T-HOD: a literature-based candidate gene database for hypertension, obesity and diabetes

Genetic Polymorphisms in Cardiovascular Disease: Effects Across Three Generations Exposed to Radiation from the Semipalatinsk Nuclear Test Site

The population in the areas neighboring the Semipalatinsk Nuclear Test Site (SNTS) in the eastern region of Kazakhstan faces increased cardiovascular disease (CVD) risk. Previous research has not explored gene polymorphisms related to CVD in this population. Therefore, the present study examines the prevalence of six CVD-associated genotypes in three generations exposed to SNTS radiation. The genotyping of ApoE Leu28 → Pro, AGT Met174 → Thr, AGT Met235 → Thr, eNOS T786 → C, PON1 Gln192 → Arg, and EDN 1 Lys198 → Asn was performed using real-time polymerase chain reaction. The present study encompassed a cohort of 218 participants with a familial history of arterial hypertension and/or carotid artery disease spanning at least three generations. The analysis unveiled significant disparities in the prevalence of ApoE Leu28 → Pro, eNOS T786 → C, and PON1 Gln192 → Arg genotypes across different generations. Furthermore, a substantial variation in the distribution of the eNOS T786 → C genotype was observed between individuals of Kazakh and Russian ethnicities. Nevertheless, no significant discrepancies were detected in the frequencies of the investigated genotypes between genders. Further research in this area is warranted to enhance the understanding of the genetic factors contributing to CVD in the population exposed to radiation from the SNTS. Specifically, future studies should broaden the scope of genetic polymorphisms investigated and include representatives of healthy individuals who have not been exposed to radiation as controls.

Prioritization of genes associated with type 2 diabetes mellitus for functional studies

Existing therapies for type 2 diabetes mellitus (T2DM) show limited efficacy or have adverse effects. Numerous genetic variants associated with T2DM have been identified, but progress in translating these findings into potential drug targets has been limited. Here, we describe the tools and platforms available to identify effector genes from T2DM-associated coding and non-coding variants and prioritize them for functional studies. We discuss QSER1 and SLC12A8 as examples of genes that have been identified as possible T2DM candidate genes using these tools and platforms. We suggest further approaches, including the use of sequencing data with increased sample size and ethnic diversity, single-cell omics data for analyses, glycaemic trait associations to predict gene function and, potentially, human induced pluripotent stem cell 'village' cultures, to strengthen current gene functionalization workflows. Effective prioritization of T2DM-associated genes for experimental validation could expedite our understanding of the genetic mechanisms responsible for T2DM to facilitate the use of precision medicine in its treatment.

PlagueKD: a knowledge graph-based plague knowledge database

Plague has been confirmed as an extremely horrific international quarantine infectious disease attributed to Yersinia pestis. It has an extraordinarily high lethal rate that poses a serious hazard to human and animal lives. With the deepening of research, there has been a considerable amount of literature related to the plague that has never been systematically integrated. Indeed, it makes researchers time-consuming and laborious when they conduct some investigation. Accordingly, integrating and excavating plague-related knowledge from considerable literature takes on a critical significance. Moreover, a comprehensive plague knowledge base should be urgently built. To solve the above issues, the plague knowledge base is built for the first time. A database is built from the literature mining based on knowledge graph, which is capable of storing, retrieving, managing and accessing data. First, 5388 plague-related abstracts that were obtained automatically from PubMed are integrated, and plague entity dictionary and ontology knowledge base are constructed by using text mining technology. Second, the scattered plague-related knowledge is correlated through knowledge graph technology. A multifactor correlation knowledge graph centered on plague is formed, which contains 9633 nodes of 33 types (e.g. disease, gene, protein, species, symptom, treatment and geographic location), as well as 9466 association relations (e.g. disease-gene, gene-protein and disease-species). The Neo4j graph database is adopted to store and manage the relational data in the form of triple. Lastly, a plague knowledge base is built, which can successfully manage and visualize a large amount of structured plague-related data. This knowledge base almost provides an integrated and comprehensive plague-related knowledge. It should not only help researchers to better understand the complex pathogenesis and potential therapeutic approaches of plague but also take on a key significance to reference for exploring potential action mechanisms of corresponding drug candidates and the development of vaccine in the future. Furthermore, it is of great significance to promote the field of plague research. Researchers are enabled to acquire data more easily for more effective research. Database URL: http://39.104.28.169:18095/.

A network pharmacology-based approach to explore the active ingredients and molecular mechanism of Lei-gong-gen formula granule on a spontaneously hypertensive rat model

Background

Lei-gong-gen formula granule (LFG) is a folk prescription derived from Zhuang nationality, the largest ethnic minority among 56 nationalities in China. It consists of three herbs, namely Eclipta prostrata (L.) L., Smilax glabra Roxb, and Centella asiatica (L.) Urb. It has been widely used as health protection tea for hundreds of years to prevent hypertension in Guangxi Zhuang Autonomous Region. The purpose of this study is to validate the antihypertensive effect of LFG on the spontaneously hypertensive rat (SHR) model, and to further identify the effective components and anti-hypertension mechanism of LFG.

