Enrico Glaab

Luxembourg Centre for Systems Biomedicine

Integrative bioinformaticsanalysis of Parkinson‘sdisease related omics data

1

Workflow and data types

Omics

Risk/protective factors& comorbidity data

Genetic mutations& polymorphisms

Animal modeldata

Networks& pathways

Differentialgene/proteinandpathwayactivity

analyses

Gene/proteinco-expressionanalyses

Cross-speciesanalyses

(Phenologs)

Clustering,predictionand

networkanalyses

2

Meta-analysis of transcriptome changes in PD

Cross-study analysis of differentially expressed genes in PD vs. controls

1) 8 post-mortem datasets analyzed using empirical Bayes moderated t-statistic (G. K. Smyth, 2004)

2) Marot et al. (2009) inverse weighted normal method to combine significance scores

3) Multiple hypothesis testing adjustment (Benjamini & Hochberg, 1995), significance cut-off: 0.05

Section of PD pathway map

showing mitochondrial

complexes IV and VRed = down-regulated

Green = up-regulated

3

Analysis of brain transcriptome changes during aging

Analysis of differentially expressed genes across age periods (HBT BrainAtlas)

1) DEGs were computed for 16 brain regions separately, across 3 age periods

(20 to 40 years, 40 to 60 years, 60 years onwards); at least 5 replicates per class

2) Multiple testing adjustment (Benjamini & Hochberg, 1995), significance cut-off: 0.05

3) Identify genes with joint deregulation patterns in PD and over aging in multiple brain regions:

MT1G expression in“healthy“ human brains

NR4A2/NURR1 expression in“healthy“ human brains

PD-linkedSNP fromGWAS

Mutated insome casesof familialPD

20-40y 40-60y >60y 20-40y 40-60y >60y

no

rma

lized

exp

ressi

on

leve

l

no

rma

lized

exp

ressi

on

leve

l

4

PD/aging related genes: Metallothionein 1G (MT1G)

• over-expressed in PD samples and

significant up-regulation in higher age

periods

• SNP for MT1G associated with PD

(p = 4.15e-05, Fung et al., Lancet Neurol.,

2006, dbSNP 135), not replicated

• binds to various heavy metals and

responds to oxidative stress (Reddy

et al., PLoS ONE, 2006), proposed as

biomarker for neurodegeneration

(Sharma and Ebadi, IIOAB J., 2011)

• up-regulation of metallothionein gene

expression observed in Parkinsonian

astrocytes (Michael et al.,

Neurogenetics, 2011)

Metallothionein 1G (MT1G)

MT1G expression in “healthy“ human brains

5

PD/aging related genes: NURR1 (NR4A2)

• under-expressed in PD samples and

significant down-regulation in higher

age periods

• mutations in first exon have been associat-

ed with late-onset familial PD (10 out of 107

cases; W. D. Le et al., Nat. Genet., 2003)

• encodes a TF controlling the expression of

genes involved in the maintenance of the

nervous system and synaptic transmission

• represses genes encoding pro-inflammatory

neurotoxic factors in microglia and

astrocytes (Saijo et al., 2009; J. K. Lee et al.,

2009)

Nuclear receptor subfamily 4, group A, member 2 (NR4A2)

NR4A2 expression in “healthy“ human brains

6

Joing PD/aging deregulated genes – PD heat map

7

Joing PD/aging deregulated genes – Aging heat map

8

Integrate information across species: Phenologs approach

Mouse Phenotype:“Decreased dopamine level”

(MGD)

Human Phenotype:“Parkinson's disease”

(OMIM)

p < 5.31e-6

15 new candidate genes (human orthologs)

Intersection with differentially expressed genes in PD microarray studies 7 candidates remaining:

mutations in early-onset dystonia

see slide 2

Alu-insertion over-represented in PD

Enrichment analysis (Fisher‘s exact test)

mutations in DOPA-responsive dystoniayes2.61sepiapterin reductase (7,8-dihydrobiopterin:NADP+ oxidoreductase)SPR

yes2.63uncoupling protein 2 (mitochondrial, proton carrier)UCP2

-5.29torsin family 1, member A (torsin A)TOR1A

yes-8.48tyrosine hydroxylaseTH

-8.74solute carrier family 6 (neurotransmitter transporter, dopamine), member 3SLC6A3

