دورية أكاديمية
Single-cell transcriptomic profiling of human pancreatic islets reveals genes responsive to glucose exposure over 24 h.
| العنوان: | Single-cell transcriptomic profiling of human pancreatic islets reveals genes responsive to glucose exposure over 24 h. |
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| المؤلفون: | Grenko CM; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.; Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN, USA., Taylor HJ; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. hjt52@cam.ac.uk.; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. hjt52@cam.ac.uk.; Heart and Lung Research Institute, University of Cambridge, Cambridge, UK. hjt52@cam.ac.uk., Bonnycastle LL; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA., Xue D; Department of Surgery, Weill Cornell Medicine, New York, NY, USA.; Center for Genomic Health, Weill Cornell Medicine, New York, NY, USA., Lee BN; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA., Weiss Z; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA., Yan T; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA., Swift AJ; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA., Mansell EC; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA., Lee A; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA., Robertson CC; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA., Narisu N; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA., Erdos MR; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA., Chen S; Department of Surgery, Weill Cornell Medicine, New York, NY, USA.; Center for Genomic Health, Weill Cornell Medicine, New York, NY, USA., Collins FS; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. francis.collins@nih.gov., Taylor DL; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. |
| المصدر: | Diabetologia [Diabetologia] 2024 Jul 05. Date of Electronic Publication: 2024 Jul 05. |
| Publication Model: | Ahead of Print |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Springer Verlag Country of Publication: Germany NLM ID: 0006777 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-0428 (Electronic) Linking ISSN: 0012186X NLM ISO Abbreviation: Diabetologia Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Berlin Springer Verlag |
| مستخلص: | Aims/hypothesis: Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycaemia, beta cell glucotoxicity and subsequently type 2 diabetes. In this study, we explored the effects of in vitro hyperglycaemic conditions on human pancreatic islet gene expression across 24 h in six pancreatic cell types: alpha; beta; gamma; delta; ductal; and acinar. We hypothesised that genes associated with hyperglycaemic conditions may be relevant to the onset and progression of diabetes. Methods: We exposed human pancreatic islets from two donors to low (2.8 mmol/l) and high (15.0 mmol/l) glucose concentrations over 24 h in vitro. To assess the transcriptome, we performed single-cell RNA-seq (scRNA-seq) at seven time points. We modelled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Additionally, we integrated genomic features and genetic summary statistics to nominate candidate effector genes. For three of these genes, we functionally characterised the effect on insulin production and secretion using CRISPR interference to knock down gene expression in EndoC-βH1 cells, followed by a glucose-stimulated insulin secretion assay. Results: In the discrete time models, we identified 1344 genes associated with time and 668 genes associated with glucose exposure across all cell types and time points. In the continuous time models, we identified 1311 genes associated with time, 345 genes associated with glucose exposure and 418 genes associated with interaction effects between time and glucose across all cell types. By integrating these expression profiles with summary statistics from genetic association studies, we identified 2449 candidate effector genes for type 2 diabetes, HbA Conclusions/interpretation: The findings of our study provide an in-depth characterisation of the 24 h transcriptomic response of human pancreatic islets to glucose exposure at a single-cell resolution. By integrating differentially expressed genes with genetic signals for type 2 diabetes and glucose-related traits, we provide insights into the molecular mechanisms underlying glucose homeostasis. Finally, we provide functional evidence to support the role of three candidate effector genes in insulin secretion and production. Data Availability: The scRNA-seq data from the 24 h glucose exposure experiment performed in this study are available in the database of Genotypes and Phenotypes (dbGap; https://www.ncbi.nlm.nih.gov/gap/ ) with accession no. phs001188.v3.p1. Study metadata and summary statistics for the differential expression, gene set enrichment and candidate effector gene prediction analyses are available in the Zenodo data repository ( https://zenodo.org/ ) under accession number 11123248. The code used in this study is publicly available at https://github.com/CollinsLabBioComp/publication-islet_glucose_timecourse . (© 2024. The Author(s).) |
| التعليقات: | Update of: bioRxiv. 2023 Jul 17:2023.06.06.543931. doi: 10.1101/2023.06.06.543931. (PMID: 37333221) |
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| معلومات مُعتمدة: | 9-22-PDFPM-06 American Diabetes Association; 1U01DK127777-01 United States DK NIDDK NIH HHS; R01 DK116075-01A1 United States DK NIDDK NIH HHS; R01 DK119667-01A1 United States DK NIDDK NIH HHS; R01 DK124463 United States DK NIDDK NIH HHS; ZIA-HG000024 United States NH NIH HHS |
| فهرسة مساهمة: | Keywords: GSIS; Genetics; Genomics; Islets; Single-cell; Transcriptomics; Type 2 diabetes |
| تواريخ الأحداث: | Date Created: 20240705 Latest Revision: 20240715 |
| رمز التحديث: | 20240715 |
| DOI: | 10.1007/s00125-024-06214-4 |
| PMID: | 38967666 |
| قاعدة البيانات: | MEDLINE |
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| DOI: | 10.1007/s00125-024-06214-4 |