An integrating strategy for serum metabolomics and microarray analysis to expand the understanding of diet-induced obesity. Issue 28 (28th June 2021)

• Record Type: Journal Article
• Authors: Liu, Wuping; Xu, Jingjing; Dai, Tao; Shen, Guiping; Feng, Jianghua
• Is Part Of: Analytical methods. Volume 13:Issue 28(2021)
• Page Start: 3127
• Page End: 3135
• Publication Date: 2021-06-28
• Subjects: Chemistry, Analytic -- Periodicals; Analytical biochemistry -- Periodicals; Chemical laboratories -- Standards -- Periodicals; 543.1905
• DOI: 10.1039/d1ay00821h
• Languages: English
• ISSNs: 1759-9660

Abstract

Abstract : Obesity is a key component of metabolic syndrome and is precipitated by complex interactions between multiple environmental and genetic factors. Abstract : Obesity is a key component of metabolic syndrome and is precipitated by complex interactions between multiple environmental and genetic factors. The integration of multi-level bioinformation is needed to understand the altered endogenous molecule and metabolic mechanisms. In this study, an integrated analytical strategy was proposed by combining microarray data from a gene expression omnibus database and in vitro serum metabolomic data to unearth bioinformation associated with cafeteria diet induced obesity. In the diet induced obese rats, 23 genes and 9 metabolites showed significant changes, in which the increased levels of alanine, lactate and lactate dehydrogenase B ( Ldhb ) and the decreased levels of citrate and pyruvate indicated an enhanced glycolysis and a disordered Krebs cycle. Furthermore, the closeness centrality of Slc27a2, Apobr, alanine and histidine in the correlations network of pathways, genes and metabolites was 0.5036, 0.5111, 0.5702, and 0.5352, respectively. These close links between metabolites and genes would be highly useful to assess the degree of obesity and to understand the developmental mechanism of obesity. The pathway enrichment analysis of genes and metabolites proved that a disturbed glucose metabolism and biosynthesis of amino acids are typical metabolic features of cafeteria-induced obesity. The metabolomics combined with microarray data not only could identify the biomarkers, but also would be beneficial to the follow-up research of obesity treatment, especially providing a methodological basis for the study of other diseases.