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Enrico Glaab
Luxembourg Centre for Systems Biomedicine
Integrative bioinformatics
analysis of Parkinson‘s
disease related omics data

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Workflow and data types
Omics
Risk/protective factors
& comorbidity data
Genetic mutations
& polymorphisms
Animal model
data
Networks
& pathways
Differentialgene/protein
andpathwayactivity
analyses
Gene/proteinco-
expressionanalyses
Cross-species
analyses
(Phenologs)
Clustering,
predictionand
networkanalyses

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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

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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-linked
SNP from
GWAS
Mutated in
some cases
of familial
PD
20-40y 40-60y >60y 20-40y 40-60y >60y
normalizedexpressionlevel
normalizedexpressionlevel

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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

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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

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Joing PD/aging deregulated genes – PD heat map

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Joing PD/aging deregulated genes – Aging heat map

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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

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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 in
PD
down-regulated
in PD
p = 0.008
p = 0.005

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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

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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

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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