Differential CpG methylation at <i>Nnat</i> in the early establishment of beta cell heterogeneity
Bibliographic record
Abstract
Abstract Aims/hypothesis Beta cells within the pancreatic islet represent a heterogenous population wherein individual sub-groups of cells make distinct contributions to the overall control of insulin secretion. These include a subpopulation of highly-connected ‘hub’ cells, important for the propagation of intercellular Ca 2+ waves. Functional subpopulations have also been demonstrated in human beta cells, with an altered subtype distribution apparent in type 2 diabetes. At present, the molecular mechanisms through which beta cell hierarchy is established are poorly understood. Changes at the level of the epigenome provide one such possibility which we explore here by focussing on the imprinted gene neuronatin ( Nnat ), which is required for normal insulin synthesis and secretion. Methods Single cell RNA-seq datasets were examined using Seurat 4.0 and ClusterProfiler running under R. Transgenic mice expressing eGFP under the control of the Nnat enhancer/promoter regions were generated for fluorescence-activated cell (FAC) sorting of beta cells and downstream analysis of CpG methylation by bisulphite and RNA sequencing, respectively. Animals deleted for the de novo methyltransferase, DNMT3A from the pancreatic progenitor stage were used to explore control of promoter methylation. Proteomics was performed using affinity purification mass spectrometry and Ca 2+ dynamics explored by rapid confocal imaging of Cal-520 and Cal-590. Insulin secretion was measured using Homogeneous Time Resolved Fluorescence Imaging. Results Nnat mRNA was differentially expressed in a discrete beta cell population in a developmental stage- and DNA methylation (DNMT3A)-dependent manner. Thus, pseudo-time analysis of embryonic data sets demonstrated the early establishment of Nnat -positive and negative subpopulations during embryogenesis. NNAT expression is also restricted to a subset of beta cells across the human islet that is maintained throughout adult life. NNAT + beta cells also displayed a discrete transcriptome at adult stages, representing a sub-population specialised for insulin production, reminiscent of recently-described “β HI ” cells and were diminished in db/db mice. ‘Hub’ cells were less abundant in the NNAT + population, consistent with epigenetic control of this functional specialization. Conclusions/interpretation These findings demonstrate that differential DNA methylation at Nnat represents a novel means through which beta cell heterogeneity is established during development. We therefore hypothesise that changes in methylation at this locus may thus contribute to a loss of beta cell hierarchy and connectivity, potentially contributing to defective insulin secretion in some forms of diabetes. Research in context What is already known about this subject? - Neuronatin ( Nnat / NNAT ) is an imprinted gene in humans and mice and is required for glucose-stimulated insulin secretion in vivo - Pancreatic beta cells are functionally heterogeneous with specific highly-connected subpopulations known to coordinate islet wide Ca 2+ dynamics - Functional subpopulations have been described in human beta cells and their distribution is altered in type 2 diabetes What is the key question? - Does NNAT mark a discrete subpopulation of functional beta cells and which epigenetic pathways coordinate its formation and maintenance? What are the new findings? - A subpopulation of NNAT + beta cells is established prior to the first week of postnatal life in mice via de novo DNA methylation at the Nnat promoter - NNAT + beta cells are transcriptionally highly differentiated and appear to be functionally specialised for insulin production, possibly corresponding to recently-described “β HI ” and “CD63 hi ” beta cells. NNAT is expressed in a subset of beta cells across the human islet, and its deficiency in human beta cells diminishes glucose-stimulated insulin secretion - NNAT + cells are likelier to belong to the population of ‘follower’, rather than ‘hub’ cells, consistent with a role in insulin production rather than glucose detection How might this impact on clinical practice in the foreseeable future? - Epigenome-modifying compounds may provide a way of enhancing beta cell function and the ensemble behaviour of the islet to stimulate insulin secretion
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".