A δ-cell subpopulation with a pro-β-cell identity contributes to efficient age-independent recovery in a zebrafish model of diabetes.
Claudio Andrés Carril PardoLaura MassozMarie A DupontDavid BergemannJordane BourdouxheArnaud LavergneEstefania Tarifeño-SaldiviaChristian Sm HelkerDidier Y R StainierBernard PeersMarianne M VozIsabelle ManfroidPublished in: eLife (2022)
Restoring damaged β-cells in diabetic patients by harnessing the plasticity of other pancreatic cells raises the questions of the efficiency of the process and of the functionality of the new Insulin -expressing cells. To overcome the weak regenerative capacity of mammals, we used regeneration-prone zebrafish to study β-cells arising following destruction. We show that most new in s ulin cells differ from the original β-cells as they coexpress Somatostatin and Insulin. These bihormonal cells are abundant, functional and able to normalize glycemia. Their formation in response to β-cell destruction is fast, efficient, and age-independent. Bihormonal cells are transcriptionally close to a subset of δ-cells that we identified in control islets and that are characterized by the expression of somatostatin 1.1 ( sst1.1 ) and by genes essential for glucose-induced Insulin secretion in β-cells such as pdx1 , s lc2a2 and gck . We observed in vivo the conversion of monohormonal sst1.1- expressing cells to sst1.1+ ins + bihormonal cells following β-cell destruction. Our findings support the conclusion that sst1.1 δ-cells possess a pro-β identity enabling them to contribute to the neogenesis of Insulin-producing cells during regeneration. This work unveils that abundant and functional bihormonal cells benefit to diabetes recovery in zebrafish.
Keyphrases
- induced apoptosis
- cell cycle arrest
- type diabetes
- stem cells
- endoplasmic reticulum stress
- signaling pathway
- oxidative stress
- cardiovascular disease
- blood pressure
- single cell
- mesenchymal stem cells
- adipose tissue
- transcription factor
- metabolic syndrome
- cell therapy
- cell proliferation
- endothelial cells
- deep learning
- blood glucose
- glycemic control
- artificial intelligence
- high resolution mass spectrometry