NK MacLennan, J Dong, S Horvath, L Ornelas, L Rahib, K Dipple and ERB McCabe.
description
Transcript of NK MacLennan, J Dong, S Horvath, L Ornelas, L Rahib, K Dipple and ERB McCabe.
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.
Glycerol Kinase
• Catalyzes the reaction
Glycerol glycerol 3-phosphate, a substrate for gluconeogenesis and lipid metabolism
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.
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).
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.
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 WTKO C
Weighted Gene Co-Expression Network Analysis
(WGCNA) Overview
http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/
Construct a networkRationale: make use of interaction patterns between genes
Identify modulesRationale: module (pathway) based analysis
Relate modules to external informationArray Information: Sample dataGene Information: EASERationale: find biologically interesting modules
Find the key drivers in interesting modulesTools: Module connectivity, causality testingRationale: experimental validation, therapeutics, biomarkers
Study Module Preservation across different data Rationale: • Same data: to check robustness of module definition• Different data: to find interesting modules
Construct a networkRationale: make use of interaction patterns between genes
Identify modulesRationale: module (pathway) based analysis
Relate modules to external informationArray Information: Sample dataGene Information: EASERationale: find biologically interesting modules
Find the key drivers in interesting modulesTools: Module connectivity, causality testingRationale: experimental validation, therapeutics, biomarkers
Study Module Preservation across different data Rationale: • Same data: to check robustness of module definition• Different data: to find interesting modules
Construct a Network
Microarray gene expression data
Gene expression correlation
Correlation Matrix
Power adjacency function generates a weighted network
| ( , ) |ij i ja cor x x
Construct a networkRationale: make use of interaction patterns between genes
Identify modulesRationale: module (pathway) based analysis
Relate modules to external informationArray Information: Sample dataGene Information: EASERationale: find biologically interesting modules
Find the key drivers in interesting modulesTools: Module connectivity, causality testingRationale: experimental validation, therapeutics, biomarkers
Study Module Preservation across different data Rationale: • Same data: to check robustness of module definition• Different data: to find interesting modules
Module Identification• WGCNA aim: Detect
modules. • Modules are groups
of highly correlated, highly connected genes.
• Defined with the standard distance measure: 1-correlation.
Construct a networkRationale: make use of interaction patterns between genes
Identify modulesRationale: module (pathway) based analysis
Relate modules to external informationArray Information: Sample dataGene Information: EASERationale: find biologically interesting modules
Find the key drivers in interesting modulesTools: Module connectivity, causality testingRationale: experimental validation, therapeutics, biomarkers
Study Module Preservation across different data Rationale: • Same data: to check robustness of module definition• Different data: to find interesting modules
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
Module Connectivity
Gen
e S
ign
ific
ance
(G
S)
Construct a networkRationale: make use of interaction patterns between genes
Identify modulesRationale: module (pathway) based analysis
Relate modules to external informationArray Information: Sample dataGene Information: EASERationale: find biologically interesting modules
Find the key drivers in interesting modulesTools: Module connectivity, causality testingRationale: experimental validation, therapeutics, biomarkers
Study Module Preservation across different data Rationale: • Same data: to check robustness of module definition• Different data: to find interesting modules
Construct a networkRationale: make use of interaction patterns between genes
Identify modulesRationale: module (pathway) based analysis
Relate modules to external informationArray Information: Sample dataGene Information: EASERationale: find biologically interesting modules
Find the key drivers in interesting modulesTools: Module connectivity, causality testingRationale: experimental validation, therapeutics, biomarkers
Study Module Preservation across different data Rationale: • Same data: to check robustness of module definition• Different data: to find interesting modules
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
Identify Modules and Study Module Preservation
Dol 1 Dol 3
Dol 3 colors Dol 1 colors
Relate Modules to Gene SignificanceGlycerol Kinase Knockout Status
DOL 1 KO• Blue: Underexpressed• Turquoise: Overexpressed
DOL 3 KO • Blue: Underexpressed• Brown: No relationship• Turquoise: Overexpressed
Relate Modules to External Information
Functional Group Enrichment Dol1 Dol3Mitotic 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.
Find the Key Drivers in Interesting ModulesDol1 Dol3
Module Connectivity
Gen
e S
ign
ific
ance
Module Connectivity
Gen
e S
ign
ific
ance
Module Connectivity
Gen
e S
ign
ific
ance
Module Connectivity
Gen
e S
ign
ific
ance
GKGPDVDAC
GKTATHNF4a
TATHNF4a
GPDVDAC
ACOTACOTPSATPSAT
BCL2BIDGADD45TRP53inp1
ACOTACOTPSATPSATPLK3PLK3
Validation Studies
• Cell Culture– ACOTACOT– PSATPSAT– PLK3PLK3
• KO Mice– ACOTACOT
Summary
• Dol 1 Blue module:
– Genes underexpressed in KO
– GK gene module membership
– Enriched with Apoptosis/ cell death genes
Summary
• Dol 3 blue module:
– Genes Underexpressed in KO
– Loss of Apoptosis/ cell death gene enrichment
Summary
• Dol 1 and 3 Turquoise module:
– Genes overexpressed in KO – ACOT, PSAT, PLK3ACOT, PSAT, PLK3 connected
Summary
• Gene validation studies supported the WGCNA.– ACOTACOT– PSATPSAT– PLK3PLK3
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
Acknowledgements
• McCabe Lab
• Dipple Lab
Cell Culture Validation
0
50
100
150
200
250
300
350
***
******
**
***
Gyk Acot Gyk Psat Gyk Plk3Clofibrate Naltrexone Paclitaxel
% o
f C
on
tro
l
GK GKGK
Choice of Power, β