Identificación de variantes genéticas poco frecuentes y raras en Diabetes mellitus tipo 2 mediante secuenciación de exoma

Identificación de variantes genéticas poco frecuentes y raras en diabetes mellitus tipo 2 mediante secuenciación de exoma. La diabetes mellitus tipo 2 (DM2) se ha convertido en una epidemia global que contribuye significativamente a la morbi-mortalidad prematura. La DM2 es resultado de múltiples factores, incluyendo ambientales, genéticos y adquiridos. Se ha demostrado que la heredabilidad de la DM2 se encuentra alrededor del 70%. No obstante, los estudios realizados sólo han podido identificar una pequeña proporción del componente genético de la enfermedad. El resto del componente podría residir, en parte, en variantes genéticas poco frecuentes (MAF <0,05) o variantes raras (MAF <0,01). Sin embargo, la implicación de estas últimas en la enfermedad no ha sido determinada por las limitaciones de los estudios genéticos realizados hasta el momento. Además, más del 90% de las mutaciones y polimorfismos asociados a enfermedades se sitúan en las regiones codificantes de proteínas, y por tanto, en el exoma. Por ello, una fracción importante de las variantes de baja frecuencia y raras puede localizarse en el exoma. Finalmente, este proyecto representa un trabajo pionero en cuanto a la técnica empleada y población a la que se dirige. Objetivos: Identificar variantes genéticas presentes en el exoma, incluyendo variantes poco frecuentes y raras, en relación a la DM2 en población española. Metodología: Secuenciación del exoma (cobertura: 20x) en 200 pacientes con DM2 y 200 controles sanos de población española; todos con un índice de masa corporal de 25 a 34,9 kg/m2 y una edad comprendida entre 40 y 65 años. Las regiones exónicas fueron capturadas y secuenciadas empleando el sistema de secuenciación de nueva generación HiScanSQ de Illumina, generando lecturas de 2x100 pares de bases en cada dirección (paired-end). A continuación, mediante el análisis bioinformático se realizaron los controles de calidad, el alineamiento de las lecturas contra el genoma de referencia y la identificación de las variantes genéticas. Se seleccionaron aquellas variantes con un MAF 20 y con valores predictivos de alteración de la funcionalidad, SIFT y PoliPhen, significativos. Posteriormente, se estudiaron las variantes genéticas de mayor impacto funcional que estuvieran presentes en controles o en casos; centrándonos en las variantes de codón de stop o parada, variantes de splicing, variantes de cambio de sentido o missense, y variantes en regiones codificantes para microARNs. Finalmente, las variantes de codón de stop o parada fueron verificadas mediante el método de secuenciación de Sanger. Resultados: La secuenciación del exoma generó aproximadamente 1.000 GB de datos iniciales que tras el análisis bioinformático se convirtieron en unos 2.000 GB en total. Se identificaron 21.822 SNPs en controles y 17.238 SNPs en casos con efecto funcional, presentes únicamente en uno de los grupos y cumpliendo con los criterios de filtrado establecidos. En concreto, 160 y 132 SNPs fueron identificados como variantes de splicing, en controles y casos, respectivamente. Mientras, 1.817 y 1.614 SNPs fueron identificados como variantes missense para el grupo control y diabéticos, respectivamente. Además, 23 SNPs fueron identificados en secuencias codificantes para microARNs en controles y 11 en casos. Finalmente, 102 variantes de codón de stop fueron identificadas en controles y 50 en casos. Conclusiones: Se han identificado un gran número de variantes genéticas que pueden estar implicadas en el desarrollo de DM2 o en la protección frente a la misma, incluyendo nuevas variantes genéticas de baja frecuencia y raras. Con la finalidad de identificar variantes genéticas válidas será necesario validar los resultados obtenidos mediante otras estrategias, su replicación en un amplio número de muestras de pacientes control y diabéticos así como el diseño de experimentos funcionales.Identification of low-frequency and rare genetic variants related to type 2 diabetes mellitus by exome sequencing. Type 2 diabetes mellitus (T2DM) has become a global epidemic contributing significantly to morbidity and premature mortality. T2DM is the result from the interaction of different factors, including environmental, genetic and acquired. It has shown that the heredability of T2DM is around 70%. Thus far genetic studies can explain only a fraction of the estimated genetic component of the disease. The rest of it could be explained in low-frequency variants (MAF <0,05) and rare variants (MAF <0,01). However, the implications of these variants in the disease have not been determined yet due to the limitations of genetic studies conducted. Moreover, more than 90% of mutations and polymorphisms associated with diseases are in the protein coding region, the exome. Therefore, an important fraction of low-frequency and rare variants could be found in the exome. Finally, this project represents a novel study because of the approach and population used. Objectives: To identify genetic variants in the exome, including low-frequency and rare variants, in relation to T2DM in a Spanish population. Methodology: Exome sequencing (coverage: 20x) in 200 patients with T2DM and 200 Spanish healthy controls; all subjects had a body mass index between 25-34.9 kg/m2 and were 40 to 65 years old. Exome regions were captured and sequenced by nextgeneration sequencing technology using Illumina HiScanSQ system to generate 2x100 bp paired end reads. A bioinformatic analysis pipeline was used to perform quality controls, to align the reads to a reference genome and identify genetic variants. We selected variants with MAF <20%, present in controls or cases, with genotyping quality (Q) >20 and significative effect predictors, SIFT and PolyPhen. We analysed genetic variants with an important functional consequences which were present in controls or cases. Specifically, we focused in the analysis of splicing variants, missense variants, variants in mature microRNAs coding sequences and stop variants. Finally, we verified the stop variants by Sanger sequencing. Results: Exome sequencing approximately generated 1,000 GB data which after the bioinformatics analysis became around 2,000 GB in total. It was identified 21,822 SNPs in controls and 17,238 in cases with a functional effect, present only in controls or cases that meet quality criteria. In particular, 160 and 132 SNPs were splicing variants which were identified in controls and cases, respectively. While 1,817 and 1,614 SNPs were missense variants in controls and cases, respectively. Furthermore, 23 SNPs were identified as mature microRNA variants in controls and 11 SNPs in cases. Finally, we identified 102 SNPs as stop variants in controls and 50 SNPs in cases. Conclusions: We have identified a large number of genetic variants, including lowfrequency and rare variants, which may be involved in the development of T2DM or in the protection from it. In order to stablish the true genetic variants involved in the disease we will need to validate them by different strategies, replication in a large sample of controls and diabetics as well as carrying out functional studies

