Dataset Information

Identification of genetic variants related to metabolic syndrome by next-generation sequencing.

ABSTRACT:

Background

Metabolic syndrome (MetS) is a cluster of conditions associated with glucose intolerance, hypertension, abdominal obesity, dyslipidemia, and insulin resistance that increase the risk of cardiovascular diseases (CVD) and type 2 diabetes (T2D). Since MetS is known as a complex symptom with a high incidence of genetic factors, it is important to identify genetic variants for each clinical characteristic of MetS.

Methods

We performed targeted next-generation sequencing (NGS) to identify genetic variants related to obesity, blood glucose, triacylglycerol (TG), and high-density lipoprotein (HDL)-cholesterol level, and hypertension in 48 subjects with MetS and in 48 healthy subjects.

Results

NGS analysis revealed that 26 of 48 subjects (54.2%) with MetS had putative non-synonymous variants related to the clinical features of MetS. Of the subjects with MetS, 8 (16.7%) had variants in 4 genes (COL6A2, FTO, SPARC, and MTHFR) related to central obesity, 17 (35.4%) had variants in 6 genes (APOB, SLC2A2, LPA, ABCG5, ABCG8, and GCKR) related to hyperglycemia, 3 (6.3%) had variants in 4 genes (APOA1, APOC2, APOA4, and LMF1) related to hypertriglyceridemia, 8 (16.7%) had variants in 4 genes (ABCA1, CETP, SCARB1, and LDLR) related to low HDL-cholesterolemia, and 5 (10.4%) had variants in ADD1 related to hypertension.

Conclusions

Our findings may contribute to broadening the genetic spectrum of risk variants related to the development of MetS.

SUBMITTER: Lee S

PROVIDER: S-EPMC9396768 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

Identification of genetic variants related to metabolic syndrome by next-generation sequencing.

Lee Sanghoo S Kim Seol-A SA Hong Jeonghoon J Kim Yejin Y Hong Gayeon G Baik SaeYun S Choi Kyeonghwan K Lee Mi-Kyeong MK Lee Kyoung-Ryul KR

Diabetology & metabolic syndrome 20220823 1

<h4>Background</h4>Metabolic syndrome (MetS) is a cluster of conditions associated with glucose intolerance, hypertension, abdominal obesity, dyslipidemia, and insulin resistance that increase the risk of cardiovascular diseases (CVD) and type 2 diabetes (T2D). Since MetS is known as a complex symptom with a high incidence of genetic factors, it is important to identify genetic variants for each clinical characteristic of MetS.<h4>Methods</h4>We performed targeted next-generation sequencing (NGS ...[more]

PMID: 35999587

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Project description:COVID-19 is caused by SARS-CoV-2, several virulent variants of which have emerged since 2019. More than 529 million people have been infected, and at least 6 million have died. Our aim was to develop a fast, accurate, low-cost method for detecting and identifying newly emerging variants of concern (VOCs) that could pose a global threat. The 341-bp DNA sequence of a specific region of the SARS-CoV-2's spike protein was amplified by a one-step PCR on RNA samples from 46 patients. The product was sequenced using next-generation sequencing (NGS). DNA sequences from seven genomes, the original Wuhan isolate and six different representative variants obtained from the GISAID website, were used as references. Complete whole-genome sequences from local isolates were also obtained from the GISAID website, and their RNA was used for comparison. We used an amplicon-based NGS method (termed VOC-NGS) for genotyping and successfully identified all 46 samples. Fifteen (32.6%) were like the original isolate. Twenty-seven were VOCs: nine (19.5%) Alpha, eight (19%) Delta, six (14%) Beta, and four (8.7%) Omicron. Two were variants of interest (VOI): one (2%) Kappa and one (2%) Zeta. Two samples were mixtures of two variants, one of Alpha and Beta and one of Alpha and Delta. The Spearman correlation between whole-genome sequencing (WGS) and VOC-NGS was significant (P < 0.001) with perfect agreement (Kappa = 0.916) for 36/38 (94.7%) samples with VOC-NGS detecting all the known VOCs. Genotyping by VOC-NGS enables rapid screening of high-throughput clinical samples that includes the identification of VOCs and mixtures of variants, at lower cost than WGS. IMPORTANCE The manuscript described SARS-Cov-2 genotyping by VOC-NGS, which presents an ideal balance of accuracy, rapidity, and cost for detecting and globally tracking VOCs and some VOI of SARS-CoV-2. A large number of clinical samples can be tested together. Rapid introduction of new mutations at a specific site of the spike protein necessitates efficient strain detection and identification to enable choice of treatment and the application of vaccination, as well as planning public health policy.

Project description:BackgroundNoonan syndrome is an autosomal dominant developmental disorder with a high phenotypic variability, which shares clinical features with other rare conditions, including LEOPARD syndrome, cardiofaciocutaneous syndrome, Noonan-like syndrome with loose anagen hair, and Costello syndrome. This group of related disorders, so-called RASopathies, is caused by germline mutations in distinct genes encoding for components of the RAS-MAPK signalling pathway. Due to high number of genes associated with these disorders, standard diagnostic testing requires expensive and time consuming approaches using Sanger sequencing. In this study we show how targeted Next Generation Sequencing (NGS) technique can enable accurate, faster and cost-effective diagnosis of RASopathies.MethodsIn this study we used a validation set of 10 patients (6 positive controls previously characterized by Sanger-sequencing and 4 negative controls) to assess the analytical sensitivity and specificity of the targeted NGS. As second step, a training set of 80 enrolled patients with a clinical suspect of RASopathies has been tested. Targeted NGS has been successfully applied over 92% of the regions of interest, including exons for the following genes: PTPN11, SOS1, RAF1, BRAF, HRAS, KRAS, NRAS, SHOC, MAP2K1, MAP2K2, CBL.ResultsAll expected variants in patients belonging to the validation set have been identified by targeted NGS providing a detection rate of 100%. Furthermore, all the newly detected mutations in patients from the training set have been confirmed by Sanger sequencing. Absence of any false negative event has been excluded by testing some of the negative patients, randomly selected, with Sanger sequencing.ConclusionHere we show how molecular testing of RASopathies by targeted NGS could allow an early and accurate diagnosis for all enrolled patients, enabling a prompt diagnosis especially for those patients with mild, non-specific or atypical features, in whom the detection of the causative mutation usually requires prolonged diagnostic timings when using standard routine. This approach strongly improved genetic counselling and clinical management.

Dataset Information

Identification of genetic variants related to metabolic syndrome by next-generation sequencing.

Background

Methods

Results

Conclusions

Publications

Identification of genetic variants related to metabolic syndrome by next-generation sequencing.

Similar Datasets

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