Network Medicine, Epigenetics and Obesity

2019-04-10 11:19:17 | BioPortfolio


The prevalence of obesity is increasing and affects more than 650 million people of all ages to become one of the foremost global health threats. Obesity is a complex syndrome that can seriously impair health through a broad range of complications such as cardiovascular disease, type 1 and 2 diabetes (T1D and T2D), cancer, musculoskeletal disorders, psychosocial imbalances, and reduced quality of life, and impacts the treatment of other conditions. Weight reduction has been shown to have a positive effect on these co-morbidities and may increase the effectiveness of treatments specific for other co-morbidities. Lifestyle modification is an integral part of the weight management journey, but is often insufficient on its own, and can be complimented by pharmacological and surgical add-on treatments to achieve greater and more sustainable weight loss, as appropriate. It is likely that there are subgroups of patients that are more suited to certain types of treatment and results risk dilution of perceived efficacy unless these groups are identified and treatment is personalised. The aim of this project is to identify pathophysiologically and clinically meaningful subgroups of obesity by performing Next Generation Sequencing (NGS) approaches and network based algorithm that will allow the optimisation of prevention and treatment of obesity and its complications.


Population study will be recruited at the Clinical Department of Internal Medicine and Specialistics, Department of Advanced Clinical and Surgical Sciences belonging to University of Campania "Luigi Vanvitelli". This study will be performed according to the principles outlined in the Helsinki Declaration. Subcutaneous adipose tissues located in the surgical incision will be withdrawn without the use of surgical devices in order to avoid the degradation of the biological sample from 50 obese subjects (25 obese subjects with Type 2 Diabetes vs 25 obese subjects without Type 2 Diabetes) undergoing bariatric surgery. As controls (n=50) we will recruit subcutaneous adipose tissues from patients without a clinical history of cardiovascular or dysmetabolic diseases undergoing to surgery for stress inguinal hernia. Clinical and demographic characteristics of the study population will be available from datasets generated by physicians.

From tissue samples the genomic DNA and the total RNA will be extracted. Genomic DNA will be extracted by using DNeasy Blood & Tissue kit (QIAGEN), according to manufacturer protocol. Pooled DNA samples consisting of equal quantities of DNA (2 µg) from cases and controls will be shipped to Genomix4Life Genomics and Bioinformatics Service, to perform a global DNA methylation analysis. For this aim, it will be used the Human Methylation 27K BeadChip platform by using Bisulfite conversion technology (BBRS-Seq). Total RNA will be extracted from tissues using RNeasy Mini Kit (QIAGEN) according to manufacturer protocol. The cDNA library preparation will be performed starting from 4 ug of total RNA by using Illumina TruSeq Libraries and then sequenced at high coverage on the Illumina HiSeq 2500 NGS platform. Nucleic acid concentrations and quality control from Genomix4Life will be assessed by using Nanodrop spectrophotometer (Thermo Fisher Scientific) and Qubit assay (Thermo Fisher Scientific) and TapeStation 4200 (Agilent). The weighted human DNA methylation PPI network (WMPN) will be construct to obtain a obesity interactome in both subgroups (obese and obese with T2D vs controls) based on differentially methylated genes in order to identify putative useful diagnostic biomarkers. The TargetScan algorithm , by searching the conserved seed pairing regions in the 3' untraslated regions (UTR) of genes based on whole genome alignment, will be used to robustly predict miRNA-target gene pairs from the same study population.

Study Design




DNA methylome and total RNA sequencing


Not yet recruiting


University of Campania "Luigi Vanvitelli"

Results (where available)

View Results


Published on BioPortfolio: 2019-04-10T11:19:17-0400

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