Urinary metals and metal mixtures and oxidative stress biomarkers in an adult population from Spain: The Hortega Study

INTRODUCTION: Few studies have investigated the role of exposure to metals and metal mixtures on oxidative stress in the general population. OBJECTIVES: We evaluated the cross-sectional association of urinary metal and metal mixtures with urinary oxidative stress biomarkers, including oxidized to reduced glutathione ratio (GSSG/GSH), malondialdehyde (MDA), and 8‑oxo‑7,8‑dihydroguanine (8-oxo-dG), in a representative sample of a general population from Spain (Hortega Study). METHODS: Urine antimony (Sb), barium (Ba), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), molybdenum (Mo), vanadium (V) and zinc (Zn) were measured by ICPMS in 1440 Hortega Study participants. RESULTS: The geometric mean ratios (GMRs) of GSSG/GSH comparing the 80th to the 20th percentiles of metal distributions were 1.15 (95% confidence intervals [95% CI]: 1.03-1.27) for Mo, 1.17 (1.05-1.31) for Ba, 1.23 (1.04-1.46) for Cr and 1.18 (1.00-1.40) for V. For MDA, the corresponding GMRs (95% CI) were 1.13 (1.03-1.24) for Zn and 1.12 (1.02-1.23) for Cd. In 8-oxo-dG models, the corresponding GMR (95% CI) were 1.12 (1.01-1.23) for Zn and 1.09 (0.99-1.20) for Cd. Cr for GSSG/GSH and Zn for MDA and 8-oxo-dG drove most of the observed associations. Principal component (PC) 1 (largely reflecting non-essential metals) was positively associated with GSSG/GSH. The association of PC2 (largely reflecting essential metals) was positive for GSSG/GSH but inverse for MDA. CONCLUSIONS: Urine Ba, Cd, Cr, Mo, V and Zn were positively associated with oxidative stress measures at metal exposure levels relevant for the general population. The potential health consequences of environmental, including nutritional, exposure to these metals warrants further investigation

Urinary metals and metal mixtures and oxidative stress biomarkers in an adult population from Spain: The Hortega Study.

