
●
●
●
●
●
●
●
●
●
TC15000070.hg.1
5
6
7
8
1 2 3 4Group
valu
e
Gene: small nucleolar RNA, C/D box 116−27
Figure 34: Group Differences for Gene Symbol SNORD116-27 : small nucleolar RNA,
C/D box 116-27 In Reduced Data Set.
●
●●
●
●
TC17000494.hg.1
5
6
7
1 2 3 4Group
valu
e
Gene: RNA, Ro−associated Y4 pseudogene 8
Figure 35: Group Differences for Gene Symbol RNY4P8 : RNA, Ro-associated Y4
pseudogene 8 In Reduced Data Set.
Commercial In Confidence 58
Page 154
Jet Fuel & Solvents HTA 2 Arrays
●
●
●
●
●
●
●
TC21000007.hg.1
8.0
8.5
9.0
9.5
1 2 3 4Group
valu
e
Gene: microRNA 3687
Figure 36: Group Differences for Gene Symbol MIR3687 : microRNA 3687 In Re-
duced Data Set.
There is evidence of a difference in expression between the control group and the high
exposure group. This difference persists, even when chips scanned before October
16th2013 are excluded. There are differences between the two analyses in terms of the
genes identified, but this is not unexpected given the relatively arbitrary nature of the
selection process.
4.2 Relationship With Health Score
The empirical Bayes analysis was repeated, using health score rather than exposure
group. The objective was to identify genes which are correlated with the health score.
Following Benjamini Hochberg correction, only 1 probeset showed a statistically sig-
nificant relationship. That probe set maps to gene symbol TTTY20 (Testis-Specific
Transcript, Y-Linked 20 (Non-Protein Coding)). The log2 fold change associated with
a 50 unit change in health score was 0.176: corresponding to a 1.13 fold increase in
expression for every 50 unit change. This is a small change in expression for a large
change in health score. It is difficult to interpret this effect with any confidence.
Figure 37 shows log2 gene expression for this gene plotted against health score. The
relationship is unimpressive.
Commercial In Confidence 59
Page 155
Jet Fuel & Solvents HTA 2 Arrays
3.2
3.6
4.0
20 40 60 80Health Score
log2
Exp
ress
ion
Relationship Between Health Score and Expression of TTTY20
Figure 37: Gene Expression for TTTY20 (testis-specific transcript, Y-linked 20 – non-
protein coding) vs Health Score
With only a single gene showing change in expression further analysis (e.g. gene on-
tology enrichment) was infeasible.
5 Ontology Enrichment
It can be difficult to interpret expression changes in a large number of genes. We
adopt the approach of Alexa et al[3], using hypogeometric tests of enrichment of gene
ontology[14] terms in the list of differential genes. The analysis was performed for each
of the Gene Ontology consortium domains: molecular function, biological process and
cellular component.
Ontology enrichment analysis was performed using the genes statistically significant in
the comparison of Group 1 (control) with Group 4 (high exposure). The analysis was
performed separately for gene which were over expressed (54 genes) and for genes
which were under expressed (64 genes) in the high exposure group. Genes which
had low overall expression (median log2 expression < 2.4) or low overall variability
(median absolute deviation from median log2 expression < 0.064) were filtered out of
the analysis.
Commercial In Confidence 60
Page 156
Jet Fuel & Solvents HTA 2 Arrays
The statistically significant ontology terms for the over expressed genes in the full data
set are shown in table 6. Results are restricted to Gene Ontology terms for which the
category size (the maximum number of genes that could be significant for the cate-
gory) is ≥ 10. Only three molecular function terms are significant; they are associated
with mRNA catabolism. Four biological of the five statistically significant Biological
Process terms process terms are associated with inter-species interactions. No cellular
component terms were statistically significant.
Commercial In Confidence 61
Page 157
Jet Fuel & Solvents HTA 2 Arrays
PO
dd
sE
xp
ecte
dO
bse
rved
Do
mai
nG
OID
Ter
mval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
BP
GO
:00
16
03
2v
iral
pro
cess
0.0
06
15
64
6
BP
GO
:0044764
mult
i-org
anis
mce
llula
rpro
cess
0.0
06
15
657
BP
GO
:00
06
28
1D
NA
rep
air
0.0
07
14
40
2
BP
GO
:0044419
inte
rspec
ies
inte
ract
ion
bet
wee
norg
anis
ms
0.0
05
15
708
BP
GO
:00
44
40
3sy
mb
iosi
s,en
com
pas
sin
gm
utu
alis
mth
rou
gh
par
a-
siti
sm
0.0
05
15
70
8
MF
GO
:00
08
16
8m
ethy
ltra
nsf
eras
eac
tiv
ity
0.0
01
10
31
87
MF
GO
:00
16
74
1tr
ansf
eras
eac
tiv
ity,
tran
sfer
rin
go
ne-
carb
on
gro
up
s0
.00
11
03
19
5
MF
GO
:00
03
82
4ca
taly
tic
acti
vit
y0
.00
38
15
50
89
Tab
le6
:G
ene
On
tolo
gy
En
rich
men
tfo
rG
enes
wh
ich
are
Over
Ex
pre
ssed
inG
rou
p4
vs
Gro
up
1.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.
Commercial In Confidence 62
Pag
e 15
8
Jet Fuel & Solvents HTA 2 Arrays
The statistically significant ontology terms for the under expressed genes in the full
data set are shown in table 7. Only terms with category size (the maximum gene count
that could be obtained) ≥ 10 are tabulated. Many of the biological process terms
are associated with viral response. Others are associated with RNA metabolism. The
molecular function terms are mostly associated with transcription factor binding. The
cellular components are mostly intracellular.
Commercial In Confidence 63
Page 159
Jet Fuel & Solvents HTA 2 Arrays
PO
dd
sE
xp
ecte
dO
bse
rved
Do
mai
nG
OID
Ter
mval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
BP
GO
:00
16
03
2v
iral
pro
cess
0.0
06
.40
29
64
6
BP
GO
:0044764
mult
i-org
anis
mce
llula
rpro
cess
0.0
06.2
82
9657
BP
GO
:0044419
inte
rspec
ies
inte
ract
ion
bet
wee
norg
anis
ms
0.0
05.8
02
9708
BP
GO
:00
44
40
3sy
mb
iosi
s,en
com
pas
sin
gm
utu
alis
mth
rou
gh
par
a-
siti
sm
0.0
05
.80
29
70
8
BP
GO
:00
07
21
9N
otc
hsi
gn
alin
gp
ath
way
0.0
01
1.5
80
41
43
BP
GO
:00
06
40
6m
RN
Aex
po
rtfr
om
nu
cleu
s0
.00
18
.15
03
68
BP
GO
:00
32
92
2ci
rcad
ian
reg
ula
tio
no
fg
ene
exp
ress
ion
0.0
05
1.1
90
21
7
BP
GO
:00
06
40
5R
NA
exp
ort
fro
mn
ucl
eus
0.0
01
5.9
30
37
7
BP
GO
:0046329
neg
ativ
ere
gula
tion
of
JNK
casc
ade
0.0
042.6
50
220
BP
GO
:00
16
56
8ch
rom
atin
mo
difi
cati
on
0.0
05
.58
16
45
5
BP
GO
:00
06
36
6tr
ansc
rip
tio
nfr
om
RN
Ap
oly
mer
ase
IIp
rom
ote
r0
.00
3.3
94
11
14
86
BP
GO
:00
10
46
7g
ene
exp
ress
ion
0.0
02
.63
14
23
50
46
BP
GO
:0070303
neg
ativ
ere
gula
tion
of
stre
ss-a
ctiv
ated
pro
tein
kin
ase
sig
nal
ing
casc
ade
0.0
03
1.9
80
22
6
BP
GO
:0032873
neg
ativ
ere
gula
tion
of
stre
ss-a
ctiv
ated
MA
PK
casc
ade
0.0
031.9
80
226
BP
GO
:00
50
69
0re
gu
lati
on
of
def
ense
resp
on
seto
vir
us
by
vir
us
0.0
02
8.4
20
22
9
BP
GO
:0051276
chro
moso
me
org
aniz
atio
n0.0
04.0
52
7735
BP
GO
:0006325
chro
mat
inorg
aniz
atio
n0.0
04.5
12
6557
BP
GO
:00
51
02
8m
RN
Atr
ansp
ort
0.0
01
0.3
10
31
17
CC
GO
:00
05
65
4n
ucl
eop
lasm
0.0
05
.33
41
51
39
8
CC
GO
:0070013
intr
acel
lula
rorg
anel
lelu
men
0.0
04.1
37
20
2634
CC
GO
:00
44
42
8n
ucl
ear
par
t0
.00
4.1
97
19
24
11
CC
GO
:0043233
org
anel
lelu
men
0.0
04.0
37
20
2691
CC
GO
:00
31
97
4m
emb
ran
e-en
clo
sed
lum
en0
.00
3.9
48
20
27
41
Tab
le7
:G
ene
On
tolo
gy
En
rich
men
tfo
rfo
rG
enes
wh
ich
are
Un
der
Ex
pre
ssed
inG
rou
p4
vs
Gro
up
1.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
e
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.org/amigo
.
Commercial In Confidence 64
Pag
e 16
0
Jet Fuel & Solvents HTA 2 Arrays
PO
dd
sE
xp
ecte
dO
bse
rved
Do
mai
nG
OID
Ter
mval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
CC
GO
:00
31
98
1n
ucl
ear
lum
en0
.00
4.2
36
17
20
39
CC
GO
:00
44
42
4in
trac
ellu
lar
par
t0
.00
15
.56
33
44
11
95
4
CC
GO
:00
05
62
2in
trac
ellu
lar
0.0
01
4.9
33
44
41
20
81
CC
GO
:00
44
45
1n
ucl
eop
lasm
par
t0
.00
6.4
12
96
14
CC
GO
:0044446
intr
acel
lula
rorg
anel
lepar
t0.0
02.9
517
29
6168
CC
GO
:00
00
11
8h
isto
ne
dea
cety
lase
com
ple
x0
.00
24
.43
03
50
CC
GO
:0044422
org
anel
lepar
t0.0
02.8
218
29
6335
CC
GO
:00
05
64
3n
ucl
ear
po
re0
.00
19
.12
03
63
CC
GO
:00
05
63
4n
ucl
eus
0.0
02
.72
16
27
57
58
CC
GO
:19
02
49
4ca
taly
tic
com
ple
x0
.00
4.5
72
87
36
CC
GO
:00
17
05
3tr
ansc
rip
tio
nal
rep
ress
or
com
ple
x0
.00
17
.38
03
69
CC
GO
:00
46
93
0p
ore
com
ple
x0
.00
15
.08
03
79
MF
GO
:00
03
68
2ch
rom
atin
bin
din
g0
.00
7.0
11
63
57
MF
GO
:00
05
48
7n
ucl
eocy
top
lasm
ictr
ansp
ort
erac
tiv
ity
0.0
06
8.4
80
21
3
MF
GO
:00
03
71
2tr
ansc
rip
tio
nco
fact
or
acti
vit
y0
.00
5.2
61
64
70
MF
GO
:00
05
51
5p
rote
inb
ind
ing
0.0
02
.76
22
31
78
12
MF
GO
:00
00
98
9tr
ansc
rip
tio
nfa
cto
rb
ind
ing
tran
scri
pti
on
fact
or
acti
v-
ity
0.0
04
.89
16
50
4
MF
GO
:00
00
98
8p
rote
inb
ind
ing
tran
scri
pti
on
fact
or
acti
vit
y0
.00
4.8
51
65
08
MF
GO
:00
17
12
4S
H3
do
mai
nb
ind
ing
0.0
01
0.2
80
31
15
Tab
le7
:G
ene
On
tolo
gy
En
rich
men
tfo
rG
enes
wh
ich
are
Un
der
Ex
pre
ssed
inG
rou
p4
vs
Gro
up
1.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.
Commercial In Confidence 65
Pag
e 16
1
Jet Fuel & Solvents HTA 2 Arrays
The enrichment analysis was repeated using genes that were statistically significant
in the reduced data set (all chips scanned on or after 16thOctober 2013). The results
for over expressed genes are shown in Table ??. OInly results with a category size
(maximum possible number of differential genes) ≥ 10 are reported.
Most of the biological process results for over expressed genes are associated with
nucleic acid metabolism and chromatin binding. Most of the molecular function results
are associated with chromatin binding.
Commercial In Confidence 66
Page 162
Jet Fuel & Solvents HTA 2 Arrays
PO
dd
sE
xp
ecte
dO
bse
rved
Do
mai
nG
OID
Ter
mval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
BP
GO
:00
65
00
4p
rote
in-D
NA
com
ple
xas
sem
bly
0.0
01
20
41
34
BP
GO
:0071824
pro
tein
-DN
Aco
mple
xsu
bunit
org
aniz
atio
n0.0
011
04
157
BP
GO
:00
44
26
0ce
llu
lar
mac
rom
ole
cule
met
abo
lic
pro
cess
0.0
03
18
28
66
10
BP
GO
:00
06
28
9n
ucl
eoti
de-
exci
sio
nre
pai
r0
.00
16
03
77
BP
GO
:00
90
30
4n
ucl
eic
acid
met
abo
lic
pro
cess
0.0
03
12
21
43
60
BP
GO
:00
06
33
4n
ucl
eoso
me
asse
mb
ly0
.00
12
03
10
5
BP
GO
:00
06
13
9n
ucl
eob
ase-
con
tain
ing
com
po
un
dm
etab
oli
cp
roce
ss0
.00
21
42
35
25
7
BP
GO
:00
31
49
7ch
rom
atin
asse
mb
ly0
.00
10
03
11
8
CC
GO
:00
00
78
5ch
rom
atin
0.0
08
16
30
3
CC
GO
:0043231
intr
acel
lula
rm
embra
ne-
bounded
org
anel
le0.0
03
26
37
9298
CC
GO
:0043229
intr
acel
lula
rorg
anel
le0.0
04
29
39
10349
CC
GO
:00
44
42
7ch
rom
oso
mal
par
t0
.00
52
75
42
CC
GO
:00
00
78
6n
ucl
eoso
me
0.0
01
90
36
3
CC
GO
:19
90
10
4D
NA
ben
din
gco
mp
lex
0.0
01
90
36
3
CC
GO
:00
44
81
5D
NA
pac
kag
ing
com
ple
x0
.00
17
03
69
CC
GO
:0043227
mem
bra
ne-
bounded
org
anel
le0.0
03
28
38
10134
CC
GO
:00
05
65
4n
ucl
eop
lasm
0.0
03
41
11
39
8
CC
GO
:00
05
69
4ch
rom
oso
me
0.0
05
27
63
5
CC
GO
:00
05
84
0ri
bo
som
e0
.00
81
41
88
CC
GO
:0043226
org
anel
le0.0
03
30
39
10942
CC
GO
:0043228
non-m
embra
ne-
bounded
org
anel
le0.0
02
917
3198
CC
GO
:0043232
intr
acel
lula
rnon-m
embra
ne-
bounded
org
anel
le0.0
02
917
3198
CC
GO
:19
02
49
4ca
taly
tic
com
ple
x0
.00
42
77
36
CC
GO
:00
30
52
9ri
bo
nu
cleo
pro
tein
com
ple
x0
.00
42
65
72
CC
GO
:00
05
63
4n
ucl
eus
0.0
02
16
25
57
58
Tab
le8
:G
ene
On
tolo
gy
En
rich
men
tfo
rfo
rG
enes
wh
ich
are
Over
Ex
pre
ssed
inG
rou
p4
vs
Gro
up
1fo
rch
ips
scan
ned
on
or
afte
rO
cto
ber
16
th2
01
3.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp:
//amigo.geneontology.org/amigo
.
Commercial In Confidence 67
Pag
e 16
3
Jet Fuel & Solvents HTA 2 Arrays
PO
dd
sE
xp
ecte
dO
bse
rved
Do
mai
nG
OID
Ter
mval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
MF
GO
:00
03
67
7D
NA
bin
din
g0
.00
36
14
22
47
MF
GO
:00
03
89
9D
NA
-dir
ecte
dR
NA
po
lym
eras
eac
tiv
ity
0.0
02
20
23
8
MF
GO
:00
34
06
2R
NA
po
lym
eras
eac
tiv
ity
0.0
02
20
23
8
Tab
le8
:G
ene
On
tolo
gy
En
rich
men
tfo
rG
enes
wh
ich
are
Over
Ex
pre
ssed
inG
rou
p4
vs
Gro
up
1,
for
chip
ssc
ann
edo
no
raf
ter
Oct
ob
er1
6th
20
13
.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp:
//amigo.geneontology.org/amigo
.
Commercial In Confidence 68
Pag
e 16
4
Jet Fuel & Solvents HTA 2 Arrays
Results for under expressed genes are shown in Table 9. Only two terms were statisti-
cally significant; both were associated with the vacuolar cellular component.
Commercial In Confidence 69
Page 165
Jet Fuel & Solvents HTA 2 Arrays
PO
dd
sE
xp
ecte
dO
bse
rved
Do
mai
nG
OID
Ter
mval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
CC
GO
:00
44
43
7vac
uo
lar
par
t0
.00
24
03
32
8
CC
GO
:00
05
77
3vac
uo
le0
.00
16
03
48
4
Tab
le9
:G
ene
On
tolo
gy
En
rich
men
tfo
rG
enes
wh
ich
are
Un
der
Ex
pre
ssed
inG
rou
p4
vs
Gro
up
1.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.
Commercial In Confidence 70
Pag
e 16
6
Jet Fuel & Solvents HTA 2 Arrays
There is no consistency between the ontology terms enriched for the full set of chips
and the reduced set of chips.
6 Conclusions
1. There is evidence of differential gene expression between the control group and
the high exposure group. This is the case for the full data set, and for the reduced
data set including only data from chips scanned on or after 16thOctober 2013.
2. There is some similarity in the set of differential genes for the full data set and
for the reduced data set. But these sets are by no means identical.
3. All the gene expression changes are small, especially when compared to those
associated with acute insults.
4. There is no consistent picture for gene ontology enrichment, between the full
data set and the reduced data set.
Any signals associated with the exposure groups are numerically small, and inconsis-
tent; even if they are statistically significant.
References
[1] Quality Assessment of Exon Arrays Revision Date: 2005-09-27. Whitepaper,
Affymetrix Inc., 3420 Central Expressway, Santa Clara, CA 95051, April 2007.
[2] B. Everitt, Sabine Landau, and Morven Leese. Cluster Analysis. Edward Arnold,
4 edition, May 2001.
[3] S. Falcon and R. Gentleman. Using GOstats to test gene lists for GO term asso-
ciation. Bioinformatics, 23(2):257–8, 2007.
[4] J. A. Hartigan. Consistency of Single Linkage for High-Density Clusters. Journalof the American Statistical Association, 76(374):388–394, 1981.
[5] P. Huber. Robust Statistics. John Wiley and Sons, 1981.
[6] R. Irizarry, B. Hobbs, F. Collin, Y. Beazer-Barclay, K. Antonellis, U. Scherf, and
T. Speed. Exploration, Normalization, and Summaries of High Density Oligonu-
cleotide Array Probe Level Data. Biostatistics, 4:249 264, 2003.
[7] I. Jolliffe. Principal Components Analysis. Springer-Verlag, 1986.
[8] H. E. Lockstone. Exon array data analysis using Affymetrix power tools and R
statistical software. Briefings in Bioinformatics, 2011.
[9] I. Lonnstedt and T. Speed. Replicated Microarray Data. Statistica Sinica, pages
31–46, 2002.
Commercial In Confidence 71
Page 167
Jet Fuel & Solvents HTA 2 Arrays
[10] J. M. Lucas and M. S. Saccucci. Exponentially Weighted Moving Average Con-
trol Schemes: Properties and Enhancements. Technometrics, 32(1):1–12, 1990.
[11] P. B. Robinson and T. Y. Ho. Average Run Lengths of Geometric Moving Average
Charts by Numerical Methods. Technometrics, 20(1):85–93, 1978.
[12] B. Silverman. Density estimation for statistics and data analysis. Chapman and
Hall, London, 1986.
[13] G. K. Smyth. Limma: linear models for microarray data. In R. Gentleman,
V. Carey, S. Dudoit, R. Irizarry, and W. Huber, editors, Bioinformatics and Com-putational Biology Solutions using R and Bioconductor, pages 397–420, New
York, 2005. Springer.
[14] The Gene Ontology Consortium. Gene Ontology: tool for the unification of biol-
ogy. Nature Genetics, 25:25–29, 2000.
[15] Y. Benjamini and Y. Hochberg. Controlling the false discovery rate: a practical
and powerful approach to multiple testing. Journal of the Royal Statistical SocietySeries B, 57:289–300, 1995.
Commercial In Confidence 72
Page 168
������������������� ������� ������������������������� ������ ���������������������Appendix 4 Report from Emphron on Bioinformatic analysis of regulatory gene expression array data from leucocytes of exposed workers and controls.
��������������
ANALYSIS of Micro RNA ARRAYS����Emphron Informatics Pty Ltd Author: Mervyn Thomas Date: 11 June 2014 ��
Page 169
Jet Fuel and Solvents ExposureProject
Analysis of Micro RNA Arrays
Emphron Informatics Pty Ltd www.emphron.com
Report Number: 2012.020
Version Number: 1.0
Client: Jet Fuel and Solvents Exposure Project
Date: July 22, 2014
Author: Mervyn Thomas
Email: [email protected]
Signature: ..................................................
Page 170
Jet Fuel and Solvents Micro RNA Arrays
Executive Summary
1. Data were available from 219 high quality micro RNA gene expression assays.
Three types of micro RNAs were analysed: stem-loop pre-micro RNAs, mature
micro RNAs, and small nucleolar micro RNAs. Stem-Loop pre-micro RNAs
and mature micro RNAs were associated with messenger RNA targets using the
microRNA.org data base (generated using the miRanda algorithm[8].
2. Exploratory analysis showed that the multivariate disposition of samples in the
space of the three types of micro RNA was very similar. There was some evi-
dence of exposure group related differences in the principal components of each
type of micro RNA. Mean differences were associated with the lower order prin-
cipal components, suggesting that whilst these differences were real they were
a relatively small part of the total variation. The clinical significance of such
differences is unknown.
3. Empirical Bayes analysis of differences between exposure groups revealed clear
evidence of differential micro RNA expression for each type of micro RNA.
These differences were strongest in comparing group 1 (high exposure) to Group
4 (The controls).
4. Linking stem loop pre micro RNAs and mature micro RNAs to the target mRNAs
and conducting ontology enrichment analysis suggested that the major biological
processes involved were associated with RNA metabolism and control of gene
expression, and neuro-genesis We regard this finding as somewhat speculative,
but certainly potentially important.
5. There is reasonable evidence of a difference between controls and the high expo-
sure group in expression of small nucleolar micro RNAs. Given current knowl-
edge it is difficult to ascribe clinical significance to this finding, but impossible
to rule out clinical significance. In short, changes in these micro RNAs are hap-
pening; they may or may not be clinically important.
6. The psychological health score was associated with statistically significant changes
in expression of mature micro RNAs. Linking these to mRNAs and conducting
ontology enrichment analysis the main biological processes were again control
of gene expression and neuro-genesis
7. There is clear evidence of exposure related changes in micro RNA expression,
and a modicum of suspicion that these may be associated with psychological
health and well being of subjects.
Contents
1 Introduction 9
1.1 Background and Objectives . . . . . . . . . . . . . . . . . . . . . . . 9
1.2 Available Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Commercial In Confidence 1
Page 171
Jet Fuel and Solvents Micro RNA Arrays
2 Pre Processing of Gene Chip Data 10
2.1 Expression Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 Quality Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 Exploratory Analysis of Gene Expression 13
3.1 Exploratory Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 Mature Micro RNAs . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3 Stem-Loop Pre Micro RNAs . . . . . . . . . . . . . . . . . . . . . . 18
3.4 Small Nucleolar Micro RNAs . . . . . . . . . . . . . . . . . . . . . . 24
3.5 Summary of Exploratory Analysis Results . . . . . . . . . . . . . . . 29
4 Differential Expression 29
4.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.2 Mature Micro RNAs . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.3 Stem Loop pre Micro RNAs . . . . . . . . . . . . . . . . . . . . . . 51
4.4 Small Nucleolar Micro RNAs . . . . . . . . . . . . . . . . . . . . . . 61
5 Relationship With Health Scores 63
6 Summary and Conclusions 76
List of Tables
1 Samples By Exposure Group and Date Scanned . . . . . . . . . . . . 10
2 Analysis of Variance For Principal Components of miRNA . . . . . . 16
3 Analysis of Variance For Principal Components of Stem Loop pre-
Micro RNAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4 Analysis of Variance For Principal Components of snoRNA . . . . . . 27
5 Differentially Expressed Mature Micro RNA Species. The ID column
is the micro RNA name as defined in the miRBase database[4] accessed
via http://www.mirbase.org/. . . . . . . . . . . . . . . . . 31
5 Differentially Expressed Mature Micro RNA Species. The ID column
is the micro RNA name as defined in the miRBase database[4] accessed
via http://www.mirbase.org/. . . . . . . . . . . . . . . . . 32
Commercial In Confidence 2
Page 172
Jet Fuel and Solvents Micro RNA Arrays
6 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 33
6 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 34
6 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 35
6 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 36
6 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 37
6 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 38
6 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 39
6 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 40
Commercial In Confidence 3
Page 173
Jet Fuel and Solvents Micro RNA Arrays
7 Molecular Function Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 41
7 Molecular Function Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 42
7 Molecular Function Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 43
7 Molecular Function Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 44
7 Molecular Function Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 45
7 Molecular Function Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 46
7 Molecular Function Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 47
7 Molecular Function Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 48
Commercial In Confidence 4
Page 174
Jet Fuel and Solvents Micro RNA Arrays
8 Cellular Component Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 49
8 Cellular Component Gene Ontology Enrichment for Mature Micro RNA
Targets. The ‘GO ID’ is the unique identifier in the Gene Ontology
Consortium ontology database, and may be searched via the AMI GO
interface http://amigo.geneontology.org/amigo. The GO
Term is the descriptive text provided by the Gene ontology Consortium. 50
9 Differentially Expressed Stem Loop Pre Micro RNA Species. Differ-
entially Expressed Stem Loop Pre-Micro RNA Species. The ID col-
umn is the micro RNA name as defined in the miRBase database[4]
accessed via http://www.mirbase.org/. . . . . . . . . . . . 52
10 Biological Process Gene Ontology Enrichment for Stem Loop Pre-
Micro RNA Targets. The ‘GO ID’ is the unique identifier in the Gene
Ontology Consortium ontology database, and may be searched via the
AMI GO interface http://amigo.geneontology.org/amigo.
The GO Term is the descriptive text provided by the Gene ontology
Consortium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
10 Biological Process Gene Ontology Enrichment for Stem Loop Pre-
Micro RNA Targets. The ‘GO ID’ is the unique identifier in the Gene
Ontology Consortium ontology database, and may be searched via the
AMI GO interface http://amigo.geneontology.org/amigo.
The GO Term is the descriptive text provided by the Gene ontology
Consortium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
10 Biological Process Gene Ontology Enrichment for Stem Loop Pre-
Micro RNA Targets. The ‘GO ID’ is the unique identifier in the Gene
Ontology Consortium ontology database, and may be searched via the
AMI GO interface http://amigo.geneontology.org/amigo.
The GO Term is the descriptive text provided by the Gene ontology
Consortium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
10 Biological Process Gene Ontology Enrichment for Stem Loop Pre-
Micro RNA Targets. The ‘GO ID’ is the unique identifier in the Gene
Ontology Consortium ontology database, and may be searched via the
AMI GO interface http://amigo.geneontology.org/amigo.
The GO Term is the descriptive text provided by the Gene ontology
Consortium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
10 Biological Process Gene Ontology Enrichment for Stem Loop Pre-
Micro RNA Targets. The ‘GO ID’ is the unique identifier in the Gene
Ontology Consortium ontology database, and may be searched via the
AMI GO interface http://amigo.geneontology.org/amigo.
The GO Term is the descriptive text provided by the Gene ontology
Consortium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Commercial In Confidence 5
Page 175
Jet Fuel and Solvents Micro RNA Arrays
10 Biological Process Gene Ontology Enrichment for Stem Loop Pre-
Micro RNA Targets. The ‘GO ID’ is the unique identifier in the Gene
Ontology Consortium ontology database, and may be searched via the
AMI GO interface http://amigo.geneontology.org/amigo.
The GO Term is the descriptive text provided by the Gene ontology
Consortium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
11 Molecular Function Gene Ontology Enrichment for Stem Loop Pre-
Micro RNA Targets. The ‘GO ID’ is the unique identifier in the Gene
Ontology Consortium ontology database, and may be searched via the
AMI GO interface http://amigo.geneontology.org/amigo.
The GO Term is the descriptive text provided by the Gene ontology
Consortium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
12 Cellular Component Gene Ontology Enrichment for Stem Loop Pre-
Micro RNA Targets. The ‘GO ID’ is the unique identifier in the Gene
Ontology Consortium ontology database, and may be searched via the
AMI GO interface http://amigo.geneontology.org/amigo.
The GO Term is the descriptive text provided by the Gene ontology
Consortium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
13 Differentially Expressed Small Nucleolar Micro RNA Species. The ID
column is the micro RNA name as defined in the miRBase database[4]
accessed via http://www.mirbase.org/. . . . . . . . . . . . 62
14 Mature Micro RNA Species With Statistically Significant Relation-
ship With Health Score Following Benjamini Hochberg Adjustment.
The ID column is the micro RNA name as defined in the miRBase
database[4] accessed via http://www.mirbase.org/. . . . . . 63
15 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets Relationship With Health Score. The ‘GO ID’ is the unique
identifier in the Gene Ontology Consortium ontology database, and
may be searched via the AMI GO interface http://amigo.geneontology.org/amigo. The GO Term is the descriptive text provided by the
Gene ontology Consortium. . . . . . . . . . . . . . . . . . . . . . . . 64
15 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets Relationship With Health Score. The ‘GO ID’ is the unique
identifier in the Gene Ontology Consortium ontology database, and
may be searched via the AMI GO interface http://amigo.geneontology.org/amigo. The GO Term is the descriptive text provided by the
Gene ontology Consortium. . . . . . . . . . . . . . . . . . . . . . . . 65
15 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets Relationship With Health Score. The ‘GO ID’ is the unique
identifier in the Gene Ontology Consortium ontology database, and
may be searched via the AMI GO interface http://amigo.geneontology.org/amigo. The GO Term is the descriptive text provided by the
Gene ontology Consortium. . . . . . . . . . . . . . . . . . . . . . . . 66
Commercial In Confidence 6
Page 176
Jet Fuel and Solvents Micro RNA Arrays
15 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets Relationship With Health Score. The ‘GO ID’ is the unique
identifier in the Gene Ontology Consortium ontology database, and
may be searched via the AMI GO interface http://amigo.geneontology.org/amigo. The GO Term is the descriptive text provided by the
Gene ontology Consortium. . . . . . . . . . . . . . . . . . . . . . . . 67
15 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets Relationship With Health Score. The ‘GO ID’ is the unique
identifier in the Gene Ontology Consortium ontology database, and
may be searched via the AMI GO interface http://amigo.geneontology.org/amigo. The GO Term is the descriptive text provided by the
Gene ontology Consortium. . . . . . . . . . . . . . . . . . . . . . . . 68
15 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets Relationship With Health Score. The ‘GO ID’ is the unique
identifier in the Gene Ontology Consortium ontology database, and
may be searched via the AMI GO interface http://amigo.geneontology.org/amigo. The GO Term is the descriptive text provided by the
Gene ontology Consortium. . . . . . . . . . . . . . . . . . . . . . . . 69
15 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets Relationship With Health Score. The ‘GO ID’ is the unique
identifier in the Gene Ontology Consortium ontology database, and
may be searched via the AMI GO interface http://amigo.geneontology.org/amigo. The GO Term is the descriptive text provided by the
Gene ontology Consortium. . . . . . . . . . . . . . . . . . . . . . . . 70
15 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets Relationship With Health Score. The ‘GO ID’ is the unique
identifier in the Gene Ontology Consortium ontology database, and
may be searched via the AMI GO interface http://amigo.geneontology.org/amigo. The GO Term is the descriptive text provided by the
Gene ontology Consortium. . . . . . . . . . . . . . . . . . . . . . . . 71
15 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets Relationship With Health Score. The ‘GO ID’ is the unique
identifier in the Gene Ontology Consortium ontology database, and
may be searched via the AMI GO interface http://amigo.geneontology.org/amigo. The GO Term is the descriptive text provided by the
Gene ontology Consortium. . . . . . . . . . . . . . . . . . . . . . . . 72
15 Biological Process Gene Ontology Enrichment for Mature Micro RNA
Targets Relationship With Health Score. The ‘GO ID’ is the unique
identifier in the Gene Ontology Consortium ontology database, and
may be searched via the AMI GO interface http://amigo.geneontology.org/amigo. The GO Term is the descriptive text provided by the
Gene ontology Consortium. . . . . . . . . . . . . . . . . . . . . . . 73
Commercial In Confidence 7
Page 177
Jet Fuel and Solvents Micro RNA Arrays
16 Molecular Function Gene Ontology Enrichment for Mature Micro RNA
Targets Relationship With Health Score. The ‘GO ID’ is the unique
identifier in the Gene Ontology Consortium ontology database, and
may be searched via the AMI GO interface http://amigo.geneontology.org/amigo. The GO Term is the descriptive text provided by the
Gene ontology Consortium. . . . . . . . . . . . . . . . . . . . . . . 73
16 Molecular Function Gene Ontology Enrichment for Mature Micro RNA
Targets Relationship With Health Score. The ‘GO ID’ is the unique
identifier in the Gene Ontology Consortium ontology database, and
may be searched via the AMI GO interface http://amigo.geneontology.org/amigo. The GO Term is the descriptive text provided by the
Gene ontology Consortium. . . . . . . . . . . . . . . . . . . . . . . 74
17 Cellular Component Gene Ontology Enrichment for Mature Micro RNA
Targets Relationship With Health Score. The ‘GO ID’ is the unique
identifier in the Gene Ontology Consortium ontology database, and
may be searched via the AMI GO interface http://amigo.geneontology.org/amigo. The GO Term is the descriptive text provided by the
Gene ontology Consortium. . . . . . . . . . . . . . . . . . . . . . . 74
17 Cellular Component Gene Ontology Enrichment for Mature Micro RNA
Targets Relationship With Health Score. The ‘GO ID’ is the unique
identifier in the Gene Ontology Consortium ontology database, and
may be searched via the AMI GO interface http://amigo.geneontology.org/amigo. The GO Term is the descriptive text provided by the
Gene ontology Consortium. . . . . . . . . . . . . . . . . . . . . . . 75
List of Figures
1 Exponentially Weighted Moving Average (EWMA) Control Chart for
Mean Absolute Deviation of Residuals . . . . . . . . . . . . . . . . . 12
2 Distribution of Mean Absolute Deviation of Residuals . . . . . . . . . 13
3 Variance By Principal Component For Mature Micro RNAs . . . . . . 14
4 Scatter Plot For First Two Components of mature Micro RNAs . . . . 15
5 Box And Whisker Plots By Exposure Group For Principal Components
of mature Micro RNAs . . . . . . . . . . . . . . . . . . . . . . . . . 16
6 Single Linkage Cluster Analysis For Mature Micro RNAs . . . . . . . 17
7 Average Linkage Cluster Analysis For Mature Micro RNAs . . . . . . 17
8 Complete Linkage Cluster Analysis For Mature Micro RNAs . . . . . 18
9 Variance By Principal Component For Stem Loop pre-Micro RNAs . 19
10 Scatter Plot For First Two Components of Stem Loop pre-Micro RNAs 20
Commercial In Confidence 8
Page 178
Jet Fuel and Solvents Micro RNA Arrays
11 Box And Whisker Plots By Exposure Group For Principal Components
of Stem Loop pre-Micro RNAs . . . . . . . . . . . . . . . . . . . . . 21
12 Single Linkage Cluster Analysis For Mature Micro RNAs . . . . . . . 22
13 Average Linkage Cluster Analysis For Mature Micro RNAs . . . . . . 23
14 Complete Linkage Cluster Analysis For Mature Micro RNAs . . . . . 23
15 Variance By Principal Component For Small Nucleolar Micro RNAs . 24
16 Scatter Plot For First Two Components of Small Nucleolar Micro RNAs 25
17 Box And Whisker Plots By Exposure Group For Principal Components
of mature Micro RNAs . . . . . . . . . . . . . . . . . . . . . . . . . 26
18 Single Linkage Cluster Analysis For Mature Micro RNAs . . . . . . . 27
19 Average Linkage Cluster Analysis For Mature Micro RNAs . . . . . . 28
20 Complete Linkage Cluster Analysis For Mature Micro RNAs . . . . . 28
1 Introduction
1.1 Background and Objectives
The Jet Fuel and Solvents Exposure study has been set up to investigate physiological
and other differences between ex Air Force employees who were exposed to avia-
tion fuel and solvents during their service. Subjects were stratified into four exposure
groups, where Group 4 is a control group (representing no known exposure) and Group
1 is the highest level of exposure.
