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To address the challenge of translating genetic discoveries for type 1 diabetes (T1D) into mechanistic insight, we have developed the T1D Knowledge Portal (T1DKP), an open-access resource for hypothesis development and target discovery in T1D.
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Diabetes Mellitus Tipo 1 , Humanos , Diabetes Mellitus Tipo 1/genética , Genômica , Genética HumanaRESUMO
The staggering complexity of the genome controls for developmental processes is revealed through massively parallel cis-regulatory analysis using new methods of perturbation and readout. The choice of combinations of these new methods is tailored to the system, question and resources at hand. Our focus is on issues that include the necessity or sufficiency of given cis-regulatory modules, cis-regulatory function in the normal spatial genomic context, and easily accessible high throughput and multiplexed analysis methods. In the sea urchin embryonic model, recombineered BACs offer new opportunities for consecutive modes of cis-regulatory analyses that answer these requirements, as we here demonstrate on a diverse suite of previously unstudied sea urchin effector genes expressed in skeletogenic cells. Positively active cis-regulatory modules were located in single Nanostring experiments per BAC containing the gene of interest, by application of our previously reported "barcode" tag vectors of which>â¯100 can be analyzed at one time. Computational analysis of DNA sequences that drive expression, based on the known skeletogenic regulatory state, then permitted effective identification of functional target site clusters. Deletion of these sub-regions from the parent BACs revealed module necessity, as simultaneous tests of the same regions in short constructs revealed sufficiency. Predicted functional inputs were then confirmed by site mutations, all generated and tested in multiplex formats. There emerged the simple conclusion that each effector gene utilizes a small subset of inputs from the skeletogenic GRN. These inputs may function to only adjust expression levels or in some cases necessary for expression. Since we know the GRN architecture upstream of the effector genes, we could then conceptually isolate and compare the wiring of the effector gene driver sub-circuits and identify the inputs whose removal abolish expression.
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Regulação da Expressão Gênica no Desenvolvimento/fisiologia , Engenharia Genética/métodos , Análise de Sequência de DNA/métodos , Animais , Cromossomos Artificiais Bacterianos/genética , Regulação da Expressão Gênica no Desenvolvimento/genética , Redes Reguladoras de Genes/genética , Redes Reguladoras de Genes/fisiologia , Genes Reporter/genética , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Modelos Biológicos , Ouriços-do-Mar/embriologia , Ouriços-do-Mar/genética , Fatores de Transcrição/metabolismoRESUMO
Type 1 diabetes (T1D) has a large genetic component, and expanded genetic studies of T1D can lead to novel biological and therapeutic discovery and improved risk prediction. In this study, we performed genetic association and fine-mapping analyses in 817,718 European ancestry samples genome-wide and 29,746 samples at the MHC locus, which identified 165 independent risk signals for T1D of which 19 were novel. We used risk variants to train a machine learning model (named T1GRS) to predict T1D, which highly differentiated T1D from non-disease and type 2 diabetes (T2D) in Europeans as well as African Americans at or beyond the level of current standards. We identified extensive non-linear interactions between risk loci in T1GRS, for example between HLA-DQB1*57 and INS, coding and non-coding HLA alleles, and DEXI, INS and other beta cell loci, that provided mechanistic insight and improved risk prediction. T1D individuals formed distinct clusters based on genetic features from T1GRS which had significant differences in age of onset, HbA1c, and renal disease severity. Finally, we provided T1GRS in formats to enhance accessibility of risk prediction to any user and computing environment. Overall, the improved genetic discovery and prediction of T1D will have wide clinical, therapeutic, and research applications.
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Physiological variability in pancreatic cell type gene regulation and the impact on diabetes risk is poorly understood. In this study we mapped gene regulation in pancreatic cell types using single cell multiomic (joint RNA-seq and ATAC-seq) profiling in 28 non-diabetic donors in combination with single cell data from 35 non-diabetic donors in the Human Pancreas Analysis Program. We identified widespread associations with age, sex, BMI, and HbA1c, where gene regulatory responses were highly cell type- and phenotype-specific. In beta cells, donor age associated with hypoxia, apoptosis, unfolded protein response, and external signal-dependent transcriptional regulators, while HbA1c associated with inflammatory responses and gender with chromatin organization. We identified 10.8K loci where genetic variants were QTLs for cis regulatory element (cRE) accessibility, including 20% with lineage- or cell type-specific effects which disrupted distinct transcription factor motifs. Type 2 diabetes and glycemic trait associated variants were enriched in both phenotype- and QTL-associated beta cell cREs, whereas type 1 diabetes showed limited enrichment. Variants at 226 diabetes and glycemic trait loci were QTLs in beta and other cell types, including 40 that were statistically colocalized, and annotating target genes of colocalized QTLs revealed genes with putatively novel roles in disease. Our findings reveal diverse responses of pancreatic cell types to phenotype and genotype in physiology, and identify pathways, networks, and genes through which physiology impacts diabetes risk.
