Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules

Molecular regulation of PPARγ/RXRα signaling by the novel cofactor ZFP407

Cofactors interacting with PPARγ can regulate adipogenesis and adipocyte metabolism by modulating the transcriptional activity and selectivity of PPARγ signaling. ZFP407 was previously demonstrated to regulate PPARγ target genes such as GLUT4, and its overexpression improved glucose homeostasis in mice. Here, using a series of molecular assays, including protein-interaction studies, mutagenesis, and ChIP-seq, ZFP407 was found to interact with the PPARγ/RXRα protein complex in the nucleus of adipocytes. Consistent with this observation, ZFP407 ChIP-seq peaks significantly overlapped with PPARγ ChIP-seq peaks, with more than half of ZFP407 peaks overlapping with PPARγ peaks. Transcription factor binding motifs enriched in these overlapping sites included CTCF, RARα/RXRγ, TP73, and ELK1, which regulate cellular development and function within adipocytes. Site-directed mutagenesis of frequent PPARγ phosphorylation or SUMOylation sites did not prevent its regulation by ZFP407, while mutagenesis of ZFP407 domains potentially necessary for RXR and PPARγ binding abrogated any impact of ZFP407 on PPARγ activity. These data suggest that ZFP407 controls the activity of PPARγ, but does so independently of post-translational modifications, likely by direct binding, establishing ZFP407 as a newly identified PPARγ cofactor. In addition, ZFP407 ChIP-seq analyses identified regions that did not overlap with PPARγ peaks. These non-overlapping peaks were significantly enriched for the transcription factor binding motifs of TBX19, PAX8, HSF4, and ZKSCAN3, which may contribute to the PPARγ-independent functions of ZFP407 in adipocytes and other cell types.

Obesity-Dependent Association of the rs10454142 PPP1R21 with Breast Cancer

The purpose of this work was to find a link between the breast cancer (BC)-risk effects of sex hormone-binding globulin (SHBG)-associated polymorphisms and obesity. The study was conducted on a sample of 1498 women (358 BC; 1140 controls) who, depending on the presence/absence of obesity, were divided into two groups: obese (119 BC; 253 controls) and non-obese (239 BC; 887 controls). Genotyping of nine SHBG-associated single nucleotide polymorphisms (SNP)-rs17496332 PRMT6, rs780093 GCKR, rs10454142 PPP1R21, rs3779195 BAIAP2L1, rs440837 ZBTB10, rs7910927 JMJD1C, rs4149056 SLCO1B1, rs8023580 NR2F2, and rs12150660 SHBG-was executed, and the BC-risk impact of these loci was analyzed by logistic regression separately in each group of obese/non-obese women. We found that the BC-risk effect correlated by GWAS with the SHBG-level polymorphism rs10454142 PPP1R21 depends on the presence/absence of obesity. The SHBG-lowering allele C rs10454142 PPP1R21 has a risk value for BC in obese women (allelic model: CvsT, OR = 1.52, 95%CI = 1.10-2.11, and pperm = 0.013; additive model: CCvsTCvsTT, OR = 1.71, 95%CI = 1.15-2.62, and pperm = 0.011; dominant model: CC + TCvsTT, OR = 1.95, 95%CI = 1.13-3.37, and pperm = 0.017) and is not associated with the disease in women without obesity. SNP rs10454142 PPP1R21 and 10 proxy SNPs have adipose-specific regulatory effects (epigenetic modifications of promoters/enhancers, DNA interaction with 51 transcription factors, eQTL/sQTL effects on five genes (PPP1R21, RP11-460M2.1, GTF2A1L, STON1-GTF2A1L, and STON1), etc.), can be "likely cancer driver" SNPs, and are involved in cancer-significant pathways. In conclusion, our study detected an obesity-dependent association of the rs10454142 PPP1R21 with BC in women.

Prediction of interactomic hub genes in PBMC cells in type 2 diabetes mellitus, dyslipidemia, and periodontitis

BACKGROUND AND OBJECTIVE

In recent years, the complex interplay between systemic health and oral well-being has emerged as a focal point for researchers and healthcare practitioners. Among the several important connections, the convergence of Type 2 Diabetes Mellitus (T2DM), dyslipidemia, chronic periodontitis, and peripheral blood mononuclear cells (PBMCs) is a remarkable example. These components collectively contribute to a network of interactions that extends beyond their domains, underscoring the intricate nature of human health. In the current study, bioinformatics analysis was utilized to predict the interactomic hub genes involved in type 2 diabetes mellitus (T2DM), dyslipidemia, and periodontitis and their relationships to peripheral blood mononuclear cells (PBMC) by machine learning algorithms.