Data are also available from other genomic modalities (including cytogentics data and
High Throughput mRNA gene expression data) and from proteomic sources. Only the
results of micro RNA expression from blood samples, estimated using the Affymetrix
micro RNA 2 chip are described in this report.
Each subject was also asked to complete a Health questionnaire (Ref), and data from
the psycho social scale of the questionnaire were investigated in conjunction with the
gene expression.
Commercial In Confidence 9
Page 179
Jet Fuel and Solvents Micro RNA Arrays
1.2 Available Data
Micro RNA gene chip data were available from 224 subjects. Five of these gene chips
failed quality assessments and were excluded from analysis (vide infra)1 The break
down of subjects with high quality chips by exposure group is shown in Table 1. We
note that all group 4 chips were scanned on or after 16thOctober 2013. The compar-
ison of Group 4 with the other groups is therefore partially confounded with date of
scanning. This issue is addressed in the Methods section below.
Date Exposure Group
Scanned 1 2 3 4
2013-01-25 11 6 7 0
2013-07-18 8 8 4 0
2013-07-24 15 9 8 0
2013-10-02 8 3 6 13
2013-10-10 9 1 10 11
2013-11-22 15 7 2 5
2014-01-01 10 3 5 5
2014-01-03 5 2 0 23
Table 1: Samples By Exposure Group and Date Scanned
2 Pre Processing of Gene Chip Data
2.1 Expression Estimates
Raw chip data were processed to give estimates of micro RNA expression using the
RMA algorithm[7], as implemented in the Affymetrix Power Tools Suite[13]. The
Affymetrix micro RNA chip contains sequences from a number of species, and of a
number of types. Only human sequence probe sets were retained for analysis, and data
sets were generated for mature micro RNAs ( 20 nucleotides), stem-loop pre-micro
RNAs ( 70 nucleotides) and small nucleolar RNAs.
Throughout the remainder of this report it should be remembered that stem-loop pre-
micro RNAs act as precursors to mature micro RNAs. They are cleaved by the ‘dicer’
enzyme to produce the mature micro RNA. Mature micro RNAs bind with comple-
mentary sequences on mRNAs, and either inhibit translation, silence translation, or
promote mRNA degradation. High levels of expression of stem-loop pre-micro RNAs
are therefore associated with high levels of micro RNAs of matching species, and de-
creased translation or abundance of the matching mRNAs.
Small nucleolar RNAs are mostly associated with the methylation and pseudouridyla-
tion of ribosomal RNAs, but they can also interact with mRNAs[19].
1This is actually rather better than standard performance for a genomics laboratory, and should not be
considered as a criticism of laboratory processing of these complex assays.
Commercial In Confidence 10
Page 180
Jet Fuel and Solvents Micro RNA Arrays
Affymetrix micro RNA annotation is inconsistent and incomplete. Small nucleolar
RNAs were considered to be those probesets with sequence type ”HAcaBox”, ”CD-
Box” or snoRNA”. Affymetrix annotate probe sets with sequence type ”snoRNA”
with Ensembl IDs, whereas those with type ”HAcaBox” or ”CDBox” are annotated
with the gene ID as defined in the miRBase database[4] accessed via http://www.mirbase.org/. Mature micro RNAs and stem-loop micro RNAs are annotated with
the miRBase transcript name. Annotations for sequence type ”snoRNA” were con-
verted from Ensembl IDs to external gene names using Ensembl Biomart[10].
Relatively good target information exists for pre-micro RNAs and for mature micro
RNAs[2]. Target relationships were obtained from the microRNA.org database[2] ac-
cessed at http://www.microrna.org. Target relationships that were charac-
terised by strong complementarity and good cross species conservation [2, page D150]
were retained for analysis. Good conservation was defined by a conservation score of
0.57 or more: which is approximately the cut off for conservation within mammals.
2.2 Quality Control
Standard quality metrics were generated as recommended by Affymetrix[1].
One of those quality metrics were selected for further examination: ‘all+probesets mad residual mean’
This is the mean of the absolute deviation of the residuals from the median. The RMA
algorithm produces a model for probe level responses, based on the particular probe
and the probe set to which it belongs. Residuals from this model represent departures
from the model prediction for the given probe on the given chip. The mean absolute
deviation for a chip provides a measure of how well the particular chip fits the RMA
model: which was developed across all chips. A high mean absolute deviation of the
residuals from the median suggests quality problems for the chip concerned.
Chips were sorted by date and time of scanning, and an exponentially weighted moving
average control (EWMA) charts[15, 16] was generated for the metric. The EWMA
chart is a powerful method of identifying when a process moved out of control. It is
not intended to identify individual extreme values; the EWMA is more sensitive to runs
of extreme values. It generally lags the onset of extreme values by a few observations.
Histograms were constructed for the metric, and robust Huber estimates[6] of the mean
and standard deviation were used to generate mean ± 3 standard deviation ranges.
Figure 1 shows the EWMA plot for mean absolute deviation of residuals. There is a
period towards the end of the sequence in which the process appears to be out of con-
trol. Figure 2 shows a histogram and kernel density estimate[17] of the distribution of
this metric. The distribution is relatively well behaved, with a small number of extreme
values. These extreme values occur during the period highlighted on the EWMA chart.
Inspection of the data revealed that these extreme values were for five RNA samples
obtained by manual extraction. The samples were dropped from down stream analysis.
Commercial In Confidence 11
Page 181
Jet Fuel and Solvents Micro RNA Arrays
EWMA Chartfor Median Absolute Deviation of Residuals
Group
Gro
up S
umm
ary
Sta
tistic
s
1 13 27 41 55 69 83 97 113 131 149 167 185 203 221
0.35
0.40
0.45
0.50
0.55
0.60
●
●
●●●●●●●●●●●●
●
●●●●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●●●
●●●
●●●
●
●●
●●
●●●●
●
●●
●●●
●●●●●●
●●●●●●
●●●●●●
●
●
●
●●●●●●●
●
●
●●
●●
●●
●●●●●●
●●
●●●
●
●●●●●●●●●●●
●●●
●●●●●
●●●
●●●●●●
●
●
●
●●●●●●●●
●
●●●●
●●●●
●●●
●●
●●●●●●
●●
●●
●
●●
●
●
●
●
●●
●
●
●●●●●●
●
●
●●
●
●
●
●
●
●●
●●●●
●●●
●●●●●
●●●●●
●
●●●●●●●●
LCL
UCL●
●
●
●●●●●●●●●●
●●●●●● ●
●●●
●
●
●
●
●
●
Number of groups = 224Center = 0.464396StdDev = 0.03891935
Smoothing parameter = 0.2Control limits at 3*sigmaNo. of points beyond limits = 29
Figure 1: Exponentially Weighted Moving Average (EWMA) Control Chart for Mean
Absolute Deviation of Residuals
Commercial In Confidence 12
Page 182
Jet Fuel and Solvents Micro RNA Arrays
Distribution of Mean Relative Log Expression
Mean RLE
Den
sity
0.35 0.40 0.45 0.50 0.55 0.60
02
46
810
12
Figure 2: Distribution of Mean Absolute Deviation of Residuals
The quality metrics calculated for these data were very satisfactory. A small number of
extreme values were associated with documented differences in chip processing. These
chips have been excluded from further analysis.
Probe sets which had low overall expression (median across all chips log2 expression
< 2.4) or low overall variability (median absolute deviation from median across all
chips log2 expression < 0.064) were filtered out of all further analysis analysis.
3 Exploratory Analysis of Gene Expression
3.1 Exploratory Methods
Exploratory analysis was conducted separately for each of the sequence types: mature
micro RNAs, stem-loop pre-micro RNAs and small nucleolar micro RNAs. Princi-
pal components[9] of the gene expression matrix (represented as samples X genes)
were calculated. The scree plots were generated, showing the variance of each princi-
pal component,m and each sample was plotted in the space of then first two principal
components. Observations were colour coded by exposure group. Box and whisker
plots were generated showing the distribution of the first eight principal components
by group, and a between groups analysis of variance was conducted for the first eight
principal components.
Commercial In Confidence 13
Page 183
Jet Fuel and Solvents Micro RNA Arrays
The gene expression data were clustered using three hierarchical clustering algorithms[3]:
complete, average and single linkage[5]. Cluster plots are often used to over-interpret
data. Apparent cluster structure is frequently illusory. Cluster solutions should only be
interpreted when they are markedly consistent across different algorithms.
3.2 Mature Micro RNAs
Figure 3 shows the scree plot for the principal components analysis: giving the variance
for each of the first ten principal components. The first principal component dominates,
representing 4 times the variance of the second component.
Scree Plot For miRNA
Varia
nces
050
100
150
200
Figure 3: Variance By Principal Component For Mature Micro RNAs
Figure 4 shows the scatter plot for the first two components of the mature micro RNA
data. The majority of samples have first component scores close to zero, but there is a
long tail of samples with large and negative values. There is some suggestion that more
of the exposed samples are in this tail than the control samples.
Commercial In Confidence 14
Page 184
Jet Fuel and Solvents Micro RNA Arrays
●
●●
●
●
●
●
●
●
●
●
●●●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●●
●
●
●●
●
●
●
●
●
●●●
●
●
●
●
●
●
●●●
●
●
●
●
●
●●
●
●
●
●
●● ●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●●
●
●●
●
●
●
●●
●
●
●
● ●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●●
●
●●
●●●
●●
●
● ●
●
●
●
●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●
● ●
●●
●
●
●
●●
●●
●
●●
●
●
●
●
●
● ●●
●
−50
−25
0
25
−80 −60 −40 −20 0Component 1
Com
pone
nt 2
Exposed●
●
Control
Exposed
First Two Principal Components for miRNA
Figure 4: Scatter Plot For First Two Components of mature Micro RNAs
Figure 5 shows box and whisker plots of the first nine principal components by expo-
sure group. There is a suggestion of between group differences for components 5, 7,
8 and 9. These are associated with exposure group (the controls) for components 5, 7
and 9, and with group 1 (the highest exposure group) for component 8.
The analysis of variance for each component is summarised by Table 2. This conforms
the existence of statistically significant between group differences for components 5,
7, 8 and 9. This analysis suggests that there may be differences between groups in
mature micro RNAs, mostly associated with group 4. These differences, however, are
in relatively low order principal components and do not represent a large contribution
ton total variation in micro RNA expression.
Commercial In Confidence 15
Page 185
Jet Fuel and Solvents Micro RNA Arrays
●
●●●●●
●●
●●●
●
●
●●
●
●
●
●
●
●
●
●
●●●
●
●
●
●●
●●
●
●
●
●●●●
●●●●
●
●●
●
●
●●●
●
●●
●
●
●●
●●●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
PC1 PC2 PC3
PC4 PC5 PC6
PC7 PC8 PC9
−80
−60
−40
−20
0
−50
−25
0
25
−40
−20
0
−10
0
10
20
−30
−20
−10
0
10
−10
−5
0
5
10
−10
0
10
−5
0
5
10
−10
−5
0
5
10
1 2 3 4 1 2 3 4 1 2 3 4
1 2 3 4 1 2 3 4 1 2 3 4
1 2 3 4 1 2 3 4 1 2 3 4Exposure Group
Com
pone
nt S
core
Figure 5: Box And Whisker Plots By Exposure Group For Principal Components of
mature Micro RNAs
Df Sum Sq Mean Sq F value Pr(>F)
PC 1 3 1032.81 344.27 1.55 0.2035
PC 2 3 244.19 81.40 1.29 0.2788
PC 3 3 148.90 49.63 0.93 0.4269
PC 4 3 140.02 46.67 1.76 0.1560
PC 5 3 306.55 102.18 4.19 0.0066
PC 6 3 24.38 8.13 0.35 0.7887
PC 7 3 479.91 159.97 8.31 0.0000
PC 8 3 207.17 69.06 5.01 0.0022
PC 9 3 181.14 60.38 5.53 0.0011
Table 2: Analysis of Variance For Principal Components of miRNA
Single, average and complete linkage cluster dendrograms are shown in Figures 6, 7
and 8 respectively. Visually the dendrograms are quite different. Closer examination
reveals that they are similar only in so far as their major structure is associated with a
few extreme samples, rather than the exposure groups. That is, samples occurring as
isolated nodes or in small groups in one solution tend to do so in all solutions. Those
samples are JF1072, JF2024, JF2033, JF2068 and JF3020.
Commercial In Confidence 16
Page 186
Jet Fuel and Solvents Micro RNA Arrays
2068
3020
2033
1072
2024
4045
1099
1095
3046 10
9040
5810
4540
4420
3010
2710
6940
5610
5610
0830
0310
6610
0730
1610
0210
36 1032
2040 10
0610
3110
4430
2830
6140
5320
5520
6440
7740
7940
1130
3630
5210
8920
8910
2620
6540
0110
8240
3230
5910
8520
4840
8120
8110
0910
8420
3240
0410
8630
3530
1710
7910
2130
2420
5110
8320
5920
2710
0430
6640
0610
2540
1210
9220
7410
7510
9130
0710
8830
5810
9730
6320
2510
7020
7930
5730
4330
4130
4430
0240
2040
1810
3520
3520
67 1013
1076
3054
1093
4031
3045
3060 40
2840
2510
6530
1940
2710
0540
3430
1540
2440
2920
7810
6320
5830
0430
5630
3810
5920
8020
1440
4010
3440
5010
8720
3630
5110
5830
3740
0740
1010
3840
0310
3040
4840
7410
4110
50 2045
4065
2057
1037
2069
4042
4052
1080
4084
2003
1047
2039
3040
1039
2042
4083
4043
4066
4080
1073
1051
4055
1062
1048
3055
1081
1003
1014
4064
2013
2041
1053
2049
3023
1100
2056
3047
4038
1023
3033
1105
4033
1012
3025
1040
4076
1067
4039 10
4240
6340
82 4072
4062
4078
2031
1022
4002
4009
4014
4008
4013
4015
1094
4036
1098
3064
1020
4005
3021
3022
2038
3032
1052
1057
3027
2050
1043
1046
1060
1028
1029
1024
204715
2025
3035
4045
Single Linkage
dwork
Hei
ght
Figure 6: Single Linkage Cluster Analysis For Mature Micro RNAs
3020
2033
1027
1052
1057
3027 2
038
3032 10
9940
45 106
910
9530
4610
9040
5820
2430
6120
5540
11 404
440
5640
7740
7910
9110
7020
78 1092
3044 30
5810
0530
1520
1440
40 1063
4034 1013
3043
3045
3060
3056
3038
1059
2080 20
5830
0440
3140
2740
2440
29 4018
4028
3007
4020
4025 2
074
3041
1065
3019 20
2510
3520
3520
67 1093
1076
3054 30
0210
9730
57 1075
1088
2079 3
063
4012 10
8910
2630
5210
5830
59 1044
3028 20
3030
2130
2240
3220
6540
81 108
510
8220
8920
4840
04 1009
1073
4064 40
4310
4030
2510
1240
7610
6740
3911
0540
3320
5630
4740
38 1062
2049
1023
3033 2
036
3017
1003
1014
2013
2041 20
4210
4830
55 3023
1047
1051
4055 10
3010
2510
3420
0340
84 2045
1042
4063
4082
4062
4078 40
8320
3930
4040
6640
8040
6540
72 1081
2051
1079
2081
1086
4042
4052 20
5730
3530
2410
3720
6910
3830
3710
5310
3911
00 2027
1004
4074
1041
1050 20
5940
0710
8720
3110
9440
3640
0940
1440
0240
0840
1340
15 1022
4010
1084
4050
1083
3051
1080
4048
1098
3064
1020
4005
2032
4001
1021
4003
3066
4006
4053
2064
3036
3016
1002
1036 1007
1066
1032
2040
1006
1031 10
5610
0830
0320
5010
2810
2910
2420
4710
4510
4310
4620
6810
6010
72
1020
3040
5060
Average Linkage
dwork
Hei
ght
Figure 7: Average Linkage Cluster Analysis For Mature Micro RNAs
Commercial In Confidence 17
Page 187
Jet Fuel and Solvents Micro RNA Arrays
1099
4045
1095
3046
1090
4058
3021
3022 20
3010
4430
2840
3220
6540
8110
2630
5210
5830
5920
2420
5540
1110
8930
61 106
940
7740
79 404
440
5640
1220
7430
63 1009
1003
1014
2013
2041
4008
4013
4015
1047
1051
4055 10
3010
2510
34 2045
4083
4066
4080
2039
3040
4065
4072 30
3520
5740
02 4004
1004
4074
2027
2031
1094
4036 20
5940
0710
2240
1040
0940
14 1084
4050
1082
2089
4042
4052 1
085
1086
1087
1080
4048
1083
3051
1041
1050
1053
3023
3037
1039
1100
1038
3024
2003
4084
4062
4078
1042
4063
4082 20
4210
4830
55 3066
4006
4003
1020
4005 10
2110
9830
6410
3720
69 1081
2051
1079
2081 20
4820
3220
3630
1740
5340
0120
6430
3610
0710
6630
1610
0210
3610
4030
25 2056
1073
4064
1062
2049
1023
3033 40
4310
1240
7610
6740
3911
0540
3330
4740
3810
0530
1520
1440
4010
6340
3440
3140
2740
2440
29 3007
1065
3019
4020
4025
4018
4028 30
5820
7830
3830
56 3041
2025
1035
2035
2067 10
8820
7910
9310
9730
5730
0210
7630
5420
5830
0410
5920
8030
4530
60 1013
3043
1070
1091
1075
1092
3044
1060
1072
1032
2040
1006
1031 10
5610
0830
0310
4510
4310
4620
5010
2810
2910
2420
4720
6830
2020
3320
3830
32 102
710
5210
5730
27
020
4060
80
Complete Linkage
dwork
Hei
ght
Figure 8: Complete Linkage Cluster Analysis For Mature Micro RNAs
3.3 Stem-Loop Pre Micro RNAs
Figure 9 shows the scree plot for the principal components analysis: giving the variance
for each of the first ten principal components. The first principal component dominates,
representing 3 times the variance of the second component.
Commercial In Confidence 18
Page 188
Jet Fuel and Solvents Micro RNA Arrays
Scree Plot For stemLoop
Varia
nces
050
100
150
Figure 9: Variance By Principal Component For Stem Loop pre-Micro RNAs
Figure 10 shows the scatter plot for the first two components of the Stem Loop pre-
micro RNA data. The majority of samples have first and second component scores
close to zero, but there is a long tail of samples with large and negative values on com-
ponent 1. Most of these samples are from the exposed groups. This plot is markedly
similar to that seen for mature micro RNAs, and the correlation between the first princi-
pal component scores for mature micro RNAs and stem loop pre-micro RNAs is 0.99.
The correlation coefficient for the second component scores is 0.78. There are very
strong similarities between the multivariate disposition of samples in the space of the
stem loop pre-micro RNAs and the space of the micro RNAs.
It would be possible to establish this more powerfully with a generalised procrustes
analysis[12, Chapter 5], but the point is well demonstrated by these simple correlations.
Commercial In Confidence 19
Page 189
Jet Fuel and Solvents Micro RNA Arrays
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●●●
●
●
●
●
● ●
●
●
●
●
●
●●●
●
●●
●
●●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●●
●
●
●
●●
●●●
●
●●
●
●
●●●●
●
●●
●
●●●
●●
●
●
●
●
●
●
●
●●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●●●●●
●
●
●
●
●
●
●
●●
●
●●
●●
●●
●
●
●
●
●
●
●●
●●
●●
●
●
●
●
●●
●●●●
●
●
●●
●
●
●
●
●●
●●
●
●
●
●
●●●●
●
●
●
●
●
●●
●
●●
●
●
●●●
●●
●
●
●●
●●●
●
●
●●●●
●●
● ●
●
●
●
●
●
●●●
−25
0
25
50
−60 −40 −20 0Component 1
Com
pone
nt 2
Exposed●
●
Control
Exposed
First Two Principal Components for stemLoop
Figure 10: Scatter Plot For First Two Components of Stem Loop pre-Micro RNAs
Figure 11 shows box and whisker plots of the first nine principal components by ex-
posure group. There is a suggestion of between group differences for components 4, 6
and 8. These are mostly associated with exposure group 4, but there is some suggestion
of a trend across increasing exposure groups for component 8.
The analysis of variance for each component is summarised by Table 3. This conforms
the existence of statistically significant between group differences for components 3,
4, 6 and 8. This analysis suggests that there may be differences between groups in
Stem Loop pre-micro RNAs, mostly associated with differences between the control
group and the exposed groups. These differences, however, are in relatively low order
principal components and do not represent a large contribution to total variation in
micro RNA expression.
Commercial In Confidence 20
Page 190
Jet Fuel and Solvents Micro RNA Arrays
●
●
●
●● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●●●●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●●
●
●
●●
●●
●
●●
●
●
●●●
●●
●
●
●
●
●
●
PC1 PC2 PC3
PC4 PC5 PC6
PC7 PC8 PC9
−60
−40
−20
0
−25
0
25
50
−10
0
10
20
30
−15
−10
−5
0
5
10
−30
−20
−10
0
10
−10
−5
0
5
10
−10
−5
0
5
−5
0
5
10
−10
−5
0
5
1 2 3 4 1 2 3 4 1 2 3 4
1 2 3 4 1 2 3 4 1 2 3 4
1 2 3 4 1 2 3 4 1 2 3 4Exposure Group
Com
pone
nt S
core
Figure 11: Box And Whisker Plots By Exposure Group For Principal Components of
Stem Loop pre-Micro RNAs
Commercial In Confidence 21
Page 191
Jet Fuel and Solvents Micro RNA Arrays
Df Sum Sq Mean Sq F value Pr(>F)
PC 1 3 930.97 310.32 2.00 0.1151
PC 2 3 201.70 67.23 1.46 0.2269
PC 3 3 346.82 115.61 5.13 0.0019
PC 4 3 464.73 154.91 8.20 0.0000
PC 5 3 64.56 21.52 1.11 0.3453
PC 6 3 322.95 107.65 8.29 0.0000
PC 7 3 33.07 11.02 0.87 0.4550
PC 8 3 132.16 44.05 4.11 0.0073
PC 9 3 17.73 5.91 0.60 0.6154
Table 3: Analysis of Variance For Principal Components of Stem Loop pre-Micro
RNAs
Single, average and complete linkage cluster dendrograms are shown in Figures 12, 13
and 14 respectively. Visually the dendrograms are quite different. Closer examination
reveals that they are similar only in so far as their major structure is associated with a
few extreme samples, rather than the exposure groups. That is, samples occurring as
isolated nodes or in small groups in one solution tend to do so in all solutions. Those
samples are JF1072, JF2068 and JF3020. These samples also formed isolated clusters
for the mature micro RNAs.
2068
1072
3020
2050
1045
2024
1044
2030
3022
4001
2055
3061
4045
1099
3046
1090
4058 2
048
1026
1095
4053
3028
4077
4079 30
3620
8920
6430
3530
2130
5230
2410
7930
2340
8110
8910
4840
1040
5530
5940
8320
5940
1140
0430
3740
5020
2510
5010
5840
3240
8210
8320
3210
8440
7420
6940
0630
1730
5110
3710
8510
4140
8440
0320
4930
6620
5110
8620
3920
5710
0910
3840
7810
3420
6510
4710
2120
3640
4840
3310
2210
3910
8720
8110
8230
3340
0710
8040
7240
1430
5530
6340
0810
6210
0330
6440
3640
1510
5310
0410
5111
0020
2710
4240
4220
0310
2040
0240
6340
4320
4210
2540
0540
3830
4020
7940
6210
9840
6510
9720
4520
3140
0940
1220
1310
6710
8110
1430
4710
3040
6440
3910
1210
7510
9240
8020
7440
6610
7030
0710
7330
4110
9130
5840
1320
5611
0530
2510
3510
7610
4040
7630
4320
6720
4130
5440
2710
1330
5710
2310
6540
2040
1810
8820
3530
4440
2530
1930
0210
9340
2830
0440
3120
7830
4530
6030
5610
0520
8020
5840
2440
2910
6330
3840
3440
4020
1410
5930
1510
9440
5240
5610
6940
4410
2810
6010
3220
40 1031
1008
1007
1066
1036
3016 10
0230
03 100
610
56 102
910
2420
4720
3310
2710
5220
3830
3210
5730
2710
4310
46
1015
2025
Single Linkage
dwork
Hei
ght
Figure 12: Single Linkage Cluster Analysis For Mature Micro RNAs
Commercial In Confidence 22
Page 192
Jet Fuel and Solvents Micro RNA Arrays
2068
2033
3020
1052
2038
1027
3032
1057
3027
1060
1072
1043
1046
1045
1007
1002
3003 10
6610
3630
1610
3110
3220
40 1008
1006
1056
2050
1028
1029
1024
2047 40
4510
9930
4610
9040
58 4056
4077
4079 10
9510
6940
44 202
420
5530
6140
0110
2640
5320
4820
8930
2830
3530
3630
2140
1140
8140
8320
6540
3220
6410
4830
24 3017
1079
2025
4084 3051
3037
2032
3023
1089
3059
4082
3052
4010
2049
2059
4055
1009
4004
1037
1058
2039
1083
4074
4043
4050
1084
1087
1038
1062
2069
3041
1082
1021
3066 30
6310
8640
0620
5140
0310
5010
4110
4720
4510
2210
8510
3420
5710
8030
5520
8140
3340
0240
4840
7810
5140
0840
7220
0320
4240
6340
1440
4220
3610
3030
3310
3910
5340
1540
3630
6410
1210
2010
0320
1340
0940
0710
0410
1410
4240
6620
2720
3130
4040
6440
8040
6240
6511
0030
4740
3910
7340
7610
2320
56 4038
1105
2041
1094
4052
1098
4005
1025
4013 20
7910
8110
6710
4030
25 1097
1093
4012
1035
2067
1075
2074
1091
1092
3007
3058
1070
4020
2035
1065
3043 40
2710
7610
1330
1930
5730
4530
60 3044
2078
4028
4031
4024
4029
3056
4034
4040 10
0520
5830
0420
8020
1410
5930
1510
6330
38 4018
1088
3002
3054
4025
1044
2030
3022
1015
2025
3035
40
Average Linkage
dwork
Hei
ght
Figure 13: Average Linkage Cluster Analysis For Mature Micro RNAs
2068
2024
1044
3061
2055
3035
3021
4011 4
001
4056
4077
4079 20
8920
2520
4810
4830
24 2064
2049
3037
2032
3023
3052
4010
1089
4074
1080
3055 20
3920
5940
5510
4220
2720
3110
7330
4040
6440
8040
6240
65 1009
4004
4081
4083 30
3610
5810
5010
4110
4720
45 3017
4007
1004
1014
1030
3033
1067
1040
3025 1038
4038
4039
2036
1039
1053 10
3710
6220
6920
3510
6530
43 3041
3004
3019 20
7930
5840
1840
2530
0230
5410
0520
5810
7630
5730
4530
60 1075
2074 3063
3044
2078
1059
3015
1063
3038
2014
2080 10
3520
67 1034
2003
2042
4063 4002
4048
1087
4066
4072
4008
1051
1097
1025
4013
1088
4028
4031 4027
4024
4029
3056
4034
4040 1
093
4012
1070
4020
1091
1092
1013
3007 40
3640
1510
9840
05 2081
1100
1003
2013
4009 40
7830
6410
1210
2010
8130
4740
7610
2320
5611
0520
41 4014
4042
1022
1094
4052 30
5940
8210
7940
8410
2130
6610
8210
8610
8540
43 1083
2057
4050 30
5140
0620
5140
0310
8440
33 302
220
3030
2840
5310
2620
6540
3210
9510
6940
4440
4510
9930
4610
9040
5820
3330
2030
3210
5730
2710
2710
5220
3810
3110
2420
4710
4510
3220
4010
0810
0610
5620
5010
0710
0230
03 1066
1036
3016
1043
1046 1
072
1028
1029
1060
1015
2025
3035
4045
Complete Linkage
dwork
Hei
ght
Figure 14: Complete Linkage Cluster Analysis For Mature Micro RNAs
Commercial In Confidence 23
Page 193
Jet Fuel and Solvents Micro RNA Arrays
3.4 Small Nucleolar Micro RNAs
Figure 15 shows the scree plot for the principal components analysis: giving the vari-
ance for each of the first ten principal components. The first principal component
dominates, representing 4 times the variance of the second component.
Scree Plot For snoRNA
Varia
nces
050
100
150
200
Figure 15: Variance By Principal Component For Small Nucleolar Micro RNAs
Figure 16 shows the scatter plot for the first two components of the small nucleolar
micro RNA data. The majority of samples have first component scores close to zero,
but there is a long tail of samples with large and negative values. Most of the samples
in this long tail are from subjects in the exposed groups. This pattern is very similar to
that seen for the mature micro RNAs and for the stem-loop micro RNAs. The Pearson
correlation coefficient between the first component score for mature micro RNAs and
small nucleolar micro RNAs is 0.98, and the correlation between scores on the first
component for stem-loop micro RNAs and small nucleolar RNAs is 0.99. Correlations
on the second component are 0.88 and 0.93.
That is, the multivariate disposition of samples in the space of the small nucleolar
RNAs is very similar to the disposition in the space of the stem loop micro RNAs
and the mature micro RNAs. The question. of course, arises as to whether this reflects
common underlying biological processes or common artefacts in laboratory processing.
Commercial In Confidence 24
Page 194
Jet Fuel and Solvents Micro RNA Arrays
●
●●●
●
●
●
●●
●
●
●●●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●●
●
●
●
●
●●●
●
●
●●
●●●
●
●●
●
●
●
●
●
●
●● ●
●
●
●●
●●● ●●●
●
●
●●●●
●
●●
●
●
●●●●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●●
●
●
●●●●●
●
●
●
●
●
●●
●
●
●
●
●●
●●●
●
●
●
●
●
●● ●●●● ●
●
●
●●
● ●
●
●●
●
● ●
●
●
●
●●
●
●●
●●●●●
●
●●
●
●
●
●
●
●●●
●
●
●
●●●●
●●●●●
●
●
●●
●
●●
●
●
●●●
●
●●
●●
●
●
●
●●
●●●
−60
−30
0
30
−80 −60 −40 −20 0Component 1
Com
pone
nt 2
Exposed●
●
Control
Exposed
First Two Principal Components for snoRNA
Figure 16: Scatter Plot For First Two Components of Small Nucleolar Micro RNAs
Figure 17 shows box and whisker plots of the first nine principal components by ex-
posure group. There is a suggestion of between group differences for components 3, 4
and 6. These are mostly associated with the control group for components 4 and 6, but
the high exposure group for component 3.
The analysis of variance for each component is summarised by Table 4. This conforms
the existence of statistically significant between group differences for components 3, 4
and 6. This analysis suggests that there may be differences between groups in small
nucleolar micro RNAs, mostly associated with differences between the control group
and exposed groups. These differences, however, are not in the dominant principal
components.
Commercial In Confidence 25
Page 195
Jet Fuel and Solvents Micro RNA Arrays
●
●●
●● ●●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●●●●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●●
●●●
●
●
●●
●
●
●
●●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
PC1 PC2 PC3
PC4 PC5 PC6
PC7 PC8 PC9
−80
−60
−40
−20
0
−60
−30
0
30
−30
−20
−10
0
10
−10
0
10
20
30
−10
0
10
−10
−5
0
5
10
−10
0
10
−10
0
10
−10
0
10
1 2 3 4 1 2 3 4 1 2 3 4
1 2 3 4 1 2 3 4 1 2 3 4
1 2 3 4 1 2 3 4 1 2 3 4Exposure Group
Com
pone
nt S
core
Figure 17: Box And Whisker Plots By Exposure Group For Principal Components of
mature Micro RNAs
Commercial In Confidence 26
Page 196
Jet Fuel and Solvents Micro RNA Arrays
Df Sum Sq Mean Sq F value Pr(>F)
PC 1 3 1595.89 531.96 2.40 0.0684
PC 2 3 413.08 137.69 1.98 0.1182
PC 3 3 582.57 194.19 5.31 0.0015
PC 4 3 310.72 103.57 3.59 0.0146
PC 5 3 132.31 44.10 1.81 0.1461
PC 6 3 402.11 134.04 7.21 0.0001
PC 7 3 92.47 30.82 1.89 0.1315
PC 8 3 102.43 34.14 2.41 0.0679
PC 9 3 86.60 28.87 2.11 0.1000
Table 4: Analysis of Variance For Principal Components of snoRNA
Single, average and complete linkage cluster dendrograms are shown in Figures 18, 19
and 20 respectively. Visually the dendrograms are quite different. Closer examination
reveals that they are similar only in so far as their major structure is associated with a
few extreme samples, rather than the exposure groups. That is, samples occurring as
isolated nodes or in small groups in one solution tend to don son in all solutions. Those
samples are JF2033, JF2068 and JF3020; these samples were also in isolated clusters
for mature micro RNAs.
2068
3020
2033
1072
1027
1052
2038
3032
1057
3027
1060
2050
1029
2024
1043
1028
1046
1031
1024
2047
1044
2030
2055
3022
1095
4045
3061
3021
4053
4056
3028
1099
1090
4058 4
044
3046
4079
1069
4077 3035
2064
4001
3052
2048
1026
2089
3036
4004
4010
2059
4032
1050
2057
4011
2069
1079
3051
3059
3017
3024
2027
1089
1038
1087
3037
1084
4012
4036
2065
2081
1058
1039
4055
4081
2032
1004
4007
1083
2042
4083
4008
4009
4002
1037
3066
1082
3055
4062
3023
1048
2036
4072
1094
4050
4006
4074
2051
2039
1021
4080
1022
2031
4005
3040
4082
2045
1086
2003
4063
1003
4066
4003
4014
4065
3064
1053
1030
2074
1100
1047
1041
1051
4078
1042
4048
4033
1014
1080
2049
4015
1085
4042
3057
1009
1034
4013
4043
4038
1098
4052
1076
3047
1012
1081
1075
3033
3002
1025
4027
4064
1067
3007
4020
4039
2056
2013
1023
3063
1013
3025
1062
1020
1073
1097
1092
3041
4031
1070
2079
2041
1065
3054
1035
1091
4025
4076
4018
1088
3043
3045
1105
4028
4024
3056
2058
3004
1093
2035
1040
3019
1005
3060
2067
3058
3015
2078
2080
1059
3038
4029
3044
1063
2014
4034
4040
2025
4084
1045
1066
1056
1007
3016
1002
1036
1008
3003 10
0610
3220
40
1015
2025
3035
Single Linkage
dwork
Hei
ght
Figure 18: Single Linkage Cluster Analysis For Mature Micro RNAs
Commercial In Confidence 27
Page 197
Jet Fuel and Solvents Micro RNA Arrays
4045
4044
4056
4079
1069
4077 1
095
1090
4058 10
9930
4620
2430
2140
5340
0120
6430
5220
8920
2540
84 2048
4004 2069
2057
4012
4032
2065
3059
4011
3036
4010
2059
1038
1089
3017
2036
3023
1048
1050
3024
3037
2032
3051 1058
1079
4072
2027
1039
1082
1087
4008
1003
1083
4080
4033
4036
4055
4081
4066
2039
3040 40
0230
6610
8440
5010
2220
7410
9420
8120
3140
0540
7840
0340
06 3055
1037
1086
2042
4062
4083
2003
4074
1021
1051
3025
2045
4082
1047
2051
4065
1004
1042
4063
1100
1053
1041
1030
1062
2049
3033 40
1510
1440
1410
8040
1340
4810
8520
1310
0910
1210
7510
7630
5710
9110
9740
2740
3130
6310
6510
3520
7910
9210
8830
0740
20 1070
1040
3041
3002
3054
4024
4025
3056
4028 1013
4018
1105
3043
1093
3045
3058
2058
3004
2067
3019
2035
3038
1005
2078
2080
3015 3060
1059
1063
3044
4029
2014
4034
4040
1098
3064
1081
4038
4042
1025
1034
1020
4052
1067
2041
4043
4064
1073
4076
3047
2056
1023
4039
4007
4009 10
2630
2830
35 2055
3061 20
3010
4430
2210
4310
2910
2810
3110
2420
4720
5010
0830
03 105
610
0610
3220
4010
6610
0730
1610
0210
3630
2020
3310
4510
46 102
710
5730
2730
3210
5220
3820
6810
6010
72
1015
2025
3035
4045
50
Average Linkage
dwork
Hei
ght
Figure 19: Average Linkage Cluster Analysis For Mature Micro RNAs
4045
1095
1090
4058 10
9930
46 1006
1032
2040 1
056
1008
3003
1066
1007
3016
1002
1036 40
4440
5640
7910
6940
77 1038
3036
1050
3024
3037
1048
1030
1041
1037
1053
1062
2049
3033 20
3630
2320
6910
3930
17 2064
2057
4010
1087
4008
1082
1021
1051 4
001
2089
2025
4084 2048
4004
1089
1084
4050
1047
3004
2078
2080
3015 20
5830
25 2051
3055 20
5940
3240
7210
7940
8120
3930
4040
0240
5510
0440
4340
6410
7340
76 1042
4063
2042
4062
4082
4083
2045
2003
4074
4066
4080
4033
4036
2032
3051
1022
1094
1014
4014
4038
4042
1081
1020
1067
2041 40
0940
0720
5610
2340
3930
4740
5240
6510
2510
34 1086
1098
3064 10
5840
0340
0640
0540
7810
0910
1210
0340
1510
7510
9220
7930
5430
4430
6010
0540
2940
3440
40 305
730
6310
3520
6710
5910
63 3043
3019
2035
2014
3038 10
4030
4110
6510
70 2074
4027
4031
3007
4020
3002
1088
3056
4028
4024
4025 10
7610
9330
4530
5811
0510
1340
1810
9110
97 3066
2031
2081
1083
1100
1080
4013
4048
1085
2013
2024
2055
3061
3022
3028
3035 10
4420
3030
2140
5330
5220
2740
11 4012
1026
2065
3059 10
5730
2730
3210
5220
3810
2710
4510
4620
3330
2020
6810
6010
7220
5010
3110
2420
4710
4310
2810
29
1020
3040
5060
70
Complete Linkage
dwork
Hei
ght
Figure 20: Complete Linkage Cluster Analysis For Mature Micro RNAs
Commercial In Confidence 28
Page 198
Jet Fuel and Solvents Micro RNA Arrays
3.5 Summary of Exploratory Analysis Results
• All sequence types (mature micro RNAs, stem loop pre micro RNAs and small
nucleolar micro RNAs showed ) some suggestion of exposure group related dif-
ferences.
