1
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Coate KC, Dai C, Singh A, Stanley J, Covington BA, Bradley A, Oladipupo F, Gong Y, Wisniewski S, Spears E, Poffenberger G, Bustabad A, Rodgers T, Dey N, Shultz LD, Greiner DL, Yan H, Powers AC, Chen W, Dean ED. Interruption of glucagon signaling augments islet non-alpha cell proliferation in SLC7A2- and mTOR-dependent manners. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.06.606926. [PMID: 39149351 PMCID: PMC11326219 DOI: 10.1101/2024.08.06.606926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Objective Dysregulated glucagon secretion and inadequate functional beta cell mass are hallmark features of diabetes. While glucagon receptor (GCGR) antagonism ameliorates hyperglycemia and elicits beta cell regeneration in pre-clinical models of diabetes, it also promotes alpha and delta cell hyperplasia. We sought to investigate the mechanism by which loss of glucagon action impacts pancreatic islet non-alpha cells, and the relevance of these observations in a human islet context. Methods We used zebrafish, rodents, and transplanted human islets comprising six different models of interrupted glucagon signaling to examine their impact on delta and beta cell proliferation and mass. We also used models with global deficiency of the cationic amino acid transporter, SLC7A2, and mTORC1 inhibition via rapamycin, to determine whether amino acid-dependent nutrient sensing was required for islet non-alpha cell growth. Results Inhibition of glucagon signaling stimulated delta cell proliferation in mouse and transplanted human islets, and in mouse islets. This was rapamycin-sensitive and required SLC7A2. Likewise, gcgr deficiency augmented beta cell proliferation via SLC7A2- and mTORC1-dependent mechanisms in zebrafish and promoted cell cycle engagement in rodent beta cells but was insufficient to drive a significant increase in beta cell mass in mice. Conclusion Our findings demonstrate that interruption of glucagon signaling augments islet non-alpha cell proliferation in zebrafish, rodents, and transplanted human islets in a manner requiring SLC7A2 and mTORC1 activation. An increase in delta cell mass may be leveraged for future beta cell regeneration therapies relying upon delta cell reprogramming.
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Affiliation(s)
- Katie C. Coate
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN
- Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN
| | - Chunhua Dai
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Ajay Singh
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Jade Stanley
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN
| | - Brittney A. Covington
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN
| | - Amber Bradley
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Favour Oladipupo
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Yulong Gong
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN
| | - Scott Wisniewski
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Erick Spears
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Greg Poffenberger
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Alexandria Bustabad
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Tyler Rodgers
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Nandita Dey
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | | | - Dale L. Greiner
- Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Chan Medical School, Worcester, MA
| | - Hai Yan
- REMD Biotherapeutics Inc., Camarillo, CA
| | - Alvin C. Powers
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN
- Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN
| | - Wenbiao Chen
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN
| | - E. Danielle Dean
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN
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2
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Mummey HM, Elison W, Korgaonkar K, Elgamal RM, Kudtarkar P, Griffin E, Benaglio P, Miller M, Jha A, Fox JEM, McCarthy MI, Preissl S, Gloyn AL, MacDonald PE, Gaulton KJ. Single cell multiome profiling of pancreatic islets reveals physiological changes in cell type-specific regulation associated with diabetes risk. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.03.606460. [PMID: 39149326 PMCID: PMC11326183 DOI: 10.1101/2024.08.03.606460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
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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Affiliation(s)
- Hannah M Mummey
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla CA
| | - Weston Elison
- Biomedical Sciences Program, University of California San Diego, La Jolla CA, USA
| | - Katha Korgaonkar
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Ruth M Elgamal
- Biomedical Sciences Program, University of California San Diego, La Jolla CA, USA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Emily Griffin
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Paola Benaglio
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Michael Miller
- Center for Epigenomics, University of California San Diego, La Jolla CA, USA
| | - Alokkumar Jha
- Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford CA, USA
| | - Jocelyn E Manning Fox
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Mark I McCarthy
- Wellcome Trust Center for Human Genetics, University of Oxford, Oxford, UK*
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego, La Jolla CA, USA
- Department of Genetics, Stanford School of Medicine, Stanford University, Stanford CA, USA
| | - Anna L Gloyn
- Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford CA, USA
- Department of Genetics, Stanford School of Medicine, Stanford University, Stanford CA, USA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA, USA
| | - Patrick E MacDonald
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
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3
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Wright JJ, Eskaros A, Windon A, Bottino R, Jenkins R, Bradley AM, Aramandla R, Philips S, Kang H, Saunders DC, Brissova M, Powers AC. Exocrine Pancreas in Type 1 and Type 2 Diabetes: Different Patterns of Fibrosis, Metaplasia, Angiopathy, and Adiposity. Diabetes 2024; 73:1140-1152. [PMID: 37881846 PMCID: PMC11189834 DOI: 10.2337/db23-0009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 07/18/2023] [Indexed: 10/27/2023]
Abstract
