Dual Effect of the GHRL Gene Variant in the Molecular Pathogenesis of Obesity

Establishment of a novel obesity mouse model: the induction of intestinal microbiota dysbiosis

To establish and evaluate an intestinal microbiota dysbiosis-induced obesity mouse model. 50 C57BL/6 J male healthy mice were randomly divided into an obesity model group and the control group. The body weight, body length, and Lee's index of the two groups of mice at week 1 and week 10 were compared. Serum glucose (GLU), total cholesterol (TC) and triglyceride (TG) were measured by enzyme-labeled colorimetric methods. Illumina HiSeq 16S rDNA high-throughput sequencing technology was used to characterize intestinal microbiota in feces. The success rate of model establishment in obese mice was 52%. The body weight, body length, Lee's index, and abdominal fat (wet weight) in the obese model group were all higher than those in the control group, and the differences were statistically significant (P < 0.01). Serum GLU and TC levels in the obesity model group were higher than those in the control group (P < 0.05), and there was no difference in TG levels between the two groups (P > 0.05). The control group contained more abundant intestinal microbiota phyla and genera than did the obesity model group; the differences between the two groups were significant (FDR ≤ 0.05, P ≤ 0.05). Intestinal microbiota dysbiosis can be used to generate an obesity model in mice.

Effect of the GHRL gene (rs696217) polymorphism on the metabolic disorders in patients with obesity in the Ukrainian population

Objective. Over the past four decades, the prevalence of obesity has tripled and limited genetic studies with specific SNPs have been conducted, but no investigations using ghrelin and obestatin prepropeptide (GHRL) gene have been reported in the Ukrainians population. The aim of this study was to evaluate changes in the level of metabolic hormones in the blood of obese patients in relation to the GHRL (rs696217) polymorphism. Methods. The study involved 53 obesity cases and 48 non-obesity subjects (controls). The GHRL (rs696217) polymorphism was genotyped using a TaqMan real-time polymerase chain reaction method. Blood hormones were determined with commercially available kits using a Multi-skan FC analyzer. Results. Carriers of the T allele of the GHRL (rs696217) polymorphism were statistically significantly more in patients diagnosed with obesity compared to controls indicating a genetically determined cause of obesity. We also established a significant effect of the presence of the T allele of the GHRL (rs696217) polymorphism on the decrease in the adiponectin level and the increase of resistin level in obese patients. The study of the effect of genotypes (TT, GT, GG) of the GHRL (rs696217) polymorphism on the metabolic hormone levels in the blood of obese patients did not show reliably significant differences. Conclusions. The presence of the T allele of the GHRL (rs696217) polymorphism in Ukrainian population indicates an increased risk of the obesity development regardless on the homozygous or heterozygous genotype.

Syndromic and Nonsyndromic Obesity: Underlying Genetic Causes in Humans

Growing evidence supports syndromic and nonsyndromic causes of obesity, including genome-wide association studies, candidate gene analysis, advanced genetic technology using next-generation sequencing (NGS), and identification of copy number variants. Identification of susceptibility genes impacts mechanistic understanding and informs precision medicine. The cause of obesity is heterogeneous with complex biological processes playing a role by controlling peptides involved in regulating appetite and food intake, cellular energy, and metabolism. Evidence for heritability shows genetic components contributing to 40%-70% of obesity. Monogenic causes and obesity-related syndromes are discussed and illustrated as well as biological pathways, gene interactions, and factors contributing to the obesity phenotype. Over 550 obesity-related single genes have been identified and summarized in tabular form with approximately 20% of these genes have been added to obesity gene panels for testing by commercially available laboratories. Early studies show that about 10% of patients with severe obesity using NGS testing have a pathogenic gene variant. Discussion to help characterize gene-gene interactions and disease mechanisms for early diagnosis, treatment, and risk factors contributing to disease is incorporated in this review.

Transcriptome analysis to reveal the mechanism of the effect of Echinops latifolius polysaccharide B on palmitate-induced insulin-resistant

Hepatic insulin resistance is a crucial pathological process in type 2 diabetes mellitus (T2DM) associated with visceral adiposity and metabolic disorders. Echinops latifolius polysaccharide B (ETPB), a polysaccharide extracted from Echinops latifolius Tausch, increases insulin sensitivity in the high-fat diet-fed and STZ induced SD rat model and even prevented hepatic metabolic disorders. However, the mechanism by which ETPB improves carbohydrate and lipid metabolisms in the liver with insulin resistance remains largely unknown. In the present work, an lnsulin resistance cell model (IR-HepG2) was established. Glucose consumption, glycogen content, triglycerides (TG), and free fatty acids (FFAs) levels were detected. The result revealed that the intervention of ETPB significantly increased glucose consumption and glycogen synthesis and reduced FFAs and TG production in IR-HepG2 cells. Further, we also employed RNA-seq to identify differentially expressed miRNAs (DEMs) and mRNAs (DEGs) with a fold change of ≥ 1.5 and p-value of <0.05. Finally, we identified 1028, 682, 382, 1614, 519 and 825 DEGs, and 71, 113, 94, 68, 52 and 38 DEMs in different comparisons, respectively. Based on a short time-series expression miner (STEM) analysis, six profiles were chosen for further analysis. Seventeen insulin resistance-associated dynamic DEGs were identified during ETPB stimulation. Based on these dynamic DEGs, the related miRNAs were acquired from DEMs, and an integrated miRNA-mRNA regulatory network was subsequently constructed. Besides, some DEGs and DEMs were validated using qPCR. This study provides transcriptomic evidence of the molecular mechanism involved in HepG2 insulin resistance, leading to the discovery of miRNA-based target therapies for ETPB.