• These differences were mostly confined to exposure group 4, and represented a
relatively small component of the total variation.
• The multivariate disposition of samples is fundamentally very similar for the
three different types of micro RNA.
4 Differential Expression
4.1 Methods
Between group differences were evaluated with a linear model, and empirical Bayes
moderated t tests[14] based on linear model parameter estimates. These were imple-
mented using Smyth’s limma methodology[18]. The following between group com-
parisons were performed:
1. Group 4 (Control) vs group 3 (Low exposure);
2. Group 4 (Control) vs group 2 (Moderate exposure);
3. Group 4 (Control) vs group 1 (High Exposure);
Individual p values for each micro RNA species were adjusted to maintain a false dis-
covery rate of 5% using the Benjamini Hochberg procedure[20]. This procedure was
adopted separately for each between groups comparison.
The empirical Bayes analysis was then repeated using health score as a covariate. Indi-
vidual p values for the health score covariate for each micro RNA species were adjusted
to maintain a false discovery rate of 5% using the Benjamini Hochberg procedure[20].
4.2 Mature Micro RNAs
Differentially expressed mature micro RNA species are shown in Table 5. One micro
RNA appears to be differentially expressed in comparison between Group 1 and Group
4. There are 45 differentially expressed mature micro RNAs in comparison between
Groups 1 (High exposure) and 4 (Control).
The largest fold change in expression is 4.7 fold. The mature micro RNAs re markedly
more dynamic that the mRNA expression data. Even so, only seven species of micro
RNAs achieved a greater than 2 fold change in expression in the High Exposure vs
Control comparison.
Commercial In Confidence 29
Page 199
Jet Fuel and Solvents Micro RNA Arrays
Unfortunately, these 7 micro RNA species have strongly conserved high complemen-
tarity bindings with 4,403 mRNA species.
Commercial In Confidence 30
Page 200
Jet Fuel and Solvents Micro RNA Arrays
ID logFC AveExpr t P.Value adj.P.Val B Contrast
hsa-miR-4282 -0.16 1.20 -3.16 0.00 0.05 -1.42 G1 v Control
hsa-miR-568 -0.17 1.18 -3.30 0.00 0.04 -1.02 G1 v Control
hsa-miR-519a-star -0.18 1.02 -3.63 0.00 0.02 0.00 G1 v Control
hsa-miR-519c -0.18 1.16 -3.56 0.00 0.02 -0.23 G1 v Control
hsa-miR-186 -0.19 1.25 -3.41 0.00 0.03 -0.68 G1 v Control
hsa-miR-376a 0.22 1.25 3.77 0.00 0.01 0.46 G1 v Control
hsa-miR-33b -0.23 1.25 -3.27 0.00 0.04 -1.12 G1 v Control
hsa-miR-627 0.24 1.31 3.17 0.00 0.05 -1.42 G1 v Control
hsa-miR-4264 -0.25 1.27 -3.54 0.00 0.02 -0.29 G1 v Control
hsa-miR-25 0.30 12.36 3.27 0.00 0.04 -1.12 G1 v Control
hsa-miR-107 -0.33 11.58 -4.53 0.00 0.00 3.25 G1 v Control
hsa-miR-103 -0.34 11.93 -4.22 0.00 0.00 2.05 G1 v Control
hsa-miR-296 -0.35 1.81 -3.16 0.00 0.05 -1.44 G1 v Control
hsa-miR-208a 0.38 1.42 3.83 0.00 0.01 0.68 G1 v Control
hsa-miR-1468 0.42 1.53 3.41 0.00 0.03 -0.68 G1 v Control
hsa-miR-93 -0.46 10.64 -4.43 0.00 0.00 2.87 G1 v Control
hsa-miR-486 -0.50 3.21 -3.23 0.00 0.04 -1.23 G1 v Control
hsa-miR-766 -0.50 5.20 -3.43 0.00 0.03 -0.64 G1 v Control
hsa-miR-888 0.50 1.40 3.76 0.00 0.01 0.42 G1 v Control
hsa-miR-1234 -0.51 3.23 -3.68 0.00 0.01 0.17 G1 v Control
hsa-miR-1825 -0.52 4.73 -3.23 0.00 0.04 -1.22 G1 v Control
hsa-miR-3130-5p -0.55 3.32 -3.14 0.00 0.05 -1.48 G1 v Control
hsa-miR-17 -0.58 10.16 -4.22 0.00 0.00 2.04 G1 v Control
hsa-miR-106a -0.59 9.88 -4.06 0.00 0.01 1.46 G1 v Control
hsa-miR-18b -0.61 2.30 -3.15 0.00 0.05 -1.48 G1 v Control
hsa-miR-1270 -0.62 3.77 -3.13 0.00 0.05 -1.52 G1 v Control
hsa-miR-548c 0.64 1.55 3.30 0.00 0.04 -1.04 G1 v Control
hsa-miR-559 0.70 2.10 3.67 0.00 0.01 0.15 G1 v Control
hsa-miR-27a -0.75 3.03 -3.23 0.00 0.04 -1.25 G1 v Control
hsa-miR-129-3p 0.76 2.18 4.46 0.00 0.00 2.97 G1 v Control
hsa-miR-20b -0.77 7.17 -3.97 0.00 0.01 1.15 G1 v Control
hsa-miR-1294 0.80 2.58 3.17 0.00 0.05 -1.39 G1 v Control
hsa-miR-1255a 0.85 1.90 3.96 0.00 0.01 1.13 G1 v Control
hsa-miR-20a -0.92 7.67 -4.21 0.00 0.00 2.03 G1 v Control
hsa-miR-129-star 0.95 2.00 4.20 0.00 0.00 1.98 G1 v Control
hsa-miR-1255b 0.96 3.84 4.29 0.00 0.00 2.30 G1 v Control
hsa-miR-548f 0.96 1.76 3.62 0.00 0.02 -0.02 G1 v Control
hsa-miR-548e 0.99 1.70 3.70 0.00 0.01 0.23 G1 v Control
hsa-miR-548a-3p 1.05 3.35 3.26 0.00 0.04 -1.15 G1 v Control
hsa-miR-606 1.12 1.83 4.29 0.00 0.00 2.32 G1 v Control
hsa-miR-18a -1.19 4.18 -4.45 0.00 0.00 2.94 G1 v Control
hsa-miR-576 1.54 2.03 4.58 0.00 0.00 3.45 G1 v Control
hsa-miR-335 1.63 3.12 3.95 0.00 0.01 1.10 G1 v Control
hsa-miR-3148 2.16 2.91 4.19 0.00 0.00 1.94 G1 v Control
hsa-miR-3128 2.22 3.11 4.43 0.00 0.00 2.84 G1 v Control
hsa-let-7d -0.38 11.38 -3.77 0.00 0.05 0.48 Group 3 v Control
hsa-miR-93 -0.46 10.64 -3.76 0.00 0.05 0.46 Group 3 v Control
hsa-miR-17 -0.62 10.16 -3.82 0.00 0.05 0.64 Group 3 v Control
Table 5: Differentially Expressed Mature Micro RNA Species. The ID column is the
micro RNA name as defined in the miRBase database[4] accessed via http://www.mirbase.org/.
Commercial In Confidence 31
Page 201
Jet Fuel and Solvents Micro RNA Arrays
ID logFC AveExpr t P.Value adj.P.Val B Contrast
hsa-miR-106a -0.66 9.88 -3.85 0.00 0.05 0.74 Group 3 v Control
hsa-miR-20a -1.00 7.67 -3.89 0.00 0.05 0.89 Group 3 v Control
Table 5: Differentially Expressed Mature Micro RNA Species. The ID column is the
micro RNA name as defined in the miRBase database[4] accessed via http://www.mirbase.org/.
Gene Ontology enrichment analyses for the target mRNAs are shown in Tables 6, 7,
and 8 for Biological Process, Molecular Function and Cellular Component domains
respectively. We note the for the Biological process domain there are a number of
terms associated with RNA metabolic processes and transcription control, and also
terms associated with central nervous system development and function. A primary
ontology association with CNS development is not necessarily inconsistent with a role
in CNS function and cognition in adulthood (this is certainly the case in the dentate
gyrus which is known to have a high rate of neuro-genesis in adult rats).
The Molecular Function domain shows enrichment in several ontology terms associ-
ated with methylation / de-methylation and transcription factor binding. There are no
obvious groupings in the Cellular Component domain.
Commercial In Confidence 32
Page 202
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
07
39
9n
ervo
us
syst
emd
evel
op
men
t0
.00
1.4
64
84
.96
60
21
62
3
GO
:20
00
11
2re
gu
lati
on
of
cell
ula
rm
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.37
77
5.7
09
14
25
96
GO
:0006996
org
anel
leorg
aniz
atio
n0.0
01.4
0645.4
2774
2160
GO
:00
10
55
6re
gu
lati
on
of
mac
rom
ole
cule
bio
syn
thet
icp
roce
ss0
.00
1.3
77
99
.60
93
82
67
6
GO
:00
10
62
8p
osi
tive
reg
ula
tio
no
fg
ene
exp
ress
ion
0.0
01
.57
30
2.0
93
95
10
11
GO
:00
51
25
2re
gu
lati
on
of
RN
Am
etab
oli
cp
roce
ss0
.00
1.3
77
35
.96
86
72
46
3
GO
:00
09
88
9re
gu
lati
on
of
bio
syn
thet
icp
roce
ss0
.00
1.3
48
42
.63
97
82
82
0
GO
:00
31
32
6re
gu
lati
on
of
cell
ula
rb
iosy
nth
etic
pro
cess
0.0
01
.34
83
4.2
69
68
27
92
GO
:00
80
09
0re
gu
lati
on
of
pri
mar
ym
etab
oli
cp
roce
ss0
.00
1.3
11
13
1.5
71
27
63
78
7
GO
:00
45
89
3p
osi
tive
reg
ula
tio
no
ftr
ansc
rip
tio
n,
DN
A-t
emp
late
d0
.00
1.5
62
77
.29
36
29
28
GO
:00
19
21
9re
gu
lati
on
of
nu
cleo
bas
e-co
nta
inin
gco
mp
ou
nd
met
abo
lic
pro
cess
0.0
01
.33
88
4.1
61
01
82
95
9
GO
:00
44
76
7si
ng
le-o
rgan
ism
dev
elo
pm
enta
lp
roce
ss0
.00
1.3
01
19
2.8
31
33
83
99
2
GO
:00
06
35
1tr
ansc
rip
tio
n,
DN
A-t
emp
late
d0
.00
1.3
47
77
.19
90
42
60
1
GO
:00
51
25
4p
osi
tive
reg
ula
tio
no
fR
NA
met
abo
lic
pro
cess
0.0
01
.53
29
2.8
33
78
98
0
GO
:00
60
25
5re
gu
lati
on
of
mac
rom
ole
cule
met
abo
lic
pro
cess
0.0
01
.30
10
69
.72
12
08
35
80
GO
:00
32
50
2d
evel
op
men
tal
pro
cess
0.0
01
.29
12
04
.78
13
47
40
32
GO
:00
31
32
3re
gu
lati
on
of
cell
ula
rm
etab
oli
cp
roce
ss0
.00
1.2
91
16
2.0
51
30
33
88
9
GO
:00
10
55
8n
egat
ive
reg
ula
tio
no
fm
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.55
26
7.1
33
48
89
4
GO
:20
00
11
3n
egat
ive
reg
ula
tio
no
fce
llu
lar
mac
rom
ole
cule
bio
syn
thet
icp
roce
ss0
.00
1.5
62
55
.78
33
58
56
GO
:00
51
17
1re
gu
lati
on
of
nit
rog
enco
mp
ou
nd
met
abo
lic
pro
cess
0.0
01
.31
90
5.9
81
03
63
03
2
GO
:00
45
93
5p
osi
tive
reg
ula
tio
no
fn
ucl
eob
ase-
con
tain
ing
com
po
un
dm
etab
oli
cp
ro-
cess
0.0
01
.49
32
6.5
94
14
10
93
GO
:20
01
14
1re
gu
lati
on
of
RN
Ab
iosy
nth
etic
pro
cess
0.0
01
.34
71
3.5
58
33
23
88
GO
:00
10
60
4p
osi
tive
reg
ula
tio
no
fm
acro
mo
lecu
lem
etab
oli
cp
roce
ss0
.00
1.3
95
18
.43
62
41
73
5
GO
:00
10
46
8re
gu
lati
on
of
gen
eex
pre
ssio
n0
.00
1.3
18
68
.63
99
62
90
7
Tab
le6
:B
iolo
gic
alP
roce
ssG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 33
Pag
e 20
3
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
06
35
5re
gu
lati
on
of
tran
scri
pti
on
,D
NA
-tem
pla
ted
0.0
01
.34
70
4.2
88
22
23
57
GO
:19
02
68
0p
osi
tive
reg
ula
tio
no
fR
NA
bio
syn
thet
icp
roce
ss0
.00
1.5
12
87
.15
36
89
61
GO
:00
51
17
3p
osi
tive
reg
ula
tio
no
fn
itro
gen
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.4
73
32
.87
41
91
11
4
GO
:00
48
85
6an
ato
mic
alst
ruct
ure
dev
elo
pm
ent
0.0
01
.29
10
31
.47
11
63
34
52
GO
:00
10
55
7p
osi
tive
reg
ula
tio
no
fm
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.47
32
4.5
04
09
10
86
GO
:00
31
32
7n
egat
ive
reg
ula
tio
no
fce
llu
lar
bio
syn
thet
icp
roce
ss0
.00
1.5
02
79
.08
35
79
34
GO
:00
09
89
0n
egat
ive
reg
ula
tio
no
fb
iosy
nth
etic
pro
cess
0.0
01
.50
28
2.0
73
60
94
4
GO
:00
09
89
3p
osi
tive
reg
ula
tio
no
fm
etab
oli
cp
roce
ss0
.00
1.3
55
65
.94
67
01
89
4
GO
:00
32
77
4R
NA
bio
syn
thet
icp
roce
ss0
.00
1.3
17
95
.42
91
32
66
2
GO
:1902589
single
-org
anis
morg
anel
leorg
aniz
atio
n0.0
01.4
0428.7
8521
1435
GO
:00
10
62
9n
egat
ive
reg
ula
tio
no
fg
ene
exp
ress
ion
0.0
01
.51
25
3.0
93
25
84
7
GO
:00
09
89
1p
osi
tive
reg
ula
tio
no
fb
iosy
nth
etic
pro
cess
0.0
01
.42
35
0.8
04
33
11
74
GO
:00
31
32
8p
osi
tive
reg
ula
tio
no
fce
llu
lar
bio
syn
thet
icp
roce
ss0
.00
1.4
33
44
.52
42
61
15
3
GO
:00
50
79
4re
gu
lati
on
of
cell
ula
rp
roce
ss0
.00
1.2
51
96
9.4
22
10
16
59
1
GO
:00
34
65
4n
ucl
eob
ase-
con
tain
ing
com
po
un
db
iosy
nth
etic
pro
cess
0.0
01
.28
86
5.3
49
81
28
96
GO
:00
21
53
7te
len
cep
hal
on
dev
elo
pm
ent
0.0
02
.44
46
.02
78
15
4
GO
:00
10
60
5n
egat
ive
reg
ula
tio
no
fm
acro
mo
lecu
lem
etab
oli
cp
roce
ss0
.00
1.4
03
88
.15
47
31
29
9
GO
:00
31
32
5p
osi
tive
reg
ula
tio
no
fce
llu
lar
met
abo
lic
pro
cess
0.0
01
.34
53
7.2
56
34
17
98
GO
:00
06
36
6tr
ansc
rip
tio
nfr
om
RN
Ap
oly
mer
ase
IIp
rom
ote
r0
.00
1.3
94
00
.10
48
51
33
9
GO
:00
19
22
2re
gu
lati
on
of
met
abo
lic
pro
cess
0.0
01
.25
12
84
.56
14
10
42
99
GO
:00
09
65
3an
ato
mic
alst
ruct
ure
mo
rph
og
enes
is0
.00
1.3
25
69
.22
66
61
90
5
GO
:00
32
98
9ce
llu
lar
com
po
nen
tm
orp
ho
gen
esis
0.0
01
.45
28
9.5
43
62
96
9
GO
:0007275
mult
icel
lula
rorg
anis
mal
dev
elopm
ent
0.0
01.2
51060.7
61179
3550
GO
:00
30
18
2n
euro
nd
iffe
ren
tiat
ion
0.0
01
.44
29
0.7
43
63
97
3
GO
:00
44
26
0ce
llu
lar
mac
rom
ole
cule
met
abo
lic
pro
cess
0.0
01
.23
16
40
.44
17
67
54
90
GO
:00
44
27
1ce
llu
lar
nit
rog
enco
mp
ou
nd
bio
syn
thet
icp
roce
ss0
.00
1.2
78
96
.12
10
08
29
99
Tab
le6
:B
iolo
gic
alP
roce
ssG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 34
Pag
e 20
4
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
19
43
8ar
om
atic
com
po
un
db
iosy
nth
etic
pro
cess
0.0
01
.27
88
1.7
79
93
29
51
GO
:19
02
67
9n
egat
ive
reg
ula
tio
no
fR
NA
bio
syn
thet
icp
roce
ss0
.00
1.5
02
30
.38
29
57
71
GO
:0016043
cell
ula
rco
mponen
torg
aniz
atio
n0.0
01.2
41188.6
51309
3978
GO
:00
06
35
7re
gu
lati
on
of
tran
scri
pti
on
fro
mR
NA
po
lym
eras
eII
pro
mo
ter
0.0
01
.40
35
3.1
94
31
11
82
GO
:00
45
89
2n
egat
ive
reg
ula
tio
no
ftr
ansc
rip
tio
n,
DN
A-t
emp
late
d0
.00
1.5
02
25
.60
28
97
55
GO
:00
06
46
4ce
llu
lar
pro
tein
mo
difi
cati
on
pro
cess
0.0
01
.29
68
1.8
77
82
22
82
GO
:00
36
21
1p
rote
inm
od
ifica
tio
np
roce
ss0
.00
1.2
96
81
.87
78
22
28
2
GO
:00
21
54
3p
alli
um
dev
elo
pm
ent
0.0
02
.84
29
.58
54
99
GO
:00
48
69
9g
ener
atio
no
fn
euro
ns
0.0
01
.41
31
4.3
43
87
10
52
GO
:00
51
25
3n
egat
ive
reg
ula
tio
no
fR
NA
met
abo
lic
pro
cess
0.0
01
.48
23
7.8
53
02
79
6
GO
:00
09
05
9m
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.25
10
41
.93
11
56
34
87
GO
:0007010
cyto
skel
eton
org
aniz
atio
n0.0
01.4
9220.2
2282
737
GO
:00
34
64
5ce
llu
lar
mac
rom
ole
cule
bio
syn
thet
icp
roce
ss0
.00
1.2
51
01
2.6
51
12
53
38
9
GO
:00
00
90
2ce
llm
orp
ho
gen
esis
0.0
01
.44
27
5.2
03
43
92
1
GO
:00
18
13
0h
eter
ocy
cle
bio
syn
thet
icp
roce
ss0
.00
1.2
68
80
.58
98
82
94
7
GO
:00
48
52
2p
osi
tive
reg
ula
tio
no
fce
llu
lar
pro
cess
0.0
01
.26
88
3.5
79
91
29
57
GO
:00
48
73
1sy
stem
dev
elo
pm
ent
0.0
01
.25
88
4.4
69
91
29
60
GO
:00
65
00
7b
iolo
gic
alre
gu
lati
on
0.0
01
.23
21
85
.16
23
01
73
13
GO
:00
00
90
4ce
llm
orp
ho
gen
esis
invo
lved
ind
iffe
ren
tiat
ion
0.0
01
.50
20
1.9
92
60
67
6
GO
:00
16
07
0R
NA
met
abo
lic
pro
cess
0.0
01
.25
94
8.1
11
05
63
17
3
GO
:00
45
93
4n
egat
ive
reg
ula
tio
no
fn
ucl
eob
ase-
con
tain
ing
com
po
un
dm
etab
oli
cp
ro-
cess
0.0
01
.44
26
1.1
63
26
87
4
GO
:00
16
56
8ch
rom
atin
mo
difi
cati
on
0.0
01
.65
12
9.0
81
76
43
2
GO
:0071840
cell
ula
rco
mponen
torg
aniz
atio
nor
bio
gen
esis
0.0
01.2
31207.4
71322
4041
GO
:00
06
13
9n
ucl
eob
ase-
con
tain
ing
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.2
21
29
4.1
21
41
04
33
1
GO
:00
45
94
4p
osi
tive
reg
ula
tio
no
ftr
ansc
rip
tio
nfr
om
RN
Ap
oly
mer
ase
IIp
rom
ote
r0
.00
1.5
01
95
.72
25
26
55
Tab
le6
:B
iolo
gic
alP
roce
ssG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 35
Pag
e 20
5
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
22
00
8n
euro
gen
esis
0.0
01
.38
33
1.9
74
03
11
11
GO
:19
01
36
2o
rgan
iccy
clic
com
po
un
db
iosy
nth
etic
pro
cess
0.0
01
.24
90
4.4
81
00
93
02
7
GO
:00
30
90
0fo
reb
rain
dev
elo
pm
ent
0.0
01
.78
90
.84
13
03
04
GO
:00
50
78
9re
gu
lati
on
of
bio
log
ical
pro
cess
0.0
01
.22
20
68
.03
21
83
69
21
GO
:00
51
17
2n
egat
ive
reg
ula
tio
no
fn
itro
gen
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.4
22
65
.94
33
08
90
GO
:0006325
chro
mat
inorg
aniz
atio
n0.0
01.5
8146.4
1195
490
GO
:00
07
50
7h
eart
dev
elo
pm
ent
0.0
01
.69
10
7.5
71
49
36
0
GO
:00
48
66
7ce
llm
orp
ho
gen
esis
invo
lved
inn
euro
nd
iffe
ren
tiat
ion
0.0
01
.60
13
4.1
61
80
44
9
GO
:00
48
46
8ce
lld
evel
op
men
t0
.00
1.3
24
32
.07
50
91
44
6
GO
:00
09
89
2n
egat
ive
reg
ula
tio
no
fm
etab
oli
cp
roce
ss0
.00
1.3
34
21
.91
49
81
41
2
GO
:00
43
41
2m
acro
mo
lecu
lem
od
ifica
tio
n0
.00
1.2
67
04
.28
79
72
35
7
GO
:00
07
41
7ce
ntr
aln
ervo
us
syst
emd
evel
op
men
t0
.00
1.4
71
99
.60
25
46
68
GO
:00
48
52
3n
egat
ive
reg
ula
tio
no
fce
llu
lar
pro
cess
0.0
01
.25
79
8.4
18
95
26
72
GO
:00
48
86
9ce
llu
lar
dev
elo
pm
enta
lp
roce
ss0
.00
1.2
57
93
.63
89
02
65
6
GO
:00
31
32
4n
egat
ive
reg
ula
tio
no
fce
llu
lar
met
abo
lic
pro
cess
0.0
01
.33
38
9.6
44
62
13
04
GO
:00
90
30
4n
ucl
eic
acid
met
abo
lic
pro
cess
0.0
01
.22
10
69
.12
11
72
35
78
GO
:00
48
51
8p
osi
tive
reg
ula
tio
no
fb
iolo
gic
alp
roce
ss0
.00
1.2
29
83
.67
10
84
32
92
GO
:00
44
70
8si
ng
le-o
rgan
ism
beh
avio
r0
.00
1.7
68
3.6
71
19
28
0
GO
:0051276
chro
moso
me
org
aniz
atio
n0.0
01.4
7193.0
3245
646
GO
:00
31
17
5n
euro
np
roje
ctio
nd
evel
op
men
t0
.00
1.4
52
05
.58
25
96
88
GO
:0030030
cell
pro
ject
ion
org
aniz
atio
n0.0
01.3
8277.5
9338
929
GO
:00
10
46
7g
ene
exp
ress
ion
0.0
01
.20
12
37
.95
13
42
41
43
GO
:00
44
08
7re
gu
lati
on
of
cell
ula
rco
mp
on
ent
bio
gen
esis
0.0
01
.61
11
2.6
51
52
37
7
GO
:00
48
81
2n
euro
np
roje
ctio
nm
orp
ho
gen
esis
0.0
01
.49
16
3.4
52
10
54
7
GO
:00
30
15
4ce
lld
iffe
ren
tiat
ion
0.0
01
.23
74
7.6
18
36
25
02
GO
:00
61
56
4ax
on
dev
elo
pm
ent
0.0
01
.55
13
2.0
71
74
44
2
Tab
le6
:B
iolo
gic
alP
roce
ssG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 36
Pag
e 20
6
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
21
98
7ce
reb
ral
cort
exd
evel
op
men
t0
.00
2.8
82
1.2
23
97
1
GO
:00
48
51
9n
egat
ive
reg
ula
tio
no
fb
iolo
gic
alp
roce
ss0
.00
1.2
28
66
.53
95
82
90
0
GO
:00
50
89
0co
gn
itio
n0
.00
2.0
04
9.0
07
51
64
GO
:0030036
acti
ncy
tosk
elet
on
org
aniz
atio
n0.0
01.5
7119.8
2159
401
GO
:00
06
41
7re
gu
lati
on
of
tran
slat
ion
0.0
01
.84
63
.05
92
21
1
GO
:00
48
66
6n
euro
nd
evel
op
men
t0
.00
1.4
31
96
.31
24
56
57
GO
:00
72
35
8ca
rdio
vas
cula
rsy
stem
dev
elo
pm
ent
0.0
01
.43
19
1.8
32
40
64
2
GO
:00
72
35
9ci
rcu
lato
rysy
stem
dev
elo
pm
ent
0.0
01
.43
19
1.8
32
40
64
2
GO
:00
32
99
0ce
llp
art
mo
rph
og
enes
is0
.00
1.4
11
99
.90
24
86
69
GO
:00
06
72
5ce
llu
lar
aro
mat
icco
mp
ou
nd
met
abo
lic
pro
cess
0.0
01
.18
13
32
.37
14
30
44
59
GO
:0051128
regula
tion
of
cell
ula
rco
mponen
torg
aniz
atio
n0.0
01.2
9388.4
5452
1300
GO
:00
10
60
8p
ost
tran
scri
pti
on
alre
gu
lati
on
of
gen
eex
pre
ssio
n0
.00
1.5
81
05
.78
14
13
54
GO
:00
07
04
9ce
llcy
cle
0.0
01
.29
38
6.9
54
50
12
95
GO
:00
34
64
1ce
llu
lar
nit
rog
enco
mp
ou
nd
met
abo
lic
pro
cess
0.0
01
.18
13
77
.49
14
74
46
10
GO
:00
46
48
3h
eter
ocy
cle
met
abo
lic
pro
cess
0.0
01
.18
13
28
.49
14
24
44
46
GO
:0051493
regula
tion
of
cyto
skel
eton
org
aniz
atio
n0.0
01.6
581.5
7112
273
GO
:00
48
85
8ce
llp
roje
ctio
nm
orp
ho
gen
esis
0.0
01
.40
19
7.2
12
43
66
0
GO
:00
07
42
0b
rain
dev
elo
pm
ent
0.0
01
.45
15
2.3
91
93
51
0
GO
:00
44
26
7ce
llu
lar
pro
tein
met
abo
lic
pro
cess
0.0
01
.20
85
9.0
69
42
28
75
GO
:00
17
14
8n
egat
ive
reg
ula
tio
no
ftr
ansl
atio
n0
.00
2.6
71
9.7
23
56
6
GO
:00
07
61
1le
arn
ing
or
mem
ory
0.0
01
.92
45
.42
68
15
2
GO
:00
43
17
0m
acro
mo
lecu
lem
etab
oli
cp
roce
ss0
.00
1.1
71
79
7.9
11
89
26
01
7
GO
:00
21
54
2d
enta
teg
yru
sd
evel
op
men
t0
.00
12
.94
3.8
81
11
3
GO
:00
44
24
9ce
llu
lar
bio
syn
thet
icp
roce
ss0
.00
1.1
81
22
3.6
11
31
44
09
5
GO
:0032956
regula
tion
of
acti
ncy
tosk
elet
on
org
aniz
atio
n0.0
01.8
056.1
881
188
GO
:00
50
79
3re
gu
lati
on
of
dev
elo
pm
enta
lp
roce
ss0
.00
1.2
64
20
.12
48
21
40
6
Tab
le6
:B
iolo
gic
alP
roce
ssG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 37
Pag
e 20
7
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
35
01
9so
mat
icst
emce
llm
ain
ten
ance
0.0
03
.62
11
.35
23
38
GO
:00
09
88
7o
rgan
mo
rph
og
enes
is0
.00
1.3
62
13
.35
25
97
14
GO
:00
42
48
1re
gu
lati
on
of
od
on
tog
enes
is0
.00
5.8
96
.27
15
21
GO
:00
07
40
9ax
on
og
enes
is0
.00
1.4
41
43
.43
18
14
80
GO
:00
42
69
2m
usc
lece
lld
iffe
ren
tiat
ion
0.0
01
.64
76
.20
10
42
55
GO
:00
30
02
9ac
tin
fila
men
t-b
ased
pro
cess
0.0
01
.46
13
2.0
71
68
44
2
GO
:00
16
56
9co
val
ent
chro
mat
inm
od
ifica
tio
n0
.00
1.5
98
4.8
61
14
28
4
GO
:0007423
senso
ryorg
andev
elopm
ent
0.0
01.4
9118.0
3152
395
GO
:00
71
23
0ce
llu
lar
resp
on
seto
amin
oac
idst
imu
lus
0.0
03
.33
12
.25
24
41
GO
:19
01
36
0o
rgan
iccy
clic
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.1
61
37
3.3
11
46
24
59
6
GO
:00
00
12
2n
egat
ive
reg
ula
tio
no
ftr
ansc
rip
tio
nfr
om
RN
Ap
oly
mer
ase
IIp
rom
ote
r0
.00
1.4
31
44
.02
18
14
82
GO
:00
42
48
7re
gu
lati
on
of
od
on
tog
enes
iso
fd
enti
n-c
on
tain
ing
too
th0
.00
21
.17
2.9
99
10
GO
:00
09
79
0em
bry
od
evel
op
men
t0
.00
1.3
22
47
.11
29
48
27
GO
:19
01
57
6o
rgan
icsu
bst
ance
bio
syn
thet
icp
roce
ss0
.00
1.1
61
24
5.7
21
33
14
16
9
GO
:00
06
46
8p
rote
inp
ho
sph
ory
lati
on
0.0
01
.28
30
9.8
63
61
10
37
GO
:00
71
49
5ce
llu
lar
resp
on
seto
end
og
eno
us
stim
ulu
s0
.00
1.3
32
25
.00
26
97
53
GO
:00
45
16
5ce
llfa
teco
mm
itm
ent
0.0
01
.68
61
.85
86
20
7
GO
:00
22
40
2ce
llcy
cle
pro
cess
0.0
01
.29
29
0.7
43
40
97
3
GO
:00
48
33
2m
eso
der
mm
orp
ho
gen
esis
0.0
02
.74
16
.14
29
54
GO
:00
21
95
3ce
ntr
aln
ervo
us
syst
emn
euro
nd
iffe
ren
tiat
ion
0.0
01
.86
42
.73
63
14
3
GO
:00
32
97
0re
gu
lati
on
of
acti
nfi
lam
ent-
bas
edp
roce
ss0
.00
1.6
56
7.8
39
32
27
GO
:0048568
embry
onic
org
andev
elopm
ent
0.0
01.5
1102.4
9133
343
GO
:00
32
26
8re
gu
lati
on
of
cell
ula
rp
rote
inm
etab
oli
cp
roce
ss0
.00
1.2
63
68
.73
42
31
23
4
GO
:00
61
06
1m
usc
lest
ruct
ure
dev
elo
pm
ent
0.0
01
.45
12
4.0
01
57
41
5
GO
:00
03
00
7h
eart
mo
rph
og
enes
is0
.00
1.7
74
9.6
07
11
66
GO
:00
22
60
7ce
llu
lar
com
po
nen
tas
sem
bly
0.0
01
.23
43
4.4
64
92
14
54
Tab
le6
:B
iolo
gic
alP
roce
ssG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 38
Pag
e 20
8
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
07
17
3ep
ider
mal
gro
wth
fact
or
rece
pto
rsi
gn
alin
gp
ath
way
0.0
01
.69
58
.86
82
19
7
GO
:19
01
69
9ce
llu
lar
resp
on
seto
nit
rog
enco
mp
ou
nd
0.0
01
.45
11
9.8
21
52
40
1
GO
:00
38
12
7E
RB
Bsi
gn
alin
gp
ath
way
0.0
01
.68
59
.76
83
20
0
GO
:00
09
05
8b
iosy
nth
etic
pro
cess
0.0
01
.16
12
61
.85
13
44
42
23
GO
:19
01
70
1ce
llu
lar
resp
on
seto
ox
yg
en-c
on
tain
ing
com
po
un
d0
.00
1.3
51
82
.57
22
16
11
GO
:00
01
56
8b
loo
dves
sel
dev
elo
pm
ent
0.0
01
.46
11
3.2
51
44
37
9
GO
:00
07
41
1ax
on
gu
idan
ce0
.00
1.4
91
00
.10
12
93
35
GO
:00
97
48
5n
euro
np
roje
ctio
ng
uid
ance
0.0
01
.49
10
0.1
01
29
33
5
GO
:00
35
33
5p
epti
dy
l-ty
rosi
ne
dep
ho
sph
ory
lati
on
0.0
02
.53
17
.33
30
58
GO
:00
01
94
4vas
cula
ture
dev
elo
pm
ent
0.0
01
.39
14
5.8
21
80
48
8
GO
:00
01
95
8en
do
cho
nd
ral
oss
ifica
tio
n0
.00
4.1
97
.47
16
25
GO
:00
36
07
5re
pla
cem
ent
oss
ifica
tio
n0
.00
4.1
97
.47
16
25
GO
:00
45
59
5re
gu
lati
on
of
cell
dif
fere
nti
atio
n0
.00
1.2
73
05
.38
35
31
02
2
GO
:00
70
84
8re
spo
nse
tog
row
thfa
cto
r0
.00
1.3
61
71
.51
20
85
74
GO
:0033043
regula
tion
of
org
anel
leorg
aniz
atio
n0.0
01.3
6171.5
1208
574
GO
:0044707
single
-mult
icel
lula
rorg
anis
mpro
cess
0.0
01.1
51343.1
31423
4495
GO
:00
01
65
4ey
ed
evel
op
men
t0
.00
1.5
77
3.5
19
82
46
GO
:00
16
57
0h
isto
ne
mo
difi
cati
on
0.0
01
.53
83
.07
10
92
78
GO
:00
06
80
7n
itro
gen
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.1
51
46
3.2
51
54
44
89
7
GO
:00
48
86
4st
emce
lld
evel
op
men
t0
.00
1.7
15
0.5
07
11
69
GO
:00
19
82
7st
emce
llm
ain
ten
ance
0.0
02
.04
28
.39
44
95
GO
:00
43
25
4re
gu
lati
on
of
pro
tein
com
ple
xas
sem
bly
0.0
01
.64
59
.16
81
19
8
GO
:0007015
acti
nfi
lam
ent
org
aniz
atio
n0.0
01.6
066.0
489
221
GO
:00
16
45
8g
ene
sile
nci
ng
0.0
02
.03
27
.79
43
93
GO
:0032501
mult
icel
lula
rorg
anis
mal
pro
cess
0.0
01.1
41388.5
51467
4647
GO
:00
07
61
2le
arn
ing
0.0
