Unraveling the role of salt-sensitivity genes in obesity with integrated network biology and co-expression analysis

Clinical relevance of circulating non-coding RNAs in metabolic diseases: Emphasis on obesity, diabetes, cardiovascular diseases and metabolic syndrome

Non-coding RNAs (ncRNAs) participate in the regulation of several cellular processes including transcription, RNA processing and genome rearrangement. The aberrant expression of ncRNAs is associated with several pathological conditions. In this review, we focused on recent information to elucidate the role of various regulatory ncRNAs i.e., micro RNAs (miRNAs), circular RNAs (circRNAs) and long-chain non-coding RNAs (lncRNAs), in metabolic diseases, e.g., obesity, diabetes mellitus (DM), cardiovascular diseases (CVD) and metabolic syndrome (MetS). The mechanisms by which ncRNAs participated in disease pathophysiology were also highlighted. miRNAs regulate the expression of genes at transcriptional and translational levels. circRNAs modulate the regulation of gene expression via miRNA sponging activity, interacting with RNA binding protein and polymerase II transcription regulation. lncRNAs regulate the expression of genes by acting as a protein decoy, miRNA sponging, miRNA host gene, binding to miRNA response elements (MRE) and the recruitment of transcriptional element or chromatin modifiers. We examined the role of ncRNAs in the disease pathogenesis and their potential role as molecular markers for diagnosis, prognosis and therapeutic targets. We showed the involvement of ncRNAs in the onset of obesity and its progression to MetS and CVD. miRNA-192, miRNA-122, and miRNA-221 were dysregulated in all these metabolic diseases. Other ncRNAs, implicated in at least three diseases include miRNA-15a, miRNA-26, miRNA-27a, miRNA-320, and miRNA-375. Dysregulation of ncRNAs increased the risk of development of DM and MetS and its progression to CVD in obese individuals. Hence, these molecules are potential targets to arrest or delay the progression of metabolic diseases.

Epigenetics of the blood pressure reactivity to salt: Is the salt sensitive phenotype correctable?

Salt sensitivity defines a state characterized by a highly reactive blood pressure to changes in salt intake. The salt-sensitive phenotype is strongly associated with hypertension, visceral adiposity/metabolic syndrome, and ageing. Obesity accounts for around 70% of hypertension in young adults, and 30% to 50% of adult hypertensives carry the salt-sensitive phenotype. It is estimated that the salt-sensitive phenotype is responsible for high blood pressure in over 600 million adults. But is the salt-sensitive phenotype correctable? Interventional, controlled, clinical trials in obese adolescents and young obese adults, demonstrated that weight-reducing lifestyle modifications revert the salt-sensitive to the salt-resistant phenotype, and restored the faulty production of nitric oxide. Correction of the salt-sensitive phenotype lowers the blood pressure by reducing its reactivity to dietary salt. In a random sample of obese adults subjected to lifestyle modifications, those who were salt-resistant at baseline, were also normotensive and failed to further lower their blood pressure despite a 12% drop in body weight. The salt-resistant phenotype protects the metabolically healthy obese from hypertension, even if their salt consumption is comparable to that of salt-sensitive obese. In summary, at early stages, the elevated blood pressure of obesity, is determined by epigenetic changes leading to a state of salt-sensitivity.