Few studies have investigated the role of exposure to metals and metal mixtures on oxidative stress in the general population. We evaluated the cross-sectional association of urinary metal and metal mixtures with urinary oxidative stress biomarkers, including oxidized to reduced glutathione ratio (GSSG/GSH), malondialdehyde (MDA), and 8‑oxo‑7,8‑dihydroguanine (8-oxo-dG), in a representative sample of a general population from Spain (Hortega Study). Urine antimony (Sb), barium (Ba), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), molybdenum (Mo), vanadium (V) and zinc (Zn) were measured by ICPMS in 1440 Hortega Study participants. The geometric mean ratios (GMRs) of GSSG/GSH comparing the 80th to the 20th percentiles of metal distributions were 1.15 (95% confidence intervals [95% CI]: 1.03-1.27) for Mo, 1.17 (1.05-1.31) for Ba, 1.23 (1.04-1.46) for Cr and 1.18 (1.00-1.40) for V. For MDA, the corresponding GMRs (95% CI) were 1.13 (1.03-1.24) for Zn and 1.12 (1.02-1.23) for Cd. In 8-oxo-dG models, the corresponding GMR (95% CI) were 1.12 (1.01-1.23) for Zn and 1.09 (0.99-1.20) for Cd. Cr for GSSG/GSH and Zn for MDA and 8-oxo-dG drove most of the observed associations. Principal component (PC) 1 (largely reflecting non-essential metals) was positively associated with GSSG/GSH. The association of PC2 (largely reflecting essential metals) was positive for GSSG/GSH but inverse for MDA. Urine Ba, Cd, Cr, Mo, V and Zn were positively associated with oxidative stress measures at metal exposure levels relevant for the general population. The potential health consequences of environmental, including nutritional, exposure to these metals warrants further investigation.This work was supported by the Strategic Action for Research in Health Sciences [CP12/03080, PI10/0082, PI13/01848, PI07/0497 and PI11/00726]; GRUPOS 03/101, PROMETEO/2009/029 and 2005/027, AMP07/075 and ACOMP/2013/039 from the Valencia Government; GRS/279/A/08 from Castilla-Leon Government; European Network of Excellence Ingenious Hypercare (EPSS-037093) from the European Commission; Retics (PREDIMED RD06/0045/0006); CIBER Fisiopatología Obesidad y Nutrición (CIBERobn) [CIBER-02-08-2009, CB06/03 and CB12/03/30016] and CIBER de Diabetes y Enfermedades Metabólicas Relacionadas (CIBERDEM CB07/0/018). The Strategic Action for Research in Health sciences, Retics, CIBEROBN and CIBERDEM are initiatives from Carlos III Health Institute Madrid and the Spanish Ministry of Economy and Competitiveness and co-funded with European Funds for Regional Development (FEDER). The authors declare they have no actual or potential competing financial interests.S

Polymorphisms in endothelin system genes, arsenic levels and obesity risk.

