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Network-based integration of molecular and physiological data elucidates regulatory mechanisms underlying adaptation to high-fat diet.

Derous D, Kelder T, van Schothorst EM, van Erk M, Voigt A, Klaus S, Keijer J, Radonjic M - Genes Nutr (2015)

Bottom Line: To effectively maintain health and prevent disease, health-relevant relations need to be understood at multiple levels of biological complexity.We built a three-layered interaction network comprising enriched biological processes, their transcriptional regulators and associated changes in physiological parameters.Apart from global understanding of the time-resolved adaptation to HFD, the multi-layered network model allows several novel mechanistic hypotheses to emerge: (1) early activation of TGF-β signalling as a trigger for structural and morphological changes in mitochondrial organization in eWAT, (2) modulation of cellular respiration as an intervention strategy to effectively deal with excess dietary fat and (3) discovery of putative intervention targets, such those in pathways related to appetite control.

View Article: PubMed Central - PubMed

Affiliation: Microbiology and Systems Biology, TNO, Zeist, The Netherlands.

ABSTRACT
Health is influenced by interplay of molecular, physiological and environmental factors. To effectively maintain health and prevent disease, health-relevant relations need to be understood at multiple levels of biological complexity. Network-based methods provide a powerful platform for integration and mining of data and knowledge characterizing different aspects of health. Previously, we have reported physiological and gene expression changes associated with adaptation of murine epididymal white adipose tissue (eWAT) to 5 days and 12 weeks of high-fat diet (HFD) and low-fat diet feeding (Voigt et al. in Mol Nutr Food Res 57:1423-1434, 2013. doi: 10.1002/mnfr.201200671 ). In the current study, we apply network analysis on this dataset to comprehensively characterize mechanisms driving the short- and long-term adaptation of eWAT to HFD across multiple levels of complexity. We built a three-layered interaction network comprising enriched biological processes, their transcriptional regulators and associated changes in physiological parameters. The multi-layered network model reveals that early eWAT adaptation to HFD feeding involves major changes at a molecular level, including activation of TGF-β signalling pathway, immune and stress response and downregulation of mitochondrial functioning. Upon prolonged HFD intake, initial transcriptional response tails off, mitochondrial functioning is even further diminished, and in turn the relation between eWAT gene expression and physiological changes becomes more prominent. In particular, eWAT weight and total energy intake negatively correlate with cellular respiration process, revealing mitochondrial dysfunction as a hallmark of late eWAT adaptation to HFD. Apart from global understanding of the time-resolved adaptation to HFD, the multi-layered network model allows several novel mechanistic hypotheses to emerge: (1) early activation of TGF-β signalling as a trigger for structural and morphological changes in mitochondrial organization in eWAT, (2) modulation of cellular respiration as an intervention strategy to effectively deal with excess dietary fat and (3) discovery of putative intervention targets, such those in pathways related to appetite control. In conclusion, the generated network model comprehensively characterizes eWAT adaptation to high-fat diet, spanning from global aspects to mechanistic details. Being open to further exploration by the research community, it provides a resource of health-relevant interactions ready to be used in a broad range of research applications.

No MeSH data available.


Multi-level network model of eWAT adaptation to 5 days of HFD. The three-layered network model comprising (1) biological processes, (2) transcription regulators (TFs) and (3) physiological parameters associated with eWAT gene expression after 5 days of HFD feeding. The processes layer includes differentially enriched biological processes as described in Fig. 1. The regulatory network layer includes TFs whose targets are enriched among the differentially expressed genes (HFD vs. LFD after 5 days of HFD feeding). TFs with highly overlapping target gene sets are clustered into single nodes. The physiological network layer includes parameters significantly correlated with eWAT expression data. The connections between the three layers are based on the overlap between underlying gene sets. The width of edges is based on the overlap between underlying gene sets. The colour coding of nodes is as described in Fig. 1, where for TFs, the direction of the expression of their targets is represented (red—activation, blue—repression)
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Fig3: Multi-level network model of eWAT adaptation to 5 days of HFD. The three-layered network model comprising (1) biological processes, (2) transcription regulators (TFs) and (3) physiological parameters associated with eWAT gene expression after 5 days of HFD feeding. The processes layer includes differentially enriched biological processes as described in Fig. 1. The regulatory network layer includes TFs whose targets are enriched among the differentially expressed genes (HFD vs. LFD after 5 days of HFD feeding). TFs with highly overlapping target gene sets are clustered into single nodes. The physiological network layer includes parameters significantly correlated with eWAT expression data. The connections between the three layers are based on the overlap between underlying gene sets. The width of edges is based on the overlap between underlying gene sets. The colour coding of nodes is as described in Fig. 1, where for TFs, the direction of the expression of their targets is represented (red—activation, blue—repression)

Mentions: Network analysis of biological processes associated with eWAT adaptation to HFD elucidates their mutual interconnectivity and changes during transition from early to late response. To place these processes in a physiological, systems context and investigate their regulation, we have built a three-layered network model comprising (1) biological processes, (2) transcription regulators and (3) physiological parameters (Figs. 3, 4; Supplemental data 8, Supplemental data 9). The connections between the three layers are based on overlap between underlying gene sets (“Methods” section).Fig. 3


Network-based integration of molecular and physiological data elucidates regulatory mechanisms underlying adaptation to high-fat diet.