Integrative weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer

Chronic obstructive pulmonary disease (COPD) is a multifactorial progressive airflow obstruction in the lungs, accounting for high morbidity and mortality across the world. This study aims to identify potential COPD blood-based biomarkers by analyzing the dysregulated gene expression patterns in blood and lung tissues with the help of robust computational approaches. The microarray gene expression datasets from blood (136 COPD and 6 controls) and lung tissues (16 COPD and 19 controls) were analyzed to detect shared differentially expressed genes (DEGs). Then these DEGs were used to construct COPD protein network-clusters and functionally enrich them against gene ontology annotation terms. The hub genes in the COPD network clusters were then queried in GWAS catalog and in several cancer expression databases to explore their pathogenic roles in lung cancers. The comparison of blood and lung tissue datasets revealed 63 shared DEGs. Of these DEGs, 12 COPD hub gene-network clusters (SREK1, TMEM67, IRAK2, MECOM, ASB4, C1QTNF2, CDC42BPA, DPF3, DET1, CCDC74B, KHK, and DDX3Y) connected to dysregulations of protein degradation, inflammatory cytokine production, airway remodeling, and immune cell activity were prioritized with the help of protein interactome and functional enrichment analysis. Interestingly, IRAK2 and MECOM hub genes from these COPD network clusters are known for their involvement in different pulmonary diseases. Additional COPD hub genes like SREK1, TMEM67, CDC42BPA, DPF3, and ASB4 were identified as prognostic markers in lung cancer, which is reported in 1% of COPD patients. This study identified 12 gene network- clusters as potential blood based genetic biomarkers for COPD diagnosis and prognosis.

The association between salt intake and blood pressure is mediated by body mass index but modified by hypertension: The ELSA-Brasil study

High salt intake has been linked to both obesity and high blood pressure (BP). Part of the variability of BP attributed to salt intake might be BMI-mediated. To investigate whether hypertension would be an effect modifier in the complex network including salt intake, obesity, and BP, we tested the hypothesis that salt intake has direct and BMI-mediated effects on systolic (SBP) and diastolic blood pressure (DBP). Data from 9,028 participants (aged 34-75 years, 53.6% women) were analyzed. A validated formula was used to estimate daily salt intake from the sodium excretion (12 h urine collection). A path model adjusted for covariates was designed in which salt intake has both a direct and a BMI-mediated effect on BP. In normotensives, standardized beta coefficients showed significant direct (Men: 0.058 and 0.052, Women: 0.072 and 0,061, P < 0.05) and BMI-mediated (Men: 0.040 and 0.065, Women: 0.038 and 0.067, P < 0.05) effect of salt intake on the SBP and DBP, respectively. However, in hypertensive individuals, neither the direct (Men: 0.006 and 0.056, Women: 0.048 and 0.017) nor the indirect effect (Men: -0.044 and 0.014, Women: 0.011 and 0.050) of salt intake on the SBP and DBP were significant. These data suggest that cardiovascular risk stratification should consider the complex interaction between salt intake and weight gain, and their effects on BP of normotensive and hypertensive individuals.

Integrative global co-expression analysis identifies Key MicroRNA-target gene networks as key blood biomarkers for obesity

Obesity is associated with the quantitative changes in miRNAs and their target genes. However, the molecular basis of their dysregulation and expression status correlations is incompletely understood. Therefore, this study aims to examine the shared differentially expressed miRNAs and their target genes between blood and adipose tissues of obese individuals to identify potential blood-based biomarkers. In this study, 3 gene expression datasets (two mRNA and one miRNA), generated from blood and adipose tissues of 68 obese and 39 lean individuals, were analyzed by a series of robust computational concepts, like protein interactome mapping, functional enrichment of biological pathways and construction of miRNA-mRNA and transcription factor gene networks. The comparison of blood versus tissue datasets has revealed the shared differential expression of 210 genes (59.5% upregulated) involved in lipid metabolism and inflammatory reactions. The blood miRNA (GSE25470) analysis has identified 79 differentially expressed miRNAs (71% downregulated). The miRNA-target gene scan identified regulation of 30 shared genes by 22miRNAs. The gene network analysis has identified the inverse expression correlation between 8 target genes (TP53, DYSF, GAB2, GFRA2, NACC2, FAM53C, JNK and GAB2) and 3 key miRNAs (hsa-mir-940, hsa-mir-765, hsa-mir-612), which are further regulated by 24 key transcription factors. This study identifies potential obesity related blood biomarkers from largescale gene expression data by computational miRNA-target gene interactome and transcription factor network construction methods.

