The document outlines an integrative bioinformatics analysis of Parkinson's disease (PD) utilizing various omics data types, including genetic mutations and differential gene/protein activity analyses. It details a meta-analysis of transcriptome changes, revealing key genes such as MT1G and NR4A2 that are regulated in PD and aging contexts, and discusses regulatory network and machine learning models for candidate biomarker prioritization. Additionally, it incorporates trans-species comparisons and causal reasoning analyses to identify novel disease-related genes and potential biomarkers.

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

2. 1
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

3. 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

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

5. 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

6. 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

7. 6
Joing PD/aging deregulated genes – PD heat map

8. 7
Joing PD/aging deregulated genes – Aging heat map

9. 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

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

11. 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

12. 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

13. 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