Leveraging type 1 diabetes human genetic and genomic data in the T1D knowledge portal.
Ontology highlight
ABSTRACT:
SUBMITTER: Kudtarkar P
PROVIDER: S-EPMC10414605 | biostudies-literature | 2023 Aug
REPOSITORIES: biostudies-literature
Publications
Leveraging type 1 diabetes human genetic and genomic data in the T1D knowledge portal.
Kudtarkar Parul P Costanzo Maria C MC Sun Ying Y Jang Dongkeun D Koesterer Ryan R Mychaleckyj Josyf C JC Nayak Uma U Onengut-Gumuscu Suna S Rich Stephen S SS Flannick Jason A JA Gaulton Kyle J KJ Burtt Noël P NP
PLoS biology 20230810 8
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. ...[more]
Similar Datasets
Project description: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.
| S-EPMC10231654 | biostudies-literature
Project description:Type 1 diabetes (T1D) is a chronic multi-factorial disorder characterized by the immune-mediated destruction of insulin-producing pancreatic beta cells. Variations at a large number of genes influence susceptibility to spontaneous autoimmune T1D in non-obese diabetic (NOD) mice, one of the most frequently studied animal models for human disease. The genetic analysis of these mice allowed the identification of many insulin-dependent diabetes (Idd) loci and candidate genes, one of them being Cd101. CD101 is a heavily glycosylated transmembrane molecule which exhibits negative-costimulatory functions and promotes regulatory T (Treg) function. It is abundantly expressed on subsets of lymphoid and myeloid cells, particularly within the gastrointestinal tract. We have recently reported that the genotype-dependent expression of CD101 correlates with a decreased susceptibility to T1D in NOD.B6 Idd10 congenic mice compared to parental NOD controls. Here we show that the knockout of CD101 within the introgressed B6-derived Idd10 region increased T1D frequency to that of the NOD strain. This loss of protection from T1D was paralleled by decreased Gr1-expressing myeloid cells and FoxP3+ Tregs and an enhanced accumulation of CD4-positive over CD8-positive T lymphocytes in pancreatic tissues. As compared to CD101+/+ NOD.B6 Idd10 donors, adoptive T cell transfers from CD101-/- NOD.B6 Idd10 mice increased T1D frequency in lymphopenic NOD scid and NOD.B6 Idd10 scid recipients. Increased T1D frequency correlated with a more rapid expansion of the transferred CD101-/- T cells and a lower proportion of recipient Gr1-expressing myeloid cells in the pancreatic lymph nodes. Fewer of the Gr1+ cells in the recipients receiving CD101-/- T cells expressed CD101 and the cells had lower levels of IL-10 and TGF-β mRNA. Thus, our results connect the Cd101 haplotype-dependent protection from T1D to an anti-diabetogenic function of CD101-expressing Tregs and Gr1-positive myeloid cells and confirm the identity of Cd101 as Idd10.
| S-EPMC6568395 | biostudies-literature
Project description:BackgroundType 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed to deliver such biomarkers, likely due to the fragmented nature of information obtained through the single omics approach. We recently demonstrated the utility of parallel multi-omics for the identification of T1D biomarker signatures. Our studies also identified challenges.MethodsHere, we evaluated a novel computational approach of data imputation and amplification as one way to overcome challenges associated with the relatively small number of subjects in these studies.ResultsUsing proprietary algorithms, we amplified our quadra-omics (proteomics, metabolomics, lipidomics, and transcriptomics) dataset from nine subjects a thousand-fold and analyzed the data using Ingenuity Pathway Analysis (IPA) software to assess the change in its analytical capabilities and biomarker prediction power in the amplified datasets compared to the original. These studies showed the ability to identify an increased number of T1D-relevant pathways and biomarkers in such computationally amplified datasets, especially, at imputation ratios close to the "golden ratio" of 38.2%:61.8%. Specifically, the Canonical Pathway and Diseases and Functions modules identified higher numbers of inflammatory pathways and functions relevant to autoimmune T1D, including novel ones not identified in the original data. The Biomarker Prediction module also predicted in the amplified data several unique biomarker candidates with direct links to T1D pathogenesis.ConclusionsThese preliminary findings indicate that such large-scale data imputation and amplification approaches are useful in facilitating the discovery of candidate integrated biomarker signatures of T1D or other diseases by increasing the predictive range of existing data mining tools, especially when the size of the input data is inherently limited.
| S-EPMC9599756 | biostudies-literature
Project description:Insect decline has become a growing concern in recent years, with studies showing alarming declines in populations of several taxa. Our knowledge about genetic spatial patterns and evolutionary history of insects still exhibits significant gaps hindering our ability to effectively conserve and manage insect populations and species. Genetic data may provide valuable insights into the diversity and the evolutionary relationships of insects' species and populations. Public repositories, such as GenBank and BOLD, containing vast archives of genetic data with associated metadata, offer an irreplaceable resource for researchers contributing to our understanding of species diversity, population structure and evolutionary relationships. However, there are some issues in using these data, as they are often scattered and may lack accuracy due to inconsistent sampling protocols and incomplete information. In this paper we describe a curated georeferenced database of genetic data collected in GenBank and BOLD, for insects listed in the International Union for Conservation of Nature (IUCN) Italian Red Lists (dragonflies, bees, saproxylic beetles and butterflies). After querying these repositories, we performed quality control and data standardization steps. We created a dataset containing approximately 33 000 mitochondrial sequences and associated metadata about taxonomy, collection localities, geographic coordinates and IUCN Red List status for 1466 species across the four insect lists. We describe the current state of geographical metadata in queried repositories for species listed under different conservation status in the Italian Red Lists to quantify data gaps posing barriers to prioritization of conservation actions. Our curated dataset is available for data repurposing and analysis, enabling researchers to conduct comparative studies. We emphasize the importance of filling knowledge gaps in insect diversity and distribution and highlight the potential of this dataset for promoting other research fields like phylogeography, macrogenetics and conservation strategies. Our database can be downloaded through the Zenodo repository in SQL format. Database URL: https://zenodo.org/records/8375181.