The endocrine and exocrine compartments of the pancreas are spatially related but functionally distinct. Multiple diseases affect both compartments, including type 1 diabetes (T1D), pancreatitis, cystic fibrosis, and pancreatic cancer. To better understand how the exocrine pancreas changes with age, obesity, and diabetes, we performed a systematic analysis of well-preserved tissue sections from the pancreatic head, body, and tail of organ donors with T1D (n = 20) or type 2 diabetes (T2D) (n = 25) and donors with no diabetes (ND; n = 74). Among ND donors, we found that the incidence of acinar-to-ductal metaplasia (ADM), angiopathy, and pancreatic adiposity increased with age, and ADM and adiposity incidence also increased with BMI. Compared with age- and sex-matched ND organs, T1D pancreata had greater rates of acinar atrophy and angiopathy, with fewer intralobular adipocytes. T2D pancreata had greater rates of ADM and angiopathy and a higher total number of T lymphocytes, but no difference in adipocyte number, compared with ND organs. Although total pancreatic fibrosis was increased in both T1D and T2D, the patterns were different, with periductal and perivascular fibrosis occurring more frequently in T1D pancreata and lobular and parenchymal fibrosis occurring more frequently in T2D. Thus, the exocrine pancreas undergoes distinct changes as individuals age or develop T1D or T2D. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Jordan J. Wright
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN
| | - Adel Eskaros
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Annika Windon
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN
| | - Rita Bottino
- Imagine Islet Center, Imagine Pharma, Pittsburgh, PA
- Institute of Cellular Therapeutics, Allegheny-Singer Research Institute, Allegheny Health Network, Pittsburgh, PA
| | - Regina Jenkins
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Amber M. Bradley
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Radhika Aramandla
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Sharon Philips
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Diane C. Saunders
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Human Pancreas Analysis Program, Nashville, TN; Philadelphia, PA; and Gainesville, FL
| | - Marcela Brissova
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Human Pancreas Analysis Program, Nashville, TN; Philadelphia, PA; and Gainesville, FL
| | - Alvin C. Powers
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN
- Human Pancreas Analysis Program, Nashville, TN; Philadelphia, PA; and Gainesville, FL
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN
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4
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Ewald JD, Lu Y, Ellis CE, Worton J, Kolic J, Sasaki S, Zhang D, dos Santos T, Spigelman AF, Bautista A, Dai XQ, Lyon JG, Smith NP, Wong JM, Rajesh V, Sun H, Sharp SA, Rogalski JC, Moravcova R, Cen HH, Manning Fox JE, Atlas E, Bruin JE, Mulvihill EE, Verchere CB, Foster LJ, Gloyn AL, Johnson JD, Pepper AR, Lynn FC, Xia J, MacDonald PE. HumanIslets: An integrated platform for human islet data access and analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.19.599613. [PMID: 38948734 PMCID: PMC11212983 DOI: 10.1101/2024.06.19.599613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Comprehensive molecular and cellular phenotyping of human islets can enable deep mechanistic insights for diabetes research. We established the Human Islet Data Analysis and Sharing (HI-DAS) consortium to advance goals in accessibility, usability, and integration of data from human islets isolated from donors with and without diabetes at the Alberta Diabetes Institute (ADI) IsletCore. Here we introduce HumanIslets.com, an open resource for the research community. This platform, which presently includes data on 547 human islet donors, allows users to access linked datasets describing molecular profiles, islet function and donor phenotypes, and to perform various statistical and functional analyses at the donor, islet and single-cell levels. As an example of the analytic capacity of this resource we show a dissociation between cell culture effects on transcript and protein expression, and an approach to correct for exocrine contamination found in hand-picked islets. Finally, we provide an example workflow and visualization that highlights links between type 2 diabetes status, SERCA3b Ca2+-ATPase levels at the transcript and protein level, insulin secretion and islet cell phenotypes. HumanIslets.com provides a growing and adaptable set of resources and tools to support the metabolism and diabetes research community.
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Affiliation(s)
- Jessica D. Ewald
- Institute of Parasitology, McGill University, Montreal, QC
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yao Lu
- Institute of Parasitology, McGill University, Montreal, QC
| | - Cara E. Ellis
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - Jessica Worton
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Surgery, University of Alberta, Edmonton, AB
| | - Jelena Kolic
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Shugo Sasaki
- Diabetes Research Group, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Surgery, School of Biomedical Engineering, University of British Columbia, Vancouver, BC
| | - Dahai Zhang
- Diabetes Research Group, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Surgery, School of Biomedical Engineering, University of British Columbia, Vancouver, BC
| | - Theodore dos Santos
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - Aliya F. Spigelman
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - Austin Bautista
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
| | - Xiao-Qing Dai
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - James G. Lyon
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
| | - Nancy P. Smith
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - Jordan M. Wong
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Surgery, University of Alberta, Edmonton, AB
| | - Varsha Rajesh
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA
| | - Han Sun
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA
| | - Seth A. Sharp
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA
| | - Jason C. Rogalski
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Renata Moravcova
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Haoning H Cen
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Jocelyn E. Manning Fox
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | | | - Ella Atlas
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON
| | - Jennifer E. Bruin
- Department of Biology & Institute of Biochemistry, Carleton University, Ottawa, ON
| | - Erin E. Mulvihill
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, ON
- University of Ottawa Heart Institute, Ottawa, ON
| | - C. Bruce Verchere
- Department of Surgery, BC Children’s Hospital Research Institute and University of British Columbia, Vancouver, BC
- Department of Pathology and Laboratory Medicine, BC Children’s Hospital Research Institute and University of British Columbia, Vancouver, BC
- Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC
| | - Leonard J. Foster
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Anna L. Gloyn
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA
| | - James D. Johnson
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Andrew R. Pepper