Methods

The effects of LFG on blood pressure, body weight, and heart rate were investigated in vivo using the SHR model. The levels of NO, ANG II, and ET-1 in the serum were measured, and pathological changes in the heart were examined by H&E staining. The main active components of LFG, their corresponding targets, and hypertension associated pathways were discerned through network pharmacology analysis based on the Traditional Chinese Medicine Systems Pharmacology (TCMSP), Traditional Chinese Medicine Integrated Database (TCMID), and the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM). Then the predicted results were further verified by molecular biology experiments such as RT-qPCR and western blot. Additionally, the potential active compounds were predicted by molecular docking technology, and the chemical constituents of LFG were analyzed and identified by UPLC-QTOF/MS technology. Finally, an in vitro assay was performed to investigate the protective effects of potential active compounds against hydrogen peroxide (H₂O₂) induced oxidative damage in human umbilical vein endothelial cells (HUVEC).

Results

LFG could effectively reduce blood pressure and increase serum NO content in SHR model. Histological results showed that LFG could ameliorate pathological changes such as cardiac hypertrophy and interstitial inflammation. From network pharmacology analysis, 53 candidate active compounds of LFG were collected, which linked to 765 potential targets, and 828 hypertension associated targets were retrieved, from which 12 overlapped targets both related to candidate active compounds from LFG and hypertension were screened and used as the potential targets of LFG on antihypertensive effect. The molecular biology experiments of the 12 overlapped targets showed that LFG could upregulate the mRNA and protein expressions of NOS3 and proto-oncogene tyrosine-protein kinase SRC (SRC) in the thoracic aorta. Pathway enrichment analysis showed that the PI3K-AKT signaling pathway was closely related to the expression of NOS3 and SRC. Moreover, western blot results showed that LFG significantly increased the protein expression levels of PI3K and phosphorylated AKT in SHR model, suggesting that LFG may active the PI3K-AKT signaling pathway to decrease hypertension. Molecular docking study further supported that p-hydroxybenzoic acid, cedar acid, shikimic acid, salicylic acid, nicotinic acid, linalool, and histidine can be well binding with NOS3, SRC, PI3K, and AKT. UPLC-QTOF/MS analysis confirmed that p-hydroxybenzoic acid, shikimic acid, salicylic acid, and nicotinic acid existed in LFG. Pre-treatment of HUVEC with nicotinic acid could alleviate the effect on cell viability induced by H₂O₂ and increase the NO level in cell supernatants.

Conclusions

LFG can reduce the blood pressure in SHR model, which might be attributed to increasing the NO level in serum for promoting vasodilation via upregulating SRC expression level and activating the PI3K-AKT-NOS3 signaling pathway. Nicotinic acid might be the potential compound for LFG antihypertensive effect.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13020-021-00507-1.

CHDGKB: a knowledgebase for systematic understanding of genetic variations associated with non-syndromic congenital heart disease

Congenital heart disease (CHD) is one of the most common birth defects, with complex genetic and environmental etiologies. The reports of genetic variation associated with CHD have increased dramatically in recent years due to the revolutionary development of molecular technology. However, CHD is a heterogeneous disease, and its genetic origins remain inconclusive in most patients. Here we present a database of genetic variations for non-syndromic CHD (NS-CHD). By manually literature extraction and analyses, 5345 NS-CHD-associated genetic variations were collected, curated and stored in the public online database. The objective of our database is to provide the most comprehensive updates on NS-CHD genetic research and to aid systematic analyses of pathogenesis of NS-CHD in molecular level and the correlation between NS-CHD genotypes and phenotypes. Database URL: http://www.sysbio.org.cn/CHDGKB/.

Association of polymorphism genes , , and with risk of hyperinsulinism and insulin resistance in the Kazakh population

Hyperinsulinism and insulin resistance are closely associated with several common diseases including type 2 of diabetes, cardiovascular diseases, and metabolic syndrome. The present study aimed to determine the association between hyperinsulinism, insulin resistance and the polymorphism of genes, including angiotensin II receptor type 1 (AGTR1), angiotensinogen (AGT), β2-adrenoreceptor (ADRB2) and lipoprotein lipase (LPL), in the Kazakh population. The design of the current research was a case-control study, involving 460 subjects (age range, 18-65 years). For every subject, plasma glucose, insulin, total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein, apolipoprotein B and apolipoprotein A1 were examined. Moreover, reverse transcription-quantitative PCR was conducted to detect the polymorphism genes LPL Ser447Ter, ADRB2 Gln27Glu, AGT Thr174Met and AGTR1 A1166C. Hyperinsulinism was considered when the insulin level was elevated >24.9 IU/ml. The homeostasis model assessment insulin resistance (HOMA-IR) was used to evaluate insulin resistance. The subjects were divided into hyperinsulinism (17 men and 24 women) and normal level insulin (214 men and 205 women) groups, which were also split into insulin resistance group (HOMA-IR >2.7; 80 men and 105 women) and those without insulin resistance group (151 men and 124 women). The results suggested that LPL Ser447Ter (rs328) allele G was associated with a lower risk of hyperinsulinism (P=0.037). Furthermore, polymorphisms of genes ADRB2 Gln27Glu (rs1042714), AGT Thr174Met (rs4762) and AGTR1 A1166C (rs5186) were not associated with hyperinsulinism and insulin resistance in the Kazakh population. No interaction was identified between LPL Ser447Ter, ADRB2 Gln27Glu, AGT Thr174Met and AGTR1 A1166C. Therefore, the results indicated that haplotype combinations were not associated with insulin resistance.

