NetBioSIG2013-KEYNOTE Esti Yeger-Lotem

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Keynote presentation for Network Biology SIG 2013 by Esti Yeger-Lotem, Senior Lecturer in Clinical Biochemistry at The National Institute for Biotechnology in the Negev, Israel

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NetBioSIG2013-KEYNOTE Esti Yeger-Lotem

  1. 1. Decoding the Tissue-Specificity of Hereditary Diseases by using Tissue Interactomes Esti Yeger-Lotem
  2. 2. Layout • Human tissue interactomes – extensive up-to-date resource • Decoding the tissue-specificity of hereditary diseases • Our open web-tool Familial Parkinson disease: SNCA aberration P1 P2 P3
  3. 3. From a global human interactome to tissue interactomes • Known protein-protein interactions (PPIs) - however no tissue context! • Use tissue expression data – Filter interactome per tissue – Most studies relied on GNF: the microarray study of Su et al, PNAS 2004, (e.g., Lehner 2008) • New large-scale data emerging (e.g., Sandberg 2009, Albrecht 2011) – RNA-Seq data & protein large-scale data available! P1 P2 P3
  4. 4. 66 tissues 78 tissues GNF HPA RNA-Seq 16 tissues 16 tissue expressomes Integrating tissue expression data • Protein=gene, no splice-variants • Used stringent cutoffs for expression Tissue GNF HPA RNA- seq Adipose 2,533 N/A 10,269 Adrenal 2,498 7,235 10,822 Brain 4,335 7,692 10,925 Breast N/A 6,526 10,698 Colon 2,807 7,244 10,519 Heart 3,345 6,189 9,827 Kidney 2,025 7,672 10,945 Liver 2,531 6,202 8,842 Lung 3,010 7,465 11,063 Lymph Node 2,441 6,183 10,973 Ovary 1,567 5,111 11,165 Prostate 3,075 6,508 11,250 Skeletal Muscle 1,751 5,805 8,851 Testis 3,176 7,744 12,567 Thyroid 3,360 6,982 10,938 White Blood Cells 5,750 N/A 9,466 Median 2,807 6,754 10,873
  5. 5. 66 tissues 78 tissues GNF HPA RNA-Seq 16 tissues 16 tissue expressomes Integrating tissue expression data • ~70% overlap between RNA-seq & GNF or HPA • Single resource not enough
  6. 6. 66 tissues 78 tissues GNF HPA RNA-Seq 16 tissues 16 tissue expressomes Integrating tissue expression data • Matching tissues correlated significantly (best match) 1 10 100 1000 10000 100000 100 1000 10000 100000 Gene expression level (GNF) RPKM(RNA-seq)
  7. 7. 66 tissues 78 tissues GNF HPA RNA-Seq 16 tissues 16 tissue expressomes Integrating tissue expression data Tissue Com- bined GNF HPA RNA- seq Adipose 10,859 2,533 N/A 10,269 Adrenal 13,592 2,498 7,235 10,822 Brain 14,000 4,335 7,692 10,925 Breast 12,669 N/A 6,526 10,698 Colon 13,312 2,807 7,244 10,519 Heart 12,766 3,345 6,189 9,827 Kidney 13,662 2,025 7,672 10,945 Liver 11,958 2,531 6,202 8,842 Lung 13,853 3,010 7,465 11,063 Lymph Node 13,185 2,441 6,183 10,973 Ovary 12,918 1,567 5,111 11,165 Prostate 13,586 3,075 6,508 11,250 Skeletal Muscle 11,736 1,751 5,805 8,851 Testis 14,819 3,176 7,744 12,567 Thyroid 13,518 3,360 6,982 10,938 White Blood Cells 10,844 5,750 N/A 9,466 Median 13,248 2,807 6,754 10,873 Tissue expressed gene: detected in ≥ 1 sample
  8. 8. 66 tissues 78 tissues Su et al HPA RNA-Seq 16 tissues MINTBIOGRID DIP INTACT 16 tissue expressomes Global human interactome Integrating expression & interactions 11,225 proteins (52% of proteins), 67,439 interactions
  9. 9. 66 tissues 78 tissues HPA RNA-Seq 16 tissues MINTBIOGRID DIP INTACT 16 tissue expressomes Global human interactome Integrating expression & interactions PPI in tissue if both proteins are expressed GNF
  10. 10. 0 5 10 15 20 25 30 35 40 45 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Percentageoftotalset Number of expressing tissues GNF HPA RNA-seq Combined Enriched for basic cellular processes (translation elongation, ..) 1. Most genes are globally expressed or tissue specific
  11. 11. 0 5000 10000 15000 20000 25000 30000 2. A common core network dominates all tissue interactomes > 50% of proteins & PPIs in each tissue appear in all tissues - 26,370 interactions, 4,989 proteins Genes PPIs
  12. 12. 3. Tissue hub proteins: persistent regulators • 451 tissue hubs: Hubs = proteins with top number of interactions (5%, > 45 interactions) • Highly enriched for regulatory processes - transcription regulation (42%, p<10-15) - protein kinase cascade (12%, p<10-8) - also relative to core proteins • Much of the regulatory components are similar across tissues Number of PPIs 30 45 150 Hubs Tissues