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Surgery, University of Alberta, Edmonton, AB
| | - Francis C. Lynn
- Diabetes Research Group, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Surgery, School of Biomedical Engineering, University of British Columbia, Vancouver, BC
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, QC
| | - Patrick E. MacDonald
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
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5
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Azzarello F, Carli F, De Lorenzi V, Tesi M, Marchetti P, Beltram F, Raimondi F, Cardarelli F. Machine-learning-guided recognition of α and β cells from label-free infrared micrographs of living human islets of Langerhans. Sci Rep 2024; 14:14235. [PMID: 38902357 PMCID: PMC11190282 DOI: 10.1038/s41598-024-65161-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 06/17/2024] [Indexed: 06/22/2024] Open
Abstract
Human islets of Langerhans are composed mostly of glucagon-secreting α cells and insulin-secreting β cells closely intermingled one another. Current methods for identifying α and β cells involve either fixing islets and using immunostaining or disaggregating islets and employing flow cytometry for classifying α and β cells based on their size and autofluorescence. Neither approach, however, allows investigating the dynamic behavior of α and β cells in a living and intact islet. To tackle this issue, we present a machine-learning-based strategy for identification α and β cells in label-free infrared micrographs of living human islets without immunostaining. Intrinsic autofluorescence is stimulated by infrared light and collected both in intensity and lifetime in the visible range, dominated by NAD(P)H and lipofuscin signals. Descriptive parameters are derived from micrographs for ~ 103 cells. These parameters are used as input for a boosted decision-tree model (XGBoost) pre-trained with immunofluorescence-derived cell-type information. The model displays an optimized-metrics performance of 0.86 (i.e. area under a ROC curve), with an associated precision of 0.94 for the recognition of β cells and 0.75 for α cells. This tool promises to enable longitudinal studies on the dynamic behavior of individual cell types at single-cell resolution within the intact tissue.
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Affiliation(s)
| | - Francesco Carli
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Pisa, Italy
| | | | - Marta Tesi
- Department of Clinical and Experimental Medicine, Islet Cell Laboratory, University of Pisa, Pisa, Italy
| | - Piero Marchetti
- Department of Clinical and Experimental Medicine, Islet Cell Laboratory, University of Pisa, Pisa, Italy
| | - Fabio Beltram
- NEST Laboratory, Scuola Normale Superiore, Pisa, Italy
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6
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Chao M, Wang M, Han H, Liu Y, Sun X, Tian T, Pang W, Cai R. Profiling of m 6A methylation in porcine intramuscular adipocytes and unravelling PHKG1 represses porcine intramuscular lipid deposition in an m 6A-dependent manner. Int J Biol Macromol 2024; 272:132728. [PMID: 38825295 DOI: 10.1016/j.ijbiomac.2024.132728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 05/10/2024] [Accepted: 05/24/2024] [Indexed: 06/04/2024]
Abstract
Intramuscular fat (IMF) content is mainly determined by intramuscular preadipocyte adipogenesis. Epigenetic modifications are known to have a regulatory effect on IMF. As N6-methyladenosine (m6A) is the most abundant epigenetic modification in eukaryotic RNAs. In the present study, we used m6A methylation and RNA sequencing (seq) to identify the m6A-modified RNAs associated with the adipogenic differentiation of intramuscular preadipocytes. Among them, the expression and m6A level of phosphorylase kinase subunit G1 (PHKG1) were found to be significantly changed during adipogenesis. Further studies revealed that knockdown of the methylase METTL3 decreased the m6A methylation of PHKG1 and led to a reduction in PHKG1. Moreover, knockdown of PHKG1 promoted adipogenic differentiation by upregulating the expression of adipogenic genes. In addition, we found that the IMF content in the longissimus thoracis (LT) of Bamei (BM) pigs was greater than that in Large White (LW) pigs, whereas the m6A and PHKG1 expression levels were lower in BM pigs. These findings indicate that the m6A level and expression of PHKG1 were significantly correlated with IMF content and meat quality. In conclusion, this study sheds light on the mechanism by which m6A modification regulates IMF deposition.
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Affiliation(s)
- Mingkun Chao
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition and Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Mingyu Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition and Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Haozhe Han
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition and Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yichen Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition and Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiaohui Sun
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition and Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Tingting Tian
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition and Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Weijun Pang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition and Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China.
| | - Rui Cai
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition and Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China.
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7
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Patil AR, Schug J, Liu C, Lahori D, Descamps HC, Naji A, Kaestner KH, Faryabi RB, Vahedi G. Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets. Cell Rep Med 2024; 5:101535. [PMID: 38677282 PMCID: PMC11148720 DOI: 10.1016/j.xcrm.2024.101535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 01/22/2024] [Accepted: 04/07/2024] [Indexed: 04/29/2024]
Abstract
Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of machine learning for early prediction of T1D using single-cell analysis of islets. Using gradient-boosting algorithms, we model changes in gene expression of single cells from pancreatic tissues in T1D and non-diabetic organ donors. We assess if mathematical modeling could predict the likelihood of T1D development in non-diabetic autoantibody-positive donors. While most autoantibody-positive donors are predicted to be non-diabetic, select donors with unique gene signatures are classified as T1D. Our strategy also reveals a shared gene signature in distinct T1D-associated models across cell types, suggesting a common effect of the disease on transcriptional outputs of these cells. Our study establishes a precedent for using machine learning in early detection of T1D.