02
.06
26
.29
41
88
Tab
le6
:B
iolo
gic
alP
roce
ssG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 39
Pag
e 20
9
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:0034968
his
tone
lysi
ne
met
hyla
tion
0.0
02.4
417.0
329
57
GO
:00
01
70
9ce
llfa
ted
eter
min
atio
n0
.00
3.1
41
0.4
62
03
5
GO
:00
06
20
8py
rim
idin
en
ucl
eob
ase
cata
bo
lic
pro
cess
0.0
0In
f1
.79
66
GO
:0071310
cell
ula
rre
sponse
toorg
anic
subst
ance
0.0
01.2
2409.3
6461
1370
GO
:2000027
regula
tion
of
org
anm
orp
hogen
esis
0.0
01.7
642.7
361
143
GO
:00
60
28
4re
gu
lati
on
of
cell
dev
elo
pm
ent
0.0
01
.35
15
7.4
71
91
52
7
GO
:00
07
16
7en
zym
eli
nked
rece
pto
rp
rote
insi
gn
alin
gp
ath
way
0.0
01
.28
25
1.5
92
93
84
2
GO
:00
43
52
5p
osi
tive
reg
ula
tio
no
fn
euro
nap
op
toti
cp
roce
ss0
.00
2.8
81
1.9
52
24
0
GO
:20
00
82
6re
gu
lati
on
of
hea
rtm
orp
ho
gen
esis
0.0
03
.77
7.7
71
62
6
GO
:00
71
36
3ce
llu
lar
resp
on
seto
gro
wth
fact
or
stim
ulu
s0
.00
1.3
41
66
.73
20
15
58
GO
:00
06
90
0m
emb
ran
ebu
dd
ing
0.0
02
.69
13
.45
24
45
GO
:00
48
59
2ey
em
orp
ho
gen
esis
0.0
01
.85
34
.66
51
11
6
GO
:00
50
76
7re
gu
lati
on
of
neu
rog
enes
is0
.00
1.4
01
25
.20
15
54
19
GO
:00
35
05
1ca
rdio
cyte
dif
fere
nti
atio
n0
.00
1.9
62
8.9
84
49
7
GO
:0071417
cell
ula
rre
sponse
toorg
anonit
rogen
com
pound
0.0
01.4
2110.8
6139
371
GO
:00
71
22
9ce
llu
lar
resp
on
seto
acid
0.0
01
.83
35
.56
52
11
9
GO
:00
48
51
4b
loo
dves
sel
mo
rph
og
enes
is0
.00
1.4
59
5.9
21
22
32
1
GO
:00
06
47
0p
rote
ind
eph
osp
ho
ryla
tio
n0
.00
1.7
73
9.7
45
71
33
GO
:00
30
53
4ad
ult
beh
avio
r0
.00
1.8
73
3.1
74
91
11
GO
:00
07
16
9tr
ansm
emb
ran
ere
cep
tor
pro
tein
tyro
sin
ek
inas
esi
gn
alin
gp
ath
way
0.0
01
.32
18
1.0
82
16
60
6
Tab
le6
:B
iolo
gic
alP
roce
ssG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 40
Pag
e 21
0
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
07
39
9n
ervo
us
syst
emd
evel
op
men
t0
.00
1.4
64
84
.96
60
21
62
3
GO
:20
00
11
2re
gu
lati
on
of
cell
ula
rm
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.37
77
5.7
09
14
25
96
GO
:0006996
org
anel
leorg
aniz
atio
n0.0
01.4
0645.4
2774
2160
GO
:00
10
55
6re
gu
lati
on
of
mac
rom
ole
cule
bio
syn
thet
icp
roce
ss0
.00
1.3
77
99
.60
93
82
67
6
GO
:00
10
62
8p
osi
tive
reg
ula
tio
no
fg
ene
exp
ress
ion
0.0
01
.57
30
2.0
93
95
10
11
GO
:00
51
25
2re
gu
lati
on
of
RN
Am
etab
oli
cp
roce
ss0
.00
1.3
77
35
.96
86
72
46
3
GO
:00
09
88
9re
gu
lati
on
of
bio
syn
thet
icp
roce
ss0
.00
1.3
48
42
.63
97
82
82
0
GO
:00
31
32
6re
gu
lati
on
of
cell
ula
rb
iosy
nth
etic
pro
cess
0.0
01
.34
83
4.2
69
68
27
92
GO
:00
80
09
0re
gu
lati
on
of
pri
mar
ym
etab
oli
cp
roce
ss0
.00
1.3
11
13
1.5
71
27
63
78
7
GO
:00
45
89
3p
osi
tive
reg
ula
tio
no
ftr
ansc
rip
tio
n,
DN
A-t
emp
late
d0
.00
1.5
62
77
.29
36
29
28
GO
:00
19
21
9re
gu
lati
on
of
nu
cleo
bas
e-co
nta
inin
gco
mp
ou
nd
met
abo
lic
pro
cess
0.0
01
.33
88
4.1
61
01
82
95
9
GO
:00
44
76
7si
ng
le-o
rgan
ism
dev
elo
pm
enta
lp
roce
ss0
.00
1.3
01
19
2.8
31
33
83
99
2
GO
:00
06
35
1tr
ansc
rip
tio
n,
DN
A-t
emp
late
d0
.00
1.3
47
77
.19
90
42
60
1
GO
:00
51
25
4p
osi
tive
reg
ula
tio
no
fR
NA
met
abo
lic
pro
cess
0.0
01
.53
29
2.8
33
78
98
0
GO
:00
60
25
5re
gu
lati
on
of
mac
rom
ole
cule
met
abo
lic
pro
cess
0.0
01
.30
10
69
.72
12
08
35
80
GO
:00
32
50
2d
evel
op
men
tal
pro
cess
0.0
01
.29
12
04
.78
13
47
40
32
GO
:00
31
32
3re
gu
lati
on
of
cell
ula
rm
etab
oli
cp
roce
ss0
.00
1.2
91
16
2.0
51
30
33
88
9
GO
:00
10
55
8n
egat
ive
reg
ula
tio
no
fm
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.55
26
7.1
33
48
89
4
GO
:20
00
11
3n
egat
ive
reg
ula
tio
no
fce
llu
lar
mac
rom
ole
cule
bio
syn
thet
icp
roce
ss0
.00
1.5
62
55
.78
33
58
56
GO
:00
51
17
1re
gu
lati
on
of
nit
rog
enco
mp
ou
nd
met
abo
lic
pro
cess
0.0
01
.31
90
5.9
81
03
63
03
2
GO
:00
45
93
5p
osi
tive
reg
ula
tio
no
fn
ucl
eob
ase-
con
tain
ing
com
po
un
dm
etab
oli
cp
ro-
cess
0.0
01
.49
32
6.5
94
14
10
93
GO
:20
01
14
1re
gu
lati
on
of
RN
Ab
iosy
nth
etic
pro
cess
0.0
01
.34
71
3.5
58
33
23
88
GO
:00
10
60
4p
osi
tive
reg
ula
tio
no
fm
acro
mo
lecu
lem
etab
oli
cp
roce
ss0
.00
1.3
95
18
.43
62
41
73
5
GO
:00
10
46
8re
gu
lati
on
of
gen
eex
pre
ssio
n0
.00
1.3
18
68
.63
99
62
90
7
Tab
le7
:M
ole
cula
rF
un
ctio
nG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 41
Pag
e 21
1
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
06
35
5re
gu
lati
on
of
tran
scri
pti
on
,D
NA
-tem
pla
ted
0.0
01
.34
70
4.2
88
22
23
57
GO
:19
02
68
0p
osi
tive
reg
ula
tio
no
fR
NA
bio
syn
thet
icp
roce
ss0
.00
1.5
12
87
.15
36
89
61
GO
:00
51
17
3p
osi
tive
reg
ula
tio
no
fn
itro
gen
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.4
73
32
.87
41
91
11
4
GO
:00
48
85
6an
ato
mic
alst
ruct
ure
dev
elo
pm
ent
0.0
01
.29
10
31
.47
11
63
34
52
GO
:00
10
55
7p
osi
tive
reg
ula
tio
no
fm
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.47
32
4.5
04
09
10
86
GO
:00
31
32
7n
egat
ive
reg
ula
tio
no
fce
llu
lar
bio
syn
thet
icp
roce
ss0
.00
1.5
02
79
.08
35
79
34
GO
:00
09
89
0n
egat
ive
reg
ula
tio
no
fb
iosy
nth
etic
pro
cess
0.0
01
.50
28
2.0
73
60
94
4
GO
:00
09
89
3p
osi
tive
reg
ula
tio
no
fm
etab
oli
cp
roce
ss0
.00
1.3
55
65
.94
67
01
89
4
GO
:00
32
77
4R
NA
bio
syn
thet
icp
roce
ss0
.00
1.3
17
95
.42
91
32
66
2
GO
:1902589
single
-org
anis
morg
anel
leorg
aniz
atio
n0.0
01.4
0428.7
8521
1435
GO
:00
10
62
9n
egat
ive
reg
ula
tio
no
fg
ene
exp
ress
ion
0.0
01
.51
25
3.0
93
25
84
7
GO
:00
09
89
1p
osi
tive
reg
ula
tio
no
fb
iosy
nth
etic
pro
cess
0.0
01
.42
35
0.8
04
33
11
74
GO
:00
31
32
8p
osi
tive
reg
ula
tio
no
fce
llu
lar
bio
syn
thet
icp
roce
ss0
.00
1.4
33
44
.52
42
61
15
3
GO
:00
50
79
4re
gu
lati
on
of
cell
ula
rp
roce
ss0
.00
1.2
51
96
9.4
22
10
16
59
1
GO
:00
34
65
4n
ucl
eob
ase-
con
tain
ing
com
po
un
db
iosy
nth
etic
pro
cess
0.0
01
.28
86
5.3
49
81
28
96
GO
:00
21
53
7te
len
cep
hal
on
dev
elo
pm
ent
0.0
02
.44
46
.02
78
15
4
GO
:00
10
60
5n
egat
ive
reg
ula
tio
no
fm
acro
mo
lecu
lem
etab
oli
cp
roce
ss0
.00
1.4
03
88
.15
47
31
29
9
GO
:00
31
32
5p
osi
tive
reg
ula
tio
no
fce
llu
lar
met
abo
lic
pro
cess
0.0
01
.34
53
7.2
56
34
17
98
GO
:00
06
36
6tr
ansc
rip
tio
nfr
om
RN
Ap
oly
mer
ase
IIp
rom
ote
r0
.00
1.3
94
00
.10
48
51
33
9
GO
:00
19
22
2re
gu
lati
on
of
met
abo
lic
pro
cess
0.0
01
.25
12
84
.56
14
10
42
99
GO
:00
09
65
3an
ato
mic
alst
ruct
ure
mo
rph
og
enes
is0
.00
1.3
25
69
.22
66
61
90
5
GO
:00
32
98
9ce
llu
lar
com
po
nen
tm
orp
ho
gen
esis
0.0
01
.45
28
9.5
43
62
96
9
GO
:0007275
mult
icel
lula
rorg
anis
mal
dev
elopm
ent
0.0
01.2
51060.7
61179
3550
GO
:00
30
18
2n
euro
nd
iffe
ren
tiat
ion
0.0
01
.44
29
0.7
43
63
97
3
GO
:00
44
26
0ce
llu
lar
mac
rom
ole
cule
met
abo
lic
pro
cess
0.0
01
.23
16
40
.44
17
67
54
90
GO
:00
44
27
1ce
llu
lar
nit
rog
enco
mp
ou
nd
bio
syn
thet
icp
roce
ss0
.00
1.2
78
96
.12
10
08
29
99
Tab
le7
:M
ole
cula
rF
un
ctio
nG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 42
Pag
e 21
2
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
19
43
8ar
om
atic
com
po
un
db
iosy
nth
etic
pro
cess
0.0
01
.27
88
1.7
79
93
29
51
GO
:19
02
67
9n
egat
ive
reg
ula
tio
no
fR
NA
bio
syn
thet
icp
roce
ss0
.00
1.5
02
30
.38
29
57
71
GO
:0016043
cell
ula
rco
mponen
torg
aniz
atio
n0.0
01.2
41188.6
51309
3978
GO
:00
06
35
7re
gu
lati
on
of
tran
scri
pti
on
fro
mR
NA
po
lym
eras
eII
pro
mo
ter
0.0
01
.40
35
3.1
94
31
11
82
GO
:00
45
89
2n
egat
ive
reg
ula
tio
no
ftr
ansc
rip
tio
n,
DN
A-t
emp
late
d0
.00
1.5
02
25
.60
28
97
55
GO
:00
06
46
4ce
llu
lar
pro
tein
mo
difi
cati
on
pro
cess
0.0
01
.29
68
1.8
77
82
22
82
GO
:00
36
21
1p
rote
inm
od
ifica
tio
np
roce
ss0
.00
1.2
96
81
.87
78
22
28
2
GO
:00
21
54
3p
alli
um
dev
elo
pm
ent
0.0
02
.84
29
.58
54
99
GO
:00
48
69
9g
ener
atio
no
fn
euro
ns
0.0
01
.41
31
4.3
43
87
10
52
GO
:00
51
25
3n
egat
ive
reg
ula
tio
no
fR
NA
met
abo
lic
pro
cess
0.0
01
.48
23
7.8
53
02
79
6
GO
:00
09
05
9m
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.25
10
41
.93
11
56
34
87
GO
:0007010
cyto
skel
eton
org
aniz
atio
n0.0
01.4
9220.2
2282
737
GO
:00
34
64
5ce
llu
lar
mac
rom
ole
cule
bio
syn
thet
icp
roce
ss0
.00
1.2
51
01
2.6
51
12
53
38
9
GO
:00
00
90
2ce
llm
orp
ho
gen
esis
0.0
01
.44
27
5.2
03
43
92
1
GO
:00
18
13
0h
eter
ocy
cle
bio
syn
thet
icp
roce
ss0
.00
1.2
68
80
.58
98
82
94
7
GO
:00
48
52
2p
osi
tive
reg
ula
tio
no
fce
llu
lar
pro
cess
0.0
01
.26
88
3.5
79
91
29
57
GO
:00
48
73
1sy
stem
dev
elo
pm
ent
0.0
01
.25
88
4.4
69
91
29
60
GO
:00
65
00
7b
iolo
gic
alre
gu
lati
on
0.0
01
.23
21
85
.16
23
01
73
13
GO
:00
00
90
4ce
llm
orp
ho
gen
esis
invo
lved
ind
iffe
ren
tiat
ion
0.0
01
.50
20
1.9
92
60
67
6
GO
:00
16
07
0R
NA
met
abo
lic
pro
cess
0.0
01
.25
94
8.1
11
05
63
17
3
GO
:00
45
93
4n
egat
ive
reg
ula
tio
no
fn
ucl
eob
ase-
con
tain
ing
com
po
un
dm
etab
oli
cp
ro-
cess
0.0
01
.44
26
1.1
63
26
87
4
GO
:00
16
56
8ch
rom
atin
mo
difi
cati
on
0.0
01
.65
12
9.0
81
76
43
2
GO
:0071840
cell
ula
rco
mponen
torg
aniz
atio
nor
bio
gen
esis
0.0
01.2
31207.4
71322
4041
GO
:00
06
13
9n
ucl
eob
ase-
con
tain
ing
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.2
21
29
4.1
21
41
04
33
1
GO
:00
45
94
4p
osi
tive
reg
ula
tio
no
ftr
ansc
rip
tio
nfr
om
RN
Ap
oly
mer
ase
IIp
rom
ote
r0
.00
1.5
01
95
.72
25
26
55
Tab
le7
:M
ole
cula
rF
un
ctio
nG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 43
Pag
e 21
3
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
22
00
8n
euro
gen
esis
0.0
01
.38
33
1.9
74
03
11
11
GO
:19
01
36
2o
rgan
iccy
clic
com
po
un
db
iosy
nth
etic
pro
cess
0.0
01
.24
90
4.4
81
00
93
02
7
GO
:00
30
90
0fo
reb
rain
dev
elo
pm
ent
0.0
01
.78
90
.84
13
03
04
GO
:00
50
78
9re
gu
lati
on
of
bio
log
ical
pro
cess
0.0
01
.22
20
68
.03
21
83
69
21
GO
:00
51
17
2n
egat
ive
reg
ula
tio
no
fn
itro
gen
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.4
22
65
.94
33
08
90
GO
:0006325
chro
mat
inorg
aniz
atio
n0.0
01.5
8146.4
1195
490
GO
:00
07
50
7h
eart
dev
elo
pm
ent
0.0
01
.69
10
7.5
71
49
36
0
GO
:00
48
66
7ce
llm
orp
ho
gen
esis
invo
lved
inn
euro
nd
iffe
ren
tiat
ion
0.0
01
.60
13
4.1
61
80
44
9
GO
:00
48
46
8ce
lld
evel
op
men
t0
.00
1.3
24
32
.07
50
91
44
6
GO
:00
09
89
2n
egat
ive
reg
ula
tio
no
fm
etab
oli
cp
roce
ss0
.00
1.3
34
21
.91
49
81
41
2
GO
:00
43
41
2m
acro
mo
lecu
lem
od
ifica
tio
n0
.00
1.2
67
04
.28
79
72
35
7
GO
:00
07
41
7ce
ntr
aln
ervo
us
syst
emd
evel
op
men
t0
.00
1.4
71
99
.60
25
46
68
GO
:00
48
52
3n
egat
ive
reg
ula
tio
no
fce
llu
lar
pro
cess
0.0
01
.25
79
8.4
18
95
26
72
GO
:00
48
86
9ce
llu
lar
dev
elo
pm
enta
lp
roce
ss0
.00
1.2
57
93
.63
89
02
65
6
GO
:00
31
32
4n
egat
ive
reg
ula
tio
no
fce
llu
lar
met
abo
lic
pro
cess
0.0
01
.33
38
9.6
44
62
13
04
GO
:00
90
30
4n
ucl
eic
acid
met
abo
lic
pro
cess
0.0
01
.22
10
69
.12
11
72
35
78
GO
:00
48
51
8p
osi
tive
reg
ula
tio
no
fb
iolo
gic
alp
roce
ss0
.00
1.2
29
83
.67
10
84
32
92
GO
:00
44
70
8si
ng
le-o
rgan
ism
beh
avio
r0
.00
1.7
68
3.6
71
19
28
0
GO
:0051276
chro
moso
me
org
aniz
atio
n0.0
01.4
7193.0
3245
646
GO
:00
31
17
5n
euro
np
roje
ctio
nd
evel
op
men
t0
.00
1.4
52
05
.58
25
96
88
GO
:0030030
cell
pro
ject
ion
org
aniz
atio
n0.0
01.3
8277.5
9338
929
GO
:00
10
46
7g
ene
exp
ress
ion
0.0
01
.20
12
37
.95
13
42
41
43
GO
:00
44
08
7re
gu
lati
on
of
cell
ula
rco
mp
on
ent
bio
gen
esis
0.0
01
.61
11
2.6
51
52
37
7
GO
:00
48
81
2n
euro
np
roje
ctio
nm
orp
ho
gen
esis
0.0
01
.49
16
3.4
52
10
54
7
GO
:00
30
15
4ce
lld
iffe
ren
tiat
ion
0.0
01
.23
74
7.6
18
36
25
02
GO
:00
61
56
4ax
on
dev
elo
pm
ent
0.0
01
.55
13
2.0
71
74
44
2
Tab
le7
:M
ole
cula
rF
un
ctio
nG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 44
Pag
e 21
4
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
21
98
7ce
reb
ral
cort
exd
evel
op
men
t0
.00
2.8
82
1.2
23
97
1
GO
:00
48
51
9n
egat
ive
reg
ula
tio
no
fb
iolo
gic
alp
roce
ss0
.00
1.2
28
66
.53
95
82
90
0
GO
:00
50
89
0co
gn
itio
n0
.00
2.0
04
9.0
07
51
64
GO
:0030036
acti
ncy
tosk
elet
on
org
aniz
atio
n0.0
01.5
7119.8
2159
401
GO
:00
06
41
7re
gu
lati
on
of
tran
slat
ion
0.0
01
.84
63
.05
92
21
1
GO
:00
48
66
6n
euro
nd
evel
op
men
t0
.00
1.4
31
96
.31
24
56
57
GO
:00
72
35
8ca
rdio
vas
cula
rsy
stem
dev
elo
pm
ent
0.0
01
.43
19
1.8
32
40
64
2
GO
:00
72
35
9ci
rcu
lato
rysy
stem
dev
elo
pm
ent
0.0
01
.43
19
1.8
32
40
64
2
GO
:00
32
99
0ce
llp
art
mo
rph
og
enes
is0
.00
1.4
11
99
.90
24
86
69
GO
:00
06
72
5ce
llu
lar
aro
mat
icco
mp
ou
nd
met
abo
lic
pro
cess
0.0
01
.18
13
32
.37
14
30
44
59
GO
:0051128
regula
tion
of
cell
ula
rco
mponen
torg
aniz
atio
n0.0
01.2
9388.4
5452
1300
GO
:00
10
60
8p
ost
tran
scri
pti
on
alre
gu
lati
on
of
gen
eex
pre
ssio
n0
.00
1.5
81
05
.78
14
13
54
GO
:00
07
04
9ce
llcy
cle
0.0
01
.29
38
6.9
54
50
12
95
GO
:00
34
64
1ce
llu
lar
nit
rog
enco
mp
ou
nd
met
abo
lic
pro
cess
0.0
01
.18
13
77
.49
14
74
46
10
GO
:00
46
48
3h
eter
ocy
cle
met
abo
lic
pro
cess
0.0
01
.18
13
28
.49
14
24
44
46
GO
:0051493
regula
tion
of
cyto
skel
eton
org
aniz
atio
n0.0
01.6
581.5
7112
273
GO
:00
48
85
8ce
llp
roje
ctio
nm
orp
ho
gen
esis
0.0
01
.40
19
7.2
12
43
66
0
GO
:00
07
42
0b
rain
dev
elo
pm
ent
0.0
01
.45
15
2.3
91
93
51
0
GO
:00
44
26
7ce
llu
lar
pro
tein
met
abo
lic
pro
cess
0.0
01
.20
85
9.0
69
42
28
75
GO
:00
17
14
8n
egat
ive
reg
ula
tio
no
ftr
ansl
atio
n0
.00
2.6
71
9.7
23
56
6
GO
:00
07
61
1le
arn
ing
or
mem
ory
0.0
01
.92
45
.42
68
15
2
GO
:00
43
17
0m
acro
mo
lecu
lem
etab
oli
cp
roce
ss0
.00
1.1
71
79
7.9
11
89
26
01
7
GO
:00
21
54
2d
enta
teg
yru
sd
evel
op
men
t0
.00
12
.94
3.8
81
11
3
GO
:00
44
24
9ce
llu
lar
bio
syn
thet
icp
roce
ss0
.00
1.1
81
22
3.6
11
31
44
09
5
GO
:0032956
regula
tion
of
acti
ncy
tosk
elet
on
org
aniz
atio
n0.0
01.8
056.1
881
188
GO
:00
50
79
3re
gu
lati
on
of
dev
elo
pm
enta
lp
roce
ss0
.00
1.2
64
20
.12
48
21
40
6
Tab
le7
:M
ole
cula
rF
un
ctio
nG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 45
Pag
e 21
5
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
35
01
9so
mat
icst
emce
llm
ain
ten
ance
0.0
03
.62
11
.35
23
38
GO
:00
09
88
7o
rgan
mo
rph
og
enes
is0
.00
1.3
62
13
.35
25
97
14
GO
:00
42
48
1re
gu
lati
on
of
od
on
tog
enes
is0
.00
5.8
96
.27
15
21
GO
:00
07
40
9ax
on
og
enes
is0
.00
1.4
41
43
.43
18
14
80
GO
:00
42
69
2m
usc
lece
lld
iffe
ren
tiat
ion
0.0
01
.64
76
.20
10
42
55
GO
:00
30
02
9ac
tin
fila
men
t-b
ased
pro
cess
0.0
01
.46
13
2.0
71
68
44
2
GO
:00
16
56
9co
val
ent
chro
mat
inm
od
ifica
tio
n0
.00
1.5
98
4.8
61
14
28
4
GO
:0007423
senso
ryorg
andev
elopm
ent
0.0
01.4
9118.0
3152
395
GO
:00
71
23
0ce
llu
lar
resp
on
seto
amin
oac
idst
imu
lus
0.0
03
.33
12
.25
24
41
GO
:19
01
36
0o
rgan
iccy
clic
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.1
61
37
3.3
11
46
24
59
6
GO
:00
00
12
2n
egat
ive
reg
ula
tio
no
ftr
ansc
rip
tio
nfr
om
RN
Ap
oly
mer
ase
IIp
rom
ote
r0
.00
1.4
31
44
.02
18
14
82
GO
:00
42
48
7re
gu
lati
on
of
od
on
tog
enes
iso
fd
enti
n-c
on
tain
ing
too
th0
.00
21
.17
2.9
99
10
GO
:00
09
79
0em
bry
od
evel
op
men
t0
.00
1.3
22
47
.11
29
48
27
GO
:19
01
57
6o
rgan
icsu
bst
ance
bio
syn
thet
icp
roce
ss0
.00
1.1
61
24
5.7
21
33
14
16
9
GO
:00
06
46
8p
rote
inp
ho
sph
ory
lati
on
0.0
01
.28
30
9.8
63
61
10
37
GO
:00
71
49
5ce
llu
lar
resp
on
seto
end
og
eno
us
stim
ulu
s0
.00
1.3
32
25
.00
26
97
53
GO
:00
45
16
5ce
llfa
teco
mm
itm
ent
0.0
01
.68
61
.85
86
20
7
GO
:00
22
40
2ce
llcy
cle
pro
cess
0.0
01
.29
29
0.7
43
40
97
3
GO
:00
48
33
2m
eso
der
mm
orp
ho
gen
esis
0.0
02
.74
16
.14
29
54
GO
:00
21
95
3ce
ntr
aln
ervo
us
syst
emn
euro
nd
iffe
ren
tiat
ion
0.0
01
.86
42
.73
63
14
3
GO
:00
32
97
0re
gu
lati
on
of
acti
nfi
lam
ent-
bas
edp
roce
ss0
.00
1.6
56
7.8
39
32
27
GO
:0048568
embry
onic
org
andev
elopm
ent
0.0
01.5
1102.4
9133
343
GO
:00
32
26
8re
gu
lati
on
of
cell
ula
rp
rote
inm
etab
oli
cp
roce
ss0
.00
1.2
63
68
.73
42
31
23
4
GO
:00
61
06
1m
usc
lest
ruct
ure
dev
elo
pm
ent
0.0
01
.45
12
4.0
01
57
41
5
GO
:00
03
00
7h
eart
mo
rph
og
enes
is0
.00
1.7
74
9.6
07
11
66
GO
:00
22
60
7ce
llu
lar
com
po
nen
tas
sem
bly
0.0
01
.23
43
4.4
64
92
14
54
Tab
le7
:M
ole
cula
rF
un
ctio
nG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 46
Pag
e 21
6
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
07
17
3ep
ider
mal
gro
wth
fact
or
rece
pto
rsi
gn
alin
gp
ath
way
0.0
01
.69
58
.86
82
19
7
GO
:19
01
69
9ce
llu
lar
resp
on
seto
nit
rog
enco
mp
ou
nd
0.0
01
.45
11
9.8
21
52
40
1
GO
:00
38
12
7E
RB
Bsi
gn
alin
gp
ath
way
0.0
01
.68
59
.76
83
20
0
GO
:00
09
05
8b
iosy
nth
etic
pro
cess
0.0
01
.16
12
61
.85
13
44
42
23
GO
:19
01
70
1ce
llu
lar
resp
on
seto
ox
yg
en-c
on
tain
ing
com
po
un
d0
.00
1.3
51
82
.57
22
16
11
GO
:00
01
56
8b
loo
dves
sel
dev
elo
pm
ent
0.0
01
.46
11
3.2
51
44
37
9
GO
:00
07
41
1ax
on
gu
idan
ce0
.00
1.4
91
00
.10
12
93
35
GO
:00
97
48
5n
euro
np
roje
ctio
ng
uid
ance
0.0
01
.49
10
0.1
01
29
33
5
GO
:00
35
33
5p
epti
dy
l-ty
rosi
ne
dep
ho
sph
ory
lati
on
0.0
02
.53
17
.33
30
58
GO
:00
01
94
4vas
cula
ture
dev
elo
pm
ent
0.0
01
.39
14
5.8
21
80
48
8
GO
:00
01
95
8en
do
cho
nd
ral
oss
ifica
tio
n0
.00
4.1
97
.47
16
25
GO
:00
36
07
5re
pla
cem
ent
oss
ifica
tio
n0
.00
4.1
97
.47
16
25
GO
:00
45
59
5re
gu
lati
on
of
cell
dif
fere
nti
atio
n0
.00
1.2
73
05
.38
35
31
02
2
GO
:00
70
84
8re
spo
nse
tog
row
thfa
cto
r0
.00
1.3
61
71
.51
20
85
74
GO
:0033043
regula
tion
of
org
anel
leorg
aniz
atio
n0.0
01.3
6171.5
1208
574
GO
:0044707
single
-mult
icel
lula
rorg
anis
mpro
cess
0.0
01.1
51343.1
31423
4495
GO
:00
01
65
4ey
ed
evel
op
men
t0
.00
1.5
77
3.5
19
82
46
GO
:00
16
57
0h
isto
ne
mo
difi
cati
on
0.0
01
.53
83
.07
10
92
78
GO
:00
06
80
7n
itro
gen
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.1
51
46
3.2
51
54
44
89
7
GO
:00
48
86
4st
emce
lld
evel
op
men
t0
.00
1.7
15
0.5
07
11
69
GO
:00
19
82
7st
emce
llm
ain
ten
ance
0.0
02
.04
28
.39
44
95
GO
:00
43
25
4re
gu
lati
on
of
pro
tein
com
ple
xas
sem
bly
0.0
01
.64
59
.16
81
19
8
GO
:0007015
acti
nfi
lam
ent
org
aniz
atio
n0.0
01.6
066.0
489
221
GO
:00
16
45
8g
ene
sile
nci
ng
0.0
02
.03
27
.79
43
93
GO
:0032501
mult
icel
lula
rorg
anis
mal
pro
cess
0.0
01.1
41388.5
51467
4647
GO
:00
07
61
2le
arn
ing
0.0
02
.06
26
.29
41
88
Tab
le7
:M
ole
cula
rF
un
ctio
nG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 47
Pag
e 21
7
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:0034968
his
tone
lysi
ne
met
hyla
tion
0.0
02.4
417.0
329
57
GO
:00
01
70
9ce
llfa
ted
eter
min
atio
n0
.00
3.1
41
0.4
62
03
5
GO
:00
06
20
8py
rim
idin
en
ucl
eob
ase
cata
bo
lic
pro
cess
0.0
0In
f1
.79
66
GO
:0071310
cell
ula
rre
sponse
toorg
anic
subst
ance
0.0
01.2
2409.3
6461
1370
GO
:2000027
regula
tion
of
org
anm
orp
hogen
esis
0.0
01.7
642.7
361
143
GO
:00
60
28
4re
gu
lati
on
of
cell
dev
elo
pm
ent
0.0
01
.35
15
7.4
71
91
52
7
GO
:00
07
16
7en
zym
eli
nked
rece
pto
rp
rote
insi
gn
alin
gp
ath
way
0.0
01
.28
25
1.5
92
93
84
2
GO
:00
43
52
5p
osi
tive
reg
ula
tio
no
fn
euro
nap
op
toti
cp
roce
ss0
.00
2.8
81
1.9
52
24
0
GO
:20
00
82
6re
gu
lati
on
of
hea
rtm
orp
ho
gen
esis
0.0
03
.77
7.7
71
62
6
GO
:00
71
36
3ce
llu
lar
resp
on
seto
gro
wth
fact
or
stim
ulu
s0
.00
1.3
41
66
.73
20
15
58
GO
:00
06
90
0m
emb
ran
ebu
dd
ing
0.0
02
.69
13
.45
24
45
GO
:00
48
59
2ey
em
orp
ho
gen
esis
0.0
01
.85
34
.66
51
11
6
GO
:00
50
76
7re
gu
lati
on
of
neu
rog
enes
is0
.00
1.4
01
25
.20
15
54
19
GO
:00
35
05
1ca
rdio
cyte
dif
fere
nti
atio
n0
.00
1.9
62
8.9
84
49
7
GO
:0071417
cell
ula
rre
sponse
toorg
anonit
rogen
com
pound
0.0
01.4
2110.8
6139
371
GO
:00
71
22
9ce
llu
lar
resp
on
seto
acid
0.0
01
.83
35
.56
52
11
9
GO
:00
48
51
4b
loo
dves
sel
mo
rph
og
enes
is0
.00
1.4
59
5.9
21
22
32
1
GO
:00
06
47
0p
rote
ind
eph
osp
ho
ryla
tio
n0
.00
1.7
73
9.7
45
71
33
GO
:00
30
53
4ad
ult
beh
avio
r0
.00
1.8
73
3.1
74
91
11
GO
:00
07
16
9tr
ansm
emb
ran
ere
cep
tor
pro
tein
tyro
sin
ek
inas
esi
gn
alin
gp
ath
way
0.0
01
.32
18
1.0
82
16
60
6
Tab
le7
:M
ole
cula
rF
un
ctio
nG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 48
Pag
e 21
8
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
05
63
4n
ucl
eus
0.0
01
.24
14
35
.67
15
73
48
51
GO
:00
05
85
6cy
tosk
elet
on
0.0
01
.32
44
3.6
35
23
14
99
GO
:00
05
62
2in
trac
ellu
lar
0.0
01
.25
29
61
.61
30
60
10
00
7
GO
:00
44
42
4in
trac
ellu
lar
par
t0
.00
1.2
42
93
6.1
63
03
29
92
1
GO
:0043229
intr
acel
lula
rorg
anel
le0.0
01.2
02546.9
82650
8606
GO
:00
05
65
4n
ucl
eop
lasm
0.0
01
.31
37
3.4
94
40
12
62
GO
:00
44
42
8n
ucl
ear
par
t0
.00
1.2
46
25
.05
70
62
11
2
GO
:00
31
98
1n
ucl
ear
lum
en0
.00
1.2
65
28
.87
60
41
78
7
GO
:00
43
22
6o
rgan
elle
0.0
01
.19
26
81
.04
27
76
90
59
GO
:00
44
46
4ce
llp
art
0.0
01
.28
33
30
.96
33
97
11
25
5
GO
:00
05
62
3ce
ll0
.00
1.2
83
33
1.2
53
39
71
12
56
GO
:0043228
non-m
embra
ne-
bounded
org
anel
le0.0
01.2
1780.7
3864
2638
GO
:0043232
intr
acel
lula
rnon-m
embra
ne-
bounded
org
anel
le0.0
01.2
1780.7
3864
2638
GO
:00
42
99
5ce
llp
roje
ctio
n0
.00
1.2
93
48
.63
40
81
17
8
GO
:00
31
51
9P
cGp
rote
inco
mp
lex
0.0
03
.58
11
.84
24
40
GO
:0005815
mic
rotu
bule
org
aniz
ing
cente
r0.0
01.4
9125.4
8162
424
GO
:00
55
03
7re
cycl
ing
end
oso
me
0.0
02
.61
20
.42
36
69
GO
:00
30
05
4ce
llju
nct
ion
0.0
01
.37
20
8.0
62
54
70
3
GO
:00
15
63
0m
icro
tubu
lecy
tosk
elet
on
0.0
01
.35
22
5.5
22
72
76
2
GO
:00
31
25
2ce
llle
adin
ged
ge
0.0
01
.61
79
.61
10
82
69
GO
:0043231
intr
acel
lula
rm
embra
ne-
bounded
org
anel
le0.0
01.1
52308.1
42396
7799
GO
:00
43
00
5n
euro
np
roje
ctio
n0
.00
1.3
61
84
.38
22
46
23
GO
:00
30
02
7la
mel
lip
od
ium
0.0
01
.92
37
.29
56
12
6
GO
:00
05
72
0n
ucl
ear
het
ero
chro
mat
in0
.00
4.4
76
.81
15
23
GO
:00
05
91
2ad
her
ens
jun
ctio
n0
.00
1.6
95
4.4
67
61
84
Tab
le8
:C
ellu
lar
Co
mp
on
ent
Gen
eO
nto
log
yE
nri
chm
ent
for
Mat
ure
Mic
roR
NA
Tar
get
s.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
eO
nto
log
y
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 49
Pag
e 21
9
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:0043227
mem
bra
ne-
bounded
org
anel
le0.0
01.1
42498.7
42578
8443
GO
:00
16
34
2ca
ten
inco
mp
lex
0.0
0In
f1
.78
66
GO
:00
05
81
3ce
ntr
oso
me
0.0
01
.46
95
.59
12
23
23
GO
:00
55
03
8re
cycl
ing
end
oso
me
mem
bra
ne
0.0
03
.30
9.1
71
83
1
Tab
le8
:C
ellu
lar
Co
mp
on
ent
Gen
eO
nto
log
yE
nri
chm
ent
for
Mat
ure
Mic
roR
NA
Tar
get
s.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
eO
nto
log
y
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 50
Pag
e 22
0
Jet Fuel and Solvents Micro RNA Arrays
4.3 Stem Loop pre Micro RNAs
Differentially expressed stem-loop pre micro RNAs are shown in Table 9. There are
several micro RNA species in common with the mature micro RNAs. These pre micro
RNAs are associated with 4,715 target mRNAs.
Gene Ontology enrichment analyses for the target mRNAs are shown in Tables 10, 11,
and 12 for Biological Process, Molecular Function and Cellular Component domains
respectively. We note the for the Biological process domain there are a number of
terms associated with RNA metabolic processes and transcription control, and also a
few terms associated with central nervous system development and function. A primary
ontology association with CNS development is not necessarily inconsistent with a role
in CNS function and cognition in adulthood.
The Molecular Function domain shows enrichment for several terms associated (either
as child or ancestor nodes) with nucleic acid binding. The Cellular Component domain
shows enrichment for cell nucleus terms.
Commercial In Confidence 51
Page 221
Jet Fuel and Solvents Micro RNA Arrays
ID logFC AveExpr t P.Value adj.P.Val B Contrast
hsa-miR-1297 -0.17 1.34 -3.39 0.00 0.03 -0.73 G1 v Control
hsa-miR-3115 -0.17 1.14 -3.43 0.00 0.03 -0.63 G1 v Control
hsa-miR-100 -0.18 1.21 -3.53 0.00 0.03 -0.29 G1 v Control
hsa-miR-513a-1 -0.18 1.31 -3.35 0.00 0.03 -0.86 G1 v Control
hsa-miR-4266 -0.19 1.40 -3.23 0.00 0.04 -1.21 G1 v Control
hsa-miR-628 0.20 1.28 3.22 0.00 0.04 -1.24 G1 v Control
hsa-miR-1-1 -0.21 1.31 -3.64 0.00 0.02 0.04 G1 v Control
hsa-miR-520e 0.22 1.29 3.44 0.00 0.03 -0.58 G1 v Control
hsa-miR-519e 0.23 1.31 3.39 0.00 0.03 -0.75 G1 v Control
hsa-miR-4262 0.25 1.49 3.lll23 0.00 0.04 -1.21 G1 v Control
hsa-miR-1289-2 0.29 1.54 3.35 0.00 0.03 -0.86 G1 v Control
hsa-miR-1279 0.29 1.38 3.49 0.00 0.03 -0.45 G1 v Control
hsa-miR-941-2 -0.33 3.08 -3.70 0.00 0.02 0.25 G1 v Control
hsa-miR-532 0.38 3.02 3.63 0.00 0.02 0.01 G1 v Control
hsa-miR-1468 0.46 1.48 3.37 0.00 0.03 -0.79 G1 v Control
hsa-miR-526a-2 0.49 1.66 3.20 0.00 0.05 -1.30 G1 v Control
hsa-miR-548n 0.55 1.73 3.23 0.00 0.04 -1.22 G1 v Control
hsa-miR-335 0.59 2.00 3.45 0.00 0.03 -0.54 G1 v Control
hsa-miR-526a-2 0.61 1.57 3.45 0.00 0.03 -0.55 G1 v Control
hsa-miR-548i-4 0.69 1.84 3.23 0.00 0.04 -1.21 G1 v Control
hsa-miR-302c 0.70 1.52 4.16 0.00 0.01 1.84 G1 v Control
hsa-miR-302c 0.71 1.60 4.24 0.00 0.00 2.13 G1 v Control
hsa-miR-1264 0.72 1.63 3.81 0.00 0.01 0.61 G1 v Control
hsa-miR-548q 0.86 2.19 3.59 0.00 0.02 -0.10 G1 v Control
hsa-miR-519e 0.87 2.16 4.43 0.00 0.00 2.86 G1 v Control
hsa-miR-548f-1 0.87 1.63 4.40 0.00 0.00 2.75 G1 v Control
hsa-miR-520g 0.90 2.60 3.25 0.00 0.04 -1.15 G1 v Control
hsa-miR-517a 0.93 1.95 3.81 0.00 0.01 0.60 G1 v Control
hsa-miR-520h 1.05 2.42 3.75 0.00 0.02 0.42 G1 v Control
hsa-miR-548n 1.10 2.01 4.29 0.00 0.00 2.31 G1 v Control
hsa-miR-522 1.14 1.97 4.20 0.00 0.00 1.99 G1 v Control
hsa-miR-1979 1.14 2.51 3.76 0.00 0.02 0.45 G1 v Control
hsa-miR-520f 1.16 1.83 4.55 0.00 0.00 3.31 G1 v Control
hsa-miR-4275 1.26 2.20 4.03 0.00 0.01 1.38 G1 v Control
hsa-miR-3133 1.38 1.90 4.40 0.00 0.00 2.73 G1 v Control
hsa-miR-520f 1.46 2.13 4.45 0.00 0.00 2.95 G1 v Control
hsa-miR-548i-4 1.47 2.28 3.98 0.00 0.01 1.21 G1 v Control
hsa-miR-3128 1.63 2.56 4.59 0.00 0.00 3.47 G1 v Control
hsa-miR-128-1 0.23 1.26 4.26 0.00 0.02 2.17 G2 v Control
hsa-miR-518f 0.23 1.13 3.83 0.00 0.04 0.70 G2 v Control
hsa-miR-1297 -0.26 1.34 -4.22 0.00 0.02 2.02 G2 v Control
hsa-miR-4320 0.26 1.32 3.80 0.00 0.04 0.60 G2 v Control
hsa-miR-608 0.27 1.39 3.95 0.00 0.04 1.08 G2 v Control
Table 9: Differentially Expressed Stem Loop Pre Micro RNA Species. Differentially
Expressed Stem Loop Pre-Micro RNA Species. The ID column is the micro RNA name
as defined in the miRBase database[4] accessed via http://www.mirbase.org/.
Commercial In Confidence 52
Page 222
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
44
26
0ce
llu
lar
mac
rom
ole
cule
met
abo
lic
pro
cess
0.0
01
.29
17
62
.86
19
20
54
27
GO
:00
31
32
3re
gu
lati
on
of
cell
ula
rm
etab
oli
cp
roce
ss0
.00
1.2
81
24
9.6
31
38
83
84
7
GO
:0006996
org
anel
leorg
aniz
atio
n0.0
01.3
3693.8
4805
2136
GO
:00
06
46
4ce
llu
lar
pro
tein
mo
difi
cati
on
pro
cess
0.0
01
.32
73
4.4
48
47
22
61
GO
:00
36
21
1p
rote
inm
od
ifica
tio
np
roce
ss0
.00
1.3
27
34
.44
84
72
26
1
GO
:00
80
09
0re
gu
lati
on
of
pri
mar
ym
etab
oli
cp
roce
ss0
.00
1.2
61
21
7.4
71
34
83
74
8
GO
:00
51
25
2re
gu
lati
on
of
RN
Am
etab
oli
cp
roce
ss0
.00
1.3
07
93
.24
90
72
44
2
GO
:00
19
21
9re
gu
lati
on
of
nu
cleo
bas
e-co
nta
inin
gco
mp
ou
nd
met
abo
lic
pro
cess
0.0
01
.28
95
1.4
31
07
22
92
9
GO
:00
51
25
3n
egat
ive
reg
ula
tio
no
fR
NA
met
abo
lic
pro
cess
0.0
01
.52
25
6.9
43
28
79
1
GO
:00
43
41
2m
acro
mo
lecu
lem
od
ifica
tio
n0
.00
1.3
17
57
.83
86
92
33
3
GO
:20
00
11
2re
gu
lati
on
of
cell
ula
rm
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.29
83
5.7
99
50
25
73
GO
:00
10
55
6re
gu
lati
on
of
mac
rom
ole
cule
bio
syn
thet
icp
roce
ss0
.00
1.2
88
61
.45
97
42
65
2
GO
:19
02
67
9n
egat
ive
reg
ula
tio
no
fR
NA
bio
syn
thet
icp
roce
ss0
.00
1.5
02
48
.82
31
67
66
GO
:00
10
55
8n
egat
ive
reg
ula
tio
no
fm
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.46
28
8.4
53
60
88
8
GO
:00
06
13
9n
ucl
eob
ase-
con
tain
ing
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.2
41
38
9.9
51
51
74
27
9
GO
:00
06
35
1tr
ansc
rip
tio
n,
DN
A-t
emp
late
d0
.00
1.2
88
36
.77
94
72
57
6
GO
:00
10
62
9n
egat
ive
reg
ula
tio
no
fg
ene
exp
ress
ion
0.0
01
.47
27
3.5
13
43
84
2
GO
:00
07
39
9n
ervo
us
syst
emd
evel
op
men
t0
.00
1.3
45
23
.30
61
51
61
1
GO
:00
45
89
2n
egat
ive
reg
ula
tio
no
ftr
ansc
rip
tio
n,
DN
A-t
emp
late
d0
.00
1.5
02
43
.62
30
97
50
GO
:00
51
17
1re
gu
lati
on
of
nit
rog
enco
mp
ou
nd
met
abo
lic
pro
cess
0.0
01
.26
97
4.8
21
08
93
00
1
GO
:20
00
11
3n
egat
ive
reg
ula
tio
no
fce
llu
lar
mac
rom
ole
cule
bio
syn
thet
icp
roce
ss0
.00
1.4
62
76
.43
34
58
51
GO
:00
06
35
5re
gu
lati
on
of
tran
scri
pti
on
,D
NA
-tem
pla
ted
0.0
01
.28
75
9.1
38
63
23
37
GO
:1902589
single
-org
anis
morg
anel
leorg
aniz
atio
n0.0
01.3
5461.5
9547
1421
GO
:20
01
14
1re
gu
lati
on
of
RN
Ab
iosy
nth
etic
pro
cess
0.0
01
.28
76
9.2
08
73
23
68
GO
:00
50
79
4re
gu
lati
on
of
cell
ula
rp
roce
ss0
.00
1.2
32
12
0.1
72
24
56
52
7
Tab
le1
0:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Ste
mL
oo
pP
re-M
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 53
Pag
e 22
3
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
16
07
0R
NA
met
abo
lic
pro
cess
0.0
01
.25
10
19
.97
11
32
31
40
GO
:00
60
25
5re
gu
lati
on
of
mac
rom
ole
cule
met
abo
lic
pro
cess
0.0
01
.24
11
51
.20
12
67
35
44
GO
:00
31
32
6re
gu
lati
on
of
cell
ula
rb
iosy
nth
etic
pro
cess
0.0
01
.26
89
8.4
81
00
52
76
6
GO
:00
09
88
9re
gu
lati
on
of
bio
syn
thet
icp
roce
ss0
.00
1.2
59
07
.58
10
13
27
94
GO
:00
43
17
0m
acro
mo
lecu
lem
etab
oli
cp
roce
ss0
.00
1.2
11
93
1.7
72
05
25
94
7
GO
:00
32
77
4R
NA
bio
syn
thet
icp
roce
ss0
.00
1.2
58
55
.93
95
82
63
5
GO
:00
10
46
8re
gu
lati
on
of
gen
eex
pre
ssio
n0
.00
1.2
49
36
.49
10
41
28
83
GO
:00
31
32
7n
egat
ive
reg
ula
tio
no
fce
llu
lar
bio
syn
thet
icp
roce
ss0
.00
1.4
03
01
.12
36
69
27
GO
:0007010
cyto
skel
eton
org
aniz
atio
n0.0
01.4
5237.7
8296
732
GO
:00
09
89
0n
egat
ive
reg
ula
tio
no
fb
iosy
nth
etic
pro
cess
0.0
01
.39
30
4.3
73
69
93
7
GO
:00
30
18
2n
euro
nd
iffe
ren
tiat
ion
0.0
01
.38
31
3.7
93
79
96
6
GO
:00
44
76
7si
ng
le-o
rgan
ism
dev
elo
pm
enta
lp
roce
ss0
.00
1.2
11
28
3.7
31
39
33
95
2
GO
:00
45
93
4n
egat
ive
reg
ula
tio
no
fn
ucl
eob
ase-
con
tain
ing
com
po
un
dm
etab
oli
cp
ro-
cess
0.0
01
.40
28
2.2
83
44
86
9
GO
:00
34
65
4n
ucl
eob
ase-
con
tain
ing
com
po
un
db
iosy
nth
etic
pro
cess
0.0
01
.23
93
1.2
91
03
02
86
7
GO
:00
32
50
2d
evel
op
men
tal
pro
cess
0.0
01
.21
12
96
.40
14
04
39
91
GO
:00
48
69
9g
ener
atio
no
fn
euro
ns
0.0
01
.36
33
9.1
24
05
10
44
GO
:00
51
17
2n
egat
ive
reg
ula
tio
no
fn
itro
gen
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.3
92
87
.15
34
88
84
GO
:00
46
48
3h
eter
ocy
cle
met
abo
lic
pro
cess
0.0
01
.20
14
25
.36
15
34
43
88
GO
:00
06
72
5ce
llu
lar
aro
mat
icco
mp
ou
nd
met
abo
lic
pro
cess
0.0
01
.20
14
29
.58
15
38
44
01
GO
:00
48
66
6n
euro
nd
evel
op
men
t0
.00
1.4
52
13
.41
26
66
57
GO
:00
90
30
4n
ucl
eic
acid
met
abo
lic
pro
cess
0.0
01
.21
11
49
.58
12
52
35
39
GO
:00
10
60
5n
egat
ive
reg
ula
tio
no
fm
acro
mo
lecu
lem
etab
oli
cp
roce
ss0
.00
1.3
14
18
.71
48
91
28
9
GO
:00
19
22
2re
gu
lati
on
of
met
abo
lic
pro
cess
0.0
01
.20
13
81
.83
14
88
42
54
GO
:00
44
26
7ce
llu
lar
pro
tein
met
abo
lic
pro
cess
0.0
01
.22
92
5.4
41
02
02
84
9
GO
:00
34
64
5ce
llu
lar
mac
rom
ole
cule
bio
syn
thet
icp
roce
ss0
.00
1.2
11
08
8.8
31
18
83
35
2
Tab
le1
0:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Ste
mL
oo
pP
re-M
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 54
Pag
e 22
4
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
32
98
9ce
llu
lar
com
po
nen
tm
orp
ho
gen
esis
0.0
01
.35
31
2.4
93
73
96
2
GO
:00
48
86
9ce
llu
lar
dev
elo
pm
enta
lp
roce
ss0
.00
1.2
28
53
.33
94
42
62
7
GO
:00
09
05
9m
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.20
11
20
.67
12
19
34
50
GO
:00
00
90
4ce
llm
orp
ho
gen
esis
invo
lved
ind
iffe
ren
tiat
ion
0.0
01
.42
21
7.3
12
68
66
9
GO
:00
31
17
5n
euro
np
roje
ctio
nd
evel
op
men
t0
.00
1.4
12
21
.86
27
36
83
GO
:00
48
52
2p
osi
tive
reg
ula
tio
no
fce
llu
lar
pro
cess
0.0
01
.21
95
1.4
31
04
42
92
9
GO
:00
00
90
2ce
llm
orp
ho
gen
esis
0.0
01
.35
29
6.9
03
55
91
4
GO
:00
48
46
8ce
lld
evel
op
men
t0
.00
1.2
84
65
.81
53
61
43
4
GO
:00
19
43
8ar
om
atic
com
po
un
db
iosy
nth
etic
pro
cess
0.0
01
.21
94
8.5
11
04
02
92
0
GO
:00
06
36
6tr
ansc
rip
tio
nfr
om
RN
Ap
oly
mer
ase
IIp
rom
ote
r0
.00
1.2
94
31
.38
49
91
32
8
GO
:00
18
13
0h
eter
ocy
cle
bio
syn
thet
icp
roce
ss0
.00
1.2
19
46
.88
10
38
29
15
GO
:00
31
32
5p
osi
tive
reg
ula
tio
no
fce
llu
lar
met
abo
lic
pro
cess
0.0
01
.25
57
9.8
26
56
17
85
GO
:00
51
05
6re
gu
lati
on
of
smal
lG
TP
ase
med
iate
dsi
gn
altr
ansd
uct
ion
0.0
01
.60
11
0.7
71
47
34
1
GO
:00
48
66
7ce
llm
orp
ho
gen
esis
invo
lved
inn
euro
nd
iffe
ren
tiat
ion
0.0
01
.51
14
5.8
51
87
44
9
GO
:00
10
46
7g
ene
exp
ress
ion
0.0
01
.19
13
31
.81
14
31
41
00
GO
:00
22
00
8n
euro
gen
esis
0.0
01
.31
35
8.2
94
20
11
03
GO
:00
34
64
1ce
llu
lar
nit
rog
enco
mp
ou
nd
met
abo
lic
pro
cess
0.0
01
.18
14
78
.31
15
79
45
51
GO
:00
50
78
9re
gu
lati
on
of
bio
log
ical
pro
cess
0.0
01
.18
22
25
.74
23
26
68
52
GO
:00
00
12
2n
egat
ive
reg
ula
tio
no
ftr
ansc
rip
tio
nfr
om
RN
Ap
oly
mer
ase
IIp
rom
ote
r0
.00
1.4
91
54
.94
19
74
77
GO
:00
10
60
4p
osi
tive
reg
ula
tio
no
fm
acro
mo
lecu
lem
etab
oli
cp
roce
ss0
.00
1.2
55
60
.01
63
41
72
4
GO
:00
44
27
1ce
llu
lar
nit
rog
enco
mp
ou
nd
bio
syn
thet
icp
roce
ss0
.00
1.2
09
64
.10
10
54
29
68
GO
:00
09
89
3p
osi
tive
reg
ula
tio
no
fm
etab
oli
cp
roce
ss0
.00
1.2
46
10
.68
68
71
88
0
GO
:00
46
62
6re
gu
lati
on
of
insu
lin
rece
pto
rsi
gn
alin
gp
ath
way
0.0
04
.87
9.7
42
13
0
GO
:00
09
65
3an
ato
mic
alst
ruct
ure
mo
rph
og
enes
is0
.00
1.2
46
13
.28
68
91
88
8
GO
:0007275
mult
icel
lula
rorg
anis
mal
dev
elopm
ent
0.0
01.1
91141.4
61234
3514
GO
:00
30
15
4ce
lld
iffe
ren
tiat
ion
0.0
01
.21
80
3.3
18
86
24
73
Tab
le1
0:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Ste
mL
oo
pP
re-M
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 55
Pag
e 22
5
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
48
81
2n
euro
np
roje
ctio
nm
orp
ho
gen
esis
0.0
01
.44
17
6.7
12
20
54
4
GO
:00
09
96
6re
gu
lati
on
of
sig
nal
tran
sdu
ctio
n0
.00
1.2
45
89
.89
66
31
81
6
GO
:00
23
05
1re
gu
lati
on
of
sig
nal
ing
0.0
01
.23
65
9.0
87
35
20
29
GO
:0071840
cell
ula
rco
mponen
torg
aniz
atio
nor
bio
gen
esis
0.0
01.1
81297.3
81391
3994
GO
:00
06
46
8p
rote
inp
ho
sph
ory
lati
on
0.0
01
.31
33
3.2
83
90
10
26
GO
:0030036
acti
ncy
tosk
elet
on
org
aniz
atio
n0.0
01.5
1129.2
8166
398
GO
:19
01
36
2o
rgan
iccy
clic
com
po
un
db
iosy
nth
etic
pro
cess
0.0
01
.19
97
2.8
71
05
92
99
5
GO
:00
31
32
4n
egat
ive
reg
ula
tio
no
fce
llu
lar
met
abo
lic
pro
cess
0.0
01
.27
41
9.6
84
82
12
92
GO
:00
48
51
8p
osi
tive
reg
ula
tio
no
fb
iolo
gic
alp
roce
ss0
.00
1.1
91
05
9.9
21
14
83
26
3
GO
:0016043
cell
ula
rco
mponen
torg
aniz
atio
n0.0
01.1
81277.5
61370
3933
GO
:00
65
00
7b
iolo
gic
alre
gu
lati
on
0.0
01
.17
23
49
.18
24
42
72
32
GO
:00
06
35
7re
gu
lati
on
of
tran
scri
pti
on
fro
mR
NA
po
lym
eras
eII
pro
mo
ter
0.0
01
.28
38
1.6
84
41
11
75
GO
:00
10
64
6re
gu
lati
on
of
cell
com
mu
nic
atio
n0
.00
1.2
26
61
.68
73
62
03
7
GO
:00
30
02
9ac
tin
fila
men
t-b
ased
pro
cess
0.0
01
.47
14
2.2
81
80
43
8
GO
:00
48
85
6an
ato
mic
alst
ruct
ure
dev
elo
pm
ent
0.0
01
.18
11
09
.95
11
96
34
17
GO
:19
01
36
0o
rgan
iccy
clic
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.1
61
47
3.4
31
56
54
53
6
GO
:00
10
62
8p
osi
tive
reg
ula
tio
no
fg
ene
exp
ress
ion
0.0
01
.29
32
6.4
63
80
10
05
GO
:19
00
07
6re
gu
lati
on
of
cell
ula
rre
spo
nse
toin
suli
nst
imu
lus
0.0
03
.69
11
.69
23
36
GO
:0051128
regula
tion
of
cell
ula
rco
mponen
torg
aniz
atio
n0.0
01.2
6418.7
1478
1289
GO
:00
09
89
2n
egat
ive
reg
ula
tio
no
fm
etab
oli
cp
roce
ss0
.00
1.2
54
54
.76
51
61
40
0
GO
:19
00
07
7n
egat
ive
reg
ula
tio
no
fce
llu
lar
resp
on
seto
insu
lin
stim
ulu
s0
.00
5.0
77
.80
17
24
GO
:0006325
chro
mat
inorg
aniz
atio
n0.0
01.4
2158.1
9196
487
GO
:00
45
89
3p
osi
tive
reg
ula
tio
no
ftr
ansc
rip
tio
n,
DN
A-t
emp
late
d0
.00
1.3
02
99
.82
35
09
23
GO
:00
01
93
2re
gu
lati
on
of
pro
tein
ph
osp
ho
ryla
tio
n0
.00
1.3
32
46
.22
29
27
58
GO
:0051493
regula
tion
of
cyto
skel
eton
org
aniz
atio
n0.0
01.5
887.7
0116
270
GO
:00
61
56
4ax
on
dev
elo
pm
ent
0.0
01
.44
14
3.5
81
79
44
2
Tab
le1
0:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Ste
mL
oo
pP
re-M
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 56
Pag
e 22
6
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:0030030
cell
pro
ject
ion
org
aniz
atio
n0.0
01.2
9298.8
4348
920
GO
:00
16
56
8ch
rom
atin
mo
difi
cati
on
0.0
01
.43
13
9.6
81
74
43
0
GO
:00
51
17
4re
gu
lati
on
of
ph
osp
ho
rus
met
abo
lic
pro
cess
0.0
01
.24
42
6.5
04
83
13
13
GO
:00
32
99
0ce
llp
art
mo
rph
og
enes
is0
.00
1.3
42
16
.34
25
86
66
GO
:00
70
84
8re
spo
nse
tog
row
thfa
cto
r0
.00
1.3
71
86
.13
22
55
73
GO
:00
07
40
9ax
on
og
enes
is0
.00
1.4
01
55
.27
19
14
78
GO
:00
32
26
8re
gu
lati
on
of
cell
ula
rp
rote
inm
etab
oli
cp
roce
ss0
.00
1.2
53
96
.62
45
11
22
1
GO
:00
46
62
7n
egat
ive
reg
ula
tio
no
fin
suli
nre
cep
tor
sig
nal
ing
pat
hw
ay0
.00
4.7
77
.47
16
23
GO
:00
06
51
3p
rote
inm
on
ou
biq
uit
inat
ion
0.0
03
.13
12
.99
24
40
GO
:19
01
69
9ce
llu
lar
resp
on
seto
nit
rog
enco
mp
ou
nd
0.0
01
.44
12
9.6
11
62
39
9
GO
:00
22
60
3re
gu
lati
on
of
anat
om
ical
stru
ctu
rem
orp
ho
gen
esis
0.0
01
.35
19
4.2
52
33
59
8
GO
:00
51
25
4p
osi
tive
reg
ula
tio
no
fR
NA
met
abo
lic
pro
cess
0.0
01
.27
31
6.7
13
65
97
5
GO
:00
70
72
7ce
llu
lar
mac
rom
ole
cule
loca
liza
tio
n0
.00
1.2
92
73
.83
31
98
43
GO
:00
10
76
9re
gu
lati
on
of
cell
mo
rph
og
enes
isin
vo
lved
ind
iffe
ren
tiat
ion
0.0
01
.64
66
.59
90
20
5
GO
:00
45
93
5p
osi
tive
reg
ula
tio
no
fn
ucl
eob
ase-
con
tain
ing
com
po
un
dm
etab
oli
cp
ro-
cess
0.0
01
.25
35
2.7
74
03
10
86
GO
:00
42
32
5re
gu
lati
on
of
ph
osp
ho
ryla
tio
n0
.00
1.2
82
95
.60
34
29
10
GO
:0033043
regula
tion
of
org
anel
leorg
aniz
atio
n0.0
01.3
5185.4
8223
571
GO
:00
71
36
3ce
llu
lar
resp
on
seto
gro
wth
fact
or
stim
ulu
s0
.00
1.3
61
80
.93
21
85
57
GO
:00
19
22
0re
gu
lati
on
of
ph
osp
hat
em
etab
oli
cp
roce
ss0
.00
1.2
34
22
.28
47
61
30
0
GO
:00
10
55
7p
osi
tive
reg
ula
tio
no
fm
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.25
35
0.4
94
00
10
79
GO
:19
02
53
1re
gu
lati
on
of
intr
acel
lula
rsi
gn
altr
ansd
uct
ion
0.0
01
.25
35
8.2
94
08
11
03
GO
:19
01
06
9g
uan
osi
ne-
con
tain
ing
com
po
un
dca
tab
oli
cp
roce
ss0
.00
1.4
21
27
.98
15
93
94
GO
:19
02
68
0p
osi
tive
reg
ula
tio
no
fR
NA
bio
syn
thet
icp
roce
ss0
.00
1.2
63
10
.54
35
79
56
GO
:00
34
61
3ce
llu
lar
pro
tein
loca
liza
tio
n0
.00
1.2
82
72
.21
31
68
38
GO
:00
51
17
3p
osi
tive
reg
ula
tio
no
fn
itro
gen
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.2
43
59
.59
40
91
10
7
Tab
le1
0:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Ste
mL
oo
pP
re-M
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 57
Pag
e 22
7
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
31
39
9re
gu
lati
on
of
pro
tein
mo
difi
cati
on
pro
cess
0.0
01
.26
31
5.4
13
62
97
1
GO
:00
06
18
4G
TP
cata
bo
lic
pro
cess
0.0
01
.42
12
7.3
31
58
39
2
GO
:00
09
89
1p
osi
tive
reg
ula
tio
no
fb
iosy
nth
etic
pro
cess
0.0
01
.24
37
8.7
54
29
11
66
GO
:00
48
52
3n
egat
ive
reg
ula
tio
no
fce
llu
lar
pro
cess
0.0
01
.17
85
9.5
09
29
26
46
GO
:00
07
26
4sm
all
GT
Pas
em
edia
ted
sig
nal
tran
sdu
ctio
n0
.00
1.3
91
46
.50
17
94
51
GO
:00
48
85
8ce
llp
roje
ctio
nm
orp
ho
gen
esis
0.0
01
.31
21
3.4
12
52
65
7
GO
:00
22
60
4re
gu
lati
on
of
cell
mo
rph
og
enes
is0
.00
1.4
71
01
.67
12
93
13
GO
:00
31
32
8p
osi
tive
reg
ula
tio
no
fce
llu
lar
bio
syn
thet
icp
roce
ss0
.00
1.2
43
71
.93
42
11
14
5
GO
:0032956
regula
tion
of
acti
ncy
tosk
elet
on
org
aniz
atio
n0.0
01.6
460.7
482
187
GO
:00
06
80
7n
itro
gen
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.1
41
56
9.5
81
64
94
83
2
GO
:00
33
13
8p
osi
tive
reg
ula
tio
no
fp
epti
dy
l-se
rin
ep
ho
sph
ory
lati
on
0.0
02
.54
16
.57
28
51
GO
:0071310
cell
ula
rre
sponse
toorg
anic
subst
ance
0.0
01.2
1441.1
2493
1358
GO
:0071417
cell
ula
rre
sponse
toorg
anonit
rogen
com
pound
0.0
01.4
2120.1
9149
370
GO
:00
06
47
0p
rote
ind
eph
osp
ho
ryla
tio
n0
.00
1.7
74
3.2
06
11
33
GO
:00
46
03
9G
TP
met
abo
lic
pro
cess
0.0
01
.40
13
0.2
61
60
40
1
GO
:00
45
44
4fa
tce
lld
iffe
ren
tiat
ion
0.0
01
.81
39
.95
57
12
3
GO
:00
32
53
5re
gu
lati
on
of
cell
ula
rco
mp
on
ent
size
0.0
01
.61
62
.04
83
19
1
GO
:0051276
chro
moso
me
org
aniz
atio
n0.0
01.3
1208.2
2245
641
GO
:19
01
06
8g
uan
osi
ne-
con
tain
ing
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.3
91
32
.21
16
24
07
GO
:00
48
81
3d
end
rite
mo
rph
og
enes
is0
.00
2.0
92
5.3
43
97
8
GO
:00
08
06
4re
gu
lati
on
of
acti
np
oly
mer
izat
ion
or
dep
oly
mer
izat
ion
0.0
01
.90
33
.46
49
10
3
GO
:00
16
31
0p
ho
sph
ory
lati
on
0.0
01
.21
42
0.9
84
71
12
96
Tab
le1
0:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Ste
mL
oo
pP
re-M
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 58
Pag
e 22
8
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
05
51
5p
rote
inb
ind
ing
0.0
01
.28
21
35
.59
22
84
66
24
GO
:00
05
48
8b
ind
ing
0.0
01
.30
30
98
.28
31
87
96
10
GO
:00
19
89
9en
zym
eb
ind
ing
0.0
01
.38
33
6.9
14
07
10
45
GO
:00
04
84
2u
biq
uit
in-p
rote
inli
gas
eac
tiv
ity
0.0
01
.77
74
.47
10
52
31
GO
:00
97
15
9o
rgan
iccy
clic
com
po
un
db
ind
ing
0.0
01
.18
14
23
.08
15
22
44
14
GO
:0016881
acid
-am
ino
acid
ligas
eac
tivit
y0.0
01.6
883.1
8114
258
GO
:19
01
36
3h
eter
ocy
clic
com
po
un
db
ind
ing
0.0
01
.18
14
04
.38
15
02
43
56
GO
:0019787
smal
lco
nju
gat
ing
pro
tein
ligas
eac
tivit
y0.0
01.7
178.9
9109
245
GO
:00
03
67
6n
ucl
eic
acid
bin
din
g0
.00
1.1
99
24
.97
10
07
28
69
GO
:00
03
67
7D
NA
bin
din
g0
.00
1.2
35
61
.30
62
91
74
1
GO
:00
19
90
0k
inas
eb
ind
ing
0.0
01
.49
12
5.4
11
60
38
9
GO
:00
04
72
1p
ho
sph
op
rote
inp
ho
sph
atas
eac
tiv
ity
0.0
01
.88
48
.04
70
14
9
GO
:00
43
02
4ri
bo
som
alsm
all
subu
nit
bin
din
g0
.00
Inf
2.5
88
8
GO
:00
43
16
7io
nb
ind
ing
0.0
01
.16
14
77
.57
15
67
45
83
GO
:00
03
68
2ch
rom
atin
bin
din
g0
.00
1.4
81
06
.07
13
53
29
GO
:00
08
09
2cy
tosk
elet
alp
rote
inb
ind
ing
0.0
01
.34
19
5.7
02
34
60
7
GO
:0016879
ligas
eac
tivit
y,fo
rmin
gca
rbon-n
itro
gen
bonds
0.0
01.5
191.5
6118
284
GO
:00
04
67
4p
rote
inse
rin
e/th
reo
nin
ek
inas
eac
tiv
ity
0.0
01
.43
12
1.8
71
52
37
8
GO
:00
05
54
51
-ph
osp
hat
idy
lin
osi
tol
bin
din
g0
.00
4.9
26
.45
14
20
GO
:00
05
08
8R
asg
uan
yl-
nu
cleo
tid
eex
chan
ge
fact
or
acti
vit
y0
.00
1.9
33
4.5
05
11
07
GO
:00
16
79
1p
ho
sph
atas
eac
tiv
ity
0.0
01
.58
70
.28
93
21
8
GO
:00
19
90
1p
rote
ink
inas
eb
ind
ing
0.0
01
.43
11
3.1
61
41
35
1
GO
:00
44
82
2p
oly
(A)
RN
Ab
ind
ing
0.0
01
.26
29
3.0
63
36
90
9
Tab
le1
1:
Mo
lecu
lar
Fu
nct
ion
Gen
eO
nto
log
yE
nri
chm
ent
for
Ste
mL
oo
pP
re-M
icro
RN
AT
arg
ets.
