Abstract

Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans.

Original languageEnglish
Article number525
Number of pages16
JournalGenes
Volume9
Issue number11
DOIs
Publication statusPublished - Nov 2018

Keywords

  • obesity
  • diet
  • adipose tissue
  • correlation networks
  • transcriptomics
  • differential expression
  • cellular processes
  • cytoscape
  • network biology
  • network visualisation
  • CARDIOVASCULAR-DISEASE
  • DOWN-REGULATION
  • OBESITY
  • PATHWAY
  • ADIPOCYTES
  • CYTOSCAPE
  • RECEPTOR
  • DATABASE
  • KINASE
  • IMPACT

Cite this

@article{b2838acadccf43e1bb82a2f0a9e7664a,
title = "Profiling cellular processes in adipose tissue during weight loss using time series gene expression",
abstract = "Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans.",
keywords = "obesity, diet, adipose tissue, correlation networks, transcriptomics, differential expression, cellular processes, cytoscape, network biology, network visualisation, CARDIOVASCULAR-DISEASE, DOWN-REGULATION, OBESITY, PATHWAY, ADIPOCYTES, CYTOSCAPE, RECEPTOR, DATABASE, KINASE, IMPACT",
author = "Tareen, {Samar H K} and Adriaens, {Michiel E} and Arts, {Ilja C W} and {de Kok}, {Theo M} and Vink, {Roel G} and Roumans, {Nadia J T} and {van Baak}, {Marleen A} and Mariman, {Edwin C M} and Evelo, {Chris T} and Martina Kutmon",
year = "2018",
month = "11",
doi = "10.3390/genes9110525",
language = "English",
volume = "9",
journal = "Genes",
issn = "2073-4425",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "11",

}

TY - JOUR

T1 - Profiling cellular processes in adipose tissue during weight loss using time series gene expression

AU - Tareen, Samar H K

AU - Adriaens, Michiel E

AU - Arts, Ilja C W

AU - de Kok, Theo M

AU - Vink, Roel G

AU - Roumans, Nadia J T

AU - van Baak, Marleen A

AU - Mariman, Edwin C M

AU - Evelo, Chris T

AU - Kutmon, Martina

PY - 2018/11

Y1 - 2018/11

N2 - Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans.

AB - Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans.

KW - obesity

KW - diet

KW - adipose tissue

KW - correlation networks

KW - transcriptomics

KW - differential expression

KW - cellular processes

KW - cytoscape

KW - network biology

KW - network visualisation

KW - CARDIOVASCULAR-DISEASE

KW - DOWN-REGULATION

KW - OBESITY

KW - PATHWAY

KW - ADIPOCYTES

KW - CYTOSCAPE

KW - RECEPTOR

KW - DATABASE

KW - KINASE

KW - IMPACT

U2 - 10.3390/genes9110525

DO - 10.3390/genes9110525

M3 - Article

C2 - 30380678

VL - 9

JO - Genes

JF - Genes

SN - 2073-4425

IS - 11

M1 - 525

ER -