Insulin Release: Synchronizing beta cells in the pancreas

The secretion of insulin from the pancreas relies on both gap junctions and subpopulations of beta cells with specific intrinsic properties.
  1. Bradford E Peercy  Is a corresponding author
  2. David J Hodson  Is a corresponding author
  1. Department of Mathematics and Statistics, University of Maryland Baltimore County (UMBC), United States
  2. Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, United Kingdom

The amount of glucose in the blood is controlled by the hormone insulin, which is released by the pancreas when glucose levels get too high. The hormone is released from beta cells that are organized into spheroid structures within the pancreas known as islets of Langerhans. Like muscle cells in the heart, beta cells are electrically coupled together by gap junctions, and this coupling enables the cells within the islet to coordinate or synchronize their behavior and release insulin in a pulsatile manner.

Gap junctions are thought to be critical for controlling the dynamics of the islets and, hence, insulin secretion. Indeed, mice lacking gap junctions are unable to release insulin in pulses (Head et al., 2012). Gap junction (or electrical) coupling between beta cells has also been shown to weaken with age as insulin secretion declines and individuals become more susceptible to type 2 diabetes (Westacott et al., 2017a).

Recent studies have shown that beta cells can be separated into subpopulations based on their genetic makeup, the proteins they make, and how they behave (Benninger and Hodson, 2018). Some of these subgroups have a greater influence over islet dynamics than others (Stožer et al., 2013; Johnston et al., 2016; Salem et al., 2019; Westacott et al., 2017b; Nasteska et al., 2021). When these cells are disrupted – either by optogenetics or gene overexpression – islet function and insulin secretion decline, reminiscent of what occurs during aging and type 2 diabetes.

These subpopulations of beta cells are not physically connected and instead rely on their intrinsic properties to influence islet dynamics (Johnston et al., 2016; Westacott et al., 2017b; Nasteska et al., 2021). However, the cells in these subpopulations are too few in number to influence electrical coupling by gap junctions (Peercy and Sherman, 2022). Additionally, gap junctions alone cannot explain the activity patterns of the subpopulations identified, or their influence over islet function. So how do these two mechanisms work together to control blood glucose levels? Now, in eLife, Richard Benninger and co-workers – including Jennifer Briggs as first author – report new findings that shine some light on the relationship between beta cell subpopulations and gap junctions (Briggs et al., 2023).

The researchers (who are based at the University of Colorado Anschutz Medical Campus and the University of Birmingham) found that the enzyme glucokinase – which senses changes in blood glucose levels – displayed elevated levels of activity in a subpopulation of beta cells. This resulted in heightened metabolism due to glucokinase breaking down more molecules of glucose to generate the high levels of ATP (usable energy) versus ADP (used energy) required for insulin release, reflecting previous findings (Johnston et al., 2016; Westacott et al., 2017b).

Notably, beta cells were more likely to synchronize their response to glucose if their metabolic activity was similar; moreover, changing these intrinsic properties led to a loss of the beta cell subpopulation. Reducing gap junction coupling also did not stop the beta cells within the islet from synchronizing their activity. It did, however, make them much weaker at transmitting electrical signals across the islet.

It has long been thought that gap junctions are the major driver of synchronized beta cell activity, and that their disruption during diabetes leads to impaired insulin secretion. However, the findings of Briggs et al. suggest that gap junctions are just one piece of the jigsaw, and that cells with similar intrinsic properties – such as metabolic actvity – also drive islet dynamics (Figure 1).

The role of gap junctions and beta cell subpopulations in insulin release.

Within the pancreas, insulin-secreting beta cells are arranged into islets, and are physically coupled together by gap junctions (dark gray rectangles/lines). In the current model (left), gap junctions (pink) spread electrical currents (mV) between beta cells within the islet, resulting in the population displaying similar oscillations of electrical activity (right hand graph, matching waves highlighted in blue) and synchronizing their insulin release. Within the islet are also subpopulations of beta cells with specific intrinsic properties (shaded in dark grey; lower panel), such as higher levels of metabolic activity or producing more usable energy (ATP) than used energy (ADP). These beta cell subpopulations also contribute to coordinated beta cell activity, but how exactly was largely unknown. In the updated model proposed by Briggs et al. (right), the beta subpopulations and gap junctions work together to control islet dynamics. The increased metabolism of the beta subpopulations makes it easier for gap junctions to spread electrical currents across the islet. Disrupting either of these mechanisms (represented by an X symbol) makes it harder for beta cells within the islet to fully synchronize their electrical activity (left hand graph), leading to a decline in insulin secretion. mV = membrane potential.

