Download Dol1 Dol3

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
WT
KO
WT
C
• Expression data was filtered for the top
10% most varying probe sets for Weighted
Gene Co-Expression Network Analysis
(WGCNA).
Weighted Gene Co-Expression
Network Analysis
(WGCNA) Overview
http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/
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
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
Construct a Network
Microarray gene
expression data
Gene expression
correlation
Correlation Matrix
Power adjacency
function generates a
weighted network

aij | cor ( xi , x j ) |
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 Identification
• WGCNA aim: Detect
modules.
• Modules are groups
of highly correlated,
highly connected
genes.
• Defined with the
standard distance
measure: 1correlation.
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
• A measure of a
gene’s connection
strength to other
genes in the whole
network.
• Use both k and GS
Gene Significance (GS)
Connectivity (k) and Gene
Significance (GS)
Module Connectivity
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
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
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 Significance
Glycerol 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
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.
Dol3
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 Modules
Dol3
Gene Significance
GK
TAT
HNF4a
BCL2
BID
GADD45
TRP53inp1
Module Connectivity
Gene Significance
GPD
VDAC
ACOT
PSAT
TAT
HNF4a
Gene Significance
Module Connectivity
Module Connectivity
GK
GPD
VDAC
Gene Significance
Dol1
ACOT
PSAT
PLK3
Module Connectivity
Validation Studies
• Cell Culture
– ACOT
– PSAT
– PLK3
• KO Mice
– ACOT
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, PLK3 connected
Summary
• Gene validation
studies supported
the WGCNA.
– ACOT
– PSAT
– PLK3
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
350
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% of Control
300
**
250
***
200
150
100
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***
50
0
GK Acot
Gyk
Clofibrate
GK Plk3
GK Psat
Gyk
Gyk
Naltrexone Paclitaxel
Choice of Power, β