Diabetologia

Modeling how a human SUR1 gene change affects insulin release in lab-grown pancreatic islet cells

Updated

Abstract

Immature SC-islets with the SUR1 R1420H variant secreted 3.4-fold more insulin than normal SC-islets under basal conditions.

  • Homozygous SUR1 R1420HH SC-islets exhibited hyperinsulinaemia during infancy, suggesting a loss of Kchannel function.
  • Heterozygous SUR1 R1420RH SC-islets showed modest hyperinsulinaemia but did not achieve statistical significance.
  • Mature 1420HH SC-islets failed to respond to glucose, while 1420RH SC-islets had reduced glucose responsiveness compared to normal.
  • Diazoxide reduced hyperinsulinaemia in both heterozygous and homozygous immature SC-islets.
  • Single-cell RNA sequencing revealed dysregulated genes in SUR1 1420RH SC-beta cells, potentially accounting for lower insulin secretion.

Simplified

Full Text

Full text is available at the source.

Funding

Competing interests

Acknowledgements: We thank the volunteers who provided blood cells for the generation of iPSCs and consented to this study. This work used the computational resources of the NIH HPC Biowulf cluster. Data availability: Further information about resources and reagents should be directed to the corresponding author. There are restrictions to the availability of human iPSCs lines used and generated in this study because these cell lines were generated from blood cells from Indigenous American individuals. The Indigenous community has not consented to the sharing of biospecimens with outside researchers and collaborators. All other reagents are available on reasonable request. Single-cell sequencing data reported in this study cannot be deposited in a public repository because data were generated from Indigenous American iPSCs. Inquiries about these data can be made to the corresponding author. See the database of Genotypes and Phenotypes (dbGaP; dbgap.ncbi.nlm.nih.gov/home; accession no.: phs002490.v1.p1) for details concerning data requests. All source codes can be found in the GitHub repository under https://github.com/Koushik-Cheranda/SC-islet-scRNAseq-analysis-R-codes . Funding: Open access funding provided by the National Institutes of Health. This research was supported by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) within the National Institutes of Health (NIH). The contributions of the NIH author(s) are considered Works of the United States Government. The findings and conclusions presented in this paper are those of the author(s) and do not necessarily reflect the views of the NIH or the US Department of Health and Human Services. The study sponsor/funder was not involved in the design of the study; the collection, analysis, and interpretation of data; writing the report; and did not impose any restrictions regarding the publication of the report. Authors’ relationships and activities: The authors declare that there are no relationships or activities that might bias, or be perceived to bias, their work. Contribution statement: AKN contributed to study design, performed stem cell culture, generation of isogenic cell lines, and differentiations, and data analysis and wrote the first version of the manuscript. KB assisted with the characterisation of stem cell lines, data acquisition, and edited the manuscript. KPC performed the bioinformatic analysis of single-cell data and in silico modelling, and assisted with data analysis and writing the manuscript. MT assisted with the characterisation of stem cell lines, generated single-cell libraries and edited the manuscript. JRS assisted with data acquisition and edited the manuscript. DA assisted with data acquisition and data analysis, and edited the manuscript. CB contributed to the study design and edited the manuscript. LJB contributed to the study design, and revised and edited the manuscript. All authors approved the final version of the manuscript. LJB is the guarantor of the work as a whole and accepts full responsibility for the work and/or the conduct of the study, had access to the data, and controlled the decision to publish.
PubMed

What Lands in Your Inbox Each Week:

  • 📚7 fresh studies
  • 📝plain-language summaries
  • ✅direct links to original studies
  • 🏅top journal indicators
  • 📅weekly delivery
  • 🧘‍♂️always free