By Renata Konickova, Alena Jiraskova, Hind Hashwah and Jana Sponarova
© NEBION AG. October 31, 2019

Abstract

Metabolic syndrome (MetS) is a complex worldwide epidemic disorder defined by a cluster of concomitantly occurring conditions, including dyslipidemia, hypertension, hyperglycemia, or abdominal obesity. MetS is chronic in nature, negatively affecting the life quality of patients, who also have an increased risk of cardiovascular disease and type 2 diabetes. Efforts to better diagnose, prevent, and treat MetS patients rely on a better understanding of this syndrome.

In an effort to demonstrate the applicability of compendium-wide analyses in bringing high-quality insights into the pathophysiology of MetS, we used the GENEVESTIGATOR^® toolset and the underlying rich compendium of curated data from the areas of obesity, nutrition, type 2 diabetes, and other metabolic disorders. Our analysis highlights the gene regulatory networks involved in the inflammation-immune responses in MetS and other related conditions. Using two different transcriptomic approaches, we identified gene signatures of subcutaneous adipose tissue (SAT) specific for certain metabolic disorders. Using the first approach, we confirmed the effectiveness of current treatment interventions which resulted in signatures very different from our identified signature. We could also confirm the translatability of our finding across platforms. Using the second approach, we identified a metabolic signature for MetS, obese, overweight, and control subjects, in which genes previously reported to be involved in these conditions were identified. One specific cluster of interest was identified, containing genes mainly associated with the inflammation-immune response of SAT. We could show that these genes are also highly expressed in immune cells, confirming the pro-inflammatory state of SAT in these conditions. Lastly, we identified co-regulated genes in obese/insulin resistant patients, all of which have been previously associated with MetS, and thereby providing a list of genes that can be used for further computational or laboratory validation. (Download PDF)