MATERIALS AND METHODS

Gene Expression Omnibus datasets were utilized to identify the genes linked to type 2 diabetes mellitus(T2DM), dyslipidemia, and Periodontitis (GSE156993).Gene Ontology (G.O.) Enrichr, Genemania, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for analysis for identification and functionalities of hub genes. The expression of hub D.E.G.s was confirmed, and an orange machine learning tool was used to predict the hub genes.

RESULT

The decision tree, AdaBoost, and Random Forest had an A.U.C. of 0.982, 1.000, and 0.991 in the R.O.C. curve. The AdaBoost model showed an accuracy of (1.000). The findings imply that the AdaBoost model showed a good predictive value and may support the clinical evaluation and assist in accurately detecting periodontitis associated with T2DM and dyslipidemia. Moreover, the genes with p-value < 0.05 and A.U.C.>0.90, which showed excellent predictive value, were thus considered hub genes.

CONCLUSION

The hub genes and the D.E.G.s identified in the present study contribute immensely to the fundamentals of the molecular mechanisms occurring in the PBMC associated with the progression of periodontitis in the presence of T2DM and dyslipidemia. They may be considered potential biomarkers and offer novel therapeutic strategies for chronic inflammatory diseases.

Shared gene expression signatures between visceral adipose and skeletal muscle tissues are associated with cardiometabolic traits in children with obesity

Obesity in children is related to the development of cardiometabolic complications later in life, where molecular changes of visceral adipose tissue (VAT) and skeletal muscle tissue (SMT) have been proven to be fundamental. The aim of this study is to unveil the gene expression architecture of both tissues in a cohort of Spanish boys with obesity, using a clustering method known as weighted gene co-expression network analysis. For this purpose, we have followed a multi-objective analytic pipeline consisting of three main approaches; identification of gene co-expression clusters associated with childhood obesity, individually in VAT and SMT (intra-tissue, approach I); identification of gene co-expression clusters associated with obesity-metabolic alterations, individually in VAT and SMT (intra-tissue, approach II); and identification of gene co-expression clusters associated with obesity-metabolic alterations simultaneously in VAT and SMT (inter-tissue, approach III). In both tissues, we identified independent and inter-tissue gene co-expression signatures associated with obesity and cardiovascular risk, some of which exceeded multiple-test correction filters. In these signatures, we could identify some central hub genes (e.g., NDUFB8, GUCY1B1, KCNMA1, NPR2, PPP3CC) participating in relevant metabolic pathways exceeding multiple-testing correction filters. We identified the central hub genes PIK3R2, PPP3C and PTPN5 associated with MAPK signaling and insulin resistance terms. This is the first time that these genes have been associated with childhood obesity in both tissues. Therefore, they could be potential novel molecular targets for drugs and health interventions, opening new lines of research on the personalized care in this pathology. This work generates interesting hypotheses about the transcriptomics alterations underlying metabolic health alterations in obesity in the pediatric population.

Consumption of Common Bean Suppresses the Obesogenic Increase in Adipose Depot Mass: Impact of Dose and Biological Sex

Obesity prevention is stated as a simple objective in the public health guidelines of most countries: avoid adult weight gain. However, the success of the global population in accomplishing this goal is limited as reflected in the persisting pandemic of overweight and obesity. While many intervention strategies have been proposed, most are directed at mitigating the consequences of obesity. Efforts intended to prevent unintentional weight gain and associated adiposity are termed anti-obesogenic. Herein, evidence is presented that a neglected category of foods, pulses, i.e., grain legumes, have anti-obesogenic activity. Using a preclinical mouse model of obesity, a dose-response study design in animals of both biological sexes, and cooked, freeze-dried, and milled common bean as a representative pulse, data are presented showing that the rate of body weight gain is slowed, and fat accumulation is suppressed when 70% of the dietary protein is provided from common bean. These anti-obesogenic effects are reduced at lower amounts of common bean (17.5% or 35%). The anti-obesogenic responsiveness is greater in female than in male mice. RNA sequence analysis indicates that the sex-related differences extend to gene expression patterns, particularly those related to immune regulation within adipose tissue. In addition, our findings indicate the potential value of a precision nutrition approach for human intervention studies that identify "pulse anti-obesogenic responders". A precision approach may reduce the concentration of pulses required in the diet for benefits, but candidate biomarkers of responsivity to pulse consumption remain to be determined.

Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes Mellitus

Background

Type 2 diabetes mellitus (T2DM) is the most common metabolic disorder. The aim of the present investigation was to identify gene signature specific to T2DM.

Methods

The next generation sequencing (NGS) dataset GSE81608 was retrieved from the gene expression omnibus (GEO) database and analyzed to identify the differentially expressed genes (DEGs) between T2DM and normal controls. Then, Gene Ontology (GO) and pathway enrichment analysis, protein-protein interaction (PPI) network, modules, miRNA (micro RNA)-hub gene regulatory network construction and TF (transcription factor)-hub gene regulatory network construction, and topological analysis were performed. Receiver operating characteristic curve (ROC) analysis was also performed to verify the prognostic value of hub genes.

Results

A total of 927 DEGs (461 were up regulated and 466 down regulated genes) were identified in T2DM. GO and REACTOME results showed that DEGs mainly enriched in protein metabolic process, establishment of localization, metabolism of proteins, and metabolism. The top centrality hub genes APP, MYH9, TCTN2, USP7, SYNPO, GRB2, HSP90AB1, UBC, HSPA5, and SQSTM1 were screened out as the critical genes. ROC analysis provides prognostic value of hub genes.

Conclusion

The potential crucial genes, especially APP, MYH9, TCTN2, USP7, SYNPO, GRB2, HSP90AB1, UBC, HSPA5, and SQSTM1, might be linked with risk of T2DM. Our study provided novel insights of T2DM into genetics, molecular pathogenesis, and novel therapeutic targets.

Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity

Background and Objectives: A subject with diabetes and obesity is a class of the metabolic disorder. The current investigation aimed to elucidate the potential biomarker and prognostic targets in subjects with diabetes and obesity. Materials and Methods: The next-generation sequencing (NGS) data of GSE132831 was downloaded from Gene Expression Omnibus (GEO) database. Functional enrichment analysis of DEGs was conducted with ToppGene. The protein-protein interactions network, module analysis, target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed. Furthermore, hub genes were validated by receiver operating characteristic (ROC) analysis. A total of 872 DEGs, including 439 up-regulated genes and 433 down-regulated genes were observed. Results: Second, functional enrichment analysis showed that these DEGs are mainly involved in the axon guidance, neutrophil degranulation, plasma membrane bounded cell projection organization and cell activation. The top ten hub genes (MYH9, FLNA, DCTN1, CLTC, ERBB2, TCF4, VIM, LRRK2, IFI16 and CAV1) could be utilized as potential diagnostic indicators for subjects with diabetes and obesity. The hub genes were validated in subjects with diabetes and obesity. Conclusion: This investigation found effective and reliable molecular biomarkers for diagnosis and prognosis by integrated bioinformatics analysis, suggesting new and key therapeutic targets for subjects with diabetes and obesity.

IncRNA XIST Promotes Cardiac Fibrosis in Mice with Diabetic Nephropathy via Sponging miR-106a-5p to Target RUNX1

Diabetic nephropathy (DN) accompanied by cardiac fibrosis (CF) increases the mortality rate among people with diabetes. This study sought to explore the molecular mechanism of long non-coding RNA X inactive specific transcript (lncRNA XIST) in CF in DN mice. The animal model of DN was established by streptozocin (STZ). The levels of lncRNA XIST, microRNA (miR)-106a-5p, and RUNX family transcription factor 1 (RUNX1) were determined by quantitative real-time polymerase chain reaction (qRT-PCR), followed by biochemical analysis, hematoxylin & eosin and Masson staining, echocardiography, and quantification of collagen I, collagen III, α-smooth muscle actin (α-SMA), and transforming growth factor-β1 (TGF-β1) levels through qRT-PCR and Western blot assay. The subcellular localization of lncRNA XIST was analyzed by nuclear/cytoplasmic fractionation assay and the bindings of miR-106a-5p to lncRNA XIST and RUNX1 were confirmed by RNA immunoprecipitation and dual-luciferase assays. Functional rescue experiments were performed to validate the role of miR-106a-5p/RUNX1 in CF in DN mice. lncRNA XIST and RUNX1 were elevated while miR-106a-5p was decreased in STZ mice. lncRNA XIST inhibition reduced myocardial injury and collagen deposition, along with decreased levels of fasting blood glucose, serum creatinine, blood urea nitrogen, and urinary microalbumin, collagen I, collagen III, α-SMA, and TGF-β1. lncRNA XIST competitively bound to miR-106a-5p to promote RUNX1 transcription. miR-106a-5p downregulation or RUXN1 upregulation reversed the protective role of lncRNA XIST inhibition in STZ mice. lncRNA XIST competitively bound to miR-106a-5p to promote RUNX1 transcription, thereby aggravating renal dysfunction and CF in DN mice.

