Implicating type 2 diabetes effector genes in relevant metabolic cellular models using promoter-focused Capture-C
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| Titel: | Implicating type 2 diabetes effector genes in relevant metabolic cellular models using promoter-focused Capture-C |
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| Autoren: | Wachowski, Nicholas A., Pippin, James A., Boehm, Keith, Lu, Sumei, Leonard, Michelle E., Manduchi, Elisabetta, Parlin, Ursula W., Wabitsch, Martin, Chesi, Alessandra, Wells, Andrew D., Grant, Struan F. A., Pahl, Matthew C. |
| Quelle: | Diabetologia |
| Verlagsinformationen: | Springer Science and Business Media LLC, 2024. |
| Publikationsjahr: | 2024 |
| Schlagwörter: | Type 2 diabetes, Humans [MeSH], Cell Line [MeSH], Hep G2 Cells [MeSH], SMCO4, Polymorphism, Single Nucleotide/genetics [MeSH], Insulin secretion, Genome-Wide Association Study [MeSH], Promoter Regions, Genetic/genetics [MeSH], Chromatin conformation, Article, Diabetes Mellitus, Type 2/genetics [MeSH], Epigenetics, Diabetes Mellitus, Type 2/metabolism [MeSH], Variant to gene mapping, Insulin-Secreting Cells/metabolism [MeSH], Diabetes Mellitus, Type 2, Insulin-Secreting Cells, Humans, Hep G2 Cells, Promoter Regions, Genetic, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Cell Line |
| Beschreibung: | Aims/hypothesis Genome-wide association studies (GWAS) have identified hundreds of type 2 diabetes loci, with the vast majority of signals located in non-coding regions; as a consequence, it remains largely unclear which ‘effector’ genes these variants influence. Determining these effector genes has been hampered by the relatively challenging cellular settings in which they are hypothesised to confer their effects. Methods To implicate such effector genes, we elected to generate and integrate high-resolution promoter-focused Capture-C, assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA-seq datasets to characterise chromatin and expression profiles in multiple cell lines relevant to type 2 diabetes for subsequent functional follow-up analyses: EndoC-BH1 (pancreatic beta cell), HepG2 (hepatocyte) and Simpson–Golabi–Behmel syndrome (SGBS; adipocyte). Results The subsequent variant-to-gene analysis implicated 810 candidate effector genes at 370 type 2 diabetes risk loci. Using partitioned linkage disequilibrium score regression, we observed enrichment for type 2 diabetes and fasting glucose GWAS loci in promoter-connected putative cis-regulatory elements in EndoC-BH1 cells as well as fasting insulin GWAS loci in SGBS cells. Moreover, as a proof of principle, when we knocked down expression of the SMCO4 gene in EndoC-BH1 cells, we observed a statistically significant increase in insulin secretion. Conclusions/interpretation These results provide a resource for comparing tissue-specific data in tractable cellular models as opposed to relatively challenging primary cell settings. Data availability Raw and processed next-generation sequencing data for EndoC-BH1, HepG2, SGBS_undiff and SGBS_diff cells are deposited in GEO under the Superseries accession GSE262484. Promoter-focused Capture-C data are deposited under accession GSE262496. Hi-C data are deposited under accession GSE262481. Bulk ATAC-seq data are deposited under accession GSE262479. Bulk RNA-seq data are deposited under accession GSE262480. Graphical Abstract |
| Publikationsart: | Article Other literature type |
| Sprache: | English |
| ISSN: | 1432-0428 0012-186X |
| DOI: | 10.1007/s00125-024-06261-x |
| Zugangs-URL: | https://pubmed.ncbi.nlm.nih.gov/39240351 https://repository.publisso.de/resource/frl:6506935 |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi.dedup.....b2946a01e213ab1b13348758fa1e0bda |
| Datenbank: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | Aims/hypothesis Genome-wide association studies (GWAS) have identified hundreds of type 2 diabetes loci, with the vast majority of signals located in non-coding regions; as a consequence, it remains largely unclear which ‘effector’ genes these variants influence. Determining these effector genes has been hampered by the relatively challenging cellular settings in which they are hypothesised to confer their effects. Methods To implicate such effector genes, we elected to generate and integrate high-resolution promoter-focused Capture-C, assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA-seq datasets to characterise chromatin and expression profiles in multiple cell lines relevant to type 2 diabetes for subsequent functional follow-up analyses: EndoC-BH1 (pancreatic beta cell), HepG2 (hepatocyte) and Simpson–Golabi–Behmel syndrome (SGBS; adipocyte). Results The subsequent variant-to-gene analysis implicated 810 candidate effector genes at 370 type 2 diabetes risk loci. Using partitioned linkage disequilibrium score regression, we observed enrichment for type 2 diabetes and fasting glucose GWAS loci in promoter-connected putative cis-regulatory elements in EndoC-BH1 cells as well as fasting insulin GWAS loci in SGBS cells. Moreover, as a proof of principle, when we knocked down expression of the SMCO4 gene in EndoC-BH1 cells, we observed a statistically significant increase in insulin secretion. Conclusions/interpretation These results provide a resource for comparing tissue-specific data in tractable cellular models as opposed to relatively challenging primary cell settings. Data availability Raw and processed next-generation sequencing data for EndoC-BH1, HepG2, SGBS_undiff and SGBS_diff cells are deposited in GEO under the Superseries accession GSE262484. Promoter-focused Capture-C data are deposited under accession GSE262496. Hi-C data are deposited under accession GSE262481. Bulk ATAC-seq data are deposited under accession GSE262479. Bulk RNA-seq data are deposited under accession GSE262480. Graphical Abstract |
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| ISSN: | 14320428 0012186X |
| DOI: | 10.1007/s00125-024-06261-x |