Derous D, Kelder T, van Schothorst EM, van Erk M, Voigt A, Klaus S, Keijer J, Radonjic M - Genes Nutr (2015)

Multi-level network model of eWAT adaptation to 5 days of HFD. The three-layered network model comprising (1) biological processes, (2) transcription regulators (TFs) and (3) physiological parameters associated with eWAT gene expression after 5 days of HFD feeding. The processes layer includes differentially enriched biological processes as described in Fig. 1. The regulatory network layer includes TFs whose targets are enriched among the differentially expressed genes (HFD vs. LFD after 5 days of HFD feeding). TFs with highly overlapping target gene sets are clustered into single nodes. The physiological network layer includes parameters significantly correlated with eWAT expression data. The connections between the three layers are based on the overlap between underlying gene sets. The width of edges is based on the overlap between underlying gene sets. The colour coding of nodes is as described in Fig. 1, where for TFs, the direction of the expression of their targets is represented (red—activation, blue—repression)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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Fig3: Multi-level network model of eWAT adaptation to 5 days of HFD. The three-layered network model comprising (1) biological processes, (2) transcription regulators (TFs) and (3) physiological parameters associated with eWAT gene expression after 5 days of HFD feeding. The processes layer includes differentially enriched biological processes as described in Fig. 1. The regulatory network layer includes TFs whose targets are enriched among the differentially expressed genes (HFD vs. LFD after 5 days of HFD feeding). TFs with highly overlapping target gene sets are clustered into single nodes. The physiological network layer includes parameters significantly correlated with eWAT expression data. The connections between the three layers are based on the overlap between underlying gene sets. The width of edges is based on the overlap between underlying gene sets. The colour coding of nodes is as described in Fig. 1, where for TFs, the direction of the expression of their targets is represented (red—activation, blue—repression)
Mentions: Network analysis of biological processes associated with eWAT adaptation to HFD elucidates their mutual interconnectivity and changes during transition from early to late response. To place these processes in a physiological, systems context and investigate their regulation, we have built a three-layered network model comprising (1) biological processes, (2) transcription regulators and (3) physiological parameters (Figs. 3, 4; Supplemental data 8, Supplemental data 9). The connections between the three layers are based on overlap between underlying gene sets (“Methods” section).Fig. 3

Bottom Line: To effectively maintain health and prevent disease, health-relevant relations need to be understood at multiple levels of biological complexity.We built a three-layered interaction network comprising enriched biological processes, their transcriptional regulators and associated changes in physiological parameters.Apart from global understanding of the time-resolved adaptation to HFD, the multi-layered network model allows several novel mechanistic hypotheses to emerge: (1) early activation of TGF-β signalling as a trigger for structural and morphological changes in mitochondrial organization in eWAT, (2) modulation of cellular respiration as an intervention strategy to effectively deal with excess dietary fat and (3) discovery of putative intervention targets, such those in pathways related to appetite control.

View Article: PubMed Central - PubMed

Affiliation: Microbiology and Systems Biology, TNO, Zeist, The Netherlands.

ABSTRACT
Health is influenced by interplay of molecular, physiological and environmental factors. To effectively maintain health and prevent disease, health-relevant relations need to be understood at multiple levels of biological complexity. Network-based methods provide a powerful platform for integration and mining of data and knowledge characterizing different aspects of health. Previously, we have reported physiological and gene expression changes associated with adaptation of murine epididymal white adipose tissue (eWAT) to 5 days and 12 weeks of high-fat diet (HFD) and low-fat diet feeding (Voigt et al. in Mol Nutr Food Res 57:1423-1434, 2013. doi: 10.1002/mnfr.201200671 ). In the current study, we apply network analysis on this dataset to comprehensively characterize mechanisms driving the short- and long-term adaptation of eWAT to HFD across multiple levels of complexity. We built a three-layered interaction network comprising enriched biological processes, their transcriptional regulators and associated changes in physiological parameters. The multi-layered network model reveals that early eWAT adaptation to HFD feeding involves major changes at a molecular level, including activation of TGF-β signalling pathway, immune and stress response and downregulation of mitochondrial functioning. Upon prolonged HFD intake, initial transcriptional response tails off, mitochondrial functioning is even further diminished, and in turn the relation between eWAT gene expression and physiological changes becomes more prominent. In particular, eWAT weight and total energy intake negatively correlate with cellular respiration process, revealing mitochondrial dysfunction as a hallmark of late eWAT adaptation to HFD. Apart from global understanding of the time-resolved adaptation to HFD, the multi-layered network model allows several novel mechanistic hypotheses to emerge: (1) early activation of TGF-β signalling as a trigger for structural and morphological changes in mitochondrial organization in eWAT, (2) modulation of cellular respiration as an intervention strategy to effectively deal with excess dietary fat and (3) discovery of putative intervention targets, such those in pathways related to appetite control. In conclusion, the generated network model comprehensively characterizes eWAT adaptation to high-fat diet, spanning from global aspects to mechanistic details. Being open to further exploration by the research community, it provides a resource of health-relevant interactions ready to be used in a broad range of research applications.

No MeSH data available.