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Affiliation(s)
- Abhijeet R Patil
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jonathan Schug
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Chengyang Liu
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Deeksha Lahori
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Hélène C Descamps
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ali Naji
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Klaus H Kaestner
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Robert B Faryabi
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Golnaz Vahedi
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
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8
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Leenders F, de Koning EJP, Carlotti F. Pancreatic β-Cell Identity Change through the Lens of Single-Cell Omics Research. Int J Mol Sci 2024; 25:4720. [PMID: 38731945 PMCID: PMC11083883 DOI: 10.3390/ijms25094720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/12/2024] [Accepted: 04/17/2024] [Indexed: 05/13/2024] Open
Abstract
The main hallmark in the development of both type 1 and type 2 diabetes is a decline in functional β-cell mass. This decline is predominantly attributed to β-cell death, although recent findings suggest that the loss of β-cell identity may also contribute to β-cell dysfunction. This phenomenon is characterized by a reduced expression of key markers associated with β-cell identity. This review delves into the insights gained from single-cell omics research specifically focused on β-cell identity. It highlights how single-cell omics based studies have uncovered an unexpected level of heterogeneity among β-cells and have facilitated the identification of distinct β-cell subpopulations through the discovery of cell surface markers, transcriptional regulators, the upregulation of stress-related genes, and alterations in chromatin activity. Furthermore, specific subsets of β-cells have been identified in diabetes, such as displaying an immature, dedifferentiated gene signature, expressing significantly lower insulin mRNA levels, and expressing increased β-cell precursor markers. Additionally, single-cell omics has increased insight into the detrimental effects of diabetes-associated conditions, including endoplasmic reticulum stress, oxidative stress, and inflammation, on β-cell identity. Lastly, this review outlines the factors that may influence the identification of β-cell subpopulations when designing and performing a single-cell omics experiment.
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Affiliation(s)
| | | | - Françoise Carlotti
- Department of Internal Medicine, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (F.L.); (E.J.P.d.K.)
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9
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Drawshy Z, Neiman D, Fridlich O, Peretz A, Magenheim J, Rozo AV, Doliba NM, Stoffers DA, Kaestner KH, Schatz DA, Wasserfall C, Campbell-Thompson M, Shapiro J, Kaplan T, Shemer R, Glaser B, Klochendler A, Dor Y. DNA Methylation-Based Assessment of Cell Composition in Human Pancreas and Islets. Diabetes 2024; 73:554-564. [PMID: 38266068 PMCID: PMC10958580 DOI: 10.2337/db23-0704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 01/21/2024] [Indexed: 01/26/2024]
Abstract
Assessment of pancreas cell type composition is crucial to the understanding of the genesis of diabetes. Current approaches use immunodetection of protein markers, for example, insulin as a marker of β-cells. A major limitation of these methods is that protein content varies in physiological and pathological conditions, complicating the extrapolation to actual cell number. Here, we demonstrate the use of cell type-specific DNA methylation markers for determining the fraction of specific cell types in human islet and pancreas specimens. We identified genomic loci that are uniquely demethylated in specific pancreatic cell types and applied targeted PCR to assess the methylation status of these loci in tissue samples, enabling inference of cell type composition. In islet preparations, normalization of insulin secretion to β-cell DNA revealed similar β-cell function in pre-type 1 diabetes (T1D), T1D, and type 2 diabetes (T2D), which was significantly lower than in donors without diabetes. In histological pancreas specimens from recent-onset T1D, this assay showed β-cell fraction within the normal range, suggesting a significant contribution of β-cell dysfunction. In T2D pancreata, we observed increased α-cell fraction and normal β-cell fraction. Methylation-based analysis provides an accurate molecular alternative to immune detection of cell types in the human pancreas, with utility in the interpretation of insulin secretion assays and the assessment of pancreas cell composition in health and disease. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Zeina Drawshy
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada (IMRIC), Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - Daniel Neiman
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada (IMRIC), Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - Ori Fridlich
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada (IMRIC), Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - Ayelet Peretz
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada (IMRIC), Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - Judith Magenheim
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada (IMRIC), Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - Andrea V. Rozo
- Human Pancreas Analysis Program, University of Pennsylvania, Philadelphia, PA
- Department of Genetics and Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Nicolai M. Doliba
- Human Pancreas Analysis Program, University of Pennsylvania, Philadelphia, PA
- Department of Genetics and Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Doris A. Stoffers
- Human Pancreas Analysis Program, University of Pennsylvania, Philadelphia, PA
- Department of Genetics and Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Klaus H. Kaestner
- Human Pancreas Analysis Program, University of Pennsylvania, Philadelphia, PA
- Department of Genetics and Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | | | - Clive Wasserfall