The landscape of expression and alternative splicing variation across human traits

Understanding the consequences of individual transcriptome variation is fundamental to deciphering human biology and disease. We implement a statistical framework to quantify the contributions of 21 individual traits as drivers of gene expression and alternative splicing variation across 46 human tissues and 781 individuals from the Genotype-Tissue Expression project. We demonstrate that ancestry, sex, age, and BMI make additive and tissue-specific contributions to expression variability, whereas interactions are rare. Variation in splicing is dominated by ancestry and is under genetic control in most tissues, with ribosomal proteins showing a strong enrichment of tissue-shared splicing events. Our analyses reveal a systemic contribution of types 1 and 2 diabetes to tissue transcriptome variation with the strongest signal in the nerve, where histopathology image analysis identifies novel genes related to diabetic neuropathy. Our multi-tissue and multi-trait approach provides an extensive characterization of the main drivers of human transcriptome variation in health and disease.

Genetics, genomics, and diet interactions in obesity in the Latin American environment

Obesity is a chronic disease characterized by abnormal or excessive fat accumulation that could impact an individual's health; moreover, the World Health Organization (WHO) has declared obesity a global epidemic since 1997. In Latin America, in 2016, reports indicated that 24.2% of the adult population was obese. The environmental factor or specific behaviors like dietary intake or physical activity have a vital role in the development of a condition like obesity, but the interaction of genes could contribute to that predisposition. Hence, it is vital to understand the relationship between genes and disease. Indeed, genetics in nutrition studies the genetic variations and their effect on dietary response; while genomics in nutrition studies the role of nutrients in gene expression. The present review represents a compendium of the dietary behaviors in the Latin American environment and the interactions of genes with their single nucleotide polymorphisms (SNPs) associated with obesity, including the risk allele frequencies in the Latin American population. Additionally, a bibliographical selection of several studies has been included; these studies examined the impact that dietary patterns in Latin American environments have on the expression of numerous genes involved in obesity-associated metabolic pathways.

Identification of Novel Metabolic Subtypes Using Multi-Trait Limited Mixed Regression in the Chinese Population

The aggregation and interaction of metabolic risk factors leads to highly heterogeneous pathogeneses, manifestations, and outcomes, hindering risk stratification and targeted management. To deconstruct the heterogeneity, we used baseline data from phase II of the Fangshan Family-Based Ischemic Stroke Study (FISSIC), and a total of 4632 participants were included. A total of 732 individuals who did not have any component of metabolic syndrome (MetS) were set as a reference group, while 3900 individuals with metabolic abnormalities were clustered into subtypes using multi-trait limited mixed regression (MFMR). Four metabolic subtypes were identified with the dominant characteristics of abdominal obesity, hypertension, hyperglycemia, and dyslipidemia. Multivariate logistic regression showed that the hyperglycemia-dominant subtype had the highest coronary heart disease (CHD) risk (OR: 6.440, 95% CI: 3.177-13.977) and that the dyslipidemia-dominant subtype had the highest stroke risk (OR: 2.450, 95% CI: 1.250-5.265). Exome-wide association studies (EWASs) identified eight SNPs related to the dyslipidemia-dominant subtype with genome-wide significance, which were located in the genes APOA5, BUD13, ZNF259, and WNT4. Functional analysis revealed an enrichment of top genes in metabolism-related biological pathways and expression in the heart, brain, arteries, and kidneys. Our findings provide directions for future attempts at risk stratification and evidence-based management in populations with metabolic abnormalities from a systematic perspective.

