Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes.

The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662057, associated with a twofold increased risk for T2D in males. rs146662057 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches

Detection and classification of somatic structural variants, and its application in the study of neuronal development

[eng] The identification and analysis of genomic variation across individuals has been central in biology, first through comparative genomics to answer evolutionary questions, and then in the context of biomedicine, where it is actually becoming central to the study of most diseases. Next generation sequence technologies are allowing the systematic analysis of thousands of different types of genetic variation, enhancing the identification of disease markers and the understanding of the molecular basis of disease. For the past years, there has been a burst of new methodology for genome analysis around diseases coming from hundreds of groups around the world. Specific computational methods and strategies are being designed and improved around the identification and interpretation of genomic variation. The identification and classification of different types of genomic variants in the context of biomedicine is a key and foundational step for the development of a personalized medicine. This has been particularly central in the field of cancer genomics, which has based the research of the past ten to fifteen years in the sequencing of genomic DNA, and the identification and interpretation of (mostly) somatic and germline variation. Throughout these years, a large number of methods for variant detection have been developed with different action ranges. Despite all these developments, the identification of genomic variants has still room for improvement, not only at the level of sensitivity and specificity, but also at the computational level. Given the emergence of many initiatives for personalized medicine around the world, and the expected number of genomes that will have to be analyzed within health care systems, we require robust algorithms, designed together with a matching implementation that will minimize the computational costs of the analysis. With this aim, during this thesis, I have pushed and designed and implemented an algorithm for the efficient processing of genomic data, in close collaboration with computer scientists of our center that defined the implementation, focusing on lowering the energy and the time of the analysis. This methodology, which relies on a reference free approach of read classification, has been protected with a patent, and is being used as the foundation for the development of SMuFin2, a more accurate and computationally efficient version of the initial SMuFin from 2014. We here show that our method is able to process whole genome sequences very fast and with a minimal energy consumption, compared with existing methods, and that has great potential for the identification of all ranges of variants, including insertions of non-human DNA. Further developments on SMuFin2 are needed to finally assess its full variant calling capabilities. Despite their great importance and their clear role in the biology of the cell, somatic variation that occurs in healthy tissues has remained diffuse in their roles. In the case of development, some hypotheses have been proposed to explain the observed somatic DNA damage that occurs during brain development (e.g., replication stress). But the real impact and the underlying mechanisms of this somatic variation are not yet understood. In order to seed light on the type and potential functional impact of somatic variation in brain development, we established a new collaboration to identify, and describe somatic DNA rearrangements induced by Pgbd5 during brain development and adult state in 36 mice neural tissue samples. The detection of somatic variants in healthy tissues presents more challenges than in the cancer scenario, where a variant is present in a significant number of cells and is easier to detect. We have identified, classified and interpreted the landscape of somatic variation in neural development and identified interesting differences between adult and embryonic variation load, and specific types of variants, as the potential result of the activity of these transposase-like genes

Genetic manipulation of temperate and tropical woody species

SIGLEAvailable from British Library Document Supply Centre- DSC:D172157 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Adaptation to environmental factors shapes the organization of regulatory regions in microbial communities

Background: It has been shown in a number of metagenomic studies that the addition and removal of specific genes have allowed microbiomes to adapt to specific environmental conditions by losing and gaining specific functions. But it is not known whether and how the regulation of gene expression also contributes to adaptation. Results: We have here characterized and analyzed the metaregulome of three different environments, as well as their impact in the adaptation to particular variable physico-chemical conditions. For this, we have developed a computational protocol to extract regulatory regions and their corresponding transcription factors binding sites directly from metagenomic reads and applied it to three well known environments: Acid Mine, Whale Fall, and Waseca Farm. Taking the density of regulatory sites in promoters as a measure of the potential and complexity of gene regulation, we found it to be quantitatively the same in all three environments, despite their different physico-chemical conditions and species composition. However, we found that each environment distributes their regulatory potential differently across their functional space. Among the functions with highest regulatory potential in each niche, we found significant enrichment of processes related to sensing and buffering external variable factors specific to each environment, like for example, the availability of co-factors in deep sea, of oligosaccharides in soil and the regulation of pH in the acid mine. Conclusions: These results highlight the potential impact of gene regulation in the adaptation of bacteria to the different habitats through the distribution of their regulatory potential among specific functions, and point to critical environmental factors that challenge the growth of any microbial community.Peer Reviewe

Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug

Metformin is widely used in the treatment of type 2 diabetes (T2D), but its mechanism of action is poorly defined. Recent evidence implicates the gut microbiota as a site of metformin action. In a double-blind study, we randomized individuals with treatment-naive T2D to placebo or metformin for 4 months and showed that metformin had strong effects on the gut microbiome. These results were verified in a subset of the placebo group that switched to metformin 6 months after the start of the trial. Transfer of fecal samples (obtained before and 4 months after treatment) from metformin-treated donors to germ-free mice showed that glucose tolerance was improved in mice that received metformin-altered microbiota. By directly investigating metformin–microbiota interactions in a gut simulator, we showed that metformin affected pathways with common biological functions in species from two different phyla, and many of the metformin-regulated genes in these species encoded metalloproteins or metal transporters. Our findings provide support for the notion that altered gut microbiota mediates some of metformin's antidiabetic effects

Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes

The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662057, associated with a twofold increased risk for T2D in males. rs146662057 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches

Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes

The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662057, associated with a twofold increased risk for T2D in males. rs146662057 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches

Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes

The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662057, associated with a twofold increased risk for T2D in males. rs146662057 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches