integrated biology a pathway-centric approach to multiomics research … · 2016. 9. 3. ·...
TRANSCRIPT
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A Pathway-centric Approach
to Multiomics Research
Powered by GeneSpring
Analytics
Sham Naal
Market Manager, Academia and
Research Europe
March 13, 2013
Integrated Biology
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3
Interpretive value comes from
integrating diverse measurements
within their biological context
Looking at results from
individual disciplines describes
only part of the picture.
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LTM Revenue* $1.1B
LTM Revenue* $1.0B
LTM Revenue* $2.7B
Agilent Overview
Electronic
Measurement Chemical Analysis Diagnostics Life Sciences Life Sciences
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LTM Revenue* $1.1B
LTM Revenue* $1.0B
LTM Revenue* $2.7B
Agilent Overview
Electronic
Measurement Chemical Analysis Diagnostics Life Sciences Life Sciences
![Page 5: Integrated Biology A Pathway-centric Approach to Multiomics Research … · 2016. 9. 3. · Software and Lab Informatics Capillary Electrophoresis Micro-fluidics and bio-analyzers](https://reader034.vdocuments.net/reader034/viewer/2022052013/602a6f2be40c7d64ab38fab5/html5/thumbnails/5.jpg)
Life – Products and Applications
Detection Separation
Sample Intro Sample
Prep
Sample
collection Detection Sample
Labeling Conversion
Data to Report Sample
Prep
Products, Systems and Integrated Solutions Enabling Customer‘s Workflows
Life Sciences Business Models
Liquid Chromatographs
Liquid Chromatographs Mass Spectrometry
Software and Lab Informatics
Capillary Electrophoresis
Micro-fluidics and bio-analyzers
Robotics (sample prep automation)
Bio-reagents
PCR and QPC instruments
Microarrays (DNA and R&A)
Consumables
Services and Support
Life Sciences
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“Omics”
7
DNA RNA Protein Metabolite
What is the “common coordinate” that
enables integrative analysis?
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Common Reference: Pathway Representation
8
R
R
Gene B
Gene A
Gene X
HO
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“Omics”
9
DNA RNA Protein Metabolite
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DNA RNA Protein Metabolite
“Omics”
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DNA RNA Protein Metabolite
RNA Protein Metabolite
RNA Protein Metabolite
DNA
DNA RNA
Protein
Protein Metabolite
DNA RNA Protein Metabolite
“Omics” Biology
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DNA RNA Protein Metabolite
RNA Protein Metabolite
RNA Protein Metabolite
DNA
DNA RNA
Protein
Protein Metabolite
DNA RNA Protein Metabolite
“Omics”
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DNA RNA Protein Metabolite
RNA Protein Metabolite
RNA Protein Metabolite
DNA
DNA RNA
Protein
Protein Metabolite
DNA RNA Protein Metabolite
“Omics”
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DNA RNA Protein Metabolite
RNA Protein Metabolite
RNA Protein Metabolite
DNA
DNA RNA
Protein
Protein Metabolite
DNA RNA Protein Metabolite
The Challenge
DNA RNA Protein Metabolite
RNA Protein Metabolite
RNA Protein Metabolite
DNA
DNA RNA
Protein
Protein Metabolite
DNA RNA Protein Metabolite
“-Omics” Biological Processes
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Common Reference: Pathway Representation
16
R
R
Gene B
Gene A
Gene X
HO
Sources
• WikiPathways
• BioCyc/MetaCyc
• Generalized BioPax
• KEGG
WikiPathways
Platforms
• GeneSpring
• IPA
• MetaCore
• others….
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R
R
HO
Gene B
Gene A
Gene X
R
R
HO
Gene B
Gene A
Gene X R
R
HO
Gene B
Gene A
Gene X
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R
R
HO
Gene B
Gene A
Gene X
• Identifies why the pathway is active
• Suggests follow-on experiments
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LC/MS
GC/MS
Microarrays Biological
Pathways
MassHunter Qual/Quant
ChemStation AMDIS
Feature Extraction GeneSpring Platform
Agilent Integrated Biology Workflows: Practically any –omics technology type imaginable
Alignment to Reference Genome NGS
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Case Studies
Study I – Infectious Disease
Kyu Rhee
Tuberculosis (Treatment)
Weil / Cornell
Study II – Cancer
Akhilesh Pandey
Chemopreventives
Johns Hopkins
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“TB can usually be cured with a combination of first-line drugs taken for several months. Shown here are the four drugs in the standard regimen of first-line drugs and their modes of action. Also shown are the dates these four drugs were discovered—all more than 40 years ago.”