Th
e‘G
OID
’is
the
un
iqu
eid
enti
fier
inth
eG
ene
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 59
Pag
e 22
9
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
05
62
2in
trac
ellu
lar
0.0
01
.32
31
73
.77
32
97
98
88
GO
:00
44
42
4in
trac
ellu
lar
par
t0
.00
1.3
13
14
6.8
13
27
09
80
4
GO
:00
05
63
4n
ucl
eus
0.0
01
.25
15
38
.10
16
81
47
92
GO
:00
44
46
4ce
llp
art
0.0
01
.39
35
68
.57
36
58
11
11
8
GO
:00
05
62
3ce
ll0
.00
1.3
93
56
8.8
93
65
81
11
19
GO
:00
43
22
6o
rgan
elle
0.0
01
.24
28
72
.38
29
90
89
49
GO
:00
44
42
8n
ucl
ear
par
t0
.00
1.2
76
67
.94
76
02
08
1
GO
:0043229
intr
acel
lula
rorg
anel
le0.0
01.2
12730.1
92844
8506
GO
:00
31
98
1n
ucl
ear
lum
en0
.00
1.2
85
65
.23
65
01
76
1
GO
:0043227
mem
bra
ne-
bounded
org
anel
le0.0
01.1
92676.2
62780
8338
GO
:00
05
85
6cy
tosk
elet
on
0.0
01
.27
47
6.6
45
47
14
85
GO
:0043228
non-m
embra
ne-
bounded
org
anel
le0.0
01.2
1837.4
2925
2609
GO
:0043232
intr
acel
lula
rnon-m
embra
ne-
bounded
org
anel
le0.0
01.2
1837.4
2925
2609
GO
:0043231
intr
acel
lula
rm
embra
ne-
bounded
org
anel
le0.0
01.1
72474.0
52577
7708
GO
:00
30
05
4ce
llju
nct
ion
0.0
01
.37
22
3.7
22
71
69
7
GO
:00
05
65
4n
ucl
eop
lasm
0.0
01
.26
40
0.2
54
58
12
47
GO
:00
35
77
0ri
bo
nu
cleo
pro
tein
gra
nu
le0
.00
2.2
32
6.3
24
28
2
GO
:00
31
25
2ce
llle
adin
ged
ge
0.0
01
.54
85
.06
11
12
65
GO
:00
44
43
0cy
tosk
elet
alp
art
0.0
01
.25
33
5.7
43
84
10
46
GO
:00
05
91
1ce
ll-c
ell
jun
ctio
n0
.00
1.5
28
3.4
51
08
26
0
GO
:19
02
49
4ca
taly
tic
com
ple
x0
.00
1.3
02
13
.13
25
16
64
GO
:00
10
49
4cy
top
lasm
icst
ress
gra
nu
le0
.00
3.4
79
.31
18
29
Tab
le1
2:
Cel
lula
rC
om
po
nen
tG
ene
On
tolo
gy
En
rich
men
tfo
rS
tem
Lo
op
Pre
-Mic
roR
NA
Tar
get
s.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
e
On
tolo
gy
Co
nso
rtiu
mo
nto
log
yd
atab
ase,
and
may
be
sear
ched
via
the
AM
IG
Oin
terf
acehttp://amigo.geneontology.org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 60
Pag
e 23
0
Jet Fuel and Solvents Micro RNA Arrays
4.4 Small Nucleolar Micro RNAs
Differentially expressed small nucleolar RNA species are shown in Table 13. The
majority of these differences are associated with the comparison between the hgigh
exposure group (Group 1) and the control group (Group 4).
Many of the probe sets are associated with U3 or U13. Unusually for small nucleolar
micro RNAs, neither of these micro RNAs are involved in methylation or pseudouridy-
lation; instead both are involved in ribosomal RNA cleavage[11]. There is no obvious
clinical relevance of this finding, given current knowledge.
Commercial In Confidence 61
Page 231
Jet Fuel and Solvents Micro RNA Arrays
ID logFC AveExpr t P.Value adj.P.Val B Contrast
U3 0.16 1.18 3.58 0.00 0.03 -0.13 G1 v Control
snoU13 -0.18 1.29 -3.35 0.00 0.05 -0.84 G1 v Control
SNORD78 -0.19 1.21 -3.49 0.00 0.04 -0.41 G1 v Control
snoU13 -0.21 2.25 -3.45 0.00 0.04 -0.52 G1 v Control
SNORD11 0.23 1.23 4.48 0.00 0.01 3.04 G1 v Control
snoU13 0.24 1.68 3.54 0.00 0.04 -0.24 G1 v Control
snoZ40 0.26 1.31 3.86 0.00 0.02 0.80 G1 v Control
SNORA73 0.26 1.32 3.80 0.00 0.02 0.58 G1 v Control
SNORA1 0.27 1.28 3.61 0.00 0.03 -0.03 G1 v Control
SNORD112 0.29 1.31 3.31 0.00 0.05 -0.96 G1 v Control
U3 0.33 1.36 4.21 0.00 0.01 2.00 G1 v Control
SNORA43 0.33 1.38 3.65 0.00 0.03 0.11 G1 v Control
SNORD112 0.36 1.55 3.32 0.00 0.05 -0.91 G1 v Control
snoU13 0.37 1.67 3.45 0.00 0.04 -0.52 G1 v Control
snoU13 0.37 2.00 3.31 0.00 0.05 -0.94 G1 v Control
SNORD112 0.38 1.32 4.00 0.00 0.02 1.27 G1 v Control
SNORD28 0.38 1.51 4.02 0.00 0.02 1.34 G1 v Control
SNORA81 0.40 1.45 3.33 0.00 0.05 -0.89 G1 v Control
snoU13 0.45 1.48 3.72 0.00 0.03 0.33 G1 v Control
SNORA31 0.45 1.67 3.94 0.00 0.02 1.07 G1 v Control
SNORA41 0.46 1.79 3.65 0.00 0.03 0.10 G1 v Control
SNORD113 0.46 1.39 3.84 0.00 0.02 0.73 G1 v Control
SNORD112 0.52 1.45 4.75 0.00 0.01 4.09 G1 v Control
SNORA1 0.52 1.49 3.51 0.00 0.04 -0.36 G1 v Control
SNORA31 0.61 1.72 3.50 0.00 0.04 -0.36 G1 v Control
U3 0.64 2.22 3.75 0.00 0.03 0.42 G1 v Control
U3 0.72 1.87 3.45 0.00 0.04 -0.53 G1 v Control
U3 0.72 1.73 3.85 0.00 0.02 0.77 G1 v Control
U3 0.77 3.70 3.36 0.00 0.05 -0.79 G1 v Control
snoU13 0.95 6.36 3.38 0.00 0.05 -0.73 G1 v Control
U3 0.98 3.25 3.43 0.00 0.04 -0.60 G1 v Control
SNORA1 1.07 2.03 4.48 0.00 0.01 3.03 G1 v Control
snosnR66 1.08 2.63 3.42 0.00 0.04 -0.63 G1 v Control
SNORA43 1.28 2.20 3.70 0.00 0.03 0.25 G1 v Control
U3 1.28 3.25 3.54 0.00 0.04 -0.25 G1 v Control
U3 1.29 3.03 3.85 0.00 0.02 0.77 G1 v Control
SNORA43 1.33 2.28 3.92 0.00 0.02 1.00 G1 v Control
SNORD50 1.62 2.09 4.36 0.00 0.01 2.57 G1 v Control
U3 0.32 1.54 4.07 0.00 0.04 1.44 G2 v Control
snoU13 0.42 1.98 4.09 0.00 0.04 1.51 G2 v Control
snoU13 0.44 1.45 4.32 0.00 0.04 2.30 G2 v Control
SNORD11 0.31 1.23 5.25 0.00 0.00 5.81 Group 3 v Control
ACA64 0.37 2.54 4.08 0.00 0.04 1.46 Group 3 v Control
U8 0.43 1.76 4.09 0.00 0.04 1.48 Group 3 v Control
SNORA70 0.44 2.45 3.96 0.00 0.04 1.03 Group 3 v Control
Table 13: Differentially Expressed Small Nucleolar Micro RNA Species. The ID
column is the micro RNA name as defined in the miRBase database[4] accessed via
http://www.mirbase.org/.
Commercial In Confidence 62
Page 232
Jet Fuel and Solvents Micro RNA Arrays
5 Relationship With Health Scores
The empirical Bayes analysis was repeated using a model involving regression of each
micro RNA species on the health score. Three mature micro RNA species were sig-
nificant following Benjamini Hochberg adjustment to a 0.05 false discovery rate. No
stem-loop or small nucleolar micro RNA species were statistically significant. The sta-
tistically significant mature micro RNA species are shown in Table 14. Note that the
log fold change is for a unit change in total health questionnaire score; the inter-quartile
range for the total score is approximately 20.
ID logFC AveExpr t P.Value adj.P.Val B
hsa-miR-103 -0.01 11.95 -4.33 0.00 0.02 0.46
hsa-miR-107 -0.01 11.59 -4.18 0.00 0.02 -0.11
hsa-miR-200b 0.01 1.38 4.21 0.00 0.02 -0.01
Table 14: Mature Micro RNA Species With Statistically Significant Relationship
With Health Score Following Benjamini Hochberg Adjustment. The ID column is
the micro RNA name as defined in the miRBase database[4] accessed via http://www.mirbase.org/.
The three statistically significant miRNA species from Table 14 are associated with
4003 mRNA targets. Gene Ontology enrichment analyses for the target mRNAs are
shown in Tables 15, 16, and 17 for Biological Process, Molecular Function and Cellular
Component domains respectively.
We note the for the Biological process domain there are a number of terms associated
with RNA metabolic processes and transcription control, and also terms associated
with central nervous system development and function. A primary ontology association
with CNS development is not necessarily inconsistent with a role in CNS function and
cognition in adulthood.
The Molecular Function domain shows enrichment in several ontology terms associ-
ated with chromatin and transcription factor binding. There are no obvious groupings
in the Cellular Component domain.
Commercial In Confidence 63
Page 233
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
48
85
6an
ato
mic
alst
ruct
ure
dev
elo
pm
ent
0.0
01
.32
95
4.9
41
09
23
45
2
GO
:00
32
50
2d
evel
op
men
tal
pro
cess
0.0
01
.30
11
15
.38
12
56
40
32
GO
:00
44
76
7si
ng
le-o
rgan
ism
dev
elo
pm
enta
lp
roce
ss0
.00
1.3
01
10
4.3
21
24
33
99
2
GO
:00
09
65
3an
ato
mic
alst
ruct
ure
mo
rph
og
enes
is0
.00
1.3
85
26
.99
63
51
90
5
GO
:00
22
00
8n
euro
gen
esis
0.0
01
.49
30
7.3
43
92
11
11
GO
:00
07
39
9n
ervo
us
syst
emd
evel
op
men
t0
.00
1.4
04
48
.97
54
81
62
3
GO
:00
48
46
8ce
lld
evel
op
men
t0
.00
1.4
24
00
.01
49
41
44
6
GO
:00
31
32
3re
gu
lati
on
of
cell
ula
rm
etab
oli
cp
roce
ss0
.00
1.2
91
07
5.8
21
20
83
88
9
GO
:00
30
18
2n
euro
nd
iffe
ren
tiat
ion
0.0
01
.51
26
9.1
63
48
97
3
GO
:00
60
25
5re
gu
lati
on
of
mac
rom
ole
cule
met
abo
lic
pro
cess
0.0
01
.29
99
0.3
41
11
93
58
0
GO
:00
48
69
9g
ener
atio
no
fn
euro
ns
0.0
01
.49
29
1.0
23
72
10
52
GO
:00
48
81
2n
euro
np
roje
ctio
nm
orp
ho
gen
esis
0.0
01
.69
15
1.3
22
11
54
7
GO
:0007275
mult
icel
lula
rorg
anis
mal
dev
elopm
ent
0.0
01.2
8982.0
51107
3550
GO
:00
06
46
4ce
llu
lar
pro
tein
mo
difi
cati
on
pro
cess
0.0
01
.33
63
1.2
87
39
22
82
GO
:00
36
21
1p
rote
inm
od
ifica
tio
np
roce
ss0
.00
1.3
36
31
.28
73
92
28
2
GO
:00
10
62
8p
osi
tive
reg
ula
tio
no
fg
ene
exp
ress
ion
0.0
01
.48
27
9.6
83
57
10
11
GO
:00
50
79
4re
gu
lati
on
of
cell
ula
rp
roce
ss0
.00
1.2
61
82
3.2
91
95
56
59
1
GO
:00
03
00
7h
eart
mo
rph
og
enes
is0
.00
2.4
14
5.9
27
91
66
GO
:00
32
99
0ce
llp
art
mo
rph
og
enes
is0
.00
1.5
91
85
.07
24
86
69
GO
:00
45
89
3p
osi
tive
reg
ula
tio
no
ftr
ansc
rip
tio
n,
DN
A-t
emp
late
d0
.00
1.4
92
56
.72
32
99
28
GO
:00
48
85
8ce
llp
roje
ctio
nm
orp
ho
gen
esis
0.0
01
.58
18
2.5
82
44
66
0
GO
:00
00
90
4ce
llm
orp
ho
gen
esis
invo
lved
ind
iffe
ren
tiat
ion
0.0
01
.57
18
7.0
02
49
67
6
GO
:00
19
22
2re
gu
lati
on
of
met
abo
lic
pro
cess
0.0
01
.25
11
89
.24
13
13
42
99
GO
:00
43
41
2m
acro
mo
lecu
lem
od
ifica
tio
n0
.00
1.3
16
52
.02
75
62
35
7
GO
:00
48
73
1sy
stem
dev
elo
pm
ent
0.0
01
.28
81
8.8
39
31
29
60
Tab
le1
5:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Mat
ure
Mic
roR
NA
Tar
get
sR
elat
ion
ship
Wit
hH
ealt
hS
core
.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.
org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 64
Pag
e 23
4
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
80
09
0re
gu
lati
on
of
pri
mar
ym
etab
oli
cp
roce
ss0
.00
1.2
61
04
7.6
11
16
73
78
7
GO
:00
10
60
4p
osi
tive
reg
ula
tio
no
fm
acro
mo
lecu
lem
etab
oli
cp
roce
ss0
.00
1.3
44
79
.96
57
11
73
5
GO
:00
07
50
7h
eart
dev
elo
pm
ent
0.0
01
.80
99
.59
14
53
60
GO
:00
51
25
2re
gu
lati
on
of
RN
Am
etab
oli
cp
roce
ss0
.00
1.3
06
81
.35
78
52
46
3
GO
:00
31
17
5n
euro
np
roje
ctio
nd
evel
op
men
t0
.00
1.5
41
90
.32
25
16
88
GO
:00
50
78
9re
gu
lati
on
of
bio
log
ical
pro
cess
0.0
01
.25
19
14
.57
20
36
69
21
GO
:00
48
86
9ce
llu
lar
dev
elo
pm
enta
lp
roce
ss0
.00
1.2
87
34
.74
84
02
65
6
GO
:00
32
98
9ce
llu
lar
com
po
nen
tm
orp
ho
gen
esis
0.0
01
.45
26
8.0
63
38
96
9
GO
:00
09
89
3p
osi
tive
reg
ula
tio
no
fm
etab
oli
cp
roce
ss0
.00
1.3
25
23
.94
61
61
89
4
GO
:00
51
25
4p
osi
tive
reg
ula
tio
no
fR
NA
met
abo
lic
pro
cess
0.0
01
.44
27
1.1
03
41
98
0
GO
:00
48
66
6n
euro
nd
evel
op
men
t0
.00
1.5
51
81
.75
24
06
57
GO
:00
48
66
7ce
llm
orp
ho
gen
esis
invo
lved
inn
euro
nd
iffe
ren
tiat
ion
0.0
01
.68
12
4.2
11
73
44
9
GO
:00
30
15
4ce
lld
iffe
ren
tiat
ion
0.0
01
.28
69
2.1
37
93
25
02
GO
:00
00
90
2ce
llm
orp
ho
gen
esis
0.0
01
.45
25
4.7
83
22
92
1
GO
:00
19
21
9re
gu
lati
on
of
nu
cleo
bas
e-co
nta
inin
gco
mp
ou
nd
met
abo
lic
pro
cess
0.0
01
.27
81
8.5
69
25
29
59
GO
:00
10
46
8re
gu
lati
on
of
gen
eex
pre
ssio
n0
.00
1.2
78
04
.17
91
02
90
7
GO
:00
65
00
7b
iolo
gic
alre
gu
lati
on
0.0
01
.25
20
23
.01
21
39
73
13
GO
:20
00
11
2re
gu
lati
on
of
cell
ula
rm
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.28
71
8.1
48
20
25
96
GO
:00
06
35
5re
gu
lati
on
of
tran
scri
pti
on
,D
NA
-tem
pla
ted
0.0
01
.29
65
2.0
27
50
23
57
GO
:00
31
32
5p
osi
tive
reg
ula
tio
no
fce
llu
lar
met
abo
lic
pro
cess
0.0
01
.32
49
7.3
95
85
17
98
GO
:00
07
40
9ax
on
og
enes
is0
.00
1.6
31
32
.78
18
24
80
GO
:20
01
14
1re
gu
lati
on
of
RN
Ab
iosy
nth
etic
pro
cess
0.0
01
.28
66
0.6
07
58
23
88
GO
:00
44
26
7ce
llu
lar
pro
tein
met
abo
lic
pro
cess
0.0
01
.26
79
5.3
28
99
28
75
GO
:00
61
56
4ax
on
dev
elo
pm
ent
0.0
01
.65
12
2.2
71
69
44
2
GO
:19
02
68
0p
osi
tive
reg
ula
tio
no
fR
NA
bio
syn
thet
icp
roce
ss0
.00
1.4
22
65
.84
33
29
61
GO
:00
44
26
0ce
llu
lar
mac
rom
ole
cule
met
abo
lic
pro
cess
0.0
01
.22
15
18
.71
16
35
54
90
Tab
le1
5:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Mat
ure
Mic
roR
NA
Tar
get
sR
elat
ion
ship
Wit
hH
ealt
hS
core
.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.
org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 65
Pag
e 23
5
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
09
88
7o
rgan
mo
rph
og
enes
is0
.00
1.4
91
97
.52
25
57
14
GO
:00
10
60
5n
egat
ive
reg
ula
tio
no
fm
acro
mo
lecu
lem
etab
oli
cp
roce
ss0
.00
1.3
63
59
.35
43
41
29
9
GO
:00
06
35
7re
gu
lati
on
of
tran
scri
pti
on
fro
mR
NA
po
lym
eras
eII
pro
mo
ter
0.0
01
.37
32
6.9
83
98
11
82
GO
:00
03
20
5ca
rdia
cch
amb
erd
evel
op
men
t0
.00
2.6
42
8.7
75
21
04
GO
:00
31
32
6re
gu
lati
on
of
cell
ula
rb
iosy
nth
etic
pro
cess
0.0
01
.26
77
2.3
68
71
27
92
GO
:00
51
17
1re
gu
lati
on
of
nit
rog
enco
mp
ou
nd
met
abo
lic
pro
cess
0.0
01
.25
83
8.7
59
40
30
32
GO
:00
07
16
5si
gn
altr
ansd
uct
ion
0.0
01
.23
10
24
.09
11
31
37
02
GO
:00
09
79
0em
bry
od
evel
op
men
t0
.00
1.4
42
28
.78
28
98
27
GO
:00
10
55
6re
gu
lati
on
of
mac
rom
ole
cule
bio
syn
thet
icp
roce
ss0
.00
1.2
67
40
.27
83
72
67
6
GO
:00
72
35
8ca
rdio
vas
cula
rsy
stem
dev
elo
pm
ent
0.0
01
.51
17
7.6
02
31
64
2
GO
:00
72
35
9ci
rcu
lato
rysy
stem
dev
elo
pm
ent
0.0
01
.51
17
7.6
02
31
64
2
GO
:00
48
56
6em
bry
on
icd
iges
tive
trac
td
evel
op
men
t0
.00
5.7
98
.85
22
32
GO
:00
03
27
9ca
rdia
cse
ptu
md
evel
op
men
t0
.00
3.6
21
5.7
73
35
7
GO
:00
09
88
9re
gu
lati
on
of
bio
syn
thet
icp
roce
ss0
.00
1.2
57
80
.10
87
72
82
0
GO
:00
23
05
2si
gn
alin
g0
.00
1.2
21
13
6.6
81
24
44
10
9
GO
:0044700
single
org
anis
msi
gnal
ing
0.0
01.2
21136.6
81244
4109
GO
:00
48
59
8em
bry
on
icm
orp
ho
gen
esis
0.0
01
.60
12
4.7
61
69
45
1
GO
:00
45
93
5p
osi
tive
reg
ula
tio
no
fn
ucl
eob
ase-
con
tain
ing
com
po
un
dm
etab
oli
cp
ro-
cess
0.0
01
.37
30
2.3
63
68
10
93
GO
:00
48
64
6an
ato
mic
alst
ruct
ure
form
atio
nin
vo
lved
inm
orp
ho
gen
esis
0.0
01
.44
21
1.0
72
67
76
3
GO
:0030030
cell
pro
ject
ion
org
aniz
atio
n0.0
01.4
0256.9
9318
929
GO
:00
51
17
3p
osi
tive
reg
ula
tio
no
fn
itro
gen
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.3
63
08
.17
37
41
11
4
GO
:00
09
96
6re
gu
lati
on
of
sig
nal
tran
sdu
ctio
n0
.00
1.2
95
06
.51
58
71
83
1
GO
:00
32
26
8re
gu
lati
on
of
cell
ula
rp
rote
inm
etab
oli
cp
roce
ss0
.00
1.3
43
41
.36
40
91
23
4
GO
:00
07
15
4ce
llco
mm
un
icat
ion
0.0
01
.21
11
52
.17
12
55
41
65
GO
:20
00
11
3n
egat
ive
reg
ula
tio
no
fce
llu
lar
mac
rom
ole
cule
bio
syn
thet
icp
roce
ss0
.00
1.4
12
36
.80
29
48
56
Tab
le1
5:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Mat
ure
Mic
roR
NA
Tar
get
sR
elat
ion
ship
Wit
hH
ealt
hS
core
.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.
org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 66
Pag
e 23
6
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
48
51
8p
osi
tive
reg
ula
tio
no
fb
iolo
gic
alp
roce
ss0
.00
1.2
29
10
.67
10
07
32
92
GO
:00
01
94
4vas
cula
ture
dev
elo
pm
ent
0.0
01
.55
13
5.0
01
79
48
8
GO
:00
43
00
9ch
ord
ate
emb
ryo
nic
dev
elo
pm
ent
0.0
01
.53
13
9.4
21
84
50
4
GO
:00
31
32
8p
osi
tive
reg
ula
tio
no
fce
llu
lar
bio
syn
thet
icp
roce
ss0
.00
1.3
43
18
.96
38
31
15
3
GO
:00
06
35
1tr
ansc
rip
tio
n,
DN
A-t
emp
late
d0
.00
1.2
47
19
.52
80
72
60
1
GO
:00
10
55
7p
osi
tive
reg
ula
tio
no
fm
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.35
30
0.4
23
62
10
86
GO
:00
01
56
8b
loo
dves
sel
dev
elo
pm
ent
0.0
01
.61
10
4.8
41
43
37
9
GO
:00
31
32
4n
egat
ive
reg
ula
tio
no
fce
llu
lar
met
abo
lic
pro
cess
0.0
01
.32
36
0.7
34
27
13
04
GO
:00
07
41
7ce
ntr
aln
ervo
us
syst
emd
evel
op
men
t0
.00
1.4
41
84
.79
23
46
68
GO
:00
51
60
3p
rote
oly
sis
invo
lved
ince
llu
lar
pro
tein
cata
bo
lic
pro
cess
0.0
01
.60
10
7.6
11
46
38
9
GO
:0048562
embry
onic
org
anm
orp
hogen
esis
0.0
01.8
164.7
395
234
GO
:00
43
63
2m
od
ifica
tio
n-d
epen
den
tm
acro
mo
lecu
leca
tab
oli
cp
roce
ss0
.00
1.6
11
02
.63
14
03
71
GO
:00
48
52
3n
egat
ive
reg
ula
tio
no
fce
llu
lar
pro
cess
0.0
01
.23
73
9.1
68
26
26
72
GO
:00
09
79
2em
bry
od
evel
op
men
ten
din
gin
bir
tho
reg
gh
atch
ing
0.0
01
.51
14
0.8
11
84
50
9
GO
:00
48
52
2p
osi
tive
reg
ula
tio
no
fce
llu
lar
pro
cess
0.0
01
.22
81
8.0
09
07
29
57
GO
:00
09
89
1p
osi
tive
reg
ula
tio
no
fb
iosy
nth
etic
pro
cess
0.0
01
.33
32
4.7
73
87
11
74
GO
:00
06
36
6tr
ansc
rip
tio
nfr
om
RN
Ap
oly
mer
ase
IIp
rom
ote
r0
.00
1.3
03
70
.41
43
61
33
9
GO
:00
10
62
9n
egat
ive
reg
ula
tio
no
fg
ene
exp
ress
ion
0.0
01
.38
23
4.3
12
88
84
7
GO
:00
48
51
9n
egat
ive
reg
ula
tio
no
fb
iolo
gic
alp
roce
ss0
.00
1.2
28
02
.23
89
02
90
0
GO
:00
10
55
8n
egat
ive
reg
ula
tio
no
fm
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.37
24
7.3
13
02
89
4
GO
:00
10
64
6re
gu
lati
on
of
cell
com
mu
nic
atio
n0
.00
1.2
55
67
.93
64
52
05
3
GO
:00
19
94
1m
od
ifica
tio
n-d
epen
den
tp
rote
inca
tab
oli
cp
roce
ss0
.00
1.5
91
01
.80
13
83
68
GO
:00
45
89
2n
egat
ive
reg
ula
tio
no
ftr
ansc
rip
tio
n,
DN
A-t
emp
late
d0
.00
1.4
02
08
.86
25
97
55
GO
:00
23
05
1re
gu
lati
on
of
sig
nal
ing
0.0
01
.25
56
5.7
16
42
20
45
GO
:00
30
90
0fo
reb
rain
dev
elo
pm
ent
0.0
01
.66
84
.10
11
73
04
GO
:00
10
46
7g
ene
exp
ress
ion
0.0
01
.19
11
46
.09
12
41
41
43
Tab
le1
5:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Mat
ure
Mic
roR
NA
Tar
get
sR
elat
ion
ship
Wit
hH
ealt
hS
core
.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.
org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 67
Pag
e 23
7
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
30
16
3p
rote
inca
tab
oli
cp
roce
ss0
.00
1.4
91
38
.59
18
05
01
GO
:00
09
89
2n
egat
ive
reg
ula
tio
no
fm
etab
oli
cp
roce
ss0
.00
1.2
93
90
.61
45
61
41
2
GO
:00
48
58
9d
evel
op
men
tal
gro
wth
0.0
01
.71
73
.31
10
42
65
GO
:00
06
46
8p
rote
inp
ho
sph
ory
lati
on
0.0
01
.33
28
6.8
73
44
10
37
GO
:00
51
25
3n
egat
ive
reg
ula
tio
no
fR
NA
met
abo
lic
pro
cess
0.0
01
.38
22
0.2
02
71
79
6
GO
:00
06
51
1u
biq
uit
in-d
epen
den
tp
rote
inca
tab
oli
cp
roce
ss0
.00
1.5
81
01
.52
13
73
67
GO
:00
51
71
6ce
llu
lar
resp
on
seto
stim
ulu
s0
.00
1.1
91
23
4.3
31
32
94
46
2
GO
:0048568
embry
onic
org
andev
elopm
ent
0.0
01.6
094.8
8129
343
GO
:00
45
93
4n
egat
ive
reg
ula
tio
no
fn
ucl
eob
ase-
con
tain
ing
com
po
un
dm
etab
oli
cp
ro-
cess
0.0
01
.36
24
1.7
82
94
87
4
GO
:00
31
32
7n
egat
ive
reg
ula
tio
no
fce
llu
lar
bio
syn
thet
icp
roce
ss0
.00
1.3
52
58
.37
31
29
34
GO
:0035264
mult
icel
lula
rorg
anis
mgro
wth
0.0
02.2
430.1
550
109
GO
:00
32
26
9n
egat
ive
reg
ula
tio
no
fce
llu
lar
pro
tein
met
abo
lic
pro
cess
0.0
01
.53
11
5.9
11
53
41
9
GO
:00
07
41
1ax
on
gu
idan
ce0
.00
1.6
09
2.6
71
26
33
5
GO
:00
97
48
5n
euro
np
roje
ctio
ng
uid
ance
0.0
01
.60
92
.67
12
63
35
GO
:19
02
67
9n
egat
ive
reg
ula
tio
no
fR
NA
bio
syn
thet
icp
roce
ss0
.00
1.3
82
13
.28
26
27
71
GO
:00
01
93
3n
egat
ive
reg
ula
tio
no
fp
rote
inp
ho
sph
ory
lati
on
0.0
01
.79
57
.54
84
20
8
GO
:00
09
89
0n
egat
ive
reg
ula
tio
no
fb
iosy
nth
etic
pro
cess
0.0
01
.34
26
1.1
43
14
94
4
GO
:00
03
20
6ca
rdia
cch
amb
erm
orp
ho
gen
esis
0.0
02
.41
24
.34
42
88
GO
:00
32
77
4R
NA
bio
syn
thet
icp
roce
ss0
.00
1.2
17
36
.40
81
62
66
2
GO
:00
43
17
0m
acro
mo
lecu
lem
etab
oli
cp
roce
ss0
.00
1.1
81
66
4.5
01
75
86
01
7
GO
:00
44
25
7ce
llu
lar
pro
tein
cata
bo
lic
pro
cess
0.0
01
.57
96
.82
13
03
50
GO
:00
07
16
7en
zym
eli
nked
rece
pto
rp
rote
insi
gn
alin
gp
ath
way
0.0
01
.35
23
2.9
22
82
84
2
GO
:00
03
28
1ven
tric
ula
rse
ptu
md
evel
op
men
t0
.00
3.5
61
1.0
72
34
0
GO
:00
31
07
6em
bry
on
icca
mer
a-ty
pe
eye
dev
elo
pm
ent
0.0
04
.04
9.1
32
03
3
GO
:0060485
mes
ench
ym
edev
elopm
ent
0.0
02.0
634.0
354
123
Tab
le1
5:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Mat
ure
Mic
roR
NA
Tar
get
sR
elat
ion
ship
Wit
hH
ealt
hS
core
.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.
org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 68
Pag
e 23
8
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
51
17
2n
egat
ive
reg
ula
tio
no
fn
itro
gen
com
po
un
dm
etab
oli
cp
roce
ss0
.00
1.3
32
46
.20
29
68
90
GO
:00
38
09
5F
c-ep
silo
nre
cep
tor
sig
nal
ing
pat
hw
ay0
.00
1.9
24
2.0
56
41
52
GO
:00
48
81
3d
end
rite
mo
rph
og
enes
is0
.00
2.4
42
1.8
53
87
9
GO
:00
03
23
1ca
rdia
cven
tric
led
evel
op
men
t0
.00
2.4
42
1.8
53
87
9
GO
:2000026
regula
tion
of
mult
icel
lula
rorg
anis
mal
dev
elopm
ent
0.0
01.3
0298.2
1352
1078
GO
:0016043
cell
ula
rco
mponen
torg
aniz
atio
n0.0
01.1
81100.4
41187
3978
GO
:00
35
55
6in
trac
ellu
lar
sig
nal
tran
sdu
ctio
n0
.00
1.2
44
94
.62
56
11
78
8
GO
:00
21
91
5n
eura
ltu
be
dev
elo
pm
ent
0.0
02
.08
32
.64
52
11
8
GO
:00
50
79
3re
gu
lati
on
of
dev
elo
pm
enta
lp
roce
ss0
.00
1.2
63
88
.95
44
81
40
6
GO
:00
07
61
0b
ehav
ior
0.0
01
.45
13
2.5
11
69
47
9
GO
:00
02
00
9m
orp
ho
gen
esis
of
anep
ith
eliu
m0
.00
1.5
49
5.7
11
27
34
6
GO
:00
40
01
2re
gu
lati
on
of
loco
mo
tio
n0
.00
1.4
51
26
.70
16
24
58
GO
:00
32
87
9re
gu
lati
on
of
loca
liza
tio
n0
.00
1.2
63
72
.90
43
01
34
8
GO
:0044707
single
-mult
icel
lula
rorg
anis
mpro
cess
0.0
01.1
71243.4
61329
4495
GO
:2000027
regula
tion
of
org
anm
orp
hogen
esis
0.0
01.9
139.5
660
143
GO
:00
48
84
4ar
tery
mo
rph
og
enes
is0
.00
3.1
51
2.1
72
44
4
GO
:00
07
42
0b
rain
dev
elo
pm
ent
0.0
01
.43
14
1.0
81
78
51
0
GO
:00
06
46
9n
egat
ive
reg
ula
tio
no
fp
rote
ink
inas
eac
tiv
ity
0.0
01
.87
42
.05
63
15
2
GO
:00
10
60
8p
ost
tran
scri
pti
on
alre
gu
lati
on
of
gen
eex
pre
ssio
n0
.00
1.5
29
7.9
31
29
35
4
GO
:00
34
65
4n
ucl
eob
ase-
con
tain
ing
com
po
un
db
iosy
nth
etic
pro
cess
0.0
01
.19
80
1.1
38
77
28
96
GO
:00
45
59
5re
gu
lati
on
of
cell
dif
fere
nti
atio
n0
.00
1.2
92
82
.72
33
31
02
2
GO
:0071840
cell
ula
rco
mponen
torg
aniz
atio
nor
bio
gen
esis
0.0
01.1
71117.8
71201
4041
GO
:00
33
67
3n
egat
ive
reg
ula
tio
no
fk
inas
eac
tiv
ity
0.0
01
.82
45
.37
67
16
4
GO
:00
45
94
4p
osi
tive
reg
ula
tio
no
ftr
ansc
rip
tio
nfr
om
RN
Ap
oly
mer
ase
IIp
rom
ote
r0
.00
1.3
71
81
.19
22
26
55
GO
:00
44
70
8si
ng
le-o
rgan
ism
beh
avio
r0
.00
1.5
97
7.4
61
05
28
0
GO
:00
40
00
7g
row
th0
.00
1.3
51
93
.09
23
56
98
Tab
le1
5:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Mat
ure
Mic
roR
NA
Tar
get
sR
elat
ion
ship
Wit
hH
ealt
hS
core
.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.
org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 69
Pag
e 23
9
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
01
94
7h
eart
loo
pin
g0
.00
3.1
81
1.6
22
34
2
GO
:00
55
02
4re
gu
lati
on
of
card
iac
mu
scle
tiss
ue
dev
elo
pm
ent
0.0
03
.18
11
.62
23
42
GO
:00
50
76
7re
gu
lati
on
of
neu
rog
enes
is0
.00
1.4
61
15
.91
14
94
19
GO
:00
48
51
3o
rgan
dev
elo
pm
ent
0.0
01
.21
57
1.8
06
38
20
67
GO
:00
48
72
9ti
ssu
em
orp
ho
gen
esis
0.0
01
.46
11
8.6
81
52
42
9
GO
:00
16
31
0p
ho
sph
ory
lati
on
0.0
01
.26
36
2.1
14
17
13
09
GO
:20
00
14
5re
gu
lati
on
of
cell
mo
tili
ty0
.00
1.4
61
17
.02
15
04
23
GO
:00
23
05
7n
egat
ive
reg
ula
tio
no
fsi
gn
alin
g0
.00
1.3
42
03
.60
24
67
36
GO
:00
09
96
8n
egat
ive
reg
ula
tio
no
fsi
gn
altr
ansd
uct
ion
0.0
01
.35
19
4.4
72
36
70
3
GO
:00
03
15
7en
do
card
ium
dev
elo
pm
ent
0.0
02
0.9
72
.49
89
GO
:00
44
26
5ce
llu
lar
mac
rom
ole
cule
cata
bo
lic
pro
cess
0.0
01
.37
17
3.7
32
13
62
8
GO
:00
40
01
1lo
com
oti
on
0.0
01
.27
31
8.6
83
70
11
52
GO
:00
00
12
2n
egat
ive
reg
ula
tio
no
ftr
ansc
rip
tio
nfr
om
RN
Ap
oly
mer
ase
IIp
rom
ote
r0
.00
1.4
21
33
.34
16
84
82
GO
:00
18
13
0h
eter
ocy
cle
bio
syn
thet
icp
roce
ss0
.00
1.1
88
15
.24
88
92
94
7
GO
:00
33
03
6m
acro
mo
lecu
lelo
cali
zati
on
0.0
01
.22
46
6.9
65
27
16
88
GO
:00
07
16
9tr
ansm
emb
ran
ere
cep
tor
pro
tein
tyro
sin
ek
inas
esi
gn
alin
gp
ath
way
0.0
01
.37
16
7.6
42
06
60
6
GO
:00
19
53
8p
rote
inm
etab
oli
cp
roce
ss0
.00
1.1
79
35
.29
10
12
33
81
GO
:00
06
93
5ch
emo
tax
is0
.00
1.4
01
46
.89
18
35
31
GO
:00
42
33
0ta
xis
0.0
01
.40
14
6.8
91
83
53
1
GO
:00
07
16
6ce
llsu
rfac
ere
cep
tor
sig
nal
ing
pat
hw
ay0
.00
1.2
05
90
.33
65
62
13
4
GO
:00
10
64
8n
egat
ive
reg
ula
tio
no
fce
llco
mm
un
icat
ion
0.0
01
.33
20
4.1
52
46
73
8
GO
:00
51
27
0re
gu
lati
on
of
cell
ula
rco
mp
on
ent
movem
ent
0.0
01
.42
13
3.6
11
68
48
3
GO
:00
48
73
6ap
pen
dag
ed
evel
op
men
t0
.00
1.8
73
8.7
35
81
40
GO
:00
60
17
3li
mb
dev
elo
pm
ent
0.0
01
.87
38
.73
58
14
0
GO
:00
44
27
1ce
llu
lar
nit
rog
enco
mp
ou
nd
bio
syn
thet
icp
roce
ss0
.00
1.1
88
29
.62
90
32
99
9
GO
:00
38
09
3F
cre
cep
tor
sig
nal
ing
pat
hw
ay0
.00
1.6
85
6.1
67
92
03
Tab
le1
5:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Mat
ure
Mic
roR
NA
Tar
get
sR
elat
ion
ship
Wit
hH
ealt
hS
core
.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.