Identifying potential functional lncRNAs in metabolic syndrome by constructing a lncRNA-miRNA-mRNA network

The metabolic syndrome (MS) is a cluster of interrelated risk factors including diabetes mellitus, abdominal obesity, high cholesterol, and hypertension, which can significantly increase mortality and disability. Accumulating evidence suggest that long non-coding RNAs (lncRNAs) are involved in the pathogenesis of human metabolic diseases. However, little is known about the regulatory role of lncRNAs in MS. In this work, we proposed a method for identifying potential MS-associated lncRNAs by constructing an lncRNA-miRNA-mRNA network (LMMN). Firstly, we constructed LMMN by integrating MS-associated genes, miRNA-mRNA interactions, miRNA-lncRNA interactions and mRNA/miRNA expression profiles in patients with MS. Then, we predicted potential MS-associated lncRNAs based on the topological properties of LMMN. As a result, we identified XIST as the most important lncRNA in LMMN. Furthermore, we focused on XIST/miR-214-3p and mir-181a-5p/PTEN axis and validated their expression in MS using real-time quantitative polymerase chain reaction (RT-qPCR). The RT-qPCR results showed that the expression of XIST and PTEN was significantly decreased (P < 0.05) while the expression of miR-214-3p was significantly increased (P < 0.05) in peripheral blood mononuclear cells (PBMCs) of patients with MS, compared with healthy controls. In addition, correlation analysis showed that XIST was negatively correlated with serum C peptide and PTEN was positively correlated with BMI of MS patients. Our findings provided new evidence for further exploring the regulatory role of XIST and other lncRNAs in MS.

ncRNA2MetS: a manually curated database for non-coding RNAs associated with metabolic syndrome

Metabolic syndrome is a cluster of the most dangerous heart attack risk factors (diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure), and has become a major global threat to human health. A number of studies have demonstrated that hundreds of non-coding RNAs, including miRNAs and lncRNAs, are involved in metabolic syndrome-related diseases such as obesity, type 2 diabetes mellitus, hypertension, etc. However, these research results are distributed in a large number of literature, which is not conducive to analysis and use. There is an urgent need to integrate these relationship data between metabolic syndrome and non-coding RNA into a specialized database. To address this need, we developed a metabolic syndrome-associated non-coding RNA database (ncRNA2MetS) to curate the associations between metabolic syndrome and non-coding RNA. Currently, ncRNA2MetS contains 1,068 associations between five metabolic syndrome traits and 627 non-coding RNAs (543 miRNAs and 84 lncRNAs) in four species. Each record in ncRNA2MetS database represents a pair of disease-miRNA (lncRNA) association consisting of non-coding RNA category, miRNA (lncRNA) name, name of metabolic syndrome trait, expressive patterns of non-coding RNA, method for validation, specie involved, a brief introduction to the association, the article referenced, etc. We also developed a user-friendly website so that users can easily access and download all data. In short, ncRNA2MetS is a complete and high-quality data resource for exploring the role of non-coding RNA in the pathogenesis of metabolic syndrome and seeking new treatment options. The website is freely available at http://www.biomed-bigdata.com:50020/index.html

miRDRN-miRNA disease regulatory network: a tool for exploring disease and tissue-specific microRNA regulatory networks

BACKGROUND

MicroRNA (miRNA) regulates cellular processes by acting on specific target genes, and cellular processes proceed through multiple interactions often organized into pathways among genes and gene products. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. These, together with huge amounts of data on gene annotation, biological pathways, and protein-protein interactions are available in public databases. Here, using such data we built a database and web service platform, miRNA disease regulatory network (miRDRN), for users to construct disease and tissue-specific miRNA-protein regulatory networks, with which they may explore disease related molecular and pathway associations, or find new ones, and possibly discover new modes of drug action.

METHODS

Data on disease-miRNA association, miRNA-target association and validation, gene-tissue association, gene-tumor association, biological pathways, human protein interaction, gene ID, gene ontology, gene annotation, and product were collected from publicly available databases and integrated. A large set of miRNA target-specific regulatory sub-pathways (RSPs) having the form (T, G 1, G 2) was built from the integrated data and stored, where T is a miRNA-associated target gene, G 1 (G 2) is a gene/protein interacting with T (G 1). Each sequence (T, G 1, G 2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways.