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL
| | - Martha Campbell-Thompson
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL
| | - James Shapiro
- Surgery Department, Faculty of Medicine and Dentistry, Li Ka Shing Centre for Research, Edmonton, Alberta, Canada
| | - Tommy Kaplan
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada (IMRIC), Hebrew University-Hadassah Medical School, Jerusalem, Israel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ruth Shemer
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada (IMRIC), Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - Benjamin Glaser
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada (IMRIC), Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - Agnes Klochendler
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada (IMRIC), Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - Yuval Dor
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada (IMRIC), Hebrew University-Hadassah Medical School, Jerusalem, Israel
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10
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Manji J, Pipella J, Brawerman G, Thompson PJ. Exploring Transcriptional Regulation of Beta Cell SASP by Brd4-Associated Proteins and Cell Cycle Control Protein p21. EPIGENOMES 2024; 8:10. [PMID: 38534794 DOI: 10.3390/epigenomes8010010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 02/14/2024] [Accepted: 02/28/2024] [Indexed: 03/28/2024] Open
Abstract
Type 1 diabetes (T1D) is a metabolic disease resulting from progressive autoimmune destruction of insulin-producing pancreatic beta cells. Although the majority of beta cells are lost in T1D, a small subset undergoes senescence, a stress response involving growth arrest, DNA damage response, and activation of a senescence-associated secretory phenotype (SASP). SASP in beta cells of the nonobese diabetic (NOD) mouse model of T1D and primary human islets is regulated at the level of transcription by bromodomain extra-terminal (BET) proteins, but the mechanisms remain unclear. To explore how SASP is transcriptionally regulated in beta cells, we used the NOD beta cell line NIT-1 to model beta cell SASP and identified binding partners of BET protein Brd4 and explored the role of the cyclin-dependent kinase inhibitor p21. Brd4 interacted with a variety of proteins in senescent NIT-1 cells including subunits of the Ino80 chromatin remodeling complex, which was expressed in beta cells during T1D progression in NOD mice and in human beta cells of control, autoantibody-positive, and T1D donors as determined from single-cell RNA-seq data. RNAi knockdown of p21 during senescence in NIT-1 cells did not significantly impact viability or SASP. Taken together, these results suggest that Brd4 interacts with several protein partners during senescence in NIT-1 cells, some of which may play roles in SASP gene activation and that p21 is dispensable for the SASP in this beta cell model.
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Affiliation(s)
- Jasmine Manji
- Diabetes Research Envisioned and Accomplished in Manitoba (DREAM) Theme, Children's Hospital Research Institute of Manitoba, Winnipeg, MB R3E 3P4, Canada
- Department of Physiology & Pathophysiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P4, Canada
| | - Jasmine Pipella
- Diabetes Research Envisioned and Accomplished in Manitoba (DREAM) Theme, Children's Hospital Research Institute of Manitoba, Winnipeg, MB R3E 3P4, Canada
- Department of Physiology & Pathophysiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P4, Canada
| | - Gabriel Brawerman
- Diabetes Research Envisioned and Accomplished in Manitoba (DREAM) Theme, Children's Hospital Research Institute of Manitoba, Winnipeg, MB R3E 3P4, Canada
- Department of Physiology & Pathophysiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P4, Canada
| | - Peter J Thompson
- Diabetes Research Envisioned and Accomplished in Manitoba (DREAM) Theme, Children's Hospital Research Institute of Manitoba, Winnipeg, MB R3E 3P4, Canada
- Department of Physiology & Pathophysiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P4, Canada
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11
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Coykendall VM, Qian MF, Tellez K, Bautista A, Bevacqua RJ, Gu X, Hang Y, Neukam M, Zhao W, Chang C, MacDonald PE, Kim SK. RFX6 Maintains Gene Expression and Function of Adult Human Islet α-Cells. Diabetes 2024; 73:448-460. [PMID: 38064570 PMCID: PMC10882151 DOI: 10.2337/db23-0483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 11/21/2023] [Indexed: 02/22/2024]
Abstract
Mutations in the gene encoding the transcription factor regulatory factor X-box binding 6 (RFX6) are associated with human diabetes. Within pancreatic islets, RFX6 expression is most abundant in islet α-cells, and α-cell RFX6 expression is altered in diabetes. However, the roles of RFX6 in regulating gene expression, glucagon output, and other crucial human adult α-cell functions are not yet understood. We developed a method for selective genetic targeting of human α-cells and assessed RFX6-dependent α-cell function. RFX6 suppression with RNA interference led to impaired α-cell exocytosis and dysregulated glucagon secretion in vitro and in vivo. By contrast, these phenotypes were not observed with RFX6 suppression across all islet cells. Transcriptomics in α-cells revealed RFX6-dependent expression of genes governing nutrient sensing, hormone processing, and secretion, with some of these exclusively expressed in human α-cells. Mapping of RFX6 DNA-binding sites in primary human islet cells identified a subset of direct RFX6 target genes. Together, these data unveil RFX6-dependent genetic targets and mechanisms crucial for regulating adult human α-cell function. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Vy M.N. Coykendall
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA
| | - Mollie F. Qian
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA
| | - Krissie Tellez
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA
| | - Austin Bautista
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Romina J. Bevacqua
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA
| | - Xueying Gu
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA
| | - Yan Hang
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA
| | - Martin Neukam
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA
| | - Weichen Zhao
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA
| | - Charles Chang
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA
| | - Patrick E. MacDonald
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Seung K. Kim
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA
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12
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Varney MJ, Benovic JL. The Role of G Protein-Coupled Receptors and Receptor Kinases in Pancreatic β-Cell Function and Diabetes. Pharmacol Rev 2024; 76:267-299. [PMID: 38351071 PMCID: PMC10877731 DOI: 10.1124/pharmrev.123.001015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 12/01/2023] [Accepted: 12/07/2023] [Indexed: 02/16/2024] Open
Abstract
Type 2 diabetes (T2D) mellitus has emerged as a major global health concern that has accelerated in recent years due to poor diet and lifestyle. Afflicted individuals have high blood glucose levels that stem from the inability of the pancreas to make enough insulin to meet demand. Although medication can help to maintain normal blood glucose levels in individuals with chronic disease, many of these medicines are outdated, have severe side effects, and often become less efficacious over time, necessitating the need for insulin therapy. G protein-coupled receptors (GPCRs) regulate many physiologic processes, including blood glucose levels. In pancreatic β cells, GPCRs regulate β-cell growth, apoptosis, and insulin secretion, which are all critical in maintaining sufficient β-cell mass and insulin output to ensure euglycemia. In recent years, new insights into the signaling of incretin receptors and other GPCRs have underscored the potential of these receptors as desirable targets in the treatment of diabetes. The signaling of these receptors is modulated by GPCR kinases (GRKs) that phosphorylate agonist-activated GPCRs, marking the receptor for arrestin binding and internalization. Interestingly, genome-wide association studies using diabetic patient cohorts link the GRKs and arrestins with T2D. Moreover, recent reports show that GRKs and arrestins expressed in the β cell serve a critical role in the regulation of β-cell function, including β-cell growth and insulin secretion in both GPCR-dependent and -independent pathways. In this review, we describe recent insights into GPCR signaling and the importance of GRK function in modulating β-cell physiology. SIGNIFICANCE STATEMENT: Pancreatic β cells contain a diverse array of G protein-coupled receptors (GPCRs) that have been shown to improve β-cell function and survival, yet only a handful have been successfully targeted in the treatment of diabetes. This review discusses recent advances in our understanding of β-cell GPCR pharmacology and regulation by GPCR kinases while also highlighting the necessity of investigating islet-enriched GPCRs that have largely been unexplored to unveil novel treatment strategies.
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Affiliation(s)
- Matthew J Varney
- Department of Biochemistry and Molecular Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Jeffrey L Benovic
- Department of Biochemistry and Molecular Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
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13
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Camunas-Soler J. Integrating single-cell transcriptomics with cellular phenotypes: cell morphology, Ca 2+ imaging and electrophysiology. Biophys Rev 2024; 16:89-107. [PMID: 38495444 PMCID: PMC10937895 DOI: 10.1007/s12551-023-01174-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/29/2023] [Indexed: 03/19/2024] Open
Abstract
I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput.
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Affiliation(s)
- Joan Camunas-Soler
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, University of Gothenburg, 405 30 Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden
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14
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Walker JT, Saunders DC, Rai V, Chen HH, Orchard P, Dai C, Pettway YD, Hopkirk AL, Reihsmann CV, Tao Y, Fan S, Shrestha S, Varshney A, Petty LE, Wright JJ, Ventresca C, Agarwala S, Aramandla R, Poffenberger G, Jenkins R, Mei S, Hart NJ, Phillips S, Kang H, Greiner DL, Shultz LD, Bottino R, Liu J, Below JE, Parker SCJ, Powers AC, Brissova M. Genetic risk converges on regulatory networks mediating early type 2 diabetes. Nature 2023; 624:621-629. [PMID: 38049589 PMCID: PMC11374460 DOI: 10.1038/s41586-023-06693-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 09/28/2023] [Indexed: 12/06/2023]
Abstract
Type 2 diabetes mellitus (T2D), a major cause of worldwide morbidity and mortality, is characterized by dysfunction of insulin-producing pancreatic islet β cells1,2. T2D genome-wide association studies (GWAS) have identified hundreds of signals in non-coding and β cell regulatory genomic regions, but deciphering their biological mechanisms remains challenging3-5. Here, to identify early disease-driving events, we performed traditional and multiplexed pancreatic tissue imaging, sorted-islet cell transcriptomics and islet functional analysis of early-stage T2D and control donors. By integrating diverse modalities, we show that early-stage T2D is characterized by β cell-intrinsic defects that can be proportioned into gene regulatory modules with enrichment in signals of genetic risk. After identifying the β cell hub gene and transcription factor RFX6 within one such module, we demonstrated multiple layers of genetic risk that converge on an RFX6-mediated network to reduce insulin secretion by β cells. RFX6 perturbation in primary human islet cells alters β cell chromatin architecture at regions enriched for T2D GWAS signals, and population-scale genetic analyses causally link genetically predicted reduced RFX6 expression with increased T2D risk. Understanding the molecular mechanisms of complex, systemic diseases necessitates integration of signals from multiple molecules, cells, organs and individuals, and thus we anticipate that this approach will be a useful template to identify and validate key regulatory networks and master hub genes for other diseases or traits using GWAS data.
