Variants influencing age at diagnosis of HNF1A-MODY.

BACKGROUND: HNF1A-MODY is a monogenic form of diabetes caused by variants in the HNF1A gene. Different HNF1A variants are associated with differences in age of disease onset, but other factors are postulated to influence this trait. Here, we searched for genetic variants influencing age of HNF1A-MODY onset. METHODS: Blood samples from 843 HNF1A-MODY patients from Czech Republic, France, Poland, Slovakia, the UK and the US were collected. A validation set consisted of 121 patients from the US. We conducted a genome-wide association study in 843 HNF1A-MODY patients. Samples were genotyped using Illumina Human Core arrays. The core analysis was performed using the GENESIS package in R statistical software. Kinship coefficients were estimated with the KING and PC-Relate algorithms. In the linear mixed model, we accounted for year of birth, sex, and location of the HNF1A causative variant. RESULTS: A suggestive association with age of disease onset was observed for rs2305198 (p = 2.09E-07) and rs7079157 (p = 3.96E-06) in the HK1 gene, rs2637248 in the LRMDA gene (p = 2.44E-05), and intergenic variant rs2825115 (p = 2.04E-05). Variant rs2637248 reached nominal significance (p = 0.019), while rs7079157 (p = 0.058) and rs2825115 (p = 0.068) showed suggestive association with age at diabetes onset in the validation set. CONCLUSIONS: rs2637248 in the LRMDA gene is associated with age at diabetes onset in HNF1A-MODY patients.

The choice of the DNA extraction method may influence the outcome of the soil microbial community structure analysis.

Metagenomics approaches and recent improvements in the next-generation sequencing methods, have become a method of choice in establishing a microbial population structure. Many commercial soil DNA extraction kits are available and due to their efficiency they are replacing traditional extraction protocols. However, differences in the physicochemical properties of soil samples require optimization of DNA extraction techniques for each sample separately. The aim of this study was to compare the efficiency, quality, and diversity of genetic material extracted with the use of commonly used kits. The comparative analysis of microbial community composition, displayed differences in microbial community structure depending on which kit was used. Statistical analysis indicated significant differences in recovery of the genetic material for 24 out of 32 analyzed phyla, and the most pronounced differences were seen for Actinobacteria. Also, diversity indexes and reproducibility of DNA extraction with the use of a given kit, varied among the tested methods. As the extraction protocol may influence the apparent structure of a microbial population, at the beginning of each project many extraction kits should be tested in order to choose one that would yield the most representative results and present the closest view to the actual structure of microbial population.

First insight into microbial community composition in a phosphogypsum waste heap soil.

The aim of this study was to investigate the soil microbial communities of a phosphogypsum waste heap. The soil microbial community structures can differ over time, as they are affected by the changing environmental conditions caused by a long-term exposure to different kinds of pollutions, like is the case of soil in the post-production waste area in Wislinka (in the northern part of Poland) currently undergoing restoration. Our analyses indicated that the most abundant phyla were Proteobacteria, Acidobacteria, and Actinobacteria, and generally such an abundance is common for most of the studied soils. The most dominant class were Alphaproteobacteria, with their participation in 33.46% of the total reads. Among this class, the most numbered order was Sphingomonadales, whereas among this order the Sphingomonadaceae family was the most abundant one. The Sphingomonadaceae family is currently in the center of interest of many researchers, due to the ability of some of its members to utilize a wide range of naturally occurring organic compounds and many types of environmental contaminants. This kind of knowledge about microbial populations can support efforts in bioremediation and can improve monitoring changes in the contaminated environments.

Analysis of gene expression to predict dynamics of future hypertension incidence in type 2 diabetic patients.

BACKGROUND: The main focus of the Genetic Analysis Workshop 19 (GAW19) is identification of genes related to the occurrence of hypertension in the cohort of patients with type 2 diabetes mellitus (T2DM). The aim of our study was to predict dynamics of the future hypertension incidence, based on gene expression profiles, systolic and diastolic blood pressure changes in time, sex, baseline age, and cigarette smoking status. We analyzed data made available to GAW19 participants, which included gene expression profiles of peripheral blood mononuclear cells (PBMCs) from the diabetic members of 20 Mexican American families. METHODS: On the basis of mid blood pressure measurements at several time points, the coefficient of regression (slope) was calculated for each individual. We corrected the slope value in patients treated with antihypertensive medications. Feature preprocessing methods were used to remove highly correlated probes and linear dependencies between them. Subsequently, multiple linear regression model was used to associate gene expression with the regression coefficient calculated for each T2DM patient. Tenfold cross-validation was used to validate the model. We used linear mixed effects model and kinship coefficients to account for the family structure. All calculations were performed in R. RESULTS: This analysis allowed us to identify 6 well-annotated genes: RTP4, FXYD6, GDF11, IFNAR1, NOX3, and HLA-DQ2, associated with dynamics of future hypertension incidence. Two of them, IFNAR1 and NOX3 were previously implicated in pathogenesis of hypertension. CONCLUSIONS: There is no obvious mechanism that links all detected genes with dynamics of hypertension incidence. Identification of possible connection with hypertension needs further investigation.