RESULTS

A web service platform, miRDRN (http://mirdrn.ncu.edu.tw/mirdrn/), was built. The database part of miRDRN currently stores 6,973,875 p-valued RSPs associated with 116 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes. miRDRN also provides facilities for the user to construct disease and tissue-specific miRNA regulatory networks from RSPs it stores, and to download and/or visualize parts or all of the product. User may use miRDRN to explore a single disease, or a disease-pair to gain insights on comorbidity. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer's disease-Type 2 diabetes, in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

A Systems Pharmacology-Based Study of the Molecular Mechanisms of San Cao Decoction for Treating Hypertension

Traditional Chinese medicine (TCM) has a longstanding history and has gained widespread clinical applications. San Cao Decoction (SCD) is an experience prescription first formulated by Prof. Duzhou Liu. We previously demonstrated its antihypertensive effects; however, to systematically explain the underlying mechanisms of action, we employed a systems pharmacology approach for pharmacokinetic screening and target prediction by constructing protein-protein interaction networks of hypertension-related and putative SCD-related targets, and Database for Annotation, Visualization, and Integrated Discovery enrichment analysis. We identified 123 active compounds in SCD and 116 hypertension-related targets. Furthermore, the enrichment analysis of the drug-target network showed that SCD acts in a multidimensional manner to regulate PI3K-Akt-endothelial nitric oxide synthase signaling to maintain blood pressure. Our results highlighted the molecular mechanisms of antihypertensive actions of medicinal herbs at a systematic level.

Adipocyte Metabolism and Insulin Signaling Perturbations: Insights from Genetics

Insulin resistance (IR) is a rapidly growing pandemic. It poses an enormous health burden given its comorbidity with obesity, type 2 diabetes (T2D), and other metabolic and cardiovascular diseases (CVDs). Adipose tissue has been established as a key regulator of whole-body metabolic homeostasis, with interest growing rapidly. Emerging evidence suggests that adipocytes play an important role in these afflictions and contribute to IR. Genome-wide association studies (GWAS) have begun to illuminate the genetics underlying obesity, T2D, and IR, and this will allow further study into the disease mechanisms of the genes implicated in these metabolic diseases. Progress towards understanding the molecular mechanisms underlying diseased adipocytes will be discussed here, with an eye towards the future in developing novel therapeutics to combat metabolic disease.

ILDgenDB: integrated genetic knowledge resource for interstitial lung diseases (ILDs)

Interstitial lung diseases (ILDs) are a diverse group of ∼200 acute and chronic pulmonary disorders that are characterized by variable amounts of inflammation, fibrosis and architectural distortion with substantial morbidity and mortality. Inaccurate and delayed diagnoses increase the risk, especially in developing countries. Studies have indicated the significant roles of genetic elements in ILDs pathogenesis. Therefore, the first genetic knowledge resource, ILDgenDB, has been developed with an objective to provide ILDs genetic data and their integrated analyses for the better understanding of disease pathogenesis and identification of diagnostics-based biomarkers. This resource contains literature-curated disease candidate genes (DCGs) enriched with various regulatory elements that have been generated using an integrated bioinformatics workflow of databases searches, literature-mining and DCGs–microRNA (miRNAs)–single nucleotide polymorphisms (SNPs) association analyses. To provide statistical significance to disease-gene association, ILD-specificity index and hypergeomatric test scores were also incorporated. Association analyses of miRNAs, SNPs and pathways responsible for the pathogenesis of different sub-classes of ILDs were also incorporated. Manually verified 299 DCGs and their significant associations with 1932 SNPs, 2966 miRNAs and 9170 miR-polymorphisms were also provided. Furthermore, 216 literature-mined and proposed biomarkers were identified. The ILDgenDB resource provides user-friendly browsing and extensive query-based information retrieval systems. Additionally, this resource also facilitates graphical view of predicted DCGs–SNPs/miRNAs and literature associated DCGs–ILDs interactions for each ILD to facilitate efficient data interpretation. Outcomes of analyses suggested the significant involvement of immune system and defense mechanisms in ILDs pathogenesis. This resource may potentially facilitate genetic-based disease monitoring and diagnosis.

Database URL: http://14.139.240.55/ildgendb/index.php

Chromosomal microarray analysis in the genetic evaluation of 279 patients with syndromic obesity

BACKGROUND

Syndromic obesity is an umbrella term used to describe cases where obesity occurs with additional phenotypes. It often arises as part of a distinct genetic syndrome with Prader-Willi syndrome being a classical example. These rare forms of obesity provide a unique source for identifying obesity-related genetic changes. Chromosomal microarray analysis (CMA) has allowed the characterization of new genetic forms of syndromic obesity, which are due to copy number variants (CNVs); however, CMA in large cohorts requires more study. The aim of this study was to characterize the CNVs detected by CMA in 279 patients with a syndromic obesity phenotype.