  13. 13. 4. PPI degree and expression levels are correlated across all tissues Gene2 Gene3 Gene4 Gene1 Gene1 Gene1 Gene2 Gene6 Gene4 Gene3 Gene8 Gene9 Gene10 Gene1Gene1 0 5 10 15 20 1 2 3 4 5 6 7 8 9 10 Degree RPKM percentile Adipose Spearman r= 0.98 • Previously shown in yeast von Mering et al, Nature 2002
  14. 14. 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Adipose 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Adrenal 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Brain 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Breast 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Heart 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Kidney 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Liver 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Colon 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Lymph Node 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Lung 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Ovary 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Prostate 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Skeletal Muscle 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Testis 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Thyroid 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 WBC 4. PPI degree and expression levels are correlated across all tissues
  15. 15. Layout • Tissue interactomes – extensive up-to-date resource • Decoding the tissue-specificity of hereditary diseases • Our open web-tool Familial Parkinson disease: SNCA aberration
  16. 16. Familial Parkinson disease: SNCA aberration SNCA expression 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Number of expressing tissues Percentageoftotal 342 hereditary diseases 266 causal disease genes The enigmatic tissue-specific manifestation of hereditary diseases 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Number of expressing tissues P • Hereditary diseases - causal genes associations: OMIM, COSMIC • Disease-tissue associations: Lage et al, PNAS 2008 Barshir et al, in revision
  17. 17. 0 10 20 30 40 50 60 disease tissues non disease tissues Factors governing tissue-specificity (TS) Disease tissues Other expressing tissues 63% of the genes, p<10-4 Expression level (RPKM) 0 0.5 1 1.5 2 2.5 Disease tissues Other expressing tissues MediannumberofTS-PPIof diseasegenes Tissue-specific PPIs 21% of the genes, p<10-4 Barshir et al, in revision
  18. 18. TS-PPIs illuminate disease-related mechanisms Hereditary breast cancer predisposition BRCA1 network in breast Familial lung adenocarcinoma EGFR network in lung Muscular dystrophy DAG1 network in muscle 14-16 tissues 4-13 tissues 1-3 tissues Protein expressed in: ~90% PPIs filtered out Barshir et al, in revision
  19. 19. Factors distribution across hereditary diseases TS-PPIs 15% TS-PPIs + elevated expression 12% Elevated expression: 33% Unknown 33% Disease genes tissue- specific: 7% Barshir et al, in revision
  20. 20. Layout • Tissue interactomes – extensive up-to-date resource • Decoding the tissue-specificity of hereditary diseases • Our open web-tool Familial Parkinson disease: SNCA aberration
  21. 21. Barshir et al, NAR 2013 TissueNet: an open database 14-16 tissues 4-13 tissues 1-3 tissues Protein expressed in: http://netbio.bgu.ac.il/tissuenet
  22. 22. Disease/Stimulus Differentially expressed genes Genetic screening (mutations) Known protein- DNA interactions Known protein-protein interactions Interactome (~60,000 edges) Identifying signaling pathways Identify regulatory pathways connecting screening data ResponseNet Yeger-Lotem et al, Nature Genetics 2009
  23. 23. The ResponseNet web-server http://netbio.bgu.ac.il/respnet Basha et al, Nucleic Acids Research 2013 Mutations Diff. exp. genes Human tissue interactomes
  24. 24. Identifying context-sensitive pathways http://netbio.bgu.ac.il/ContextNet
  25. 25. Thanks! Marie Curie International Reintegration Grant TissueNet Galila Agam Haim Belmaker Assaf Rudich Vered Chalifa-Caspi Inbar plaschkes My lab @ BGU Ruth Barshir Omer Basha Alex Lan Ilan Smoly Shoval Tirman Amir Eluk Omer Schwartz ContextNet Michal Ziv-Ukelson ResponseNet Ernest Fraenkel Susan Lindquist

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