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Integrative bioinformatics analysis of Parkinson's disease related omics data

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Integrative bioinformatics analysis of Parkinson's disease related omics data

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Presentation on statistical meta analysis of omics data from Parkinson's disease case-control studies. The results are used for a comparative analysis against aging-related omics alterations in the brain and a prioritization of new candidate disease genes using the phenologs approach.

Presentation on statistical meta analysis of omics data from Parkinson's disease case-control studies. The results are used for a comparative analysis against aging-related omics alterations in the brain and a prioritization of new candidate disease genes using the phenologs approach.

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Integrative bioinformatics analysis of Parkinson's disease related omics data

  1. 1. Enrico Glaab Luxembourg Centre for Systems Biomedicine Integrative bioinformatics analysis of Parkinson‘s disease related omics data
  2. 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. 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. 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. 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. 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. 7. 6 Joing PD/aging deregulated genes – PD heat map
  8. 8. 7 Joing PD/aging deregulated genes – Aging heat map
  9. 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. 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. 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. 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. 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

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