A family with the Arg103Pro mutation in the NEUROD1 gene detected by next-generation sequencing - Clinical characteristics of mutation carriers.

UNLABELLED: Until now only a few families with early onset autosomal diabetes due to the NEUROD1 gene mutations have been identified. Moreover, only some of them meet strict MODY (maturity-onset diabetes of the young) criteria. Next-generation sequencing (NGS) provides an opportunity to detect more pathogenic mutations in this gene. Here, we evaluated the segregation of the Arg103Pro mutation in the NEUROD1 gene in a pedigree in which it was detected, and described the clinical characteristics of the mutation carriers. METHODS: We included 156 diabetic probands of MODY families, among them 52 patients earlier tested for GCK-MODY and/or HNF1A-MODY by Sanger sequencing with negative results. Genetic testing was performed by targeted NGS sequencing using a panel of 28 monogenic diabetes genes. RESULTS: As detected by NGS, one patient had the missense Arg103Pro (CGC/CCC) mutation in the gene NEUROD1 changing the amino-acid structure of the DNA binding domain of this transcription factor. We confirmed this sequence difference by Sanger sequencing. This family had previously been tested with negative results for HNF1A gene mutations. 17 additional members of this family were invited for further testing. We confirmed the presence of the mutation in 11 subjects. Seven adult mutation carriers (all but one) from three generations had been already diagnosed with diabetes. There were 3 individuals with the Arg103Pro mutation diagnosed before the age of 30 years in the family. The range of age of the four unaffected mutation carriers (3 minors and 1 adult) was 3-48 years. Interestingly, one mutation carrier had a history of transient neonatal hypoglycemia, of which the clinical course resembled episodes typical for HNF4A-MODY. CONCLUSIONS: We report a family with autosomal dominant diabetes related to a new NEUROD1 mutation, one of very few meeting MODY criteria. The use of the NGS method will facilitate identification of more families with rare forms of MODY.

Qualitative Parameters of the Colonic Flora in Patients with HNF1A-MODY Are Different from Those Observed in Type 2 Diabetes Mellitus.

Background. Type 2 diabetes mellitus (T2DM) is determined by genetic and environmental factors. There have been many studies on the relationship between the composition of the gastrointestinal bacterial flora, T2DM, and obesity. There are no data, however, on the gut microbiome structure in monogenic forms of the disease including Maturity Onset Diabetes of the Young (MODY). Methods. The aim of the investigation was to compare the qualitative parameters of the colonic flora in patients with HNF1A-MODY and T2DM and healthy individuals. 16S sequencing of bacterial DNA isolated from the collected fecal samples using the MiSeq platform was performed. Results. There were significant between-group differences in the bacterial profile. At the phylum level, the amount of Proteobacteria was higher (p = 0.0006) and the amount of Bacteroidetes was lower (p = 0.0005) in T2DM group in comparison to the control group. In HNF1A-MODY group, the frequency of Bacteroidetes was lower than in the control group (p = 0.0143). At the order level, Turicibacterales was more abundant in HNF1A-MODY group than in T2DM group. Conclusions. It appears that there are differences in the gut microbiome composition between patients with HNF1A-MODY and type 2 diabetes. Further investigation on this matter should be conducted.

Genetic testing for monogenic diabetes using targeted next-generation sequencing in patients with maturity-onset diabetes of the young.

INTRODUCTION: Molecular diagnosis of monogenic diabetes mellitus is important for individualized patient care. Next-generation sequencing (NGS) enables a simultaneous analysis of multiple genes in a single test. OBJECTIVES: We aimed to assess the feasibility of using NGS for detecting mutations in a set of known monogenic diabetes gene mutations in a cohort of Polish patients with maturity-onset diabetes of the young (MODY) with earlier negative Sanger sequencing results for HNF1A-MODY or GCK-MODY. PATIENTS AND METHODS: We selected a panel of 28 chromosomal genes in which mutations have been reported to cause monogenic diabetes. The MiSeq platform was used for NGS. An exon-capture assay was designed to include coding regions and splice sites. A total of 54 patients with existing negative Sanger sequencing screening results for HNF1A or GCK gene mutations were selected for the study. RESULTS: NGS results were generated for all 54 patients and 9 positive controls with previously identified HNF1A or GCK gene mutation. All selected positive controls were confirmed by NGS. Among 28 genes, mutations were detected in 16. The type of the analyzed genetic changes was described in the NGS study as high (n = 3) or moderate (n = 76). Among the detected mutations, there were 4 known GCK gene mutations that had been previously missed in Sanger sequencing. So far, Sanger sequencing allowed us to confirm 21 gene mutations detected by NGS, and segregation with diabetes in 14 pedigrees. CONCLUSIONS: Our pilot study using NGS for monogenic diabetes screening in the MODY cohort confirmed that it improves the detection of diabetes-related sequence differences. The screening with NGS should also include diabetic patients for whom Sanger-based screening for particular subtypes of MODY provided negative results.