RESULTS

Pathogenic CNVs were detected in 61 patients (22%) and, among them, 35 had overlapping/recurrent CNVs. Genomic imbalance disorders known to cause syndromic obesity were found in 8.2% of cases, most commonly deletions of 1p36, 2q37 and 17p11.2 (5.4%), and we also detected deletions at 1p21.3, 2p25.3, 6q16, 9q34, 16p11.2 distal and proximal, as well as an unbalanced translocation resulting in duplication of the GNB3 gene responsible for a syndromic for of childhood obesity. Deletions of 9p terminal and 22q11.2 proximal/distal were found in 1% and 3% of cases, respectively. They thus emerge as being new putative obesity-susceptibility loci. We found additional CNVs in our study that overlapped with CNVs previously reported in cases of syndromic obesity, including a new case of 13q34 deletion (CHAMP1), bringing to 7 the number of patients in whom such defects have been described in association with obesity. Our findings implicate many genes previously associated with obesity (e.g. PTBP2, TMEM18, MYT1L, POU3F2, SIM1, SH2B1), and also identified other potentially relevant candidates including TAS1R3, ALOX5AP, and GAS6.

CONCLUSION

Understanding the genetics of obesity has proven difficult, and considerable insight has been obtained from the study of genomic disorders with obesity associated as part of the phenotype. In our study, CNVs known to be causal for syndromic obesity were detected in 8.2% of patients, but we provide evidence for a genetic basis of obesity in as many as 14% of cases. Overall, our results underscore the genetic heterogeneity in syndromic forms of obesity, which imposes a substantial challenge for diagnosis.

Convergence between biological, behavioural and genetic determinants of obesity

Multiple biological, behavioural and genetic determinants or correlates of obesity have been identified to date. Genome-wide association studies (GWAS) have contributed to the identification of more than 100 obesity-associated genetic variants, but their roles in causal processes leading to obesity remain largely unknown. Most variants are likely to have tissue-specific regulatory roles through joint contributions to biological pathways and networks, through changes in gene expression that influence quantitative traits, or through the regulation of the epigenome. The recent availability of large-scale functional genomics resources provides an opportunity to re-examine obesity GWAS data to begin elucidating the function of genetic variants. Interrogation of knockout mouse phenotype resources provides a further avenue to test for evidence of convergence between genetic variation and biological or behavioural determinants of obesity.

Network analysis identifies common genes associated with obesity in six obesity-related diseases

Obesity has been reported to be associated with many diseases. However, common obesity-induced biological processes have not been evaluated across these diseases. We identified genes associated with obesity and obesity-related diseases, and used them to construct protein‒protein interaction networks. We also analyzed gene ontology (GO) in those genes overlapping between obesity and disease. Our work identifies gene modules common to obesity and obesity-related diseases, which can provide a basis for understanding the process of how obesity induces disease.

T2DiACoD: A Gene Atlas of Type 2 Diabetes Mellitus Associated Complex Disorders

We performed integrative analysis of genes associated with type 2 Diabetes Mellitus (T2DM) associated complications by automated text mining with manual curation and also gene expression analysis from Gene Expression Omnibus. They were analysed for pathogenic or protective role, trends, interaction with risk factors, Gene Ontology enrichment and tissue wise differential expression. The database T2DiACoD houses 650 genes, and 34 microRNAs associated with T2DM complications. Seven genes AGER, TNFRSF11B, CRK, PON1, ADIPOQ, CRP and NOS3 are associated with all 5 complications. Several genes are studied in multiple years in all complications with high proportion in cardiovascular (75.8%) and atherosclerosis (51.3%). T2DM Patients' skeletal muscle tissues showed high fold change in differentially expressed genes. Among the differentially expressed genes, VEGFA is associated with several complications of T2DM. A few genes ACE2, ADCYAP1, HDAC4, NCF1, NFE2L2, OSM, SMAD1, TGFB1, BDNF, SYVN1, TXNIP, CD36, CYP2J2, NLRP3 with details of protective role are catalogued. Obesity is clearly a dominant risk factor interacting with the genes of T2DM complications followed by inflammation, diet and stress to variable extents. This information emerging from the integrative approach used in this work could benefit further therapeutic approaches. The T2DiACoD is available at www.http://t2diacod.igib.res.in/ .