Bima A, Almukadi H, Ibrahim Kutbi H, Jayasheela Shetty P, Ahmad Shaik N, Banaganapalli B. Identifying Significant Genes and Functionally Enriched Pathways in Familial Hypercholesterolemia Using Integrated Gene Co-Expression Network Analysis

Familial hypercholesterolemia (FH) is a monogenic lipid disorder which promotes atherosclerosis and cardiovascular diseases. Owing to the lack of sufficient published information, this study aims to identify the potential genetic biomarkers for FH by studying the global gene expression profile of blood cells. The microarray expression data of FH patients and controls was analyzed by different computational biology methods like differential expression analysis, protein network mapping, hub gene identification, functional enrichment of biological pathways, and immune cell restriction analysis. Our results showed the dysregulated expression of 115 genes connected to lipid homeostasis, immune responses, cell adhesion molecules, canonical Wnt signaling, mucin type O-glycan biosynthesis pathways in FH patients. The findings from expanded protein interaction network construction with known FH genes and subsequent Gene Ontology (GO) annotations have also supported the above findings, in addition to identifying the involvement of dysregulated thyroid hormone and ErbB signaling pathways in FH patients. The genes like CSNK1A1, JAK3, PLCG2, RALA, and ZEB2 were found to be enriched under all GO annotation categories. The subsequent phenotype ontology results have revealed JAK3I, PLCG2, and ZEB2 as key hub genes contributing to the inflammation underlying cardiovascular and immune response related phenotypes. Immune cell restriction findings show that above three genes are highly expressed by T-follicular helper CD4⁺ T cells, naïve B cells, and monocytes, respectively. These findings not only provide a theoretical basis to understand the role of immune dysregulations underlying the atherosclerosis among FH patients but may also pave the way to develop genomic medicine for cardiovascular diseases.

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.

Exploring celiac disease candidate pathways by global gene expression profiling and gene network cluster analysis

Celiac disease (CeD) is a gastrointestinal autoimmune disorder, whose specific molecular basis is not yet fully interpreted. Therefore, in this study, we compared the global gene expression profile of duodenum tissues from CeD patients, both at the time of disease diagnosis and after two years of the gluten-free diet. A series of advanced systems biology approaches like differential gene expression, protein–protein interactions, gene network-cluster analysis were deployed to annotate the candidate pathways relevant to CeD pathogenesis. The duodenum tissues from CeD patients revealed the differential expression of 106 up- and 193 down-regulated genes. The pathway enrichment of differentially expressed genes (DEGs) highlights the involvement of biological pathways related to loss of cell division regulation (cell cycle, p53 signalling pathway), immune system processes (NOD-like receptor signalling pathway, Th1, and Th2 cell differentiation, IL-17 signalling pathway) and impaired metabolism and absorption (mineral and vitamin absorptions and drug metabolism) in celiac disease. The molecular dysfunctions of these 3 biological events tend to increase the number of intraepithelial lymphocytes (IELs) and villous atrophy of the duodenal mucosa promoting the development of CeD. For the first time, this study highlights the involvement of aberrant cell division, immune system, absorption, and metabolism pathways in CeD pathophysiology and presents potential novel therapeutic opportunities.

Myocardial infarction biomarker discovery with integrated gene expression, pathways and biological networks analysis

Myocardial infarction (MI) is the most prevalent coronary heart disease caused by the complex molecular interactions between multiple genes and environment. Here, we aim to identify potential biomarkers for the disease development and for prognosis of MI. We have used gene expression dataset (GSE66360) generated from 51 healthy controls and 49 patients experiencing acute MI and analyzed the differentially expressed genes (DEGs), protein-protein interactions (PPI), gene network-clusters to annotate the candidate pathways relevant to MI pathogenesis. Bioinformatic analysis revealed 810 DEGs. Their functional annotations have captured several MI targeting biological processes and pathways like immune response, inflammation and platelets degranulation. PPI network identify seventeen hub and bottleneck genes, whose involvement in MI was further confirmed by DisGeNET database. OpenTarget Platform reveal unique bottleneck genes as potential target for MI. Our findings identify several potential biomarkers associated with early stage MI providing a new insight into molecular mechanism underlying the disease.