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Affiliation(s)
- John T Walker
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Diane C Saunders
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Vivek Rai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Hung-Hsin Chen
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Peter Orchard
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chunhua Dai
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yasminye D Pettway
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Alexander L Hopkirk
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Conrad V Reihsmann
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yicheng Tao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Simin Fan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Shristi Shrestha
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Arushi Varshney
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Lauren E Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan J Wright
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Christa Ventresca
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Samir Agarwala
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Radhika Aramandla
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Greg Poffenberger
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Regina Jenkins
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shaojun Mei
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nathaniel J Hart
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sharon Phillips
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dale L Greiner
- Department of Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Medical School, Worcester, MA, USA
| | | | - Rita Bottino
- Imagine Pharma, Devon, PA, USA
- Institute of Cellular Therapeutics, Allegheny-Singer Research Institute, Allegheny Health Network, Pittsburgh, PA, USA
| | - Jie Liu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephen C J Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
| | - Alvin C Powers
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA.
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- VA Tennessee Valley Healthcare System, Nashville, TN, USA.
| | - Marcela Brissova
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
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15
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Elgamal RM, Kudtarkar P, Melton RL, Mummey HM, Benaglio P, Okino ML, Gaulton KJ. An Integrated Map of Cell Type-Specific Gene Expression in Pancreatic Islets. Diabetes 2023; 72:1719-1728. [PMID: 37582230 PMCID: PMC10588282 DOI: 10.2337/db23-0130] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 08/03/2023] [Indexed: 08/17/2023]
Abstract
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 β-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. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Ruth M. Elgamal
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California, San Diego, La Jolla, CA
| | - Rebecca L. Melton
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA
| | - Hannah M. Mummey
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA
| | - Paola Benaglio
- Department of Pediatrics, University of California, San Diego, La Jolla, CA
| | - Mei-Lin Okino
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA
| | - Kyle J. Gaulton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA
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16
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Lee MYY, Kaestner KH, Li M. Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data. Genome Biol 2023; 24:244. [PMID: 37875977 PMCID: PMC10594700 DOI: 10.1186/s13059-023-03073-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 09/25/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND Single-cell RNA-sequencing (scRNA-seq) measures gene expression in single cells, while single-nucleus ATAC-sequencing (snATAC-seq) quantifies chromatin accessibility in single nuclei. These two data types provide complementary information for deciphering cell types and states. However, when analyzed individually, they sometimes produce conflicting results regarding cell type/state assignment. The power is compromised since the two modalities reflect the same underlying biology. Recently, it has become possible to measure both gene expression and chromatin accessibility from the same nucleus. Such paired data enable the direct modeling of the relationships between the two modalities. Given the availability of the vast amount of single-modality data, it is desirable to integrate the paired and unpaired single-modality datasets to gain a comprehensive view of the cellular complexity. RESULTS We benchmark nine existing single-cell multi-omic data integration methods. Specifically, we evaluate to what extent the multiome data provide additional guidance for analyzing the existing single-modality data, and whether these methods uncover peak-gene associations from single-modality data. Our results indicate that multiome data are helpful for annotating single-modality data. However, we emphasize that the availability of an adequate number of nuclei in the multiome dataset is crucial for achieving accurate cell type annotation. Insufficient representation of nuclei may compromise the reliability of the annotations. Additionally, when generating a multiome dataset, the number of cells is more important than sequencing depth for cell type annotation. CONCLUSIONS Seurat v4 is the best currently available platform for integrating scRNA-seq, snATAC-seq, and multiome data even in the presence of complex batch effects.
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Affiliation(s)
- Michelle Y Y Lee
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Philadelphia, PA, 19104, USA
| | - Klaus H Kaestner
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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17
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Zhang J, Katada K, Mosleh E, Yuhas A, Peng G, Golson ML. The leptin receptor has no role in delta-cell control of beta-cell function in the mouse. Front Endocrinol (Lausanne) 2023; 14:1257671. [PMID: 37850099 PMCID: PMC10577419 DOI: 10.3389/fendo.2023.1257671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 09/05/2023] [Indexed: 10/19/2023] Open
Abstract
Introduction Leptin inhibits insulin secretion from isolated islets from multiple species, but the cell type that mediates this process remains elusive. Several mouse models have been used to explore this question. Ablation of the leptin receptor (Lepr) throughout the pancreatic epithelium results in altered glucose homeostasis and ex vivo insulin secretion and Ca2+ dynamics. However, Lepr removal from neither alpha nor beta cells mimics this result. Moreover, scRNAseq data has revealed an enrichment of LEPR in human islet delta cells. Methods We confirmed LEPR upregulation in human delta cells by performing RNAseq on fixed, sorted beta and delta cells. We then used a mouse model to test whether delta cells mediate the diminished glucose-stimulated insulin secretion in response to leptin. Results Ablation of Lepr within mouse delta cells did not change glucose homeostasis or insulin secretion, whether mice were fed a chow or high-fat diet. We further show, using a publicly available scRNAseq dataset, that islet cells expressing Lepr lie within endothelial cell clusters. Conclusions In mice, leptin does not influence beta-cell function through delta cells.