Image credit: Figure created with BioRender.com.

So which mechanism fails first during diabetes: gap junctions or intrinsic cellular properties? Small decreases in the number of gap junctions and their associated electrical signalling, which occurs during diabetes, would make it much harder for beta cells within a subpopulation to synchronize. On the other hand, small changes in intrinsic cellular properties might render gap junction synchronization much less effective. Complicating matters further, loss of gap junction coupling likely influences the intrinsic properties of beta cells and vice versa. Therefore, the disrupted islet dynamics and impaired insulin release observed in patients with diabetes is probably due in part to both mechanisms failing simultaneously.

The study by Briggs et al. shows that no single mechanism drives synchronized beta cell activity: rather, subpopulations and gap junctions come together to shape islet behaviour. Further computational modelling could help tease out – or even predict – how the critical relationship between beta cell subpopulations and gap junctions influences insulin release. Further experimental work is also warranted to understand how the interplay between beta cell subpopulations and gap junctions is altered during diabetes.

References

Article and author information

Author details

  1. Bradford E Peercy

    Bradford E Peercy is in the Department of Mathematics and Statistics, University of Maryland Baltimore County (UMBC), Baltimore, United States

    For correspondence
    bpeercy@umbc.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8597-2508
  2. David J Hodson

    David J Hodson is in the Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom

    For correspondence
    david.hodson@ocdem.ox.ac.uk
    Competing interests
    has filed patents related to diabetes therapy; receives licensing revenue from Celtarys Research for the provision of chemical probes
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8641-8568

Publication history

  1. Version of Record published:

Copyright

© 2024, Peercy and Hodson

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,330
    views
  • 145
    downloads
  • 2
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Bradford E Peercy
  2. David J Hodson
(2024)
Insulin Release: Synchronizing beta cells in the pancreas
eLife 13:e95103.
https://doi.org/10.7554/eLife.95103

Further reading

    1. Cell Biology
    2. Computational and Systems Biology
    Sarah De Beuckeleer, Tim Van De Looverbosch ... Winnok H De Vos
    Research Article

    Induced pluripotent stem cell (iPSC) technology is revolutionizing cell biology. However, the variability between individual iPSC lines and the lack of efficient technology to comprehensively characterize iPSC-derived cell types hinder its adoption in routine preclinical screening settings. To facilitate the validation of iPSC-derived cell culture composition, we have implemented an imaging assay based on cell painting and convolutional neural networks to recognize cell types in dense and mixed cultures with high fidelity. We have benchmarked our approach using pure and mixed cultures of neuroblastoma and astrocytoma cell lines and attained a classification accuracy above 96%. Through iterative data erosion, we found that inputs containing the nuclear region of interest and its close environment, allow achieving equally high classification accuracy as inputs containing the whole cell for semi-confluent cultures and preserved prediction accuracy even in very dense cultures. We then applied this regionally restricted cell profiling approach to evaluate the differentiation status of iPSC-derived neural cultures, by determining the ratio of postmitotic neurons and neural progenitors. We found that the cell-based prediction significantly outperformed an approach in which the population-level time in culture was used as a classification criterion (96% vs 86%, respectively). In mixed iPSC-derived neuronal cultures, microglia could be unequivocally discriminated from neurons, regardless of their reactivity state, and a tiered strategy allowed for further distinguishing activated from non-activated cell states, albeit with lower accuracy. Thus, morphological single-cell profiling provides a means to quantify cell composition in complex mixed neural cultures and holds promise for use in the quality control of iPSC-derived cell culture models.

    1. Computational and Systems Biology
    Franck Simon, Maria Colomba Comes ... Herve Isambert
    Tools and Resources

    Live-cell microscopy routinely provides massive amounts of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell–cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on-chip cellular ecosystem under therapeutically relevant conditions. In particular, we find that cancer-associated fibroblasts directly inhibit cancer cell apoptosis, independently from anticancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications.