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.
- Department of Mathematics and Statistics, University of Maryland Baltimore County (UMBC), United States
- 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).
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
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Beta cell hubs dictate pancreatic islet responses to glucoseCell Metabolism 24:389–401.https://doi.org/10.1016/j.cmet.2016.06.020
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Do oscillations in pancreatic islets require pacemaker cells?Journal of Biosciences 47:14.https://doi.org/10.1007/s12038-021-00251-6
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Functional connectivity in islets of Langerhans from mouse pancreas tissue slicesPLOS Computational Biology 9:e1002923.https://doi.org/10.1371/journal.pcbi.1002923
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- Version of Record published: January 25, 2024 (version 1)
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© 2024, Peercy and Hodson
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Insulin Release: Synchronizing beta cells in the pancreas
eLife 13:e95103.
https://doi.org/10.7554/eLife.95103
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Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model and provides a 2.24 Å average RMSDCα between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody–antigen interactions. This structural prediction tool can be used to optimize antibody–antigen binding and engineer therapeutic antibodies with biophysical properties for specialized drug administration route.
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Background:
Preterm birth is the leading cause of neonatal morbidity and mortality worldwide. Most cases of preterm birth occur spontaneously and result from preterm labor with intact (spontaneous preterm labor [sPTL]) or ruptured (preterm prelabor rupture of membranes [PPROM]) membranes. The prediction of spontaneous preterm birth (sPTB) remains underpowered due to its syndromic nature and the dearth of independent analyses of the vaginal host immune response. Thus, we conducted the largest longitudinal investigation targeting vaginal immune mediators, referred to herein as the immunoproteome, in a population at high risk for sPTB.
Methods:
Vaginal swabs were collected across gestation from pregnant women who ultimately underwent term birth, sPTL, or PPROM. Cytokines, chemokines, growth factors, and antimicrobial peptides in the samples were quantified via specific and sensitive immunoassays. Predictive models were constructed from immune mediator concentrations.
Results:
Throughout uncomplicated gestation, the vaginal immunoproteome harbors a cytokine network with a homeostatic profile. Yet, the vaginal immunoproteome is skewed toward a pro-inflammatory state in pregnant women who ultimately experience sPTL and PPROM. Such an inflammatory profile includes increased monocyte chemoattractants, cytokines indicative of macrophage and T-cell activation, and reduced antimicrobial proteins/peptides. The vaginal immunoproteome has improved predictive value over maternal characteristics alone for identifying women at risk for early (<34 weeks) sPTB.
Conclusions:
The vaginal immunoproteome undergoes homeostatic changes throughout gestation and deviations from this shift are associated with sPTB. Furthermore, the vaginal immunoproteome can be leveraged as a potential biomarker for early sPTB, a subset of sPTB associated with extremely adverse neonatal outcomes.
Funding:
This research was conducted by the Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS) under contract HHSN275201300006C. ALT, KRT, and NGL were supported by the Wayne State University Perinatal Initiative in Maternal, Perinatal and Child Health.