Construction of competitive endogenous RNA network reveals regulatory role of long non-coding RNAs in type 2 diabetes mellitus

Increasing epidemic of type 2 diabetes mellitus (T2DM) and its comorbidities makes it urgent to understand the pathogenesis and regulatory mechanism. However, little is known about the regulatory role of lncRNAs in diabetes. Here, we constructed a T2DM‐related competitive endogenous RNA (ceRNA) network (DMCN) to explore biological function of lncRNAs during the development of diabetes mellitus. This network contained 351 nodes including 98 mRNAs, 86 microRNAs and 167 lncRNAs. Functional analysis showed that the mRNAs in DMCN were annotated into some diabetes‐related pathways. Furthermore, mTOR‐centred subnetwork was extracted and ncRNA‐involved mTOR pathway was established. Finally, we validated that NEAT1 was potentially communicated with mTOR signalling target protein mLST8 via the association with miR‐181b. These findings provide significant insight into lncRNA regulatory network in T2DM.

Potential Susceptibility Loci Identified for Renal Cell Carcinoma by Targeting Obesity-Related Genes

Background: Obesity is an established risk factor for renal cell carcinoma (RCC). Although genome-wide association studies (GWAS) of RCC have identified several susceptibility loci, additional variants might be missed due to the highly conservative selection.Methods: We conducted a multiphase study utilizing three independent genome-wide scans at MD Anderson Cancer Center (MDA RCC GWAS and MDA RCC OncoArray) and National Cancer Institute (NCI RCC GWAS), which consisted of a total of 3,530 cases and 5,714 controls, to investigate genetic variations in obesity-related genes and RCC risk.Results: In the discovery phase, 32,946 SNPs located at ±10 kb of 2,001 obesity-related genes were extracted from MDA RCC GWAS and analyzed using multivariable logistic regression. Proxies (R² > 0.8) were searched or imputation was performed if SNPs were not directly genotyped in the validation sets. Twenty-one SNPs with P < 0.05 in both MDA RCC GWAS and NCI RCC GWAS were subsequently evaluated in MDA RCC OncoArray. In the overall meta-analysis, significant (P < 0.05) associations with RCC risk were observed for SNP mapping to IL1RAPL2 [rs10521506-G: OR_meta = 0.87 (0.81-0.93), P_meta = 2.33 × 10^-5], PLIN2 [rs2229536-A: OR_meta = 0.87 (0.81-0.93), P_meta = 2.33 × 10^-5], SMAD3 [rs4601989-A: OR_meta = 0.86 (0.80-0.93), P_meta = 2.71 × 10^-4], MED13L [rs10850596-A: OR_meta = 1.14 (1.07-1.23), P_meta = 1.50 × 10^-4], and TSC1 [rs3761840-G: OR_meta = 0.90 (0.85-0.97), P_meta = 2.47 × 10^-3]. We did not observe any significant cis-expression quantitative trait loci effect for these SNPs in the TCGA KIRC data.Conclusions: Taken together, we found that genetic variation of obesity-related genes could influence RCC susceptibility.Impact: The five identified loci may provide new insights into disease etiology that reveal importance of obesity-related genes in RCC development. Cancer Epidemiol Biomarkers Prev; 26(9); 1436-42. ©2017 AACR.

Serum microRNA profiling and bioinformatics analysis of patients with type 2 diabetes mellitus in a Chinese population

Type 2 diabetes mellitus (T2DM) is characterized by islet β-cell dysfunction and insulin resistance, which leads to an inability to maintain blood glucose homeostasis. Circulating microRNAs (miRNAs) have been suggested as novel biomarkers for T2DM prediction or disease progression. However, miRNAs and their roles in the pathogenesis of T2DM remain to be fully elucidated. In the present study, the serum miRNA expression profiles of T2DM patients in Chinese cohorts were examined. Total RNA was extracted from serum samples of 10 patients with T2DM and five healthy controls, and these was used in reverse-transcription‑quantitative polymerase chain reaction analysis with the Exiqon PCR system of 384 serum/plasma miRNAs. A total of seven miRNAs were differentially expressed between the two groups (fold change >3 or <0.33; P<0.05). The serum expression levels of miR‑455‑5p, miR‑454‑3p, miR‑144‑3p and miR‑96‑5p were higher in patients with T2DM, compared with those of healthy subjects, however, the levels of miR‑409‑3p, miR‑665 and miR‑766‑3p were lower. Hierarchical cluster analysis indicated that it was possible to separate patients with T2DM and control individuals into their own similar categories by these differential miRNAs. Target prediction showed that 97 T2DM candidate genes were potentially modulated by these seven miRNAs. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that 24 pathways were enriched for these genes, and the majority of these pathways were enriched for the targets of induced and repressed miRNAs, among which insulin, adipocytokine and T2DM pathways, and several cancer‑associated pathways have been previously associated with T2DM. In conclusion, the present study demonstrated that serum miRNAs may be novel biomarkers for T2DM and provided novel insights into the pathogenesis of T2DM.

