Identification of Novel Type 2 Diabetes Candidate Genes Involved in the Crosstalk between the Mitochondrial and the Insulin Signaling Systems

Type 2 Diabetes (T2D) is a highly prevalent chronic metabolic disease with strong co-morbidity with obesity and cardiovascular diseases. There is growing evidence supporting the notion that a crosstalk between mitochondria and the insulin signaling cascade could be involved in the etiology of T2D and insulin resistance. In this study we investigated the molecular basis of this crosstalk by using systems biology approaches. We combined, filtered, and interrogated different types of functional interaction data, such as direct protein–protein interactions, co-expression analyses, and metabolic and signaling dependencies. As a result, we constructed the mitochondria-insulin (MITIN) network, which highlights 286 genes as candidate functional linkers between these two systems. The results of internal gene expression analysis of three independent experimental models of mitochondria and insulin signaling perturbations further support the connecting roles of these genes. In addition, we further assessed whether these genes are involved in the etiology of T2D using the genome-wide association study meta-analysis from the DIAGRAM consortium, involving 8,130 T2D cases and 38,987 controls. We found modest enrichment of genes associated with T2D amongst our linker genes (p = 0.0549), including three already validated T2D SNPs and 15 additional SNPs, which, when combined, were collectively associated to increased fasting glucose levels according to MAGIC genome wide meta-analysis (p = 8.12×10−5). This study highlights the potential of combining systems biology, experimental, and genome-wide association data mining for identifying novel genes and related variants that increase vulnerability to complex diseases

Identification of novel type 2 diabetes candidate genes involved in the crosstalk between the mitochondrial and the insulin signaling systems

Type 2 Diabetes (T2D) is a highly prevalent chronic metabolic disease with strong co-morbidity with obesity and cardiovascular diseases. There is growing evidence supporting the notion that a crosstalk between mitochondria and the insulin signaling cascade could be involved in the etiology of T2D and insulin resistance. In this study we investigated the molecular basis of this crosstalk by using systems biology approaches. We combined, filtered, and interrogated different types of functional interaction data, such as direct protein-protein interactions, co-expression analyses, and metabolic and signaling dependencies. As a result, we constructed the mitochondria-insulin (MITIN) network, which highlights 286 genes as candidate functional linkers between these two systems. The results of internal gene expression analysis of three independent experimental models of mitochondria and insulin signaling perturbations further support the connecting roles of these genes. In addition, we further assessed whether these genes are involved in the etiology of T2D using the genome-wide association study meta-analysis from the DIAGRAM consortium, involving 8,130 T2D cases and 38,987 controls. We found modest enrichment of genes associated with T2D amongst our linker genes (p = 0.0549), including three already validated T2D SNPs and 15 additional SNPs, which, when combined, were collectively associated to increased fasting glucose levels according to MAGIC genome wide meta-analysis (p = 8.12×10(-5)). This study highlights the potential of combining systems biology, experimental, and genome-wide association data mining for identifying novel genes and related variants that increase vulnerability to complex diseases

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

Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis

Allergic rhinitis is the most common clinical presentation of allergy, affecting 400 million people worldwide, with increasing incidence in westernized countries1,2. To elucidate the genetic architecture and understand the underlying disease mechanisms, we carried out a meta-analysis of allergic rhinitis in 59,762 cases and 152,358 controls of European ancestry and identified a total of 41 risk loci for allergic rhinitis, including 20 loci not previously associated with allergic rhinitis, which were confirmed in a replication phase of 60,720 cases and 618,527 controls. Functional annotation implicated genes involved in various immune pathways, and fine mapping of the HLA region suggested amino acid variants important for antigen binding. We further performed genome-wide association study (GWAS) analyses of allergic sensitization against inhalant allergens and nonallergic rhinitis, which suggested shared genetic mechanisms across rhinitis-related traits. Future studies of the identified loci and genes might identify novel targets for treatment and prevention of allergic rhinitis

Genome-wide associations for birth weight and correlations with adult disease

Birth weight (BW) is influenced by both foetal and maternal factors and in observational studies is reproducibly associated with future risk of adult metabolic diseases including type 2 diabetes (T2D) and cardiovascular disease1. These lifecourse associations have often been attributed to the impact of an adverse early life environment. We performed a multi-ancestry genome-wide association study (GWAS) meta-analysis of BW in 153,781 individuals, identifying 60 loci where foetal genotype was associated with BW (P <5x10-8). Overall, ˜15% of variance in BW could be captured by assays of foetal genetic variation. Using genetic association alone, we found strong inverse genetic correlations between BW and systolic blood pressure (rg-0.22, P =5.5x10-13), T2D (rg-0.27, P =1.1x10-6) and coronary artery disease (rg-0.30, P =6.5x10-9) and, in large cohort data sets, demonstrated that genetic factors were the major contributor to the negative covariance between BW and future cardiometabolic risk. Pathway analyses indicated that the protein products of genes within BW-associated regions were enriched for diverse processes including insulin signalling, glucose homeostasis, glycogen biosynthesis and chromatin remodelling. There was also enrichment of associations with BW in known imprinted regions (P =1.9x10-4). We have demonstrated that lifecourse associations between early growth phenotypes and adult cardiometabolic disease are in part the result of shared genetic effects and have highlighted some of the pathways through which these causal genetic effects are mediated

Genome-wide associations for birth weight and correlations with adult disease

Birth weight (BW) has been shown to be influenced by both fetal and maternal factors and in observational studies is reproducibly associated with future risk of adult metabolic diseases including type 2 diabetes (T2D) and cardiovascular disease. These life-course associations have often been attributed to the impact of an adverse early life environment. Here, we performed a multi-ancestry genome-wide association study (GWAS) meta-analysis of BW in 153,781 individuals, identifying 60 loci where fetal genotype was associated with BW ($\textit{P}$  < 5 × 10$^{-8}$). Overall, approximately 15% of variance in BW was captured by assays of fetal genetic variation. Using genetic association alone, we found strong inverse genetic correlations between BW and systolic blood pressure ($\textit{R}$ $_{g}$ = -0.22, $\textit{P}$  = 5.5 × 10$^{-13}$), T2D ($\textit{R}$ $_{g}$ = -0.27, $\textit{P}$  = 1.1 × 10$^{-6}$) and coronary artery disease ($\textit{R}$ $_{g}$ = -0.30, $\textit{P}$  = 6.5 × 10$^{-9}$). In addition, using large -cohort datasets, we demonstrated that genetic factors were the major contributor to the negative covariance between BW and future cardiometabolic risk. Pathway analyses indicated that the protein products of genes within BW-associated regions were enriched for diverse processes including insulin signalling, glucose homeostasis, glycogen biosynthesis and chromatin remodelling. There was also enrichment of associations with BW in known imprinted regions ($\textit{P}$ = 1.9 × 10$^{-4}$). We demonstrate that life-course associations between early growth phenotypes and adult cardiometabolic disease are in part the result of shared genetic effects and identify some of the pathways through which these causal genetic effects are mediated.For a full list of the funders pelase visit the publisher's website and look at the supplemetary material provided. Some of the funders are: British Heart Foundation, Cancer Research UK, Medical Research Council, National Institutes of Health, Royal Society and Wellcome Trust

Implementation of a novel analytical framework for large-scale genetic data. Extending the genetic architecture of type 2 diabetes beyond common variants

[eng] The major landmark in modern genomic and biological research has been the first survey of the entire human genome. On June 2000 the staging of Bill Clinton along with Craig Venter and Francis Collins extolled how genome science would impact our lives by revolutionizing diagnosis, prevention and treatment for a vast number of human diseases (Collins 2010). Since that, we underwent a breathtaking progress in genome science with the unique conjunction of the development of new technologies such as Next Generation Sequencing (NGS) or genotyping arrays (Collins 2010; Hofker et al. 2014) and extensive data sharing initiatives catalysing new discoveries (Kaye et al. 2009; Collins 2010; Hood and Rowen 2013). To underscore the magnitude of this summit, the first sequence from the Human Genome Project (HGP) took 13 years and several collaborative efforts from a lace of international public research institutions entailing a 3 billion budget (U.S. Department of Energy & Human Genome Project program). Less than a decade later, NGS technologies have been implemented for clinical diagnosis, we entered in the $1,000 genome era, and the last Illumina sequencer, HiSeq X Ten is capable of producing up to 16 human genomes (1.8 terabases of data) in three days (Hayden 2014). The success of NGS led to an astonishing rate of growth of sequence data (Koboldt et al. 2013), which is doubling every seven months (Stephens et al. 2015). A downstream consequence has been the rapid accumulation of the number of sequenced genomes of many vertebrates, invertebrates, fungi, plants and microorganisms enabling tackling evolution and genome function through the rationale of comparative genomics (Collins 2010). In addition, the build-up of sequence data of thousands of human subjects contributed to catalogue the genetic differences between individuals, or also called as genetic variation (Hofker et al. 2014). There are different types of genetic variation but the most abundant are Single Nucleotide Polymorphisms (SNPs) (Stranger et al. 2011), substitutions of single nucleotides. While the HGP reported around 1.4 M of SNPs (Lander et al. 2001) more than 84 M of SNPs have been described in the new phase 3 release of the 1000 Genomes Project (1000G-Phase3) (Sudmant et al. 2015; The 1000 Genomes Project Consortium et al. 2015). A final example to illustrate the large efforts invested in more accurate descriptions of genetic variation is the last work published from the Exome Aggregation Consortium (ExAC). This study involved the aggregation and analysis of exomic regions through sequencing data of 60,706 individuals (Lek et al. 2016). The disposal of this kind of data showed a widespread mutational recurrence in human genomes, it allowed detecting genes subjected to strong selection depending on the class of mutation and it is expected to facilitate the clinical interpretation of disease-causing variants (Lek et al. 2016). Thus, the accumulation of individual genetic data has empowered researchers to unravel those specific genetic variants associated with disease liability. We also moved from biologically guided candidate single gene-studies involving a few hundreds of individuals towards hypothesis-free genome-wide analysis, performing extensive and massive genomic interrogation of thousands of individuals (Relling and Evans 2015; Wang et al. 2015). Piecing these advances all together, we have expanded our understanding of disease pathophysiology. Therefore, integrating the genetic understanding of the health-status alongside with clinical explorations constitutes the idea beneath personalized medicine. This genomic paradigm shift for clinical medicine provides a new source of therapeutic breakthroughs and diagnosis (Hood and Rowen 2013). As an example of this, targeted therapeutics have been resourceful for the treatment of lung cancer: sequence information revealed that tumours carrying specific mutations in the epidermal growth factor receptor (EGFR) were vulnerable to kinase inhibitors, resulting in higher response rates compared to traditional platinum-based chemotherapy (Levy et al. 2012; Swanton and Govindan 2016). Moreover, genetic tests are able to predict which breast cancer patients will benefit from chemotherapy (Innocenti et al. 2011; Gyorffy et al. 2015). Finally, notable successes have been achieved in pharmacogenomics, in which warfarin dose can be adjusted on the basis of genetic polymorphisms placed in CYP2C8 and VKORC1C genes (Collins 2010; Hood and Rowen 2013; Relling and Evans 2015). In line with this, there are large efforts under way to prioritize targeted therapeutics and to optimize drug selection and dosing, such as the Genomics England 100,000 Genomes Project and the US National of Health (NIH) Pharmacogenomics Research Network (Relling and Evans 2015; Wilson and Nicholls 2015). However, clear successes in clinical decision-making through genomic knowledge are anecdotal due to a poor understanding of human genetic diseases (Hofker et al. 2014; Relling and Evans 2015). For instance, Genome Wide Association Studies (GWAS) is undoubtedly one of the most important methodological advances emerging from the availability of complete human genome sequences and affordable DNA chips (Visscher et al. 2012; Hofker et al. 2014; Paul et al. 2014). GWAS have been extremely resourceful in identifying genetic variants associated with multiple diseases, but the translation of these results to clinics is sparse (Manolio et al. 2009; Collins 2010; Hofker et al. 2014). Some of the limitations lie on (1) the still small proportion of disease causing genetic factors identified for most complex diseases and (2) a lack of functional characterization and interpretation of disease associated variants, which hampers the identification of the underlying molecular mechanism (Manolio et al. 2009; Hofker et al. 2014). The genomic revolution has brought new decisive players for the future trend in biomedical research and clinical genetics. The ‘genomical’ challenge is one of the most demanding Big Data sciences in all four big computer science domains (data acquisition, storage, distribution and computation). In order to meet this rapid progress of genomic research, the build-up of whole-genome sequences and the emergence of large population biobanks (Stephens et al. 2015) urges a parallel development of computational frameworks. Moreover, a real social concern about data privacy can discourage the participation in genetic studies, which requires a major discussion about the ethical consequences of the return of information to participants seeking for genetic diagnosis (Hood and Rowen 2013; Koboldt et al. 2013). From this brief overview, the agenda of human genomics has clearly many issues to address. In this thesis I translated some of them into the following general goal: setting a cost-effective genetic research environment through the implementation of novel analytical and computational methods in order to better understand the genetics of Type 2 Diabetes (T2D). This work is a small glimpse of the frenzied activity in human genomics research and it aims to modestly contribute along with countless research efforts on this broad deployment of P4 medicine (Predictive, Preventive, Personalized, Participatory). In the next sections of this dissertation, I want to spell out this primary focus by providing several concepts that I learned during these years, which prompted this research to successfully achieve the goals of this thesis

TIGER: the translational human pancreatic islet genotype tissue-expression resource

Background and aims: The scarcity of human islets preparations from organ donors available and their scattering across research labs, limits the understanding of the genomic and regulatory landscape of human islets and type 2 diabetes (T2D). The Horizon 2020 T2DSystems Consortium set out to gather genomic, transcriptomic and epigenomic datasets from a large number of human pancreatic islet samples from several laboratories and make the data publicly available.Materials and methods: We collected RNA-seq and genotyping data from 495 human islet samples and performed harmonization, quality control, genotype phasing and imputation. We integrated a) T2D association from genome-wide association studies (GWAS) identified in large meta-analyses or included in the GWAS Catalog, b) variant annotation and characterization through Variant Effect Predictor and Gnomad, c) epigenomic marks from islet DNA-methylation sites, chromatin accessibility and CHiP-seq profiles, d) annotation from Gene Ontology, lncRNAs and islet regulome, e) gene expression from normalised islet RNA-seq counts, microarrays and the Genotype-Tissue Expression database, and f) computed expression quantitative loci (eQTL) and allelic specific expression (ASE) and created the largest regulatory variation database from human pancreatic islets.Results: We developed TIGER, a publicly accessible database (http://tiger.bsc.es) provided with a genome browser to ensure the comprehensive data integration. The platform encloses tools for visualizing, querying, and downloading human islet data. TIGER facilitates follow-up by providing genetic and molecular findings related to T2D pathophysiology with a gene or a variant summary, eQTL and ASE results, associations with T2D and other related traits or diseases, genomic context information such as the islet chromatin landscape and direct access to other genomic databases.Conclusion: The comprehensive collation in TIGER of genomic, transcriptomic and epigenetic human islet datasets, and the integration with T2D GWAS and regulatory variation, represents a formidable resource to interrogate the molecular etiology of beta-cell failure in T2D.info:eu-repo/semantics/publishe

Identification of novel type 2 diabetes candidate genes involved in the crosstalk between the mitochondrial and the insulin signaling systems

Type 2 Diabetes (T2D) is a highly prevalent chronic metabolic disease with strong co-morbidity with obesity and cardiovascular diseases. There is growing evidence supporting the notion that a crosstalk between mitochondria and the insulin signaling cascade could be involved in the etiology of T2D and insulin resistance. In this study we investigated the molecular basis of this crosstalk by using systems biology approaches. We combined, filtered, and interrogated different types of functional interaction data, such as direct protein-protein interactions, co-expression analyses, and metabolic and signaling dependencies. As a result, we constructed the mitochondria-insulin (MITIN) network, which highlights 286 genes as candidate functional linkers between these two systems. The results of internal gene expression analysis of three independent experimental models of mitochondria and insulin signaling perturbations further support the connecting roles of these genes. In addition, we further assessed whether these genes are involved in the etiology of T2D using the genome-wide association study meta-analysis from the DIAGRAM consortium, involving 8,130 T2D cases and 38,987 controls. We found modest enrichment of genes associated with T2D amongst our linker genes (p = 0.0549), including three already validated T2D SNPs and 15 additional SNPs, which, when combined, were collectively associated to increased fasting glucose levels according to MAGIC genome wide meta-analysis (p = 8.12×10(-5)). This study highlights the potential of combining systems biology, experimental, and genome-wide association data mining for identifying novel genes and related variants that increase vulnerability to complex diseases

Identification of novel type 2 diabetes candidate genes involved in the crosstalk between the mitochondrial and the insulin signaling systems

Type 2 Diabetes (T2D) is a highly prevalent chronic metabolic disease with strong co-morbidity with obesity and cardiovascular diseases. There is growing evidence supporting the notion that a crosstalk between mitochondria and the insulin signaling cascade could be involved in the etiology of T2D and insulin resistance. In this study we investigated the molecular basis of this crosstalk by using systems biology approaches. We combined, filtered, and interrogated different types of functional interaction data, such as direct protein-protein interactions, co-expression analyses, and metabolic and signaling dependencies. As a result, we constructed the mitochondria-insulin (MITIN) network, which highlights 286 genes as candidate functional linkers between these two systems. The results of internal gene expression analysis of three independent experimental models of mitochondria and insulin signaling perturbations further support the connecting roles of these genes. In addition, we further assessed whether these genes are involved in the etiology of T2D using the genome-wide association study meta-analysis from the DIAGRAM consortium, involving 8,130 T2D cases and 38,987 controls. We found modest enrichment of genes associated with T2D amongst our linker genes (p = 0.0549), including three already validated T2D SNPs and 15 additional SNPs, which, when combined, were collectively associated to increased fasting glucose levels according to MAGIC genome wide meta-analysis (p = 8.12×10(-5)). This study highlights the potential of combining systems biology, experimental, and genome-wide association data mining for identifying novel genes and related variants that increase vulnerability to complex diseases