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Biological sex affects the pathogenesis of type 2 and type 1 diabetes (T2D, T1D) including the development of ß cell failure observed more often in males. The mechanisms that drive sex differences in ß cell failure is unknown. Studying sex differences in islet regulation and function represent a unique avenue to understand the sex-specific heterogeneity in ß cell failure in diabetes. Here, we examined sex and race differences in human pancreatic islets from up to 52 donors with and without T2D (including 37 donors from the Human Pancreas Analysis Program [HPAP] dataset) using an orthogonal series of experiments including single cell RNA-seq (scRNA-seq), single nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq), dynamic hormone secretion, and bioenergetics. In cultured islets from nondiabetic (ND) donors, in the absence of the in vivo hormonal environment, sex differences in islet cell type gene accessibility and expression predominantly involved sex chromosomes. Of particular interest were sex differences in the X-linked KDM6A and Y-linked KDM5D chromatin remodelers in female and male islet cells respectively. Islets from T2D donors exhibited similar sex differences in differentially expressed genes (DEGs) from sex chromosomes. However, in contrast to islets from ND donors, islets from T2D donors exhibited major sex differences in DEGs from autosomes. Comparing ß cells from T2D and ND donors revealed that females had more DEGs from autosomes compared to male ß cells. Gene set enrichment analysis of female ß cell DEGs showed a suppression of oxidative phosphorylation and electron transport chain pathways, while male ß cell had suppressed insulin secretion pathways. Thus, although sex-specific differences in gene accessibility and expression of cultured ND human islets predominantly affect sex chromosome genes, major differences in autosomal gene expression between sexes appear during the transition to T2D and which highlight mitochondrial failure in female ß cells.
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Type 2 and type 1 diabetes (T2D, T1D) exhibit sex differences in insulin secretion, the mechanisms of which are unknown. We examined sex differences in human pancreatic islets from 52 donors with and without T2D combining single cell RNA-seq (scRNA-seq), single nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq), hormone secretion, and bioenergetics. In nondiabetic (ND) donors, sex differences in islet cells gene accessibility and expression predominantly involved sex chromosomes. Islets from T2D donors exhibited similar sex differences in sex chromosomes differentially expressed genes (DEGs), but also exhibited sex differences in autosomal genes. Comparing ß cells from T2D vs. ND donors, gene enrichment of female ß cells showed suppression in mitochondrial respiration, while male ß cells exhibited suppressed insulin secretion. Thus, although sex differences in gene accessibility and expression of ND ß cells predominantly affect sex chromosomes, the transition to T2D reveals sex differences in autosomes highlighting mitochondrial failure in females.
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Pancreatic islets consist of multiple cell types that produce hormones required for glucose homeostasis, and islet dysfunction is a major factor in type 1 and type 2 diabetes. Numerous studies have assessed transcription across individual cell types using single-cell assays; however, there is no canonical reference of gene expression in islet cell types that is also easily accessible for researchers to query and use in bioinformatics pipelines. Here we present an integrated map of islet cell type-specific gene expression from 192,203 cells from single-cell RNA sequencing of 65 donors without diabetes, donors who were type 1 diabetes autoantibody positive, donors with type 1 diabetes, and donors with type 2 diabetes from the Human Pancreas Analysis Program. We identified 10 distinct cell types, annotated subpopulations of several cell types, and defined cell type-specific marker genes. We tested differential expression within each cell type across disease states and identified 1,701 genes with significant changes in expression, with most changes observed in ß-cells from donors with type 1 diabetes. To facilitate user interaction, we provide several single-cell visualization and reference mapping tools, as well as the open-access analytical pipelines used to create this reference. The results will serve as a valuable resource to investigators studying islet biology.
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Diabetes Mellitus Tipo 1 , Diabetes Mellitus Tipo 2 , Ilhotas Pancreáticas , Humanos , Diabetes Mellitus Tipo 2/genética , Diabetes Mellitus Tipo 2/metabolismo , Diabetes Mellitus Tipo 1/genética , Diabetes Mellitus Tipo 1/metabolismo , Ilhotas Pancreáticas/metabolismo , Pâncreas/metabolismo , Expressão GênicaRESUMO
Pancreatic islets are comprised of multiple endocrine cell types that produce hormones required for glucose homeostasis, and islet dysfunction is a major factor in the development of type 1 and type 2 diabetes (T1D, T2D). Numerous studies have generated gene expression profiles in individual islet cell types using single cell assays. However, there is no canonical reference of gene expression in islet cell types in both health and disease that is also easily accessible for researchers to access, query, and use in bioinformatics pipelines. Here we present an integrated reference map of islet cell type-specific gene expression from 192,203 cells derived from single cell RNA-seq assays of 65 non-diabetic, T1D autoantibody positive (Aab+), T1D, and T2D donors from the Human Pancreas Analysis Program. We identified 10 endocrine and non-endocrine cell types as well as sub-populations of several cell types, and defined sets of marker genes for each cell type and sub-population. We tested for differential expression within each cell type in T1D Aab+, T1D, and T2D states, and identified 1,701 genes with significant changes in expression in any cell type. Most changes were observed in beta cells in T1D, and, by comparison, there were almost no genes with changes in T1D Aab+. To facilitate user interaction with this reference, we provide the data using several single cell visualization and reference mapping tools as well as open-access analytical pipelines used to create this reference. The results will serve as a valuable resource to investigators studying islet biology and diabetes.
