netbiosig2013-talk thomas kelder
DESCRIPTION
Presentation for Network Biology SIG 2013 by Thomas Kelder, Bioinformatics Scientist at TNO in The Netherlands. “Functional Network Signatures Link Anti-diabetic Interventions with Disease Parameters”TRANSCRIPT
Network signatures link hepatic effects of anti-diabetic interventions with systemic
disease parameters
Thomas KelderMicrobiology and Systems Biology, TNO, The Netherlands
Network Biology SIG, ISMB 2013, Berlin
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4
Anti-Diabetic Treatment (ADT) study
DISEASE PARAMETERS• Plasma glucose, insulin• Body and organ weights• Atherosclerosis lesion area• Plasma cholesterol• Plasma & liver triglycerides
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Dietary Lifestyle Intervention (DLI)
Fenofibrate, T0901317
Improves all disease parameters
Improves glycemiaDeteriorates dyslipidemia
Radonjic, et al., PLoS ONE, 2012
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Intervention – hepatic mechanisms – disease parameters
TRIGLYCERIDES
ATHEROSCLEROSIS
GLUCOSEINTERVENTION
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Which paths?
TRIGLYCERIDES
ATHEROSCLEROSIS
GLUCOSEINTERVENTION
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Network analysis workflow
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Link to disease parameters
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WGCNA
• Weighted Gene Co-expression Analysis*• Identify co-expressed network modules• Correlate modules to disease parameters based on their “eigengene” (1st
Principal Component)
*Langfelder et al. BMC Bioinformatics, 2008
Disease parameter
Disease parameter
Disease parameter
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Modules to disease parameters
• 14 coherent co-expression modules• 10 modules with GO annotation• 4 modules correlated with disease parameter(s)• All correlating endpoints related to dyslipidemia rather than dysglycemia
despite improvement of dysglycemia by all interventions
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Link to intervention targets
Prior knowledge-based networks
• Curated pathways• Protein-protein interactions• Transcription factor targets
• Drug targets• DLI “targets”
– Ingenuity Upstream Regulator Analysis– Enrichment of known TF targets with DEGs for DLI
(p<0.001)– 25 transcription factors– Some overlap with drug targets (e.g. PPARA)
Total network has >12,000 (gene) nodes and >75,000 edges
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Intervention specific networks
Filter by differential expression for intervention vs HFD
Network Total DEGs in dataset (p < 0.05) Connected nodes Edges
DLI 1,287 497 5,975
Fenofibrate 2,149 828 21,598
T0901317 2,924 1245 38,472
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Network signatures
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Random walks algorithm
[1] Dupont, et al. "Relevant subgraph extraction from random walks in a graph." Machine Learning (2006)[2] Faust, et al. "Pathway discovery in metabolic networks by subgraph extraction." Bioinformatics (2010)
Random w
alks
Intervention
Nodes and edges scored by probability of being visited by the random walker
Intervention
Diseaseparameter
Diseaseparameter
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Network signaturesDLI signaturesTemplate for successful intervention
Drug signaturesCircumvent this response
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Network signatures
• DLI vs drug, distinct response:• Small overlap• Opposite regulation
• Potential drug targets:• key nodes unique for DLI• cross-talk between processes
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Performance assessmentImproved ability to prioritize genes by relevance to disease parameters
8.78 fold enrichmentp = 3.44E-10
3.09 fold enrichmentp = 0.023
Cholesterol
Atherosclerosis
Liver weight
Cholesterol
Atherosclerosis
Liver weight
1TFIndirect links
5 TFsDirect links
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Conclusions
Network signatures underlying effects of interventions on dyslipidemia-related disease parameters
– Template for successful intervention or response to circumvent– Improves selection of genes relevant to disease parameters– Underlying interaction help interpretation
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Acknowledgements
• Marijana Radonjic• Lars Verschuren• Alain van Gool• Ben van Ommen• Ivana Bobeldijk
Check out our poster at ISMB on SundayNetwork Biology of Systems Flexibility
R scripts and data for this analysis available at:https://github.com/thomaskelder/ADT-liver-network
igraph
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23Study setup to investigate differential effects of anti-diabetic drug and dietary lifestyle interventions [1].
High fat diet “diseased” control group
Chow diet “healthy” control group
High fat diet DLI (switch to chow)
Fenofibrate
T0901317
0wk 9wk 16wk
LDLR-/-MICE
HEPA
TIC
TR
AN
SC
RIP
TO
ME
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DLI Fenofibrate T0901317
Hepatic transcriptome dataset: - Chow control- Dietary lifestyle intervention (DLI)- Fenofibrate- T0901317Compared to high fat diet (HFD) at 16wk.
Co-expression network modules identified by Weighted Gene Co-expression Network Analysis (WGCNA) [2]. Provides high-level overview of relevant processes.
WGCNA
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DLI Fenofibrate T0901317
WGCNA
DLI
T0901317
FENOFIBRATE
EXTEND WITH PRIOR KNOWLEDGE
FILTER FOR REGULATED GENES
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Out of ten modules that could be annotated to a biological process, three modules correlated significantly with disease parameters. All significant correlations were with dyslipidemia related disease parameters, despite the evident improvement of glycemic status by the interventions.
MODULE NO. GENES GO TERMS SIGNIFICANT CORRELATIONS
YELLOW 198Lipid biosynthetic process,Oxidoreductase activity
Liver weight (-0.91), Triglycerides (-0.90), Atherosclerosis (-0.79), Cholesterol (-0.79)
RED 161Cell activation, Immune system process, Inflammatory response
Atherosclerosis (0.80), Cholesterol (0.78), Liver weight (0.75)
BLACK 142Lipid metabolic process,Oxidation-reduction process
Liver weight (0.88); Cholesterol (0.83)
WGCNA• Weighted co-expression network analysis*• Correlate modules to other measurements (clinical, plasma proteins,
microbiome)
*Langfelder et al. BMC Bioinformatics, 2008
glucose
Chow
HF 16
weeks
Lifest
yle
Rosiglit
azone
T0901
317
0
5
10
15
20
** ***
glu
cose
(m
M)
Omics, genetics, physiological data, prior knowledge
Molecular signatures of metabolic health and disease
Mechanistic insight: Biological context ofmolecular signatures
Prognostic / diagnostics molecular signatures
Coexpression networks (WGCNA)Prior-knowledge networksCausality networksVariable selection methodsSubgraph ID/ (K-walks)topology/ network clustering
Network signatures for improved diagnostics & interventions
Link to pathological endpoint
Subgroup-specific molecular signatures prioritization and refinement