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Profiling Cellular Processes in Adipose Tissue during Weight Loss Using Time Series Gene Expression

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...

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Detalles Bibliográficos
Autores principales: Tareen, Samar H. K., Adriaens, Michiel E., Arts, Ilja C. W., de Kok, Theo M., Vink, Roel G., Roumans, Nadia J. T., van Baak, Marleen A., Mariman, Edwin C. M., Evelo, Chris T., Kutmon, Martina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6266822/
https://www.ncbi.nlm.nih.gov/pubmed/30380678
http://dx.doi.org/10.3390/genes9110525
Descripción
Sumario: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.