NK MacLennan, J Dong, S Horvath, L Ornelas, L Rahib, K Dipple and ERB McCabe.

1. NK MacLennan, J Dong, S Horvath, L Ornelas, L Rahib, K Dipple and ERB McCabe. UCLA, Los Angeles, CA, United States. Network Analysis of Glycerol Kinase Deficient Mice Predicts Genes Essential for Survival: A Systems Biology Approach

2. Glycerol Kinase • Catalyzes the reaction Glycerol glycerol 3-phosphate, a substrate for gluconeogenesis and lipid metabolism

3. Human Glycerol Kinase Deficiency (hGKD) • hGKD is an X-linked inborn error of metabolism. • Symptoms include metabolic and central nervous system deterioration. • Treatment: low-fat diet. • There is no satisfactory correlation between GKD genotype and phenotype.

4. Mouse Model of GKD • GK knockout (KO) mice model the human GKD phenotype. Huq et al., Hum Mol Genet. 1997; Kuwada et al., Biochem Biophys Res Commun. 2005 • Unlike humans, mice die at 3-4 days of life (Dol).

5. Objective • Identify genes associated with survival of WT mice using network analysis that relates a measure of differential expression to connectivity. • Highly connected highly differentially expressed genes have been found to be predictors of survival.

6. Methods • Microarray analysis on liver mRNA • Expression data was filtered for the top 10% most varying probe sets for Weighted Gene Co-Expression Network Analysis (WGCNA). WT C WT KO

7. Weighted Gene Co-Expression Network Analysis (WGCNA) Overviewhttp://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/

8. Construct a network Rationale: make use of interaction patterns between genes Identify modules Rationale: module (pathway) based analysis Relate modules to external information Array Information: Sample data Gene Information: EASE Rationale: find biologically interesting modules • Study Module Preservation across different data • Rationale: • Same data: to check robustness of module definition • Different data: to find interesting modules Find the key drivers in interesting modules Tools: Module connectivity, causality testing Rationale: experimental validation, therapeutics, biomarkers

9. Construct a network Rationale: make use of interaction patterns between genes Identify modules Rationale: module (pathway) based analysis Relate modules to external information Array Information: Sample data Gene Information: EASE Rationale: find biologically interesting modules • Study Module Preservation across different data • Rationale: • Same data: to check robustness of module definition • Different data: to find interesting modules Find the key drivers in interesting modules Tools: Module connectivity, causality testing Rationale: experimental validation, therapeutics, biomarkers

10. Overview: gene co-expression network analysis Construct a Network • Microarray gene expression data • Gene expression correlation • Correlation Matrix • Power adjacency functiongenerates a weighted network

11. Construct a network Rationale: make use of interaction patterns between genes Identify modules Rationale: module (pathway) based analysis Relate modules to external information Array Information: Sample data Gene Information: EASE Rationale: find biologically interesting modules • Study Module Preservation across different data • Rationale: • Same data: to check robustness of module definition • Different data: to find interesting modules Find the key drivers in interesting modules Tools: Module connectivity, causality testing Rationale: experimental validation, therapeutics, biomarkers

12. Module Identification • WGCNA aim: Detect modules. • Modules are groups of highly correlated, highly connected genes. • Defined with the standard distance measure: 1-correlation.

13. Construct a network Rationale: make use of interaction patterns between genes Identify modules Rationale: module (pathway) based analysis Relate modules to external information Array Information: Sample data Gene Information: EASE Rationale: find biologically interesting modules • Study Module Preservation across different data • Rationale: • Same data: to check robustness of module definition • Different data: to find interesting modules Find the key drivers in interesting modules Tools: Module connectivity, causality testing Rationale: experimental validation, therapeutics, biomarkers

14. Gene Significance (GS) Module Connectivity Connectivity (k) and Gene Significance (GS) • A measure of a gene’s connection strength to other genes in the whole network. • Use both k and GS

15. Construct a network Rationale: make use of interaction patterns between genes Identify modules Rationale: module (pathway) based analysis Relate modules to external information Array Information: Sample data Gene Information: EASE Rationale: find biologically interesting modules • Study Module Preservation across different data • Rationale: • Same data: to check robustness of module definition • Different data: to find interesting modules Find the key drivers in interesting modules Tools: Module connectivity, causality testing Rationale: experimental validation, therapeutics, biomarkers

16. Construct a network Rationale: make use of interaction patterns between genes Identify modules Rationale: module (pathway) based analysis Relate modules to external information Array Information: Sample data Gene Information: EASE Rationale: find biologically interesting modules • Study Module Preservation across different data • Rationale: • Same data: to check robustness of module definition • Different data: to find interesting modules Find the key drivers in interesting modules Tools: Module connectivity, causality testing Rationale: experimental validation, therapeutics, biomarkers

17. Unsupervised hierarchical clustering analysis revealed that overall gene expression profiles of the dol 1 and 3 KO mice differed from WT. Results Dol3 Dol 1

18. Identify Modules and Study Module Preservation Dol 3 Dol 1 Dol 1 colors Dol 3 colors

19. DOL 1 KO Blue: Underexpressed Turquoise: Overexpressed DOL 3 KO Blue: Underexpressed Brown: No relationship Turquoise: Overexpressed Relate Modules to Gene SignificanceGlycerol Kinase Knockout Status

20. Mitotic cell cycle, transcription factor binding, response to DNA damage stimulus, protein metabolism, apoptosis, cell death. Organic acid/carboxylic acid, lipid, amino acid, steroid and carbohydrate metabolism. Mitotic cell cycle, protein metabolism, epigenetic regulation of gene expression. Carboxylic acid/organic acid, fatty acid, amino acid and glucose metabolism. Relate Modules to External InformationFunctional Group EnrichmentDol1 Dol3

21. Find the Key Drivers in Interesting ModulesDol1 Dol3 Gene Significance Gene Significance Gene Significance Gene Significance Module Connectivity Module Connectivity Module Connectivity Module Connectivity GK TAT HNF4a GK GPD VDAC BCL2 BID GADD45 TRP53inp1 TAT HNF4a GPD VDAC ACOT PSAT PLK3 ACOT PSAT

22. Validation Studies • Cell Culture • ACOT • PSAT • PLK3 • KO Mice • ACOT

23. Summary • Dol 1 Blue module: • Genes underexpressed in KO • GK gene module membership • Enriched with Apoptosis/ cell death genes

24. Summary • Dol 3 blue module: • Genes Underexpressed in KO • Loss of Apoptosis/ cell death gene enrichment

25. Summary • Dol 1 and 3 Turquoise module: • Genes overexpressed in KO • ACOT, PSAT, PLK3 connected

26. Summary • Gene validation studies supported the WGCNA. • ACOT • PSAT • PLK3

27. Conclusion • WGCNA permits the reduction of high dimensionality data to low dimensionality output that is more easily understood • Revealed novel target genes possibly essential for survival of WT • Provided evidence of an apoptotic role for GK that is lost in GKD

28. Acknowledgements • McCabe Lab • Dipple Lab

29. GK GK GK

30. Choice of Power, β