Association between a polymorphic poly-T repeat sequence in the promoter of the somatostatin gene and hypertension

“Big Data” Approaches for Prevention of the Metabolic Syndrome

Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these “big data”, the prevention of MetS could be pushed to an earlier stage with different data source, data mining tools and analytic tools at different levels. In this review we briefly summarize the recent advances in the study of “big data” applications in the three-level disease prevention for MetS, and illustrate how these technologies could contribute tobetter preventive strategies.

Selection of both habitat and genes in specialized and endangered caribou

Genetic mechanisms determining habitat selection and specialization of individuals within species have been hypothesized, but not tested at the appropriate individual level in nature. In this work, we analyzed habitat selection for 139 GPS-collared caribou belonging to three declining ecotypes sampled throughout Northwestern Canada. We used Resource Selection Functions (RSFs) comparing resources at used and available locations. We found that the three caribou ecotypes differed in their use of habitat suggesting specialization. On expected grounds, we also found differences in habitat selection between summer and winter, but also, originally, among the individuals within an ecotype. We next obtained Single Nucleotide Polymorphisms (SNPs) for the same caribou individuals, we detected those associated to habitat selection, and then identified genes linked to these SNPs. These genes had functions related in other organisms to habitat and dietary specializations, and climatic adaptations. We therefore suggest that individual variation in habitat selection was based on genotypic variation in the SNPs of individual caribou, indicating that genetic forces underlie habitat and diet selection in the species. We also suggest that the associations between habitat and genes that we detected may lead to lack of resilience in the species, thus contributing to caribou endangerment. Our work emphasizes that similar mechanisms may exist for other specialized, endangered species. This article is protected by copyright. All rights reserved.

Screening and identification of potential biomarkers for obstructive sleep apnea via microarray analysis

Obstructive sleep apnea (OSA) is a common chronic disease and increases the risk of cardiovascular disease, metabolic and neuropsychiatric disorders, resulting in a considerable socioeconomic burden. This study aimed to identify potential key genes influence the mechanisms and consequences of OSA.Gene expression profiles related to OSA were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in subcutaneous adipose tissues from OSA compared with normal tissues were screened using R software, followed by gene ontology (GO) and pathway enrichment analyses. Subsequently, a protein-protein interaction (PPI) network for these DEGs was constructed by STRING, and key hub genes were extracted from the network with plugins in Cytoscape. The hub genes were further validated in another GEO dataset and assessed by receiver operating characteristic (ROC) analysis and Pearson correlation analysis.There were 373 DEGs in OSA samples in relative to normal controls, which were mainly associated with olfactory receptor activity and olfactory transduction. Upon analyses of the PPI network, GDNF, SLC2A2, PRL, and SST were identified as key hub genes. Decreased expression of the hub genes was association with OSA occurrence, and exhibited good performance in distinguishing OSA from normal samples based on ROC analysis. Besides, the Pearson method revealed a strong correlation between hub genes, which indicates that they may act in synergy, contributing to OSA and related disorders.This bioinformatics research identified 4 hub genes, including GDNF, SLC2A2, PRL, and SST which may be new potential biomarkers for OSA and related disorders.

Regulatory Mechanisms of Somatostatin Expression

Somatostatin is a peptide hormone, which most commonly is produced by endocrine cells and the central nervous system. In mammals, somatostatin originates from pre-prosomatostatin and is processed to a shorter form, i.e., somatostatin-14, and a longer form, i.e., somatostatin-28. The two peptides repress growth hormone secretion and are involved in the regulation of glucagon and insulin synthesis in the pancreas. In recent years, the processing and secretion of somatostatin have been studied intensively. However, little attention has been paid to the regulatory mechanisms that control its expression. This review provides an up-to-date overview of these mechanisms. In particular, it focuses on the role of enhancers and silencers within the promoter region as well as on the binding of modulatory transcription factors to these elements. Moreover, it addresses extracellular factors, which trigger key signaling pathways, leading to an enhanced somatostatin expression in health and disease.

Association study between a polymorphic poly-T repeat sequence in the promoter of the somatostatin gene and metabolic syndrome

Background

Metabolic syndrome is a cluster of factors associated with an increased risk of developing type 2 diabetes mellitus (T2D) and coronary artery disease (CAD). It is a complex disorder resulting from the interaction between various environmental factors and genetic susceptibility. The somatostatin (SST) gene has been shown to regulate a wide range of functions, particularly in energy homeostasis. In addition, low levels of SST have been reported to have effects on the progression of metabolic syndrome components. The aim of this study was therefore to evaluate the association between polymorphic T sequences in the promoter of the SST gene and metabolic syndrome expression.

Methods

We studied 1725 French-Canadian subjects from a founder population selected on the basis of having a positive family history of dyslipidemia, CAD or T2D. The analysis were performed on four groups created according to the poly T polymorphism length in the 5′ flanking promoter region of SST. Anova, Ancova and logistic regression models and Chi 2 analyses were used to evaluate the association between the poly T polymorphisms and metabolic syndrome components expression.

Results

Analyses showed that means, frequencies and odds ratio of metabolic syndrome components expression increase as the number of poly-T repeats in the promoter region of SST increases. Women exhibit more significant differences than men. However, the trends are the same in both genders and differences for most of the components are significant in the entire sample.

Conclusion

Those results suggest that the poly T polymorphisms in the SST promoter region may influence several metabolic processes implicated in metabolic syndrome expression. More analyses are needed to document the mechanisms that could underlie genetic regulation effect of SST on metabolic syndrome components and to clarify its specific role.

Common variants at somatostatin are significantly associated with hypertension incidence in smoking and drinking populations

Somatostatin (SST) and growth hormone-releasing hormone (GHRH) are involved in the development of hypertension. This study aimed to evaluate whether SST and GHRH contribute to genetic susceptibility to hypertension. A case-control study consisting of 2012 hypertensive patients and 2210 matched control individuals was performed, and three tagging single-nucleotide polymorphisms were genotyped. The association of these single-nucleotide polymorphisms with hypertension and ischemic stroke was further evaluated among 4098 participants in a follow-up study. Hazard ratio (HR) and 95% confidence interval were estimated by Cox proportional hazards regression. The follow-up study indicated that in smoking population, variants at SST presented significant association with hypertension incidence; the adjusted HR of rs3755792 (GA + AA vs. GG) was 0.634 (P = .037), and the adjusted HR of rs7624906 (TC + CC vs. TT) was 1.803 (P = .005). In drinking population, rs3755792 at SST was associated with hypertension incidence, and the adjusted HR was 0.580 (P = .009). Moreover, rs6032470 at GHRH had a statistical association with ischemic stroke incidence in smoking population, and the adjusted HR of the additive model was 1.625 (P = .049). These results suggested that SST and GHRH harbor genetic susceptible loci with incident hypertension and ischemic stroke and that smoking and drinking might modify the genetic effect.