| S-EPMC10878047 | biostudies-literature
Project description:In this Perspective, Jose Florez discusses how information from genetics and genomics may be able to contribute to prevention of type 2 diabetes and predicting individual responses to behavioral and other interventions.
| S-EPMC4961399 | biostudies-other
Project description:Introduction: Therapeutic application and study of type 1 diabetes disease could benefit from the use of functional β islet-like cells derived from human induced pluripotent stem cells (hiPSCs). Considerable efforts have been made to develop increasingly effective hiPSC differentiation protocols, although critical issues related to cost, the percentage of differentiated cells that are obtained, and reproducibility remain open. In addition, transplantation of hiPSC would require immunoprotection within encapsulation devices, to make the construct invisible to the host's immune system and consequently avoid the recipient's general pharmacologic immunosuppression. Methods: For this work, a microencapsulation system based on the use of "human elastin-like recombinamers" (ELRs) was tested to envelop hiPSC. Special attention was devoted to in vitro and in vivo characterization of the hiPSCs upon coating with ERLs. Results and Discussion: We observed that ELRs coating did not interfere with viability and function and other biological properties of differentiated hiPSCs, while in vivo, ELRs seemed to afford immunoprotection to the cell grafts in preliminary in vivo study. The construct ability to correct hyperglycemia in vivo is in actual progress.
| S-EPMC10166868 | biostudies-literature
Project description: Not available
| S-EPMC8997694 | biostudies-literature
Project description:BackgroundGenome-wide association studies (GWAS) have enabled the discovery of single nucleotide polymorphisms (SNPs) that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the identified variants lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. To address this problem, we developed a variant filtering workflow called 3DFAACTS-SNP to link genetic variants to target genes in a cell-specific manner. Here, we use 3DFAACTS-SNP to identify candidate SNPs and target genes associated with the loss of immune tolerance in regulatory T cells (Treg) in T1D.ResultsUsing 3DFAACTS-SNP, we identified from a list of 1228 previously fine-mapped variants, 36 SNPs with plausible Treg-specific mechanisms of action. The integration of cell type-specific chromosome conformation capture data in 3DFAACTS-SNP identified 266 regulatory regions and 47 candidate target genes that interact with these variant-containing regions in Treg cells. We further demonstrated the utility of the workflow by applying it to three other SNP autoimmune datasets, identifying 16 Treg-centric candidate variants and 60 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of all known common (> 10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 9376 candidate variants and 4968 candidate target genes, generating a list of potential sites for future T1D or other autoimmune disease research.ConclusionsWe demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function, and illustrate the power of using cell type-specific multi-omics datasets to determine disease mechanisms. Our workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.
| S-EPMC9244893 | biostudies-literature
Project description:UnlabelledExperimental models of human tissues and disease phenotypes frequently rely upon immortalized cell lines, which are easily accessible and simple to use due to their infinite capability of cell division. For decades, cell lines have been used to investigate cellular mechanisms of disease and the efficacy of drugs, most prominently for human cancers. However, the large body of knowledge with respect to human cell lines exists primarily in an unstructured fashion, that is, as free text in the scientific literature. Here we present CellLineMiner, a novel text mining-based web database that provides a comprehensive view of human cell line knowledge. The application offers a simple search in all indexed cell lines, accompanied by a rapid display of all identified literature associations. The CellLineMiner is intended to serve as a knowledge resource companion to the cellular model systems used in biomedical research.AvailabilityCellLineMiner is accessible at http://dev.pubgene.com/cellmine.
| S-EPMC3523228 | biostudies-literature
Project description:White adipose tissue plays a pivotal role in energy storage and release, and disruptions in these processes, marked by transcriptional disturbances, are linked to cardiometabolic diseases. Consequently, many efforts have focused on understanding adipose tissue function through transcriptional analyses. However, the resulting data are scattered and lack a unified approach for exploration. To address this gap, we present the Adipose Tissue Knowledge Portal (ATportal; www.adiposetissue.org) where we have systematically collected, generated, re-analyzed, and integrated transcriptomic and proteomic data from diverse clinical cohorts, multiple adipose-residing cell types, and perturbation studies of human adipocytes. Using the ATportal, we identify genes associated with lipid turnover in clinical samples and test their function in a mini-depletion screen. Our data reveal that the tyrosine-kinase receptor TYRO3 acts as a positive regulator of hormone-stimulated lipolysis. Overall, our open-access framework allows integrative analyses and serves as a powerful discovery tool to uncover new aspects of adipose biology.
2025-03-17 | PXD057443 | Pride