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Translating genetic discoveries for type 1 diabetes (T1D) into mechanistic insight can reveal novel biology and therapeutic targets but remains a major challenge. We developed the T1D Knowledge Portal (T1DKP), a disease-specific resource of genetic and functional annotation data that enables users to develop hypotheses for T1D-based research and target discovery. The T1DKP can be used to query genes and genomic regions for genetic associations, identify epigenomic features, access results of bioinformatic analyses, and obtain expert-curated resources. The T1DKP is available at http://t1d.hugeamp.org .
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Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.
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Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/genética , Acesso à Informação , Estudos Prospectivos , Genômica/métodos , FenótipoRESUMO
Single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) creates new opportunities to dissect cell type-specific mechanisms of complex diseases. Since pancreatic islets are central to type 2 diabetes (T2D), we profiled 15,298 islet cells by using combinatorial barcoding snATAC-seq and identified 12 clusters, including multiple alpha, beta and delta cell states. We cataloged 228,873 accessible chromatin sites and identified transcription factors underlying lineage- and state-specific regulation. We observed state-specific enrichment of fasting glucose and T2D genome-wide association studies for beta cells and enrichment for other endocrine cell types. At T2D signals localized to islet-accessible chromatin, we prioritized variants with predicted regulatory function and co-accessibility with target genes. A causal T2D variant rs231361 at the KCNQ1 locus had predicted effects on a beta cell enhancer co-accessible with INS and genome editing in embryonic stem cell-derived beta cells affected INS levels. Together our findings demonstrate the power of single-cell epigenomics for interpreting complex disease genetics.
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Cromatina/química , Diabetes Mellitus Tipo 2/genética , Células Secretoras de Glucagon/metabolismo , Células Secretoras de Insulina/metabolismo , Canal de Potássio KCNQ1/genética , Células Secretoras de Polipeptídeo Pancreático/metabolismo , Células Secretoras de Somatostatina/metabolismo , Glicemia/metabolismo , Diferenciação Celular , Cromatina/metabolismo , Diabetes Mellitus Tipo 2/metabolismo , Diabetes Mellitus Tipo 2/patologia , Epigenômica , Jejum , Perfilação da Expressão Gênica , Estudo de Associação Genômica Ampla , Células Secretoras de Glucagon/patologia , Sequenciamento de Nucleotídeos em Larga Escala , Células-Tronco Embrionárias Humanas/citologia , Humanos , Células Secretoras de Insulina/patologia , Canal de Potássio KCNQ1/metabolismo , Família Multigênica , Células Secretoras de Polipeptídeo Pancreático/patologia , Polimorfismo Genético , Análise de Célula Única , Células Secretoras de Somatostatina/patologia , Fatores de Transcrição/classificação , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismoRESUMO
BACKGROUND: Large comparative genomics studies and tools are becoming increasingly more compute-expensive as the number of available genome sequences continues to rise. The capacity and cost of local computing infrastructures are likely to become prohibitive with the increase, especially as the breadth of questions continues to rise. Alternative computing architectures, in particular cloud computing environments, may help alleviate this increasing pressure and enable fast, large-scale, and cost-effective comparative genomics strategies going forward. To test this, we redesigned a typical comparative genomics algorithm, the reciprocal smallest distance algorithm (RSD), to run within Amazon's Elastic Computing Cloud (EC2). We then employed the RSD-cloud for ortholog calculations across a wide selection of fully sequenced genomes. RESULTS: We ran more than 300,000 RSD-cloud processes within the EC2. These jobs were farmed simultaneously to 100 high capacity compute nodes using the Amazon Web Service Elastic Map Reduce and included a wide mix of large and small genomes. The total computation time took just under 70 hours and cost a total of $6,302 USD. CONCLUSIONS: The effort to transform existing comparative genomics algorithms from local compute infrastructures is not trivial. However, the speed and flexibility of cloud computing environments provides a substantial boost with manageable cost. The procedure designed to transform the RSD algorithm into a cloud-ready application is readily adaptable to similar comparative genomics problems.
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Biologia Computacional/métodos , Genoma , Genômica/métodos , AlgoritmosRESUMO
Respiratory failure associated with COVID-19 has placed focus on the lungs. Here, we present single-nucleus accessible chromatin profiles of 90,980 nuclei and matched single-nucleus transcriptomes of 46,500 nuclei in non-diseased lungs from donors of ~30 weeks gestation,~3 years and ~30 years. We mapped candidate cis-regulatory elements (cCREs) and linked them to putative target genes. We identified distal cCREs with age-increased activity linked to SARS-CoV-2 host entry gene TMPRSS2 in alveolar type 2 cells, which had immune regulatory signatures and harbored variants associated with respiratory traits. At the 3p21.31 COVID-19 risk locus, a candidate variant overlapped a distal cCRE linked to SLC6A20, a gene expressed in alveolar cells and with known functional association with the SARS-CoV-2 receptor ACE2. Our findings provide insight into regulatory logic underlying genes implicated in COVID-19 in individual lung cell types across age. More broadly, these datasets will facilitate interpretation of risk loci for lung diseases.
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COVID-19/genética , COVID-19/virologia , Interações entre Hospedeiro e Microrganismos/genética , Pulmão/metabolismo , Pulmão/virologia , Adulto , Fatores Etários , Células Epiteliais Alveolares/classificação , Células Epiteliais Alveolares/metabolismo , Células Epiteliais Alveolares/virologia , Enzima de Conversão de Angiotensina 2/genética , Enzima de Conversão de Angiotensina 2/metabolismo , COVID-19/metabolismo , Pré-Escolar , Mapeamento Cromossômico , Perfilação da Expressão Gênica , Variação Genética , Interações entre Hospedeiro e Microrganismos/fisiologia , Humanos , Recém-Nascido , Proteínas de Membrana Transportadoras/genética , Proteínas de Membrana Transportadoras/metabolismo , Pandemias , Receptores Virais/genética , Receptores Virais/metabolismo , SARS-CoV-2/genética , SARS-CoV-2/patogenicidade , Análise de Célula Única , Internalização do VírusAssuntos
Doenças Cardiovasculares , Infarto do Miocárdio , Humanos , Recursos Comunitários , EpigenômicaRESUMO
Database URL: http://www.echinobase.org.
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Bases de Dados Genéticas , Equinodermos/genética , Genoma , AnimaisRESUMO
Echinoderm genome sequences are a corpus of useful information about a clade of animals that serve as research models in fields ranging from marine ecology to cell and developmental biology. Genomic information from echinoids has contributed to insights into the gene interactions that drive the developmental process at the molecular level. Such insights often rely heavily on genomic information and the kinds of questions that can be asked thus depend on the quality of the sequence information. Here we describe the history of echinoderm genomic sequence assembly and present details about the quality of the data obtained. All of the sequence information discussed here is posted on the echinoderm information web system, Echinobase.org.
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Equinodermos/genética , Genoma/genética , Análise de Sequência de DNA/história , Análise de Sequência de DNA/normas , Transcriptoma/genética , Animais , História do Século XXI , Modelos Genéticos , Filogenia , Especificidade da EspécieRESUMO
BACKGROUND: Comparative genomics resources, such as ortholog detection tools and repositories are rapidly increasing in scale and complexity. Cloud computing is an emerging technological paradigm that enables researchers to dynamically build a dedicated virtual cluster and may represent a valuable alternative for large computational tools in bioinformatics. In the present manuscript, we optimize the computation of a large-scale comparative genomics resource-Roundup-using cloud computing, describe the proper operating principles required to achieve computational efficiency on the cloud, and detail important procedures for improving cost-effectiveness to ensure maximal computation at minimal costs. METHODS: Utilizing the comparative genomics tool, Roundup, as a case study, we computed orthologs among 902 fully sequenced genomes on Amazon's Elastic Compute Cloud. For managing the ortholog processes, we designed a strategy to deploy the web service, Elastic MapReduce, and maximize the use of the cloud while simultaneously minimizing costs. Specifically, we created a model to estimate cloud runtime based on the size and complexity of the genomes being compared that determines in advance the optimal order of the jobs to be submitted. RESULTS: We computed orthologous relationships for 245,323 genome-to-genome comparisons on Amazon's computing cloud, a computation that required just over 200 hours and cost $8,000 USD, at least 40% less than expected under a strategy in which genome comparisons were submitted to the cloud randomly with respect to runtime. Our cost savings projections were based on a model that not only demonstrates the optimal strategy for deploying RSD to the cloud, but also finds the optimal cluster size to minimize waste and maximize usage. Our cost-reduction model is readily adaptable for other comparative genomics tools and potentially of significant benefit to labs seeking to take advantage of the cloud as an alternative to local computing infrastructure.