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Affiliation(s)
- Jia Zhang
- Department of Genetics, University of Pennsylvania, Philadephia, PA, United States
| | - Kay Katada
- School of Medicine, University of Pennsylvania, Philadephia, PA, United States
| | - Elham Mosleh
- Department of Genetics, University of Pennsylvania, Philadephia, PA, United States
- School of Medicine, University of Pennsylvania, Philadephia, PA, United States
| | - Andrew Yuhas
- Department of Genetics, University of Pennsylvania, Philadephia, PA, United States
- School of Medicine, University of Pennsylvania, Philadephia, PA, United States
| | - Guihong Peng
- Department of Medicine, Divison of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University, Baltimore, MD, United States
| | - Maria L. Golson
- Department of Genetics, University of Pennsylvania, Philadephia, PA, United States
- School of Medicine, University of Pennsylvania, Philadephia, PA, United States
- Department of Medicine, Divison of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University, Baltimore, MD, United States
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18
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Wang G, Chiou J, Zeng C, Miller M, Matta I, Han JY, Kadakia N, Okino ML, Beebe E, Mallick M, Camunas-Soler J, Dos Santos T, Dai XQ, Ellis C, Hang Y, Kim SK, MacDonald PE, Kandeel FR, Preissl S, Gaulton KJ, Sander M. Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes. Nat Genet 2023; 55:984-994. [PMID: 37231096 PMCID: PMC10550816 DOI: 10.1038/s41588-023-01397-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 04/12/2023] [Indexed: 05/27/2023]
Abstract
Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases.
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Affiliation(s)
- Gaowei Wang
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | - Joshua Chiou
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
- Biomedical Graduate Studies Program, University of California San Diego, La Jolla, CA, USA
| | - Chun Zeng
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | - Michael Miller
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Ileana Matta
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | - Jee Yun Han
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Nikita Kadakia
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | - Mei-Lin Okino
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | - Elisha Beebe
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | - Medhavi Mallick
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | | | - Theodore Dos Santos
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Xiao-Qing Dai
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Cara Ellis
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Yan Hang
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
- Departments of Medicine and of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Seung K Kim
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
- Departments of Medicine and of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Patrick E MacDonald
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Fouad R Kandeel
- Department of Clinical Diabetes, Endocrinology & Metabolism, City of Hope, Duarte, CA, USA
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA.
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
| | - Maike Sander
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
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19
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Patil AR, Schug J, Naji A, Kaestner KH, Faryabi RB, Vahedi G. Single-cell expression profiling of islets generated by the Human Pancreas Analysis Program. Nat Metab 2023; 5:713-715. [PMID: 37188822 PMCID: PMC10731597 DOI: 10.1038/s42255-023-00806-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Affiliation(s)
- Abhijeet R Patil
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jonathan Schug
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ali Naji
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Klaus H Kaestner
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Robert B Faryabi
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Golnaz Vahedi
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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20
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Elgamal RM, Kudtarkar P, Melton RL, Mummey HM, Benaglio P, Okino ML, Gaulton KJ. An integrated map of cell type-specific gene expression in pancreatic islets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.03.526994. [PMID: 36778506 PMCID: PMC9915747 DOI: 10.1101/2023.02.03.526994] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
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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Affiliation(s)
- Ruth M Elgamal
- Biomedical Sciences graduate program, University of California San Diego, La Jolla CA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California San Diego, La Jolla CA
| | - Rebecca L Melton
- Biomedical Sciences graduate program, University of California San Diego, La Jolla CA
| | - Hannah M Mummey
- Bioinformatics and Systems Biology graduate program, University of California San Diego, La Jolla CA
| | - Paola Benaglio
- Department of Pediatrics, University of California San Diego, La Jolla CA
| | - Mei-Lin Okino
- Biomedical Sciences graduate program, University of California San Diego, La Jolla CA
| | - Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla CA
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21
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Blériot C, Dalmas É, Ginhoux F, Venteclef N. Inflammatory and immune etiology of type 2 diabetes. Trends Immunol 2023; 44:101-109. [PMID: 36604203 DOI: 10.1016/j.it.2022.12.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/06/2022] [Accepted: 12/07/2022] [Indexed: 01/04/2023]
Abstract
Type 2 diabetes (T2D) represents a global threat affecting millions of patients worldwide. However, its causes remain incompletely dissected and we lack the tools to predict which individuals will develop T2D. Although there is a clear proven clinical association of T2D with metabolic disorders such as obesity and nonalcoholic fatty liver disease (NAFLD), the existence of a significant number of nondiabetic obese subjects suggests yet-uncovered features of such relationships. Here, we propose that a significant proportion of individuals may harbor an immune profile that renders them susceptible to developing T2D. We note the heterogeneity of circulating monocytes and tissue macrophages in organs that are key to metabolic disorders such as liver, white adipose tissue (WAT), and endocrine pancreas, as well as their contribution to T2D genesis.
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Affiliation(s)
- Camille Blériot
- Institut Necker-Enfants Malades (INEM), Université Paris Cité, INSERM UMR-S1151, CNRS UMR-S8253, Paris, France; Gustave Roussy Cancer Campus, Villejuif, France.
| | - Élise Dalmas
- Institut Necker-Enfants Malades (INEM), Université Paris Cité, INSERM UMR-S1151, CNRS UMR-S8253, Paris, France.
| | - Florent Ginhoux
- Gustave Roussy Cancer Campus, Villejuif, France; Singapore Immunology Network (SIgN), Agency for Science, Technology, and Research (A∗STAR), Singapore 138648, Singapore; Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore; Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Nicolas Venteclef
- Institut Necker-Enfants Malades (INEM), Université Paris Cité, INSERM UMR-S1151, CNRS UMR-S8253, Paris, France
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