Network Analysis of Glycerol Kinase Deficient Mice Predicts Genes Essential for Survival: A Systems Biology Approach

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

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 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 WT KO WT C Expression data was filtered for the top 10% most varying probe sets for Weighted Gene Co-Expression Network Analysis (WGCNA).

7 Weighted Gene Co-Expression Network Analysis (WGCNA) Overview

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 Construct a Network Microarray gene expression data Gene expression correlation Correlation Matrix Power adjacency function generates a weighted network aij i j = cor( x, x ) β

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:

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 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 Gene Significance (GS) Module Connectivity

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

Module Preservation across different data Rationale: Same data: to check robustness of module definition Different data: to find interesting

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

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

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

20 Relate Modules to External Information Functional Group Enrichment Dol1 Mitotic cell cycle, transcription factor binding, response to DNA damage stimulus, protein metabolism, apoptosis, cell death. Dol3 Mitotic cell cycle, protein metabolism, epigenetic regulation of gene expression. Organic acid/carboxylic acid, lipid, amino acid, steroid and carbohydrate metabolism. Carboxylic acid/organic acid, fatty acid, amino acid and glucose metabolism.

21 Find the Key Drivers in Interesting Modules Dol1 Dol3 Gene Significance GK TAT HNF4a BCL2 BID GADD45 TRP53inp1 Gene Significance GK GPD VDAC Module Connectivity Module Connectivity Gene Significance Module Connectivity GPD VDAC ACOT PSAT Gene Significance Module Connectivity TAT HNF4a ACOT PSAT PLK3

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 Cell Culture Validation % of Control *** *** *** ** *** 0 Gyk GK Acot Gyk GK Psat Gyk GK Plk3 Clofibrate Naltrexone Paclitaxel

30 Choice of Power, β

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