org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 70
Pag
e 24
0
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:0032501
mult
icel
lula
rorg
anis
mal
pro
cess
0.0
01.1
61285.5
11367
4647
GO
:00
03
15
1o
utfl
ow
trac
tm
orp
ho
gen
esis
0.0
03
.02
11
.90
23
43
GO
:00
60
60
2b
ran
chel
on
gat
ion
of
anep
ith
eliu
m0
.00
7.2
14
.15
11
15
GO
:00
51
24
6re
gu
lati
on
of
pro
tein
met
abo
lic
pro
cess
0.0
01
.24
38
3.4
14
38
13
86
GO
:00
48
59
6em
bry
on
icca
mer
a-ty
pe
eye
mo
rph
og
enes
is0
.00
4.2
07
.19
16
26
GO
:00
19
43
8ar
om
atic
com
po
un
db
iosy
nth
etic
pro
cess
0.0
01
.18
81
6.3
48
89
29
51
GO
:00
16
07
0R
NA
met
abo
lic
pro
cess
0.0
01
.17
87
7.7
69
52
31
73
GO
:00
48
51
4b
loo
dves
sel
mo
rph
og
enes
is0
.00
1.5
28
8.8
01
17
32
1
GO
:00
35
29
5tu
be
dev
elo
pm
ent
0.0
01
.46
10
8.9
91
40
39
4
GO
:00
30
33
4re
gu
lati
on
of
cell
mig
rati
on
0.0
01
.45
11
1.7
61
43
40
4
GO
:0007423
senso
ryorg
andev
elopm
ent
0.0
01.4
6109.2
7140
395
GO
:00
43
25
4re
gu
lati
on
of
pro
tein
com
ple
xas
sem
bly
0.0
01
.68
54
.77
77
19
8
GO
:00
48
58
3re
gu
lati
on
of
resp
on
seto
stim
ulu
s0
.00
1.1
96
49
.81
71
62
34
9
GO
:00
32
88
0re
gu
lati
on
of
pro
tein
loca
liza
tio
n0
.00
1.4
51
11
.21
14
24
02
GO
:00
60
41
1ca
rdia
cse
ptu
mm
orp
ho
gen
esis
0.0
03
.04
11
.34
22
41
GO
:00
48
63
8re
gu
lati
on
of
dev
elo
pm
enta
lg
row
th0
.00
1.9
43
2.6
45
01
18
GO
:00
01
70
1in
ute
roem
bry
on
icd
evel
op
men
t0
.00
1.5
28
4.1
01
11
30
4
GO
:00
09
05
9m
acro
mo
lecu
leb
iosy
nth
etic
pro
cess
0.0
01
.16
96
4.6
21
03
93
48
7
GO
:00
60
84
0ar
tery
dev
elo
pm
ent
0.0
02
.74
13
.55
25
49
GO
:00
60
83
9en
do
thel
ial
cell
fate
com
mit
men
t0
.00
Inf
1.6
66
6
GO
:00
34
64
5ce
llu
lar
mac
rom
ole
cule
bio
syn
thet
icp
roce
ss0
.00
1.1
69
37
.51
10
11
33
89
GO
:00
21
53
7te
len
cep
hal
on
dev
elo
pm
ent
0.0
01
.78
42
.60
62
15
4
GO
:00
48
86
4st
emce
lld
evel
op
men
t0
.00
1.7
34
6.7
56
71
69
GO
:00
48
86
3st
emce
lld
iffe
ren
tiat
ion
0.0
01
.57
71
.37
96
25
8
GO
:00
51
24
8n
egat
ive
reg
ula
tio
no
fp
rote
inm
etab
oli
cp
roce
ss0
.00
1.4
01
31
.40
16
44
75
GO
:0006996
org
anel
leorg
aniz
atio
n0.0
01.1
9597.5
3660
2160
Tab
le1
5:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Mat
ure
Mic
roR
NA
Tar
get
sR
elat
ion
ship
Wit
hH
ealt
hS
core
.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.
org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 71
Pag
e 24
1
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
31
39
9re
gu
lati
on
of
pro
tein
mo
difi
cati
on
pro
cess
0.0
01
.27
27
1.9
33
17
98
3
GO
:00
16
35
8d
end
rite
dev
elo
pm
ent
0.0
01
.86
35
.41
53
12
8
GO
:00
70
84
8re
spo
nse
tog
row
thfa
cto
r0
.00
1.3
61
58
.79
19
45
74
GO
:00
03
14
3em
bry
on
ich
eart
tub
em
orp
ho
gen
esis
0.0
02
.74
13
.00
24
47
GO
:00
45
63
9p
osi
tive
reg
ula
tio
no
fm
yel
oid
cell
dif
fere
nti
atio
n0
.00
2.3
91
7.4
33
06
3
GO
:00
48
55
7em
bry
on
icd
iges
tive
trac
tm
orp
ho
gen
esis
0.0
05
.25
4.9
81
21
8
GO
:00
60
04
5p
osi
tive
reg
ula
tio
no
fca
rdia
cm
usc
lece
llp
roli
fera
tio
n0
.00
5.2
54
.98
12
18
GO
:0060317
card
iac
epit
hel
ial
tom
esen
chym
altr
ansi
tion
0.0
05.2
54.9
812
18
GO
:00
09
79
9sp
ecifi
cati
on
of
sym
met
ry0
.00
2.1
22
2.9
63
78
3
GO
:00
03
19
7en
do
card
ial
cush
ion
dev
elo
pm
ent
0.0
03
.94
6.9
21
52
5
GO
:00
31
05
4p
re-m
iRN
Ap
roce
ssin
g0
.00
18
.34
2.2
17
8
GO
:00
16
33
1m
orp
ho
gen
esis
of
emb
ryo
nic
epit
hel
ium
0.0
01
.89
31
.81
48
11
5
GO
:00
43
62
0re
gu
lati
on
of
DN
A-t
emp
late
dtr
ansc
rip
tio
nin
resp
on
seto
stre
ss0
.00
2.9
01
1.0
72
14
0
GO
:00
48
04
8em
bry
on
icey
em
orp
ho
gen
esis
0.0
03
.43
8.3
01
73
0
GO
:00
71
36
3ce
llu
lar
resp
on
seto
gro
wth
fact
or
stim
ulu
s0
.00
1.3
51
54
.36
18
85
58
GO
:00
43
61
8re
gu
lati
on
of
tran
scri
pti
on
fro
mR
NA
po
lym
eras
eII
pro
mo
ter
inre
spo
nse
tost
ress
0.0
03
.12
9.6
81
93
5
GO
:00
01
93
2re
gu
lati
on
of
pro
tein
ph
osp
ho
ryla
tio
n0
.00
1.3
02
12
.18
25
17
67
GO
:00
45
66
4re
gu
lati
on
of
neu
ron
dif
fere
nti
atio
n0
.00
1.4
59
5.1
61
22
34
4
GO
:00
60
25
1re
gu
lati
on
of
gli
alce
llp
roli
fera
tio
n0
.00
6.5
53
.87
10
14
GO
:00
34
50
4p
rote
inlo
cali
zati
on
ton
ucl
eus
0.0
01
.56
64
.73
87
23
4
GO
:00
14
88
7ca
rdia
cm
usc
lead
apta
tio
n0
.00
7.8
63
.32
91
2
GO
:00
45
94
8p
osi
tive
reg
ula
tio
no
ftr
ansl
atio
nal
init
iati
on
0.0
07
.86
3.3
29
12
GO
:00
44
69
9si
ng
le-o
rgan
ism
pro
cess
0.0
01
.19
26
23
.58
26
81
94
84
GO
:00
42
32
5re
gu
lati
on
of
ph
osp
ho
ryla
tio
n0
.00
1.2
62
54
.50
29
69
20
GO
:00
60
28
4re
gu
lati
on
of
cell
dev
elo
pm
ent
0.0
01
.35
14
5.7
91
78
52
7
Tab
le1
5:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Mat
ure
Mic
roR
NA
Tar
get
sR
elat
ion
ship
Wit
hH
ealt
hS
core
.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.
org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 72
Pag
e 24
2
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
09
98
7ce
llu
lar
pro
cess
0.0
01
.25
28
75
.32
29
20
10
39
4
GO
:00
48
58
5n
egat
ive
reg
ula
tio
no
fre
spo
nse
tost
imu
lus
0.0
01
.28
23
0.4
32
70
83
3
GO
:0014031
mes
ench
ym
alce
lldev
elopm
ent
0.0
02.0
124.9
039
90
GO
:00
06
92
8ce
llu
lar
com
po
nen
tm
ovem
ent
0.0
01
.22
35
7.1
34
05
12
91
GO
:1902589
single
-org
anis
morg
anel
leorg
aniz
atio
n0.0
01.2
1396.9
7447
1435
GO
:00
07
38
9p
atte
rnsp
ecifi
cati
on
pro
cess
0.0
01
.42
10
5.4
01
33
38
1
Tab
le1
5:
Bio
log
ical
Pro
cess
Gen
eO
nto
log
yE
nri
chm
ent
for
Mat
ure
Mic
roR
NA
Tar
get
sR
elat
ion
ship
Wit
hH
ealt
hS
core
.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.
org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
05
51
5p
rote
inb
ind
ing
0.0
01
.30
18
38
.01
19
86
66
95
GO
:00
05
48
8b
ind
ing
0.0
01
.41
26
68
.48
27
73
97
20
GO
:0016881
acid
-am
ino
acid
ligas
eac
tivit
y0.0
01.7
171.9
3102
262
GO
:00
04
84
2u
biq
uit
in-p
rote
inli
gas
eac
tiv
ity
0.0
01
.75
64
.52
93
23
5
GO
:0019787
smal
lco
nju
gat
ing
pro
tein
ligas
eac
tivit
y0.0
01.7
168.3
697
249
GO
:00
00
98
2R
NA
po
lym
eras
eII
core
pro
mo
ter
pro
xim
alre
gio
nse
qu
ence
-sp
ecifi
c
DN
Ab
ind
ing
tran
scri
pti
on
fact
or
acti
vit
y
0.0
02
.34
25
.26
43
92
GO
:0016879
ligas
eac
tivit
y,fo
rmin
gca
rbon-n
itro
gen
bonds
0.0
01.6
179.0
7108
288
GO
:00
44
82
2p
oly
(A)
RN
Ab
ind
ing
0.0
01
.31
25
2.5
73
01
92
0
GO
:00
03
67
6n
ucl
eic
acid
bin
din
g0
.00
1.1
97
96
.43
87
22
90
1
GO
:00
03
68
2ch
rom
atin
bin
din
g0
.00
1.5
49
1.1
51
21
33
2
GO
:19
01
36
3h
eter
ocy
clic
com
po
un
db
ind
ing
0.0
01
.16
12
09
.33
12
90
44
05
Tab
le1
6:
Mo
lecu
lar
Fu
nct
ion
Gen
eO
nto
log
yE
nri
chm
ent
for
Mat
ure
Mic
roR
NA
Tar
get
sR
elat
ion
ship
Wit
hH
ealt
hS
core
.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.
org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 73
Pag
e 24
3
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
08
27
0zi
nc
ion
bin
din
g0
.00
1.2
82
67
.95
31
49
76
GO
:00
97
15
9o
rgan
iccy
clic
com
po
un
db
ind
ing
0.0
01
.15
12
25
.25
13
04
44
63
GO
:00
46
87
2m
etal
ion
bin
din
g0
.00
1.1
78
42
.27
91
33
06
8
GO
:00
43
16
9ca
tio
nb
ind
ing
0.0
01
.16
85
6.5
59
27
31
20
GO
:00
00
98
7co
rep
rom
ote
rp
rox
imal
reg
ion
seq
uen
ce-s
pec
ific
DN
Ab
ind
ing
0.0
02
.66
13
.73
25
50
GO
:00
03
72
3R
NA
bin
din
g0
.00
1.2
43
32
.74
38
11
21
2
GO
:00
00
98
1se
qu
ence
-sp
ecifi
cD
NA
bin
din
gR
NA
po
lym
eras
eII
tran
scri
pti
on
fact
or
acti
vit
y
0.0
01
.58
64
.24
87
23
4
GO
:00
43
16
7io
nb
ind
ing
0.0
01
.15
12
74
.12
13
50
46
41
GO
:00
46
33
2S
MA
Db
ind
ing
0.0
02
.47
15
.37
27
56
Tab
le1
6:
Mo
lecu
lar
Fu
nct
ion
Gen
eO
nto
log
yE
nri
chm
ent
for
Mat
ure
Mic
roR
NA
Tar
get
sR
elat
ion
ship
Wit
hH
ealt
hS
core
.T
he
‘GO
ID’
isth
eu
niq
ue
iden
tifi
erin
the
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.
org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:00
44
45
1n
ucl
eop
lasm
par
t0
.00
1.4
91
54
.66
20
15
71
GO
:00
44
46
4ce
llp
art
0.0
01
.31
30
48
.52
31
16
11
25
5
GO
:00
05
62
3ce
ll0
.00
1.3
13
04
8.7
93
11
61
12
56
GO
:00
05
63
4n
ucl
eus
0.0
01
.17
13
13
.94
14
10
48
51
GO
:0005815
mic
rotu
bule
org
aniz
ing
cente
r0.0
01.5
1114.8
4151
424
GO
:00
05
62
2in
trac
ellu
lar
0.0
01
.21
27
10
.49
27
90
10
00
7
GO
:00
05
82
9cy
toso
l0
.00
1.2
15
88
.58
65
82
17
3
GO
:00
44
42
4in
trac
ellu
lar
par
t0
.00
1.1
92
68
7.2
02
76
29
92
1
Tab
le1
7:
Cel
lula
rC
om
po
nen
tG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets
Rel
atio
nsh
ipW
ith
Hea
lth
Sco
re.
Th
e‘G
OID
’is
the
un
iqu
e
iden
tifi
erin
the
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.
org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 74
Pag
e 24
4
Jet Fuel and Solvents Micro RNA Arrays
GO
GO
Od
ds
Ex
pec
ted
Ob
serv
ed
IDT
erm
Pval
ue
Rat
ioC
ou
nt
Co
un
tS
ize
GO
:0043229
intr
acel
lula
rorg
anel
le0.0
01.1
72331.0
22415
8606
GO
:00
05
81
3ce
ntr
oso
me
0.0
01
.51
87
.49
11
53
23
GO
:00
45
20
2sy
nap
se0
.00
1.4
01
29
.47
16
24
78
GO
:00
43
23
4p
rote
inco
mp
lex
0.0
01
.17
81
6.1
08
87
30
13
GO
:00
05
65
4n
ucl
eop
lasm
0.0
01
.24
34
1.8
23
91
12
62
GO
:00
16
60
4n
ucl
ear
bo
dy
0.0
01
.52
73
.94
98
27
3
GO
:00
05
76
8en
do
som
e0
.00
1.3
61
43
.28
17
65
29
GO
:0043231
intr
acel
lula
rm
embra
ne-
bounded
org
anel
le0.0
01.1
42112.4
32190
7799
Tab
le1
7:
Cel
lula
rC
om
po
nen
tG
ene
On
tolo
gy
En
rich
men
tfo
rM
atu
reM
icro
RN
AT
arg
ets
Rel
atio
nsh
ipW
ith
Hea
lth
Sco
re.
Th
e‘G
OID
’is
the
un
iqu
e
iden
tifi
erin
the
Gen
eO
nto
log
yC
on
sort
ium
on
tolo
gy
dat
abas
e,an
dm
ayb
ese
arch
edv
iath
eA
MI
GO
inte
rfac
ehttp://amigo.geneontology.
org/amigo
.T
he
GO
Ter
mis
the
des
crip
tive
tex
tp
rov
ided
by
the
Gen
eo
nto
log
yC
on
sort
ium
.
Commercial In Confidence 75
Pag
e 24
5
Jet Fuel and Solvents Micro RNA Arrays
6 Summary and Conclusions
1. There is clear evidence of differential expression of each family of micro RNAs
between the high exposure group and the controls.
2. Gene ontology enrichment analysis for the differential mature micro RNAs and
stem loop pre-micro RNAs revealed consistent findings. Both types of micro
RNA showed enrichment for neurological development processes and for RNA
metabolism.
3. Only the mature micro RNAs showed a statistically significant relationship with
the health score. Ontology enrichment for the micro RNA relationships with
health score showed enrichment for terms associated with neurological develop-
ment.
These ontology enrichment findings were not present in gene expression analysis for
messenger RNAs. Their clinical relevance is uncertain.
References
[1] Quality Assessment of Exon Arrays Revision Date: 2005-09-27. Whitepaper,
Affymetrix Inc., 3420 Central Expressway, Santa Clara, CA 95051, April 2007.
[2] D. Betel, M. Wilson, A. Gabow, D. S. Marks, and C. Sander. The microRNA.org
resource: targets and expression. Nucleic Acids Research, 36(suppl 1):D149–
D153, 2008.
[3] B. Everitt, Sabine Landau, and Morven Leese. Cluster Analysis. Edward Arnold,
4 edition, May 2001.
[4] S. Griffiths-Jones, H. K. Saini, S. van Dongen, and A. J. Enright. mirbase: tools
for microrna genomics. Nucleic Acids Research, 36(Database):D154D158, Dec
2007.
[5] J. A. Hartigan. Consistency of Single Linkage for High-Density Clusters. Journalof the American Statistical Association, 76(374):388–394, 1981.
[6] P. Huber. Robust Statistics. John Wiley and Sons, 1981.
[7] R. Irizarry, B. Hobbs, F. Collin, Y. Beazer-Barclay, K. Antonellis, U. Scherf, and
T. Speed. Exploration, Normalization, and Summaries of High Density Oligonu-
cleotide Array Probe Level Data. Biostatistics, 4:249 264, 2003.
[8] B. John, A. J. Enright, A. Aravin, T. Tuschl, C. Sander, and D. S. Marks. Human
MicroRNA Targets. PLoS Biology, 2(11):e363, 2004.
[9] I. Jolliffe. Principal Components Analysis. Springer-Verlag, 1986.
[10] A. Kasprzyk. BioMart: driving a paradigm change in biological data manage-
ment. Database, 2011, 2011.
Commercial In Confidence 76
Page 246
Jet Fuel and Solvents Micro RNA Arrays
[11] A. M. Kiss, B. E. Jady, E. Bertrand, and T. Kiss. Human Box H/ACA
Pseudouridylation Guide RNA Machinery. Molecular and Cellular Biology,
24(13):5797–5807, Jul 2004.
[12] W. J. Krzanowski. Principles of Multivariate Analysis: A User’s Perspective.
Number 3 in Oxford Statistical Science Series, 1988.
[13] H. E. Lockstone. Exon array data analysis using Affymetrix power tools and R
statistical software. Briefings in Bioinformatics, 2011.
[14] I. Lonnstedt and T. Speed. Replicated Microarray Data. Statistica Sinica, pages
31–46, 2002.
[15] J. M. Lucas and M. S. Saccucci. Exponentially Weighted Moving Average Con-
trol Schemes: Properties and Enhancements. Technometrics, 32(1):1–12, 1990.
[16] P. B. Robinson and T. Y. Ho. Average Run Lengths of Geometric Moving Average
Charts by Numerical Methods. Technometrics, 20(1):85–93, 1978.
[17] B. Silverman. Density estimation for statistics and data analysis. Chapman and
Hall, London, 1986.
[18] G. K. Smyth. Limma: linear models for microarray data. In R. Gentleman,
V. Carey, S. Dudoit, R. Irizarry, and W. Huber, editors, Bioinformatics and Com-putational Biology Solutions using R and Bioconductor, pages 397–420, New
York, 2005. Springer.
[19] M. P. Terns and R. M. Terns. Small nucleolar RNAs: versatile trans-acting
molecules of ancient evolutionary origin. Gene expression, 10(1-2):1–2, 2002.
[20] Y. Benjamini and Y. Hochberg. Controlling the false discovery rate: a practical
and powerful approach to multiple testing. Journal of the Royal Statistical SocietySeries B, 57:289–300, 1995.
Commercial In Confidence 77
Page 247
������������������� ������� ������������������������� ������ ��������������������
Appendix 5 Report from Emphron on Bioinformatic analysis of mitochondrial DNA array data from leucocytes of exposed workers and controls.
����������������
Page 248
Jet Fuel and Solvents ExposureProject
Mitochondrial DNA
Emphron Informatics Pty Ltd www.emphron.com
Report Number: 2012.020
Version Number: 1.0
Client: Jet Fuel and Solvents Project
Date: July 23, 2014
Author: Mervyn Thomas
Email: [email protected]
Signature: ..................................................
Page 249
Jet Fuel and Solvents Mitochondrial DNA
1 Introduction
1.1 Background and Objectives
The Jet Fuel and Solvents Exposure study has been set up to investigate physiological
and other differences between ex Air Force employees who were exposed to aviation
fuel during their service. Subjects were stratified into four exposure groups, where
Group 4 is a control group (representing no known exposure) and Group 1 is the highest
level of exposure.
Data are available from several genomic and proteomic modalities, including: cyto-
genetics data, High Throughput mRNA gene expression data, micro RNA expression
data, serum analysis by LCMS and full blood counts. This report is concerned with the
analysis of mitochondrial DNA variants.
1.2 Available Data
Mitochondrial DNA variant data are available from 245 subjects. 82 of these subjects
were in the high exposure group; 43 in the medium exposure group; 43 in the low
exposure group and 77 in the control group.
There were 770 variants in total. Most variants occurred in a small proportion of the
samples. 434 of the variants occurred in only one sample. Only 12 variants were found
in more than 20% but less than 80% of the samples. These 12 variants are the major
source of diversity.
2 Exploratory Analysis
Variants which occured in only 1 or 2 samples were dropped. A total of 224 variants
remained for analsyis.
A Bray Curtis[1] dissimilarity matrix was calculated between the samples, and the
dissimilarity matrix was used to produce a two dimensional metric scaling solution
(principal coordinates[3]). Observations were plotted in the space of the principal co-
ordinates, and coloured by exposure group.
Figure 1 shows the scaling solution. There is no relationship between the coordinates
nd exposure group. There do appear to be distinct groups in the data. This, however, is
an artefact of the definition of the scaling dimensions.
Commercial In Confidence 1
Page 250
Jet Fuel and Solvents Mitochondrial DNA
−0.2
0.0
0.2
−0.2 0.0 0.2X1
X2
GroupHigh
Medium
Low
Control
Mitochondrial DNAMultidimensional Scaling for Bray Curtis Dissimilarity
Figure 1: Sample Points in Space of First Two Principal Coordinates of Bray Curtis
Dissimilariy
Table 1 shows the correlation between incidence of particular variants and the scaling
dimensions. Only variants with a correlation of at least 0.8 on either dimension are dis-
played. The first scaling dimension is dominated by 8 variants. The second dimension
is dominated by the variants at positions 11467,12308 and 12372 .
The discrete groups in the scaling plot are generated by the small number of possible
values for this limited set of variants.
Table1 also shows the prevalence for each of the selected variants. Prevalence for these
variants is between 19% and 53%.
Commercial In Confidence 2
Page 251
Jet Fuel and Solvents Mitochondrial DNA
Correlation with:
Variant Dim. 1 Dim. 2 Prevalence
X73A2G -92 -6 50
X2706A2G -91 -11 53
X7028C2T -91 -11 53
X11467A2G -45 -81 19
X11719G2A -96 -7 47
X12308A2G -45 -81 19
X12372G2A -43 -81 20
X14766C2T -96 -6 46
Table 1: Mitochondrial DNA Variant - Scaling Dimension Correlations. The variant
code is defined by the position on the mitochondrial chromosome, and the transition.
For example a transition C2T implies substitution of the reference HG19 base ‘C’ by
base ‘T’.
Hierarchical cluster solutions[2] were generated using complete linkage, average link-
age and single linkage. Cluster solutions were visually very different indicating that
there is no true cluster structure in the data. Figure 2 shows the dendrogram for the
single linkage solution.
Commercial In Confidence 3
Page 252
Jet Fuel and Solvents Mitochondrial DNA
0.0
0.2
0.4
0.6
0.8
1.0
Mitochondrial DNASingle Linkage Cluster Solution
SamplesDistance
Hei
ght
Figure 2: Single Linkage Cluster Solution for Bray Curtis Distance matrix using Vari-
ant Incidence.
The strongest feature of the cluster solution is a relatively small number of outlying
samples. The cluster tree was cut at a distance value of 0.5: resulting in 8 putative
clusters. Membership of clusters is tabulated against exposure group in Table 2. There
is no relationship between exposure group and cluster membership.
Commercial In Confidence 4
Page 253
Jet Fuel and Solvents Mitochondrial DNA
Exposure Group:
Cluster High Medium Low Control
1 59 32 31 54
2 9 5 2 11
3 2 0 1 3
4 11 4 7 7
5 1 0 0 0
6 0 1 0 2
7 0 1 1 0
8 0 0 1 0
Table 2: Mitochondrial DNA: Cluster Membership by Exposure Group
The structure of the data is dominated by a relatively small number of variants with
prevalence between 20% and 80%. There is no indication of between group differences
in variant structure.
3 Between Group Differences in Individual Variants
Each variant was analysed by fitting a generalised linear model[4] on exposure group,
with a Bernoulli error distribution. A log likelihood χ2 statistic was calculated.
Variants with a p value ≤ 0.05 are tabulated in Table 3. P values were adjusted using
the Benjamini Hochberg procedure[5].
Seven variants showed a statistically significant group effect before correction. Given
224 variants analysed, we expect to find > 10 variants statistically significant by chance
alone. After correction to maintain a false discovery rate of 5%, however, none of the
variants were statistically significant.
variable Deviance Pr(>Chi) adj.P
X207G2A 9.9 0.019 0.579
X4580G2A 8.2 0.042 0.579
X15904C2T 8.2 0.042 0.579
X10238T2C 7.9 0.049 0.579
X15257G2A 9.3 0.026 0.579
X12007G2A 10.6 0.014 0.579
X16324T2C 9.4 0.024 0.579
Table 3: Chi Square Tests for Variant Between Group Differences
There is no evidence of any difference between groups in the incidence of any of the
variants.
Commercial In Confidence 5
Page 254
Jet Fuel and Solvents Mitochondrial DNA
References
[1] J. Bray and J. Curtis. An ordination of the upland forest communities of southern
Wisconsin. Ecological Monographs, 27(4):325–349, 1957.
[2] B. Everitt, Sabine Landau, and Morven Leese. Cluster Analysis. Edward Arnold,
4 edition, May 2001.
[3] J. C. Gower. Some distance properties of latent root and vector methods used in
multivariate analysis. Biometrika, 53(3-4):325–328, 1966.
[4] J. Nelder and R. Wedderburn. Generalised Linear Models. Journal of the RoyalStatistical Society, Series A, 135:370–384, 1972.
[5] Y. Benjamini and Y. Hochberg. Controlling the false discovery rate: a practical
and powerful approach to multiple testing. Journal of the Royal Statistical SocietySeries B, 57:289–300, 1995.
Commercial In Confidence 6
Page 255
������������������� ������� ������������������������� ������ ��������������������Appendix 6 Report from Emphron on Bioinformatic analysis of proteomic data from plasma and leucocytes of exposed workers and controls. ������
Page 256
Jet Fuel and Solvents ExposureProject
Analysis of Mass Spectrometry Data
Emphron Informatics Pty Ltd www.emphron.com
Report Number: 2012.020
Version Number: 1.0
Client: Jet Fuel and Solvents Project
Date: July 22, 2014
Author: Mervyn Thomas
Email: [email protected]
Signature: ..................................................
Page 257
Jet Fuel and Solvents Mass Spectrometry
Executive Summary
1. Data were available from 40 samples, processed using LCMS. One sample was
removed because it failed quality control. The remaining 39 samples included 15
control samples and 24 samples from subjects exposed to deseal/reseal solvents.
2. Multivariate exploratory analysis suggested differences between control and ex-
posed subjects in peptide and protein expression. These differences were not
large, however.
3. Protein FHOD3 (associated with actin fibre production) appeared to be over ex-
pressed in the control subjects. This is of unknown clinical significance.
4. No protein appeared correlated with the health score.
5. A single peptide appeared to be under expressed in exposed subjects, but the
mass to charge ratio is consistent with a large number of candidate peptides.
This finding is of unknown clinical significance.
6. A single peptide appeared to be negatively correlated with the health score,but
the mass to charge ratio is consistent with a large number of candidate peptides.
This finding is of unknown clinical significance.
Contents
1 Introduction 3
1.1 Background and Objectives . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Available Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Data Processing 4
2.1 Spectral Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Protein Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 Peptide Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3 Exploratory Analysis 5
3.1 Exploratory Analysis of Proteins . . . . . . . . . . . . . . . . . . . . 5
3.2 Exploratory Analysis for Peptide Spectra . . . . . . . . . . . . . . . . 11
3.3 Summary of Exploratory Analysis Results . . . . . . . . . . . . . . . 17
4 Differential Expression 18
4.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Commercial In Confidence 1
Page 258
Jet Fuel and Solvents Mass Spectrometry
4.2 Proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.3 Peptides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.4 Summary of Differential Expression Results . . . . . . . . . . . . . . 20
List of Tables
1 Analysis of Variance for Second Scaling Dimension of Protein Mascot
Score Bray Curtis Dissimilarity Matrix . . . . . . . . . . . . . . . . . 11
2 Analysis Of Variance For Peptide Scaling Dimensions From Bray Cur-
tis Dissimilarities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
List of Figures
1 Protein Mascot Score Bray Curtis Dissimilarity Matrix for All Samples 5
2 Protein Mascot Score Dissimilarity Matrix Excluding 1030 . . . . . . 6
3 Single Linkage Cluster Analysis For Samples Based on Bray Curtis
Dissimilarity for Protein Mascot Scores . . . . . . . . . . . . . . . . 7
4 Average Linkage Cluster Analysis For Samples Based on Bray Curtis
Dissimilarity for Protein Mascot Scores . . . . . . . . . . . . . . . . 8
5 Complete Linkage Cluster Analysis For Samples Based on Bray Curtis
Dissimilarity for Protein Mascot Scores . . . . . . . . . . . . . . . . 9
6 Scatter Plot For First Two Scaling Dimensions From Protein Mascot
Score Bray Curtis Dissimilarities . . . . . . . . . . . . . . . . . . . . 10
7 Box And Whisker Plots By Exposure Group For Protein MDS Dimen-
sions from Bray Curtis Dissimilarities. . . . . . . . . . . . . . . . . . 11
8 Peptide Bray Curtis Dissimilarity Matrix for All Samples . . . . . . . 12
9 Peptide Bray Curtis Dissimilarity Matrix Excluding 1030 . . . . . . . 13
10 Single Linkage Cluster Analysis For Samples Based on Peptide Bray
Curtis Dissimilarities . . . . . . . . . . . . . . . . . . . . . . . . . . 14
11 Average Linkage Cluster Analysis For Samples Based on Peptide Bray
Curtis Dissimilarities . . . . . . . . . . . . . . . . . . . . . . . . . . 15
12 Complete Linkage Cluster Analysis For Samples Based on Peptide
Bray Curtis Dissimilarities . . . . . . . . . . . . . . . . . . . . . . . 16
13 Scatter Plot For Scaling Dimensions From Peptide Bray Curtis Dissim-
ilarities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Commercial In Confidence 2
Page 259
Jet Fuel and Solvents Mass Spectrometry
14 Distribution of Peptide Scaling Dimension Scores from Bray Curtis
Dissimilarities By Exposure Group . . . . . . . . . . . . . . . . . . . 18
15 Differential Expression of ‘FH1/FH2 domain-containing protein 3’ In
Control and Exposed Subjects. . . . . . . . . . . . . . . . . . . . . . 19
16 Differential Expression of Peptide With Mass To Charge Ratio 2600.3. 20
17 Relationship of Signal For peptide With mass to Charge Ratio 3072.4
with Health Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1 Introduction
1.1 Background and Objectives
The Jet Fuel and Solvents Exposure study has been set up to investigate physiological
and other differences between ex Air Force employees who were exposed to aviation
fuel during their service. Subjects were stratified into four exposure groups, where
Group 4 is a control group (representing no known exposure) and Group 1 is the highest
level of exposure.
Data are available from several genomic and proteomic modalities, including: cyto-
genetics data, High Throughput mRNA gene expression data, micro RNA expression
data and from serum analysis by LCMS. Only the results of serum LCMS analysis are
described in this report.
Each subject was also asked to complete a health questionnaire, and data from the psy-
cho social scale of the questionnaire were investigated in conjunction with the serum
mass spectroscopy.
1.2 Available Data
LCMS data were available from 40 subjects. There were 15 samples from control group
subjects (Group 4), 21 samples from high exposure subjects (Group 1), 3 samples from
intermediate exposure subjects (Group 2), and 1 sample from the low exposure subjects
(Group 3). Since there were very few subjects from low or intermediate exposure
groups, subjects were simply coded as control (Group 4) or exposed (Group 1 to 3).
Commercial In Confidence 3
Page 260
Jet Fuel and Solvents Mass Spectrometry
2 Data Processing
2.1 Spectral Averaging
Each sample was subjected to two HPLC runs, and each spotted target from the HPLC
run was run through MaldiTOF mass spectroscopy twice: giving a total of four LCMS
runs per sample. Peptide and fragment masses within 0.05 Daltons were considered to
be duplicates, and the maximum count over each peptide and fragment was taken.
2.2 Protein Estimates
Peptide mz data, and fragment ion mz and signal data were passed to a Mascott search.
The highest matching proteins were returned for each sample. Each sample was then
represented by a list of proteins and matching Mascot scores.
Proteins that were present in at least 50% of the samples were retained for analysis, and
the data was represented as a sample by protein matrix, with each element representing
the Mascot score for the given protein in the given sample. A score of zero was imputed
when a protein was not identified in a given sample.
Mascot scores are considered semi-quantitative., For a given protein in a matrix of
fixed composition, Mascot score will increase monotonically with protein concentra-
tion. The relationship, however, is not interval or ratio[9]. Doubling the concentration
will not necessarily double the Mascot score. Moreover, it is not necessarily the case
that for two different proteins, the protein with the highest Mascot score in a sample
will have the highest concentration.
log2 transformed Mascot scores were analysed, to stabilise variances.
2.3 Peptide Analysis
Peptide mz values for each sample were aggregated across HPLC and MS runs, with
a window width of 0.05 Daltons. The maximum signal was chosen for the binned mz
value
Peptides that were present in 75% or more of the samples were retained for analysis,
and a data matrix was generated with rows defined by samples and columns by peptide
mz values. Each cell represented the maximum signal for the given mz value and
sample.
Commercial In Confidence 4
Page 261
Jet Fuel and Solvents Mass Spectrometry
3 Exploratory Analysis
3.1 Exploratory Analysis of Proteins
A Bray-Curtis dissimilarity matrix[2] was generated, to characterise the difference be-
tween samples. This dissimilarity measure is zero when two samples have the same
distribution of Mascot scores across proteins, and attains a maximum value (1) two
samples have no proteins with non zero Mascot scores in common.
An image of the between sample dissimilarity matrix is shown in Figure 1. The dis-
similarity matrix reveals that one sample, 1030, is markedly different from all of the
other samples. Although this sample gave apparently successful HPLC performance,
its mass spectroscopy data had previously been identified as marginal. This sample was
excluded from further analysis.
Protein Mascott Score Bray Curtis Dissimilarity Between Samples
Figure 1: Protein Mascot Score Bray Curtis Dissimilarity Matrix for All Samples
Figure 2 shows the Bray Curtis matrix excluding sample 1030. The rows and columns
of the dissimilarity matrix are organised in sample order, with Group 1 at the bottom
left, and Group 4 at the top right. Inspection of the matrix images reveals that ten of
the control samples (Group 4) have low dissimilarity (the dark block at the upper right
of the image), but that these samples are quite dissimilar to the remaining 5 control
samples. There is no clear group structure to the dissimilarity matrix.
Commercial In Confidence 5
Page 262
Jet Fuel and Solvents Mass Spectrometry
Protein Mascott Score Bray Curtis Dissimilarity Excluding Sample 1030
Figure 2: Protein Mascot Score Dissimilarity Matrix Excluding 1030
Single, average and complete linkage cluster dendrograms are shown in Figures 3, 4
and 5 respectively. The average and single linkage dendrograms show chaining[3].