-9.69potassium inwardly-rectifying channel, subfamily J, member 6KCNJ6

-9.91solute carrier family 18 (vesicular monoamine), member 2SLC18A2

MitochondrialScoreDescriptionSymbol

Dopamine transporter

Dopamine transporter

catalyzes L-DOPA formation

Function

9

Causal reasoning analysis of integrated microarray statistics

Causal reasoning analysis

(Chindelevitch et al., 2012)

• Combine known, manually

curated regulatory relations

between genes/proteins into

a graph

• Map microarray data onto

the graph and score potential

upstream causes for observed

deregulations (consistency +

statistical significance)

Example regulators found:

transforming growth factor

beta 2, adiponectin, pepti-

dylprolyl isomerase A

Legend:

up-regulated inPD

down-regulatedin PD

p = 0.008

p = 0.005

10

Multi-gene combinatorial marker model

Combinatorial biomarker

model

• Find pairs of genes with

differential relation of ex-

pression levels across the

sample classes in PD micro-

array studies on substantia

nigra cells

• Combine these pairs into

multi-gene combinatorial

marker models

Best model: 7 genes

(GBE1, TPBG, FKBP4,

MYST3, PPID, IGF2R,

ARL1), 92,5% cross-study

prediction accuracy

Model limitations: late-stage, post-mortem, platform-specific

11

Summary

• Meta-analysis of microarray data from PD case-control studies:

find more robust deregulation patterns in cellular pathways and complexes

• Integration of PD and aging microarray data, mouse phenologs and SNPs:

identify new genes with disease-related functional annotations

• Regulatory network and machine learning models:

prioritize candidate combinatorial biomarkers and select targets for animal

model experiments

12

References

1. E. Glaab, R. Schneider, Comparative pathway and network analysis of brain transcriptome changes during adult aging

and in Parkinson's disease, Neurobiology of Disease (2014), doi: 10.1016/j.nbd.2014.11.002

2. E. Glaab, A. Baudot, N. Krasnogor, R. Schneider, A. Valencia. EnrichNet: network-based gene set enrichment analysis,

Bioinformatics, 28(18):i451-i457, 2012

3. E. Glaab, R. Schneider, PathVar: analysis of gene and protein expression variance in cellular pathways using microarray

data, Bioinformatics, 28(3):446-447, 2012

4. E. Glaab, J. Bacardit, J. M. Garibaldi, N. Krasnogor, Using rule-based machine learning for candidate disease gene

prioritization and sample classification of cancer gene expression data, PLoS ONE, 7(7):e39932, 2012

5. E. Glaab, A. Baudot, N. Krasnogor, A. Valencia. TopoGSA: network topological gene set analysis,

Bioinformatics, 26(9):1271-1272, 2010

6. E. Glaab, A. Baudot, N. Krasnogor, A. Valencia. Extending pathways and processes using molecular interaction networks

to analyse cancer genome data, BMC Bioinformatics, 11(1):597, 2010

7. H. O. Habashy, D. G. Powe, E. Glaab, N. Krasnogor, J. M. Garibaldi, E. A. Rakha, G. Ball, A. R Green, C. Caldas, I. O.

Ellis, RERG (Ras-related and oestrogen-regulated growth-inhibitor) expression in breast cancer: A marker of ER-positive

luminal-like subtype, Breast Cancer Research and Treatment, 128(2):315-326, 2011

8. E. Glaab, J. M. Garibaldi and N. Krasnogor. ArrayMining: a modular web-application for microarray analysis combining

ensemble and consensus methods with cross-study normalization, BMC Bioinformatics,10:358, 2009

9. E. Glaab, J. M. Garibaldi, N. Krasnogor. Learning pathway-based decision rules to classify microarray cancer samples,

German Conference on Bioinformatics 2010, Lecture Notes in Informatics (LNI), 173, 123-134

10. E. Glaab, J. M. Garibaldi and N. Krasnogor. VRMLGen: An R-package for 3D Data Visualization on the Web, Journal of

Statistical Software, 36(8),1-18, 2010