Impact of variants of the EXT2 gene on Type 2 diabetes and its related traits in the Chinese han population

Identification of biomarkers, pathways, and potential therapeutic targets for heart failure using next-generation sequencing data and bioinformatics analysis

BACKGROUND

Heart failure (HF) is the most common cardiovascular diseases and the leading cause of cardiovascular diseases related deaths. Increasing molecular targets have been discovered for HF prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might aid the diagnosis and treatment of HF.

METHODS

We searched next-generation sequencing (NGS) dataset (GSE161472) and identified differentially expressed genes (DEGs) by comparing 47 HF samples and 37 normal control samples using limma in R package. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g: Profiler database. The protein-protein interaction (PPI) network was plotted with Human Integrated Protein-Protein Interaction rEference (HiPPIE) and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. Then, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed by Cytoscape software. Finally, we performed receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes.

RESULTS

A total of 930 DEGs, 464 upregulated genes and 466 downregulated genes, were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections, and the citric acid tricarboxylic acid (TCA) cycle and respiratory electron transport. After combining the results of the PPI network miRNA-hub gene regulatory network and TF-hub gene regulatory network, 10 hub genes were selected, including heat shock protein 90 alpha family class A member 1 (HSP90AA1), arrestin beta 2 (ARRB2), myosin heavy chain 9 (MYH9), heat shock protein 90 alpha family class B member 1 (HSP90AB1), filamin A (FLNA), epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), cullin 4A (CUL4A), YEATS domain containing 4 (YEATS4), and lysine acetyltransferase 2B (KAT2B).

CONCLUSIONS

This discovery-driven study might be useful to provide a novel insight into the diagnosis and treatment of HF. However, more experiments are needed in the future to investigate the functional roles of these genes in HF.

Genetic polymorphisms associated with obesity in the Arab world: a systematic review

BACKGROUND

Obesity, one of the most common chronic health conditions worldwide, is a multifactorial disease caused by complex genetic and environmental interactions. Several association studies have revealed a considerable number of candidate loci for obesity; however, the genotype-phenotype correlations remain unclear. To date, no comprehensive systematic review has been conducted to investigate the genetic risk factors for obesity among Arabs.

OBJECTIVES

This study aimed to systematically review the genetic polymorphisms that are significantly associated with obesity in Arabs.

METHODS

We searched four literature databases (PubMed, Science Direct, Scopus, and Google Scholar) from inception until May 2020 to obtain all reported genetic data related to obesity in Arab populations. Quality assessment and data extraction were performed individually by three investigators.

RESULTS

In total, 59 studies comprising a total of 15,488 cases and 9,760 controls were included in the systematic review. A total of 76 variants located within or near 49 genes were reported to be significantly associated with obesity. Among the 76 variants, two were described as unique to Arabs, as they have not been previously reported in other populations, and 19 were reported to be distinctively associated with obesity in Arabs but not in non-Arab populations.

CONCLUSIONS

There appears to be a unique genetic and clinical susceptibility profile of obesity in Arab patients.

No correlation between the variants of exostosin 2 gene and type 2 diabetes in Burkina Faso population

Recent genome-wide association studies and replication analyses have reported the association of variants of the exostosin- 2 gene (EXT2) and risk of type 2 diabetes (T2D) in some populations, but not in others. This study aimed to characterize the variants rs1113132, rs3740878 and rs11037909 of EXT2 and to determine the existence of a possible correlation with T2D in Burkina Faso. It is a case-control study undertaken in Burkina Faso in the city of Ouagadougou at the Hospital of Saint Camille of Ouagadougou from December 2014 to June 2015. It relates to 121 type 2 diabetes cases and 134 controls. The genotyping of these polymorphisms was done by real-time PCR using the allelic exclusion method with TaqMan probes. The minor allele frequencies (MAFs) was almost identical in diabetic and control subjects for the all three Single Nucleotide Polymorphisms (SNPs) with no statistical significance, p>0.05: rs1113132 (OR=0.89; p=0.82); rs11037909 (OR=0.89; p=0.74) and rs3740878 (OR=1.52; p=0.42). None of the three polymorphisms studied was associated with the risk of DT2. However, an association between the BMI, age and type 2 diabetes was noted. The variants of EXT2 would not be associated to the risk of T2D in the African black population of Burkina Faso.

Search for clinical predictors of good glycemic control in patients starting or intensifying oral hypoglycemic pharmacological therapy: A multicenter prospective cohort study

AIMS

Our aim was to search for clinical predictors of good glycemic control in patients starting or intensifying oral hypoglycemic pharmacological therapy.

METHODS

A multicenter, prospective cohort of 499 diabetic subjects was enrolled in this study: patients with newly diagnosed diabetes (NDM group) or poor glycemic control with oral antidiabetic drugs (OADs) (PDM group). All subjects then started or intensified OADs therapy and followed up for 91 days. Glycemic control was determined according to HbA1c at day 91 with HbA1c <7% considered good.

RESULTS

The proportions of patients with good glycemic control after follow up for 91 days were 66.9% and 34.8% in NDM group and PDM group respectively. Logistic regression analysis showed that the change in GA at 28 days was the only predictor of good glycemic control in NDM patients (OR = 1.630, 95% CI 1.300-2.044, P < 0.001). In PDM patients, changes in GA at 28 days, CPI, baseline HbA1c, diabetic duration, and BMI were all independent predictors of good glycemic control (All P < 0.05).

CONCLUSIONS

GA decline is a good predictor of future success in newly diagnosed patients. In patients intensifying therapy, beside GA decline, other individualized clinical characteristics should also be considered.