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Differential Network Analysis in Mouse Expression Data

Differential Network Analysis in Mouse Expression Data. Tova Fuller Steve Horvath Department of Human Genetics University of California, Los Angeles BIOCOMP’07 Conference, 6/26/07. Outline. Introduction: Single versus differential network analysis Differential Network construction

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Differential Network Analysis in Mouse Expression Data

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  1. Differential Network Analysis in Mouse Expression Data Tova Fuller Steve Horvath Department of Human Genetics University of California, Los Angeles BIOCOMP’07 Conference, 6/26/07

  2. Outline • Introduction: • Single versus differential network analysis • Differential Network construction • Results • Functional Analysis • Conclusion

  3. Goals of Single Network Analysis • Identifying genetic pathways (modules) • Finding key drivers (hub genes) • Modeling the relationships between: • Transcriptome • Clinical traits / Phenotypes • Genetic marker data

  4. Validationset 1 Validationset 2 Single Network WGCNA 1 gene co-expression network Multiple data sets may be used for validation

  5. Goals of Differential Network Analysis • Uncover differences in modules and connectivity in different data sets • Ex: Human versus chimpanzee brains (Oldham et al. 2006) • Differing toplogy in multiple networks reveals genes/pathways that are wired differently in different sample populations Oldham MC, Horvath S, Geschwind DH (2006) Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci U S A 103, 17973-17978.

  6. Differential Network WGCNA NETWORK 1 NETWORK 2 2+ gene co-expression networks Identify genes and pathways that are: • Differentially expressed • Differentially wired

  7. 135 FEMALES NETWORK 1 NETWORK 2 BxH Mouse Data • Single network analysis female BxH mice revealed a weight-related module (Ghazalpour et al. 2006) • Samples: Constructed networks from mice from extrema of weight spectrum: • Network 1: 30 leanest mice • Network 2: 30 heaviest mice • Transcripts: Used 3421 most connected and varying transcripts Ghazalpour A, Doss S, Zhang B, Wang S, Plaisier C, Castellanos R, Brozell A, Schadt EE, Drake TA, Lusis AJ, Horvath S (2006) Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS genetics 2, e130

  8. Methods • Compute Comparison Metrics • Difference in expression: t-test statistic • Compare difference in connectivity: DiffK Identify significantly different genes/pathways Permutation test Functional analysis of significant genes/pathways DAVID database Primary literature

  9. Computing Comparison Metrics DIFFERENTIAL EXPRESSION t-test statistic computed for each gene, t(i) DIFFERENTIAL CONNECTIVITY K1(i) = k1(i) K2(i) = k2(i) max(k1) max(k2) DiffK(i): difference in normalized connectivities for each gene: DiffK(i) = K1(i) – K2(i)

  10. Sector Plot • We visualize the comparison metrics via a sector plot: • x-axis: DiffK • y-axis: t statistics • We establish sector boundaries to identify regions of differentially expressed and/or connected regions • |t| = 1.96 corresponding to p = 0.05 • |DiffK| = 0.4

  11. NETWORK 1 NETWORK 2 Permutation test:Identifying significant sectors no.perms: number of permutations For each sector j, we compare the number of genes in unpermuted and permuted sectors (nobs and nperm) PERMUTE

  12. X 0.001 0.001 X X 0.01 0.001 X Sector Plot Results

  13. Functional Analysis SECTOR 3 High t statistic High DiffK Yellow module in lean Grey in obese (63 genes) SECTOR 5 Low t statistic High Diff K (28 genes) Genes in these sectors have higher connectivity in lean than obese mice: ~ pathways potentially disregulated in obesity ~

  14. Sector 3:Functional Analysis Results DAVID Database • “Extracellular”: • extracellular region (38% of genes p = 1.8 x 10-4) • extracellular space (34% of genes p = 5.7 x 10-4) • signaling (36% of genes p = 5.4 x 10-4) • cell adhesion (16% of genes p = 7.7 x 10-4) • glycoproteins (34% of genes p = 1.6 x 10-3) • 12 terms for epidermal growth factor or its related proteins • EGF-like 1 (8.2% of genes p = 8.7 x 10-4), • EGF-like 3 (6.6% of genesp = 1.6 x 10-3), • EGF-like 2 (6.6% of genes p = 6.0 x 10-3), • EGF (8.2% of genes p = 0.013) • EGF_CA (6.6% of genes p = 0.015)

  15. Sector 3:Functional Analysis Results Primary Literature • Results supported by a study on EGF levels in mice (Kurachi et al. 1993) • EGF found to be increased in obese mice • Obesity was reversed in these mice by: • Administration of anti-EGF • Sialoadenectomy Kurachi H, Adachi H, Ohtsuka S, Morishige K, Amemiya K, Keno Y, Shimomura I, Tokunaga K, Miyake A, Matsuzawa Y, et al. (1993) Involvement of epidermal growth factor in inducing obesity in ovariectomized mice. The American journal of physiology 265, E323-331

  16. Sector 5: Functional Analysis ResultsDAVID Database • Enzyme inhibitor activity (p = 2.9 x 10-3)* • Protease inhibitor activity (p = 6.0 x 10-3) • Endopeptidase inhibitor activity (p = 6.0 x 10-3) • Dephosphorylation (p = 0.012) • Protein amino acid dephosphorylation (p = 0.012) • Serine-type endopeptidase inhibitor activity (p = 0.042) * p values shown are corrected using Bonferroni correction

  17. Sector 5: Functional Analysis ResultsPrimary Literature Itih1 and Itih3 • Enriched for all categories shown previously • Located near a QTL for hyperinsulinemia (Almind and Kahn 2004) • Itih3 identified as a gene candidate for obesity-related traits based on differential expression in murine hypothalamus (Bischof and Wevrick 2005) Serpina3n and Serpina10 • Enriched for enzyme inhibitor, protease inhibitor, and endopeptidase inhibitor • Serpina10, or Protein Z-dependent protease inhibitor (ZPI) has been found to be associated with venous thrombosis (Van de Water et al. 2004) Almind K, Kahn CR (2004) Genetic determinants of energy expenditure and insulin resistance in diet-induced obesity in mice. Diabetes 53, 3274-3285 Bischof JM, Wevrick R (2005) Genome-wide analysis of gene transcription in the hypothalamus. Physiological genomics 22, 191-196 Van de Water N, Tan T, Ashton F, O'Grady A, Day T, Browett P, Ockelford P, Harper P (2004) Mutations within the protein Z-dependent protease inhibitor gene are associated with venous thromboembolic disease: a new form of thrombophilia. Bjh 127, 190-194

  18. Conclusions • Differential Network Analysis reveals pathways that are both differentially regulated and connected in mouse obesity • Genes that are differentially connected may/may not be differentially expressed • Primary literature supports biological plausibility of these pathways in weight related disorders • Sector 3 & EGF pathways: potential EGF causality in obesity • Sector 5 & serine protease pathways: potential link between obesity and venous thrombosis • These results help identify targets for validation with biological experiments

  19. Acknowledgements Guidance HORVATH LAB Steve Horvath Jason Aten Jun Dong Peter Langfelder Ai Li Wen Lin Anja Presson Lin Wang Wei Zhao Collaboration LUSIS LAB Jake Lusis Anatole Ghazalpour Thomas Drake Funding UCLA Medical Scientist Training Program (MD/PhD) An R tutorial may be found at: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/DifferentialNetworkAnalysis

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