BACKGROUND/OBJECTIVES:; Obesity has been linked to morbidity and mortality through increased risk for many chronic diseases. Endothelin (EDN) system has been related to endothelial function but it can be involved in lipid metabolism regulation: Receptor type A (EDNRA) activates lipolysis in adipocytes, the two endothelin receptors mediate arsenic-stimulated adipocyte dysfunction, and endothelin system can regulate adiposity by modulating adiponectin activity in different situations and, therefore, influence obesity development. The aim of the present study was to analyze if single nucleotide polymorphisms (SNPs) in the EDN system could be associated with human obesity. SUBJECTS/METHODS:; We analyzed two samples of general-population-based studies from two different regions of Spain: the VALCAR Study, 468 subjects from the area of Valencia, and the Hortega Study, 1502 subjects from the area of Valladolid. Eighteen SNPs throughout five genes were analyzed using SNPlex. RESULTS:; We found associations for two polymorphisms of the EDNRB gene which codifies for EDN receptor type B. Genotypes AG and AA of the rs5351 were associated with a lower risk for obesity in the VALCAR sample (p=0.048, OR=0.63) and in the Hortega sample (p=0.001, OR=0.62). Moreover, in the rs3759475 polymorphism, genotypes CT and TT were also associated with lower risk for obesity in the Hortega sample (p=0.0037, OR=0.66) and in the VALCAR sample we found the same tendency (p=0.12, OR=0.70). Furthermore, upon studying the pooled population, we found a stronger association with obesity (p=0.0001, OR=0.61 and p=0.0008, OR=0.66 for rs5351 and rs3759475, respectively). Regarding plasma arsenic levels, we have found a positive association for the two SNPs studied with obesity risk in individuals with higher arsenic levels in plasma: rs5351 (p=0.0054, OR=0.51) and rs3759475 (p=0.009, OR=0.53). CONCLUSIONS:; Our results support the hypothesis that polymorphisms of the EDNRB gene may influence the susceptibility to obesity and can interact with plasma arsenic levels.This work was supported by the funds for research in health sciences from Carlos III Health Institute (PI07/0497 and PI11/00726), by CIBER de Diabetes y Enfermedades Metabólicas Relacionadas (CIBERDEM); CIBERDEM is an initiative by Carlos III Health Institute in Madrid and the Spanish Health Ministry, by PROMETEO/2009/029, AP-091/11, and ACOMP/2013/039 from the Valencian Government, and by GRS 279/a/08 research project from JUNTA DE CASTILLA Y LEON Government.Ye

Association between obesity and BMI and SNPs of EDNRB genes adjusted by age and sex in: A-Valcar Study / B-Hortega Study / C-Both.

<p>HWE, Hardy—Weinberg equilibrium; MAF, minor allele frequency; SNP, single-nucleotide polymorphism; OR, odds ratio. Values are mean ± standard error. Bold indicates significance.</p><p>*dbSNP 129.</p><p>Association between obesity and BMI and SNPs of EDNRB genes adjusted by age and sex in: A-Valcar Study / B-Hortega Study / C-Both.</p

Linkage Disequilibrium (LD) plot of studied EDNRB SNPs. a) D´ b) R².

<p>Linkage Disequilibrium (LD) plot of studied EDNRB SNPs. a) D´ b) R².</p

General characteristics of the population.

<p>Values are mean ± standard deviation. For qualitative variables, data are expressed as (n, %)</p><p>* <i>p-value < 0</i>.<i>0001</i></p><p>General characteristics of the population.</p

Relation with Arsenic serum levels.

<p>Association between obesity and BMI and SNPs of EDNRB genes adjusted by age and sex. OR, odds ratio. Values are mean ± standard error. Bold indicates significance.</p><p>Relation with Arsenic serum levels.</p

Haplotype association analysis of rs5351 and rs3759475 of EDNRB gene with BMI and obesity risk adjusted by age and sex in relation with Arsenic serum levels.

<p>Single nucleotide polymorphisms used in the haplotype construction: rs5351, rs3759475.</p><p>Haplotype association analysis of rs5351 and rs3759475 of EDNRB gene with BMI and obesity risk adjusted by age and sex in relation with Arsenic serum levels.</p

Characteristics of selected Polymorphisms.

<p>HGN, The HUGO Gene Nomenclature.</p><p>^¥:Tag-SNP by Hapmap in Caucasian patients. Ensemble ID: Ensemble human release 53. SNP name: dbSNP129 (NCBI´s build 36, version 3)</p><p>Characteristics of selected Polymorphisms.</p