Dapagliflozin attenuates residual cardiac remodeling after surgical ventricular reconstruction in mice with an enlarged heart after myocardial infarction

BACKGROUND

Severe heart failure refractory to conventional therapy requires alternative treatment modalities. Surgical ventricular reconstruction (SVR) has been used to reverse cardiac remodeling in post-myocardial infarction (MI) patients with large left ventricular (LV) aneurysm, however, residual LV remodeling and dysfunction remain postoperatively. It is unclear whether SVR recovers response to drug treatment and whether the sodium-glucose co-transporter 2 inhibitor dapagliflozin (DAPA) reverses residual LV remodeling after SVR.

METHODS

Adult male C57 mice were subjected to MI or sham surgery. Four-week later, MI mice with LV aneurysm underwent modified SVR or second open-chest sham operation and were randomized to DAPA or vehicle for four-week. Cardiac remodeling, LV function, and the underlying mechanisms were evaluated by echocardiography, invasive LV hemodynamic measurements, mRNA sequencing, and bioinformatics analysis.

RESULTS

SVR significantly decreased LV volume; increased myocardial strain, LV pressure change rates and end-systolic elastance; and decreased heart-to-body weight ratio and myocardial fibrosis. However, significant residual cardiac remodeling remained. DAPA significantly attenuated residual cardiac remodeling and improved LV function in SVR mice but did not have curative effects in non-SVR mice. Of the 1532 genes differentially expressed in SVR and MI mice, 1037 were associated with cardiac metabolism; Src, Crebbp, Fn1, Grb2, and Mapk14 were the top 5 hub genes. Unlike sham surgery, MI upregulated those 5 genes, and treatment with SVR + DAPA normalized their expression.

CONCLUSIONS

SVR restores therapeutic response in the post-MI heart with large LV aneurysm, and DAPA attenuates residual cardiac remodeling after SVR by normalizing some cardiac metabolism-related hub genes.

Hydrolyzed Proteins and Vegetable Peptides: Anti-Inflammatory Mechanisms in Obesity and Potential Therapeutic Targets

Chronic low-grade inflammation is present in overweight and obesity, causing changes in several metabolic pathways. It impairs systemic functioning and positively feeds back the accumulation of more adipose tissue. Studies with hydrolyzed proteins and plant peptides have demonstrated a potential anti-inflammatory and immunomodulatory effect of these peptides. However, it is challenging and necessary to explore the mechanism of action of such molecules because understanding their effects depends on their structural characterizations. Furthermore, the structure might also give insights into safety, efficacy and efficiency, with a view of a possible health application. Thus, the present narrative review aimed to discuss the mechanisms of action of hydrolyzed proteins and plant peptides as anti-inflammatory agents in obesity. Keywords and related terms were inserted into databases for the search. Based on the studies evaluated, these biomolecules act by different pathways, favoring the reduction of inflammatory cytokines and adipokines and the polarization of macrophages to the M2 phenotype. Finally, as a future perspective, bioinformatics is suggested as a tool to help understand and better use these molecules considering their applicability in pre-clinical and clinical studies.

Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease

In fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of this study was to use an integrated bioinformatics approach to not only uncover Differentially Expressed Genes (DEGs), their associated functions, and pathways but also give a glimpse of how these two conditions are related at the molecular level. We started with GEO2R/R program (version 3.6.3, 64 bit) to get DEGs by comparing gene expression microarray data from CVD and CKD. Thereafter, the online STRING version 11.1 program was used to look for any correlations between all these common and/or overlapping DEGs, and the results were visualized using Cytoscape (version 3.8.0). Further, we used MCODE, a cytoscape plugin, and identified a total of 15 modules/clusters of the primary network. Interestingly, 10 of these modules contained our genes of interest (key genes). Out of these 10 modules that consist of 19 key genes (11 downregulated and 8 up-regulated), Module 1 (RPL13, RPLP0, RPS24, and RPS2) and module 5 (MYC, COX7B, and SOCS3) had the highest number of these genes. Then we used ClueGO to add a layer of GO terms with pathways to get a functionally ordered network. Finally, to identify the most influential nodes, we employed a novel technique called Integrated Value of Influence (IVI) by combining the network's most critical topological attributes. This method suggests that the nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are intended to have the most impact on the information flow in the network) are the most influential or essential nodes in a network. Thus, based on IVI values, hubness score, and spreading score, top 20 nodes were extracted, in which RPS27A non-seed gene and RPS2, a seed gene, came out to be the important node in the network.

Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes

Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1, and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.