Change of transcriptomic signature in subcutaneous adipose tissue induced by weight loss - Sopić, Miron; Ninić, Ana; Munjas, Jelena; Miljković, Milica; Erceg, Sanja; Guzonjić, Azra; Gagić, Jelena; Bogavac-Stanojević, Nataša; Kotur-Stevuljević, Jelena (Biologia Serbica, 2022)
Obesity is a chronic disease underlined as one of greatest public health challenges in 21st century that significantly increase the risk of developing diabetes, cardiovascular diseases, liver disease and cancer. Current strategies in obesity prevention focus on the lifestyle changes, calorie-restriction diets, pharmacological and surgical interventions. Although obese subjects share some phenotype characteristics, others can be significantly different. For example, up to 30% of obese patients are metabolically healthy and do not display the “typical” metabolic obesity-associated complications. These phenotype difference also lead to variation in success rate of treatments among different individuals. Since obesity cannot be looked at as one simple pathological entity, we need novel tools to define different phenotypes of obesity in order to improve stratifications of patients and facilitate the development of personalized treatments. The use of next-generation sequencing enables comprehensive view on interaction between different genes, and the discovery of novel pathways that are dysregulated in different pathophysiological processes. So far, this approach has identified novel coding and non-coding regions of DNA that are implicated in the development and progression of obesity and help to identify potential tissue-specific biomarkers that could be used for successful predictions of intervention outcomes. Another interesting layer of information that recently is being explored is related to the modifications of RNA and its implications in adipose tissue physiology. Animal as well as human studies have confirmed critical role that some RNA modification play in the dysfunctionality of adipose tissue in obesity. Future efforts should aim to integrate transcriptomics data with other omics (genomics, epigenomics, proteomics and metabolomics) through the use of machine learning algorithms in order to get more holistic view and deeper understanding of different obesity phenotypes.