NIAID
Pathway-informed Drug Discovery for Tuberculosis (Dr. Kyu Rhee, Weill Cornell Medical College)
http://www.niaid.nih.gov/topics/tuberculosis/Understanding/
WhatIsTB/ScientificIllustrations/Pages/firstLineIllustration.aspx
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Metabolome
(Label-free)
GX Microarray
(Custom) Transcriptome
(2-color)
LC/MS
(6520 iFunnel
Q-TOF)
GeneSpring
•Corroborate known
mechanisms of action
•Discover unknown cellular
responses
•Suggest new druggable
targets.
Drug X
Drug Z
Drug Y
Multi-omics Workflow: Transcriptomics + Metabolomics
low, mid, high
low, mid, high
low, mid, high
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Example Analysis– Drug X
All Genes
(Note increased
change with
dose)
Dose
Low Med High
Fo
ld C
ha
ng
e
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Example Analysis– Drug X
Genes among top
5% up-regulated
in all dosages
Genes among
bottom 5% down-
regulated in all
dosages
Dose
Low Med High
Fo
ld C
ha
ng
e
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Enrichment: Find “interesting” pathways
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Down-
Regulated
Genes
Pathway
Database GO
Database
Enriched
Pathways
Enriched
GO terms
• Pathway source: WikiPathways
• Enrichment: HG via GeneSpring
• Identifier Mapping: GeneSpring +
BridgeDB
Up-Regulated
Genes
Pathway
Database GO
Database
Enriched
Pathways
Enriched
GO terms
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BridgeDb: Mapping Entities Onto Pathways
Resolves Mapping Problem Between Databases
Metabolite Identifiers •KEGG
•HMDB
•ChEBI
•CAS
Protein Identifiers: •Swiss-Prot
•UniProt
•UniProt/TrEMBL
Gene Identifiers : •Entrez Gene, GenBank, Ensembl
•EC Number, RefSeq, UniGene, HUGO
•HGNC, EMBL
BridgeDB
Pathways DB
• Gene ID
• Protein ID
• Metabolite ID
• Interactive
• Map entities
HomoloGene
September 2012
LBMSDG
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Identifiers in
“omics” Data
Identifiers in
Pathway DBs
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Enrichment: Find “interesting” pathways
27
Down-
Regulated
Genes
Pathway
Database GO
Database
Enriched
Pathways
Enriched
GO terms
• Pathway source: WikiPathways
• Enrichment: HG via GeneSpring
• Identifier Mapping: GeneSpring +
BridgeDB
Up-Regulated
Genes
Pathway
Database GO
Database
Enriched
Pathways
Enriched
GO terms
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Pathway & GO Enrichment: Drug X
• MOA
• Suggestion of
secondary
mechanisms
• Direction for
further
exploration
DOWNREGULATED
Pathway p-value (Genes) GO Term P-value
Mx_Oxidative_phosphorylation_WP1680_41452 1.38E-04 phospholipase C activity 3.34E-04 Mx_Peptidoglycan_biosynthesis_WP1685_41461 0.002111 lipase activity 2.01E-04 Mx_D-Glutamine_and_D-glutamate_metabolism_WP1643_41425 0.002212 phospholipase activity 0.001113
Mx_Ether_lipid_metabolism_WP1645_41436 0.00711 Mx_Inositol_phosphate_metabolism_WP1664_41480 0.014402 Mx_Riboflavin_metabolism_WP1696_41424 0.023819
UPREGULATED
Pathway p-value (Genes) GO Term P-value
Mx_Glyoxylate_and_dicarboxylate_metabolism_WP1661_41479 0.030887 biological regulation 9.75E-06
0.074453 regulation of biological process 7.11E-06 response to chemical stimulus 3.50E-06 response to stimulus 1.42E-05
negative regulation of biological process 2.97E-05 regulation of growth 8.20E-05 negative regulation of growth 1.85E-04 propionate catabolic process, 2-methylcitrate cycle 5.96E-04 propionate metabolic process, methylcitrate cycle 5.96E-04 regulation of metabolic process 4.66E-04
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Multi-omics Analysis: Learning more about
significant genes, metabolites, paths, etc.
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Question: How can I understand
what is biologically
relevant about this
pathway?
Answer: Pathway visualization
to find enriched paths
Peptidoglycan biosynthesis
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Pathway: Peptidoglycan Biosynthesis
30
Significantly
downregulated
in all dosages
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Pathway: Peptidoglycan Biosynthesis
Enriched Genes
31
Significantly
downregulated
in all dosages
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Pathway: Peptidoglycan Biosynthesis
Metabolite Changes
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Control vs. Drug
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Pathway: Peptidoglycan Biosynthesis
All Measurements
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Candidates for
re-mining or for
targeted assay
Candidates for
targeted
proteomics
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Metabolome
(Label-free)
GX Microarray
(Custom) Transcriptome
(2-color)
LC/MS
(6520 iFunnel
Q-TOF)
GeneSpring
•Corroborate known
mechanisms of action
•Discover unknown cellular
responses
•Suggest new druggable
targets.
Drug X
Drug Z
Drug Y
Multi-omics Workflow: Transcriptomics + Metabolomics
low, mid, high
low, mid, high
low, mid, high
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Metabolome
(Label-free)
GX Microarray
(Custom) Transcriptome
(2-color)
LC/MS
(6520 iFunnel
Q-TOF)
GeneSpring
•Corroborate known
mechanisms of action
•Discover unknown cellular
responses
•Suggest new druggable
targets.
Drug X
Drug Z
Drug Y
Proteome
(Targeted)
Multi-omics Workflow: Transcriptomics + Metabolomics Targeted Proteomics
low, mid, high
low, mid, high
low, mid, high
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• Suggests next experiments
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Case Study: Looking for Evidence of Pathway Intervention to Advance
the Search for New Breast Cancer Chemopreventives
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Suhr Y-J. Nat. Rev. Cancer, 2003, 768-80.
siRNA
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The Workflow:
Manual
37
GeneSpring 11.5 SpectrumMill MS
IPA IPA
Joint Pathway Analysis
(Manual)
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Keap1- Nrf2 Pathway:
Transcriptome in GeneSpring Integrated Biology
Control
Control
Knockdown
SFN treated
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Control
Control
Knockdown
SFN treated
Keap1- Nrf2 Pathway:
Proteome in GeneSpring Integrated Biology
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KEAP1
NQO1
GCLC
SQSTM1
AKR1B10
AKR1C1/2
GAPDH
VC
SF
N
NT
C
KD
Genetic Pharmacologic
VC
SF
N
NT
C
KD
Genetic Pharmacologic
MCF10A MCF12A
ALDH3A1
AKR1C3
Western blot validation of SILAC data
Candidate markers
*
*
IHC validation
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VC SFN
NTC KEAP1 Knockdown
AKR1C3 Validation by immunohistochemistry
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Insights from Integrated Analysis
• “Big Picture” view
• Pharmacodynamic profile
• Identify activated pathways …including known cytoprotective pathways
• Identify off-target behaviors, and theorize as to mechanism
(or even toxicity) (e.g. SFN does more than inhibit KEAP1)
42
Key Pathways from SFN Exploration
• Xenobiotic metabolism and antioxidants
• Glutathione metabolism
• Carbohydrate metabolism and NAD(P)H
generation
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Integrated Biology:
Enabling pathway-based multi-omic discovery-to-validation
MULTI-OMIC
PATHWAY-CENTRIC
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Feedback Loop:
Multi Omics Discovery Targeted proteomics
Multi-omic Analysis in GeneSpring
44
Spectrum Mill or Skyline for targeted
proteomics
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Propose new experiments based on pathway
analysis
1. Re-mine originally acquired (or legacy)
untargeted metabolomics data based on
pathway analysis—create db
2. Design new experiments (metabolite,
protein or genes) based on pathway results
interpretation
Multi-Omic Analysis in GeneSpring
Pathway-directed re-mining of data or designing the next
experiment
Build custom metabolite database
PCDL
Export protein IDs to Peptide Selector
for targeted MS/MS
Spectrum Mill
Upload select pathway genes for
custom microarray or NGS design
eArray
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GeneSpring:
Combining Modules for Multi-Omics Capabilities
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Integrated Biology
48
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Thank you!
Acknowledgements Kyu Rhee Cornell Weil Medical College
Akhilesh Pandey Johns Hopkins University
Agilent Technologies
• Steve Fischer, Metabolomics &
Proteomics
• Theo Sana, Metabolomics/IB
• Chris Miller & Alex Apfel, Proteomics/IB
• Allan Kuchinsky, Agilent Labs
• Leo Bonilla, Life Sciences Marketing
Learn more!
New Agilent IB Website:
http://biology.chem.agilent.com/
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