An analysis of disease-gene relationship from Medline abstracts by DigSee

Diseases are developed by abnormal behavior of genes in biological events such as gene regulation, mutation, phosphorylation, and epigenetics and post-translational modification. Many studies of text mining attempted to identify the relationship between gene and disease by mining the literature, but they did not consider the biological events in which genes show abnormal behaviour in response to diseases. In this study, we propose to identify disease-related genes that are involved in the development of disease through biological events from Medline abstracts. We identified associations between 13,054 genes and 4,494 disease types, which cover more disease-related genes than manually curated databases for all disease types (e.g., Online Mendelian Inheritance in Man) and also than those for specific diseases (e.g., Alzheimer’s disease and hypertension). We show that the text mining findings are reliable, as per the PubMed scale, in that the disease-disease relationships inferred from the literature-wide findings are similar to those inferred from manually curated databases in a well-known study. In addition, literature-wide distribution of biological events across disease types reveals different characteristics of disease types.

In-Cardiome: integrated knowledgebase for coronary artery disease enabling translational research

Coronary artery disease (CAD) is a leading cause of death worldwide. Prevention, diagnosis and clinical interventions are dependent on the conventional risk factors like hypertension, diabetes and obesity. However, these conventional risk factors do not completely identify high risk individuals. One major hurdle in the improvement of diagnosis and treatment for CAD is the lack of integration of knowledge from different areas of research like molecular, clinical and drug development. In order to provide comprehensive information from hitherto dispersed data, we developed an integrative knowledgebase called “In-Cardiome or Integrated Cardiome” for all the stake holders in healthcare such as scientists, clinicians and pharmaceutical companies. It is created by integrating 16 different data sources, 995 curated genes classified into 12 different functional categories associated with disease, 1204 completed clinical trials, 12 therapy or drug classifications with 62 approved drugs and drug target networks. This knowledgebase gives the most needed opportunity to understand the disease process and therapeutic impact along with gene expression data from both animal models and patients. The data is classified into three different search categories functional groups, risk factors and therapy/drug based classes. One more unique aspect of In-Cardiome is integration of clinical data of 10,217 subject data from our ongoing Indian Atherosclerosis Research Study (IARS) (6357 unaffected and 3860 CAD affected). IARS data showing demographics and associations of individual and combinations of risk factors in Indian population along with molecular information will enable better translational and drug development research.

MicroRNA profiling in clear cell renal cell carcinoma tissues potentially links tumorigenesis and recurrence with obesity

Background:

Twenty to 40% localised RCC patients may experience recurrence after curative surgery. Limited miRNA predictors have been identified for ccRCC recurrence.

Methods:

Through a multi-phase study design, we analysed miRNAs in tissues obtained from 203 ccRCC patients. Paired t-test was used for tumour–normal comparisons and Cox regression model was performed to compute hazard ratios (HRs) and corresponding 95% CIs.

Results:

A 17-miRNA signature was identified that can concordantly classify >95% of tumour/adjacent normal samples. Significant enrichment was found as 6 out of 17 miRNAs were associated with obesity (binomial probability=0.001). Decreased levels of miR-204-5p and miR-139-5p were each associated with an approximately three-fold increased risk of recurrence (P<0.01). Risk score was generated based on expressions of miR-204-5p and miR-139-5p, and the trend test was significant in both discovery and validation sets (P_{for trend}<0.05). Striking MST reduction was observed for patients with a high-risk score (high vs low: discovery, 9.4 vs >97.7 months; validation, 20.8 vs >70.3 months). Expressions of miR-204-5p were also associated with body mass index (β=5.64, P<0.001). Significant inverse correlations were observed and validated between miR-204-5p and 13 obesity-related genes (r<0, P<0.01).

Conclusions:

We identified 17 miRNAs dysregulated in ccRCC tissues and showed that low expressions of miR-204-5p and miR-139-5p were associated with the higher risk of recurrence. The link between miR-204-5p and ccRCC recurrence may be partially mediated by regulating the expression of targeted obesity-related genes.

Systematic Characterization and Prediction of Human Hypertension Genes

Hypertension is a major cardiovascular risk factor and accounts for a large part of cardiovascular mortality. In this work, we analyzed the properties of hypertension genes and found that when compared with genes not yet known to be involved in hypertension regulation, known hypertension genes display distinguishing features: (1) hypertension genes tend to be located at network center; (2) hypertension genes tend to interact with each other; and (3) hypertension genes tend to enrich in certain biological processes and show certain phenotypes. Based on these features, we developed a machine-learning algorithm to predict new hypertension genes. One hundred and seventy-seven candidates were predicted with a posterior probability >0.9. Evidence supporting 17 of the predictions has been found.

Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment

Accurate assessment of genetic variation in human DNA sequencing studies remains a nontrivial challenge in clinical genomics and genome informatics. Ascribing functional roles and/or clinical significances to single nucleotide variants identified from a next-generation sequencing study is an important step in genome interpretation. Experimental characterization of all the observed functional variants is yet impractical; thus, the prediction of functional and/or regulatory impacts of the various mutations using in silico approaches is an important step toward the identification of functionally significant or clinically actionable variants. The relationships between genotypes and the expressed phenotypes are multilayered and biologically complex; such relationships present numerous challenges and at the same time offer various opportunities for the design of in silico variant assessment strategies. Over the past decade, many bioinformatics algorithms have been developed to predict functional consequences of single nucleotide variants in the protein coding regions. In this review, we provide an overview of the bioinformatics resources for the prediction, annotation and visualization of coding single nucleotide variants. We discuss the currently available approaches and major challenges from the perspective of protein sequence, structure, function and interactions that require consideration when interpreting the impact of putatively functional variants. We also discuss the relevance of incorporating integrated workflows for predicting the biomedical impact of the functionally important variations encoded in a genome, exome or transcriptome. Finally, we propose a framework to classify variant assessment approaches and strategies for incorporation of variant assessment within electronic health records.

Gene Expression in Experimental Aortic Coarctation and Repair: Candidate Genes for Therapeutic Intervention?

Coarctation of the aorta (CoA) is a constriction of the proximal descending thoracic aorta and is one of the most common congenital cardiovascular defects. Treatments for CoA improve life expectancy, but morbidity persists, particularly due to the development of chronic hypertension (HTN). Identifying the mechanisms of morbidity is difficult in humans due to confounding variables such as age at repair, follow-up duration, coarctation severity and concurrent anomalies. We previously developed an experimental model that replicates aortic pathology in humans with CoA without these confounding variables, and mimics correction at various times using dissolvable suture. Here we present the most comprehensive description of differentially expressed genes (DEGs) to date from the pathology of CoA, which were obtained using this model. Aortic samples (n=4/group) from the ascending aorta that experiences elevated blood pressure (BP) from induction of CoA, and restoration of normal BP after its correction, were analyzed by gene expression microarray, and enriched genes were converted to human orthologues. 51 DEGs with >6 fold-change (FC) were used to determine enriched Gene Ontology terms, altered pathways, and association with National Library of Medicine Medical Subject Headers (MeSH) IDs for HTN, cardiovascular disease (CVD) and CoA. The results generated 18 pathways, 4 of which (cell cycle, immune system, hemostasis and metabolism) were shared with MeSH ID’s for HTN and CVD, and individual genes were associated with the CoA MeSH ID. A thorough literature search further uncovered association with contractile, cytoskeletal and regulatory proteins related to excitation-contraction coupling and metabolism that may explain the structural and functional changes observed in our experimental model, and ultimately help to unravel the mechanisms responsible for persistent morbidity after treatment for CoA.

FTO-dependent demethylation of N6-methyladenosine regulates mRNA splicing and is required for adipogenesis

The role of Fat Mass and Obesity-associated protein (FTO) and its substrate N6-methyladenosine (m⁶A) in mRNA processing and adipogenesis remains largely unknown. We show that FTO expression and m⁶A levels are inversely correlated during adipogenesis. FTO depletion blocks differentiation and only catalytically active FTO restores adipogenesis. Transcriptome analyses in combination with m⁶A-seq revealed that gene expression and mRNA splicing of grouped genes are regulated by FTO. M⁶A is enriched in exonic regions flanking 5′- and 3′-splice sites, spatially overlapping with mRNA splicing regulatory serine/arginine-rich (SR) protein exonic splicing enhancer binding regions. Enhanced levels of m⁶A in response to FTO depletion promotes the RNA binding ability of SRSF2 protein, leading to increased inclusion of target exons. FTO controls exonic splicing of adipogenic regulatory factor RUNX1T1 by regulating m⁶A levels around splice sites and thereby modulates differentiation. These findings provide compelling evidence that FTO-dependent m⁶A demethylation functions as a novel regulatory mechanism of RNA processing and plays a critical role in the regulation of adipogenesis.

FTO and obesity: mechanisms of association

The Fat mass and obesity associated (FTO) gene is a newly identified genetic factor for obesity. However, the exact molecular mechanisms responsible for the effect of FTO on obesity remain largely unknown. Recent studies from genome-wide associated studies reveal that genetic variants in the FTO gene are associated not only with human adiposity and metabolic disorders, but also with cancer, a highly obesity-associated disease as well. Data from animal and cellular models further demonstrate that the perturbation of FTO enzymatic activity dysregulates genes related to energy metabolism, causing the malfunction of energy and adipose tissue homeostasis in mice. The most significant advance about FTO research is the recent discovery of FTO as the first N6-methyl-adenosine (m(6)A) RNA demethylase that catalyzes the m(6)A demethylation in α-ketoglutarate - and Fe(2+)-dependent manners. This finding provides the strong evidence that the dynamic and reversible chemical m(6)A modification on RNA may act as a novel epitranscriptomic marker. Furthermore, the FTO protein was observed to be partially localized onto nuclear speckles enriching mRNA processing factors, implying a potential role of FTO in regulating RNA processing. This review summarizes the recent progress about biological functions of FTO through disease-association studies as well as the data from in vitro and in vivo models, and highlights the biochemical features of FTO that might be linked to obesity.