This is a common phenomenon when there is no true cluster structure. The complete
linkage solution identifies a group of 5 control chips as being somewhat different from
the other chips, but otherwise there is no clear group structure. Given the marked
differences between the complete linkage and the other solutions, there is no evidence
of any real multivariate structure to the samples.
Commercial In Confidence 6
Page 263
Jet Fuel and Solvents Mass Spectrometry
jf400
3jf4
005
jf204
8jf4
002
jf107
2jf4
001
jf103
7jf2
032
jf102
4jf1
051
jf400
4jf3
035
jf107
3jf1
045
jf203
6jf1
028
jf105
2jf1
039
jf104
0jf1
043
jf407
0jf1
063
jf400
8jf1
065
jf104
2jf1
060
jf405
2jf4
044
jf105
7jf1
022
jf105
4jf4
063
jf406
4jf4
059
jf406
8jf4
057
jf102
5jf1
061
jf405
30.15
0.20
0.25
0.30
Single Linkage Cluster Analysis
Samples
Hei
ght
Figure 3: Single Linkage Cluster Analysis For Samples Based on Bray Curtis Dissim-
ilarity for Protein Mascot Scores
Commercial In Confidence 7
Page 264
Jet Fuel and Solvents Mass Spectrometry
jf400
3jf4
005
jf204
8jf4
001
jf400
2jf1
072
jf102
4jf4
004
jf203
2jf1
037
jf303
5jf1
051
jf107
3jf1
045
jf203
6jf1
028
jf105
2jf1
039
jf104
0jf1
043
jf407
0jf1
063
jf400
8jf1
042
jf106
0jf1
065
jf105
7jf4
052
jf102
2jf4
044
jf105
4jf4
068
jf406
3jf4
064
jf405
9jf4
057
jf102
5jf1
061
jf405
30.15
0.20
0.25
0.30
Average Linkage Cluster Analysis
Samples
Hei
ght
Figure 4: Average Linkage Cluster Analysis For Samples Based on Bray Curtis Dis-
similarity for Protein Mascot Scores
Commercial In Confidence 8
Page 265
Jet Fuel and Solvents Mass Spectrometry
jf204
8jf1
024
jf103
7jf1
051
jf203
2 jf107
2jf3
035
jf103
9jf1
040
jf104
3jf1
042
jf106
0 jf407
0jf1
063
jf400
8jf4
052
jf404
4jf4
068
jf406
3jf4
059
jf102
5jf1
061
jf405
3 jf405
7jf4
064
jf106
5jf1
057
jf102
2jf1
054
jf107
3jf1
045
jf203
6jf1
028
jf105
2jf4
003
jf400
5jf4
001
jf400
2jf4
004
0.15
0.20
0.25
0.30
0.35
Complete Linkage Cluster Analysis
Samples
Hei
ght
Figure 5: Complete Linkage Cluster Analysis For Samples Based on Bray Curtis Dis-
similarity for Protein Mascot Scores
A three dimensional non-metric scaling1 solution[6] to the dissimilarity matrix gener-
ated a stress of 13.6%: which is considered an acceptable solution.
Figure 6 shows samples plotted in the space of the first and second dimensions from the
non metric scaling analysis. There is some suggestion of a separation between control
and other samples on dimension 2: though this is by no means strong.
1Non metric scaling seeks to find a set of coordinates in k dimensional space, such that the Euclidean
distance between sample points in that space approximates a given dissimilarity matrix. The scaling is non-
metric in that it assumes a monotonic but not necessarily linear, relationship between Euclidean distance in
the coordinate k space and dissimilarity.
Commercial In Confidence 9
Page 266
Jet Fuel and Solvents Mass Spectrometry
−0.1
0.0
0.1
0.2
−0.1 0.0 0.1 0.2Dimension 1
Dim
ensi
on 2
Group1
2
3
4
Multidimensional Scaling Solution
Figure 6: Scatter Plot For First Two Scaling Dimensions From Protein Mascot Score
Bray Curtis Dissimilarities
Figure 7 shows box and whisker plots of the three scaling dimensions by exposure
group. There is no evidence between group differences for dimensions 1 and 3, but
there is evidence of a between groups difference for dimension 2.
Commercial In Confidence 10
Page 267
Jet Fuel and Solvents Mass Spectrometry
●
●
●
●
●●
●
●
●
●
●
●●
●
●
Dimension1 Dimension2 Dimension3
−0.2
−0.1
0.0
0.1
0.2
Control Exposed Control Exposed Control ExposedExposure Group
Dim
ensi
onProtein Multidimensional Scaling Dimensions By Group
Figure 7: Box And Whisker Plots By Exposure Group For Protein MDS Dimensions
from Bray Curtis Dissimilarities.
The analysis of variance for dimension 2 is summarised by Table 1. This conforms the
existence of statistically significant between group differences for dimension 2.
Df Sum Sq Mean Sq F value Pr(>F)
Exposure 1 0.07 0.07 17.73 0.0002
Residuals 37 0.16 0.00
Table 1: Analysis of Variance for Second Scaling Dimension of Protein Mascot Score
Bray Curtis Dissimilarity Matrix
3.2 Exploratory Analysis for Peptide Spectra
An image of the between sample peptide dissimilarity matrix is shown in Figure 8. The
dissimilarity matrix reveals that one sample, 1030, is markedly different from all of the
other samples. Although this sample gave apparently successful HPLC performance,
its mass spectroscopy data had previously been identified as marginal. This sample was
excluded from further analysis.
Commercial In Confidence 11
Page 268
Jet Fuel and Solvents Mass Spectrometry
Bray Curtis Dissimilarity Peptide Data
Figure 8: Peptide Bray Curtis Dissimilarity Matrix for All Samples
Figure 9 shows the Bray Curtis matrix excluding sample 1030. The rows and columns
of the dissimilarity matrix are organised in sample order, with Group 1 at the bottom
left, and Group 4 at the top right. Inspection of the matrix images reveals that nine
of the control samples (Group 4) have low dissimilarity (the dark block at the upper
right of the image), but that these samples are quite dissimilar to the remaining control
samples. There is no clear group structure to the dissimilarity matrix.
Commercial In Confidence 12
Page 269
Jet Fuel and Solvents Mass Spectrometry
Bray Curtis Dissimilarity Peptide Data Excluding Sample 1030
Figure 9: Peptide Bray Curtis Dissimilarity Matrix Excluding 1030
Single, average and complete linkage cluster dendrograms are shown in Figures 10,
11 and 12 respectively.The cluster solutions are visually quite different, indicting that
there is no strong cluster solution. There is no suggestion that clusters are associated
with exposure group.
Commercial In Confidence 13
Page 270
Jet Fuel and Solvents Mass Spectrometry
jf204
8jf2
036
jf400
4jf4
070
jf400
3jf4
068
jf106
1jf1
060
jf104
3jf4
063
jf404
4jf4
052 jf4
001
jf400
8jf4
064
jf405
7jf1
057
jf102
5jf1
054
jf405
3jf4
059
jf106
3jf1
065
jf103
9jf1
040
jf104
2jf4
005
jf102
2jf4
002
jf107
2jf1
052
jf107
3jf3
035
jf102
4jf1
028
jf104
5jf1
051
jf103
7jf2
032
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
Single Linkage Cluster Analysis
Samples
Hei
ght
Figure 10: Single Linkage Cluster Analysis For Samples Based on Peptide Bray Curtis
Dissimilarities
Commercial In Confidence 14
Page 271
Jet Fuel and Solvents Mass Spectrometry
jf203
6jf4
004
jf407
0jf1
061
jf104
3jf1
039
jf104
0jf1
042
jf106
0jf4
063
jf400
8jf1
063
jf106
5 jf400
1jf1
057
jf102
5jf1
054 jf4
044
jf405
2jf4
064
jf405
7jf4
053
jf405
9jf4
003
jf406
8jf4
005
jf102
2jf4
002
jf204
8jf1
072
jf103
7jf2
032
jf105
2jf1
073
jf303
5jf1
024
jf102
8jf1
045
jf105
1
0.20
0.25
0.30
0.35
0.40
0.45
0.50
Average Linkage Cluster Analysis
Samples
Hei
ght
Figure 11: Average Linkage Cluster Analysis For Samples Based on Peptide Bray
Curtis Dissimilarities
Commercial In Confidence 15
Page 272
Jet Fuel and Solvents Mass Spectrometry
jf400
4jf4
068
jf400
5jf1
022
jf400
2 jf400
8jf4
001
jf404
4jf4
052
jf102
5jf1
054
jf405
3jf4
059 jf4
057
jf406
4jf1
061
jf105
7jf1
063
jf106
5jf4
003
jf106
0jf4
063
jf203
6jf2
048
jf407
0jf1
043
jf103
9jf1
040
jf104
2jf1
024
jf102
8jf1
045
jf105
1jf1
052
jf107
3jf3
035
jf107
2jf1
037
jf203
2
0.2
0.3
0.4
0.5
0.6
Complete Linkage Cluster Analysis
Samples
Hei
ght
Figure 12: Complete Linkage Cluster Analysis For Samples Based on Peptide Bray
Curtis Dissimilarities
A two dimensional non metric scaling solution resulted in a stress of 11 %: which
is generally considered an acceptable fit. Figure 13 shows a scatter plot of the two
scaling dimensions, with samples coloured according to exposure group. There is no
clear evidence of any group separation.
Commercial In Confidence 16
Page 273
Jet Fuel and Solvents Mass Spectrometry
−0.5
0.0
0.5
−0.5 0.0 0.5 1.0Dimension 1
Dim
ensi
on 2
Group1
2
3
4
Peptides Multidimensional Scaling Solution
Figure 13: Scatter Plot For Scaling Dimensions From Peptide Bray Curtis Dissimilari-
ties
Figure 14 shows box and whisker plots for the peptide scaling dimensions by exposure
group. There is some suggestion that both scaling dimensions differ between groups,
though the effects are not large.
Dimension Effect Df Sum Sq Mean Sq F value Pr(>F)
Dimension 1 Exposure 1 1.07 1.07 11.48 0.0017
Residuals 37 3.46 0.09
Dimension 2 Exposure 1 0.50 0.50 5.04 0.0308
Residuals 37 3.65 0.10
Table 2: Analysis Of Variance For Peptide Scaling Dimensions From Bray Curtis Dis-
similarities.
3.3 Summary of Exploratory Analysis Results
• The cluster analysis reveals no suggestion of a strong cluster solution for the
peptides or for the proteins. None of the cluster solutions appear to be associated
with jet fuel and solvents exposure groups.
• The multidimensional scaling results for the protein dissimilarity suggest a dif-
ference between exposure groups on the second scaling dimension, but this dif-
ference is not large.
Commercial In Confidence 17
Page 274
Jet Fuel and Solvents Mass Spectrometry
Dimension1 Dimension2
−0.5
0.0
0.5
1.0
Control Exposed Control ExposedExposure Group
Dim
ensi
on S
core
Multidimensional Scaling Dimension 2 By Group
Figure 14: Distribution of Peptide Scaling Dimension Scores from Bray Curtis Dis-
similarities By Exposure Group
• The multidimensional scaling results for the peptide dissimilarity suggest a dif-
ference between exposure groups on the first and second scaling dimensions, but
these differences are not large.
4 Differential Expression
4.1 Methods
Between group differences were evaluated with a linear model, and empirical Bayes
moderated t tests[7] based on linear model parameter estimates. These were imple-
mented using Smyth’s limma methodology[8]. The between group comparison was
between control subjects, and subjects with any levels of exposure (pooled from Groups
1, 2 and 3).
Individual p values for each protein or peptide species were adjusted to maintain a false
discovery rate of 5% using the Benjamini Hochberg procedure[10].
Commercial In Confidence 18
Page 275
Jet Fuel and Solvents Mass Spectrometry
This procedure was adopted separately for proteins (using log2 Mascot scores) and
peptides (using log2 signals).
The empirical Bayes analysis was then repeated using health score as a covariate. In-
dividual p values for the health score covariate for each protein or peptide species
were adjusted to maintain a false discovery rate of 5% using the Benjamini Hochberg
procedure[10].
This procedure was adopted separately for proteins (using log2 Mascot scores) and
peptides (using log2 signals).
4.2 Proteins
The only protein to be differentially expressed between exposed and control subjects,
following Benjamini Hochberg correction was Swiss-Prot[1] ID FHOD3 HUMAN,
with recommended protein name ‘FH1/FH2 domain-containing protein 3’. The Mascot
score was 16 fold greater in the exposed subjects than in the control subjects. Box and
whisker plots are shown in Figure 15.
●0
2
4
6
FALSE TRUEExposure
log2
FH
OD
3
log2 FHOD3 Concentration by Exposure Group
Figure 15: Differential Expression of ‘FH1/FH2 domain-containing protein 3’ In Con-
trol and Exposed Subjects.
FHOD3 is an actin-organising protein that may be associated with stress fibre formation
together with cell elongation. It has an essential role in caridogenesis[5]. It appears to
be down-regulated in human and murine dilated cardiomyopathy[4].
Commercial In Confidence 19
Page 276
Jet Fuel and Solvents Mass Spectrometry
We have been unable to discover published neurological associations with FHOD3.
The messenger RNA associated with this protein was not differentially expressed in
the gene expression analysis.
No proteins were statistically significantly associated with the health score following
Benjamini Hochberg correction.
4.3 Peptides
Only one peptide showed differential expression between exposed and control subjects
following Benjamini Hochberg correction. This peptide had a Mass to charge ratio
of 2600.3 and was down regulated (an approximately 5 fold change) in exposed sub-
jects. This peptide is therefore not a constituent of FHOD3 which was up-regulated in
exposed subjects. The box and whisker plot for this peptide is shown in Figure 16.
10
12
14
Control ExposedExposure
log2
mz2
600.
3
log2 mz2600.3 Concentration by Exposure Group
Figure 16: Differential Expression of Peptide With Mass To Charge Ratio 2600.3.
Only one peptide, with mass to charge ratio 3072.4 showed a statistically significant
relationship with health score following Benjamini Hochberg correction. This rela-
tionship is illustrated in Figure 17. The relationship is strongly influenced by one point
which is extreme both for health score and for peptide signal.
4.4 Summary of Differential Expression Results
1. There is evidence that Protein FHOD3 is over expressed in the exposed group.
The clinical significance of this finding is uncertain.
Commercial In Confidence 20
Page 277
Jet Fuel and Solvents Mass Spectrometry
0
5
10
15
20 40 60 80Health Score
log2
mz3
072.
4
GroupControl
Exposed
log2 mz3072.4 Concentration by Health Score
Figure 17: Relationship of Signal For peptide With mass to Charge Ratio 3072.4 with
Health Score.
2. No protein appears to be correlated with the health score.
3. A peptide with mass to charge ratio 2600.3 appears to be under expressed in the
exposed group. This is of unknown clinical significance. Unfortunately the set
of peptides with this mass to charge ratio is too large for useful interpretation.
4. A peptide with mass to charge ratio 3072.4 appears to be negatively correlated
with the health score. There are too many peptides with this mass to charge ratio
for this result to be clinically useful.
References
[1] E. Boutet, D. Lieberherr, M. Tognolli, M. Schneider, and A. Bairoch.
Uniprotkb/swiss-prot. In D. Edwards, editor, Plant Bioinformatics, volume 406
of Methods in Molecular Biology, pages 89–112. Humana Press, 2007.
[2] J. Bray and J. Curtis. An ordination of the upland forest communities of southern
Wisconsin. Ecological Monographs, 27(4):325–349, 1957.
[3] B. Everitt, Sabine Landau, and Morven Leese. Cluster Analysis. Edward Arnold,
4 edition, May 2001.
Commercial In Confidence 21
Page 278
Jet Fuel and Solvents Mass Spectrometry
[4] T. Iskratsch, S. Lange, J. Dwyer, A. L. Kho, C. d. Remedios, and E. Ehler. Formin
follows function: a muscle-specific isoform of FHOD3 is regulated by CK2 phos-
phorylation and promotes myofibril maintenance. The Journal of Cell Biology,
191(6):1159–1172, Dec 2010.
[5] M. Kan-O, R. Takeya, T. Abe, N. Kitajima, M. Nishida, R. Tominaga, H. Kurose,
and H. Sumimoto. Mammalian formin Fhod3 plays an essential role in cardiogen-
esis by organizing myofibrillogenesis. Biology Open, 1(9):889–896, Sep 2012.
[6] J. B. Kruskal and M. O. Wish. Multidimensional Scaling. Number 07-011 in
Sage University Paper series on Quantitative Applications in the Social Sciences.
Sage Publications, Newbury Park, CA., 1978.
[7] I. Lonnstedt and T. Speed. Replicated Microarray Data. Statistica Sinica, pages
31–46, 2002.
[8] G. K. Smyth. Limma: linear models for microarray data. In R. Gentleman,
V. Carey, S. Dudoit, R. Irizarry, and W. Huber, editors, Bioinformatics and Com-putational Biology Solutions using R and Bioconductor, pages 397–420, New
York, 2005. Springer.
[9] S. S. Stevens. On the Theory of Scales of Measurement. Science, 103(2684):677–
680, 1946.
[10] Y. Benjamini and Y. Hochberg. Controlling the false discovery rate: a practical
and powerful approach to multiple testing. Journal of the Royal Statistical SocietySeries B, 57:289–300, 1995.
Commercial In Confidence 22
Page 279
������������������� ������� ������������������������� ������ ��������������������
Appendix 7 List of abbreviations ADHREC Australian Defence Human Research Ethics Committee AML acute myeloid leukaemia ATP adenosine tri-phosphate CBC complete blood count CHS commercial human serum CRF case report form DMF dimethylformamide DMSO dimethyl sulfoxide DNA Deoxyribonucleic acid DSRS Deseal/Reseal DVA Department of Veterans Affairs FBC full blood count FBS foetal bovine serum GHQ 30 General Health Questionnaire 30 IARC International Agency for Research on Cancer IOC International Olympic Committee ITA Intent-to-analyse JFES Jet Fuel Exposure Study JIP Jet fuel infused plasma JP-8 Jet propulsion 8 MAYAC Mater Adolescent and Young Adult Clinic MDS myelodysplastic syndromes MHS HREC Mater Health Services Human Research Ethics Committee miRNA micro RNA MRI(UQ) Mater Research Institute (University of Queensland) NHL non-Hodgkin lymphoma NHMRC National Health and Medical Research Council PP Per-Protocol RAAF Royal Australian Air Force RNA Ribonucleic acid RSL Returned Services League SAP Statistical Analysis Plan SD Standard deviation SHOAMP Study of Health Outcomes in Aircraft Maintenance Personnel SNP Single nucleotide polymorphism WBC White blood cells
Page 280
������������������� ������� ������������������������� ������ ��������������������Appendix 8 Glossary
Apoptosis�
is the process of programmed cell death that may occur in multicellular organisms SNP array�
is a type of DNA microarray which is used to detect polymorphisms within a population, it is a useful tool for studying slight variations between whole genomes Bioinformatics �
combines computer science, statistics, mathematics and engineering to study and process biological data Controls �
a group of people who have had no exposure to jet fuel. Controls are used to increase the reliability of results. Cytotoxicity �
is the quality of being toxic to cells. Epigenetics�
is the study of changes in gene expression caused by certain base pairs in DNA, or RNA, being “turned off” or “turned on” again, through chemical reactions. Gene transcription/translation � �
cells use a two-step process of transcription and translation to read each gene and produce the string of amino acids that make up a protein. Genomes�
the complete set of DNA within a single cell of an organism. Genomics�
is a discipline in genetics to sequence, assemble, and analyse the function and structure of genomes Mass spectrometry�
is an analytical chemistry technique that measures the mass-to-charge ratio and abundance of gas-phase ions. Methylation�
denotes the addition of a methyl group to a substrate or the substitution of an atom or group by a methyl group. Mitochondria�
are membrane-bound organelles found in most animal and plant cells. Proteomics�
is the large-scale study of proteins, particularly their structures and functions. workers�
Australian Air Force personnel working on the Deseal/Reseal project. �
Page 281
������������������� ������� ������������������������� ������ ��������������������
Appendix 9 References
Adams JM, Cory S. Bcl-2-regulated apoptosis: mechanism and therapeutic
potential. Curr Opin Immunol 19, 488-496 (2007).
Adams JM, Cory S. The Bcl-2 apoptotic switch in cancer development and
therapy. Oncogene 26, 1324-1337 (2007).
Australian Institute of Health and Welfare. Third Study of Mortality and Cancer Incidence in Aircraft Maintenance Personnel: A Continuing Study of F1-11 Deseal/Reseal Personnel. 2009.
Bayat A. Science, medicine, and the future: bioinformatics. BMJ 324 (2002) 1018–1022
Bleier J, McFarlane A, McGuire A, Treloar S, Waller M, Dobson A. Risk of adverse health outcomes associated with frequency and duration of deployment with the Australian Defence Force. Mil Med. 2011 Feb;176(2):139-46.
Bollati V, Baccarelli A, Hou L, et al. Changes in DNA methylation patterns in subjects exposed to low-dose benzene. Cancer Res. 67 (2007) 876–880.
Bollati V, Baccarelli A. Environmental Epigenetics (review). Heredity. 2010, February 24 [advance online publication]
Briefing notes to Chief of Air Force on research into Mitochondrial Disease in DeSeal/ReSeal personnel, 23 January 2008.
Briefing notes to the Commonwealth of Australia Parliamentary Committee Hearing, 16 April 2009.
Brittion DA, Okoniewski MJ, Connolly Y, Miller CJ. Exon level integration of proteomics and microarray data. BMC Bioninformatics. 2008;9:118.
Cho JY. Suppressive effect of hydroquinone, a benzene metabolite, on in vitro inflammatory responses mediated by macrophages, monocytes and lymphocytes. Mediat. Inflamm. (2008) 298010.
Cline MS, Smoot M, Cerami E, et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2 (2007) 2366–2382.
Page 282
������������������� ������� ������������������������� ������ ��������������������Crowley JJ, Sullivan PF, McLeod HL. Pharmacogenomic genome-wide association studies: lessons learned thus far. Pharmacogenomics 10 (2009) 161–163.
Dixon-McIver A, East P, Mein CA, et al. Distinctive patterns of microRNA expression associated with karyotype in acute myeloid leukaemia, PLoS ONE 3 (2008) e2141
Dolinoy DC, Jirtle RL. Environmental epigenomics in human health and disease. Environ Mol Mutagen. 49 (2008) 4–8.
Edwards SW, Preston RJ. Systems biology and mode of action based risk assessment. Toxicol Sci. 106 (2008) 312–318.
Ellis L, Atadja PW, Johnstone RW. Epigenetics in cancer: targeting chromatin
modifications. Mol Cancer Ther 8, 1409-1420 (2009).
Forrest MS, Lan Q, Hubbard AE, et al. Discovery of novel biomarkers by microarray analysis of peripheral blood mononuclear cell gene expression in benzene-exposed workers. Environ. Health Perspect. 113 (2005) 801– 807.
Frew AJ, Johnstone RW, Bolden JE. Enhancing the apoptotic and therapeutic
effects of HDAC inhibitors. Cancer Lett 280, 125-133 (2009).
Galvan N, Lim S, Zmugg S, Smith MT, Zhang L. Depletion of WRN enhances DNA damage in HeLa cells exposed to the benzene metabolite hydroquinone. Mutat. Res. 649 (2008) 54–61.
Glass DC, Gray CN, Jolley DJ, et al. Leukemia risk associated with low-level benzene exposure. Epidemiology (Cambridge, MA) 14 (2003) 569–577.
Goldstein JC, Waterhouse NJ, Juin P, Evan GI, Green DR. The coordinate release of cytochrome c during apoptosis is rapid, complete and kinetically
invariant. Nat Cell Biol 2, 156-162 (2000).
Hart D, Venter D. Dendritic cells in leukemia and lymphoma. Leuk Lymphoma.
47(4):613-22.
Page 283
������������������� ������� ������������������������� ������ ��������������������Hayes RB, Songnian Dosemeci YM, Linet M. Benzene and lymphohematopoietic malignancies in humans. Am. J. Ind. Med. 40 (2001) 117–126.
Hoboken, NJ, Smith M, Rappaport S. Building exposure biology centres to put the E into “G°øE” interaction studies. Environ. Health Perspect. 117 (2009) A334–A335.
International Agency for Research on Cancer (IARC). Some Industrial Chemicals and Dyestuffs. Monogr. Eval. Carcinog. Risks Hum., 1982.
International Agency for Research on Cancer (IARC). Monographs on the Evaluation of Carcinogenic Risks to Humans Overall Evaluations of Carcinogenicity, An Updating of IARC Monographs. 1987.
Ji Z, Zhang L, Guo W, McHale CM, Smith MT. The benzene metabolite, hydroquinone and etoposide both induce endoreduplication in human lymphoblastoid TK6 cells. Mutagenesis 24 (2009) 367–372.
Jirtle RL, Skinner MK. Environmental epigenomics and disease susceptibility. Nat. Rev. 8 (2007) 253–262.
Kim E, Kang BY, Kim TS. Inhibition of interleukin-12 production in mouse macrophages by hydroquinone, a reactive metabolite of benzene, via suppression of nuclear factor-kappaB binding activity. Immunol. Lett. 99 (2005) 24–29.
Kirschner MW. The meaning of systems biology. Cell 121 (2005) 503–504.
Kitano H. Systems biology: a brief overview. Science (New York, NY) 295 (2002) 1662–1664.
Knight JA, Skol AD, Shinde A, et al. Genome-wide association study to identify novel loci associated with therapy-related myeloid leukemia susceptibility. Blood 113 (2009) 5575–5582.
Lan Q, Zhang L, Li G, et al. Hematotoxicity in workers exposed to low levels of benzene. Science (New York, NY) 306 (2004) 1774–1776.
Lan Q, Vermeulen R, Zhang L, et al. Benzene exposure and hematotoxicity. Science (New York, NY) 312 (2006) 998–999.
Page 284
������������������� ������� ������������������������� ������ ��������������������Lan Q, Zhang L, Shen M, et al. Large-scale evaluation of candidate genes identifies associations between DNA repair and genomic maintenance and development of benzene hematotoxicity. Carcinogenesis 30 (2009) 50–58.
Lan Q, Zhang L, Shen M, et al. Polymorphisms in cytokine and cellular adhesion molecule genes and susceptibility to hematotoxicity among workers exposed to benzene. Cancer Res. 65 (2005) 9574–9581.
Lebaron MJ, Rasoulpour RJ, Klapacz J, Ellis-Hutchings RG, Hollnagel HM, Gollapudi BB. Epigenetics and chemical safety assessment. Mutat Res. 2010, April 23 [Epub ahead of print]
Liao TT1, Jia RW, Shi YL, Jia JW, Wang L, Chua H. Propidium iodide staining method for testing the cytotoxicity of 2,4,6-trichlorophenol and perfluorooctane sulfonate at low concentrations with Vero cells. J Environ Sci Health A Tox Hazard Subst Environ Eng. 2011;46(14):1769-75.
Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics (Oxford, England) 21 (2005) 3448–3449.
Martin SJ, et al. Early redistribution of plasma membrane phosphatidylserine is a general feature of apoptosis regardless of the initiating stimulus:
inhibition by overexpression of Bcl-2 and Abl. J Exp Med 182, 1545-1556 (1995).
Mattingly CJ, Rosenstein MC, Colby GT, Forrest Jr. JN, Boyer JL. The Comparative Toxicogenomics Database (CTD): a resource for comparative toxicological studies. J. Exp. Zool. A: Comp. Exp. Biol. 305 (2006) 689–692. McHale C, Zhang L, Hubbard AE, Smith M. Toxicogenomic studies in human populations, in: Boverhof DR, Gollapudi BB (Eds.), Applications of Toxicogenomics in Safety Evaluation and Risk Assessment, John Wiley & Sons, Inc., p177-206.
McHale CM, Zhang L, Lan Q, et al. Changes in the peripheral blood transcriptome associated with occupational benzene exposure identified by cross-comparison on two microarray platforms. Genomics 93 (2009) 343– 349.
Page 285
������������������� ������� ������������������������� ������ ��������������������McHale C, Zhang L, Lan Q, et al. Low-dose, occupational exposure to the leukemogen benzene induces robust changes in the blood transcriptome associated with altered immune system biology. American Society of Hematology, San Francisco, CA, 2008.
Mota NP, Medved M, Whitney D, Hiebert-Murphy D, Sareen J. Protective factors for mental disorders and psychological distress in female, compared with male, service members in a representative sample. Can J Psychiatry. 2013 Oct;58(10):570-8.
Muller CI, Luong QT, Shih LY, et al. Identification of marker genes including RUNX3 (AML2) that discriminate between different myeloproliferative neoplasms and normal individuals. Leukemia 22 (2008) 1773–1778.
National Research Council (NRC), Toxicity Testing in the 21st Century: A Vision and a Strategy, Committee on Toxicity Testing and Assessment of Environmental Agents, National Academies Press, Washington, DC, 2007.
Neufeld G, Shraga-Heled N, Lange T, et al. Semaphorins in cancer. Front. Biosci. 10 (2005) 751–760.
North M, Shuga J, Loguinov A, et al. Utilizing functional genomics in yeast to discover novel biomarkers of benzene toxicity in humans, in: International Symposium on Benzene Health Effects and Mechanisms of Bone Marrow Toxicity, Munich, Germany, 2009.
Ouyang Y, Virasch N, Hao P, et al. Suppression of human IL-1beta, IL-2, IFNgamma, and TNF-alpha production by cigarette smoke extracts. J. Allergy Clin. Immunol. 106 (2000) 280–287.
Paschka P. Core binding factor acute myeloid leukemia. Semin. Oncol. 35 (2008) 410–417.
Pedersen-Bjergaard J, Andersen MK, Andersen MT, Christiansen DH. Genetics of therapy-related myelodysplasia and acute myeloid leukemia. Leukemia 22 (2008) 240–248.
Page 286
������������������� ������� ������������������������� ������ ��������������������Ren X, Lim S, Smith MT, Zhang L. Werner syndrome protein, WRN, protects cells from DNA damage induced by the benzene metabolite hydroquinone. Toxicol. Sci. 107 (2009) 367–375.
Ross D, Siegel D, Schattenberg DG, Sun XM, Moran JL. Cell-specific activation and detoxification of benzene metabolites in mouse and human bone marrow: identification of target cells and a potential role for modulation of apoptosis in benzene toxicity. Environ. Health Perspect. 104 (Suppl. 6) (1996) 1177–1182.
Savitz DA, Andrews KW. Review of epidemiologic evidence on benzene and lymphatic and hematopoietic cancers. Am. J. Ind. Med. 31 (1997) 287–295.
Sedelies KA, et al. Blocking granule-mediated death by primary human NK cells requires both protection of mitochondria and inhibition of caspase
activity. Cell Death Differ 15, 708-717 (2008).
Shen M, Zhang L, Bonner MR, Liu CS, Li G, Vermeulen R, Dosemeci M, Yin S, Lan Q. Association between mitochondrial DNA copy number, blood cell
counts, and occupational benzene exposure. Environ Mol Mutagen 49(6): 453-7. (2008)
Shen M, Lan Q, Zhang L, et al. Polymorphisms in genes involved in DNA double-strand break repair pathway and susceptibility to benzene-induced hematotoxicity. Carcinogenesis 27 (2006) 2083–2089
Singh DK, Ahn B, Bohr VA. Roles of RECQ helicases in recombination based DNA repair, genomic stability and aging. Biogerontology 10 (2009) 235– 252.
Skibola CF, Bracci PM, Halperin E, et al. Genetic variants at 6p21.33 are associated with susceptibility to follicular lymphoma. Nat. Genet. 41 (2009) 873–875.
Slade T, Grove R, Burgess P, Kessler RC. Psychological Distress Scale: normative data from the 2007 Australian National Survey of Mental Health and Wellbeing. Aust N Z J Psychiatry. 2011 Apr;45(4):308-16.
Smith MT, Jones RM, Smith AH. Benzene exposure and risk of non-Hodgkin lymphoma. Cancer Epidemiol. Biomark. Prev. 16 (2007) 385–391.
Page 287
������������������� ������� ������������������������� ������ ��������������������Smith MT. The mechanism of benzene-induced leukemia: a hypothesis and speculations on the causes of leukemia. Environ. Health Perspect. 104 (Suppl. 6) (1996) 1219–1225.
Smith MT, Zhang L, Wang Y, et al. Increased translocations and aneusomy in chromosomes 8 and 21 among workers exposed to benzene. Cancer Res. 58 (1998) 2176–2181.
Snyder R, Hedli CC, An overview of benzene metabolism. Environ. Health Perspect. 104 (Suppl. 6) (1996) 1165–1171.
Study of Health Outcomes in Aircraft Maintenance Personnel (SHOAMP), Phase II Mortality and Cancer Incidence Study Interim Report, July 2003, Australian Department of Defence, Canberra; July 2003.
Subrahmanyam VV, Ross D, Eastmond DA, Smith MT. Potential role of free radicals in benzene-induced myelotoxicity and leukemia. Free Radic Biol Med. 11 (1991) 495–515.
Trosko JE. Commentary on “Toxicity testing in the 21st century: a vision and a strategy”: stem cells and cell-cell communication as fundamental targets in assessing the potential toxicity of chemicals. Hum Exp Toxicol. 2010; 29(1):21-29.
Van der Laan MJ, Pollard KS. A new algorithm for hybrid hierarchial clustering with visualization and the bootstrap. J Stat Plann Inference (2003) 275–303.
Vermeulen R, Lan Q, Zhang L, et al. Decreased levels of CXC-chemokines in serum of benzene-exposed workers identified by array-based proteomics. Proc Natl Acad Sci. U.S.A. 102 (2005) 17041–17046.
Vlaanderen J, Moore LE, Smith MT, et al. Application of omics technologies in occupational and environmental health research: current status and projections. Occup Environ Med. (2009).
Warrener R, et al. Tumor cell-selective cytotoxicity by targeting cell cycle
checkpoints. Faseb J 17, 1550-1552 (2003).
Page 288
������������������� ������� ������������������������� ������ ��������������������Waterhouse NJ, Goldstein JC, Kluck RM, Newmeyer DD, Green DR. The (Holey) study of mitochondria in apoptosis. Methods Cell Biol 66, 365-391 (2001).
Waterhouse NJ, Trapani JA. A new quantitative assay for cytochrome c
release in apoptotic cells. Cell Death Differ 10, 853-855 (2003).
Waterhouse NJ. The cellular energy crisis: mitochondria and cell death. Med
Sci Sports Exerc 35, 105-110 (2003).
Waterhouse NJ, Ricci JE, Green DR. And all of a sudden it's over:
mitochondrial outer-membrane permeabilization in apoptosis. Biochimie 84, 113-121 (2002).
Waterhouse NJ, et al. Functional dissociation of DeltaPsim and cytochrome c release defines the contribution of mitochondria upstream of caspase
activation during granzyme B-induced apoptosis. Cell Death Differ 13, 607-618 (2006).
Waters M, Stasiewicz S, Merrick BA, et al. CEBS—Chemical Effects in Biological Systems: a public data repository integrating study design and toxicity data with microarray and proteomics data. Nucleic Acids Res. 36 (2008) D892– 900.
Witzmann FA, Carpenter RL, Ritchie GD, Wilson CL, Nordholm AF, Ross J. Toxicity of chemical mixtures: Proteomic analysis of persisting liver and kidney protein alterations inducted by repeated exposure of rats to JP-8 jet fuel vapor. Electrophoresis. 2000;21:2138-2147.
Yoon BI, Hirabayashi Y, Kawasaki Y, et al. Aryl hydrocarbon receptor mediates benzene-induced hematotoxicity. Toxicol. Sci. 70 (2002) 150–156.
Zhang L, McHale CM, Rothman N, et al. Systems Biology of human benzene exposure. Chem Biol Interact. 2010;184(1-2):86-93.
Page 289
������������������� ������� ������������������������� ������ ��������������������Zhang L, Smith M, Bandy B, Tamaki SJ, Davison AJ. Role of Quinones, active oxygen species and metals in the genotoxicity of 1,2,4-benzenetriol, ametabolite of benzene, in: H. Nhol. H. Esterbauer, C. Rice-Evans (Eds.), Free Radicals in the Environment, Medicine and Toxicology, Richelieu, London, 1994, pp. 521–562.
Zhang L, Eastmond DA, Smith MT. The nature of chromosomal aberrations detected in humans exposed to benzene. Crit. Rev. Toxicol. 32 (2002) 1–42.
Zhang L, Rothman N, Wang Y, et al. Increased aneusomy and long arm deletion of chromosomes 5 and 7 in the lymphocytes of Chinese workers exposed to benzene. Carcinogenesis 19 (1998) 1955–1961.
Page 290