Design Principles of Pancreatic Islets: Glucose-Dependent Coordination of Hormone Pulses

A primer on modelling pancreatic islets: from models of coupled β-cells to multicellular islet models

Pancreatic islets are mini-organs composed of hundreds or thousands of ɑ, β and δ-cells, which, respectively, secrete glucagon, insulin and somatostatin, key hormones for the regulation of blood glucose. In pancreatic islets, hormone secretion is tightly regulated by both internal and external mechanisms, including electrical communication and paracrine signaling between islet cells. Given its complexity, the experimental study of pancreatic islets has been complemented with computational modeling as a tool to gain a better understanding about how all the mechanisms involved at different levels of organization interact. In this review, we describe how multicellular models of pancreatic cells have evolved from the early models of electrically coupled β-cells to models in which experimentally derived architectures and both electrical and paracrine signals have been considered.

Mathematical modeling clarifies the paracrine roles of insulin and glucagon on the glucose-stimulated hormonal secretion of pancreatic alpha- and beta-cells

Introduction

Blood sugar homeostasis relies largely on the action of pancreatic islet hormones, particularly insulin and glucagon. In a prototypical fashion, glucagon is released upon hypoglycemia to elevate glucose by acting on the liver while elevated glucose induces the secretion of insulin which leads to sugar uptake by peripheral tissues. This simplified view of glucagon and insulin does not consider the paracrine roles of the two hormones modulating the response to glucose of α- and β-cells. In particular, glucose-stimulated glucagon secretion by isolated α-cells exhibits a Hill-function pattern, while experiments with intact pancreatic islets suggest a 'U'-shaped response.

Methods

To this end, a framework was developed based on first principles and coupled to experimental studies capturing the glucose-induced response of pancreatic α- and β-cells influenced by the two hormones. The model predicts both the transient and steady-state profiles of secreted insulin and glucagon, including the typical biphasic response of normal β-cells to hyperglycemia.

Results and discussion

The results underscore insulin activity as a differentiating factor of the glucagon secretion from whole islets vs. isolated α-cells, and highlight the importance of experimental conditions in interpreting the behavior of islet cells in vitro. The model also reproduces the hyperglucagonemia, which is experienced by diabetes patients, and it is linked to a failure of insulin to inhibit α-cell activity. The framework described here is amenable to the inclusion of additional islet cell types and extrapancreatic tissue cells simulating multi-organ systems. The study expands our understanding of the interplay of insulin and glucagon for pancreas function in normal and pathological conditions.

Determinants and dynamics of pancreatic islet architecture

The islets of Langerhans are highly organized structures that have species-specific, three-dimensional tissue architecture. Islet architecture is critical for proper hormone secretion in response to nutritional stimuli. Islet architecture is disrupted in all types of diabetes mellitus and in cadaveric islets for transplantation during isolation, culture, and perfusion, limiting patient outcomes. Moreover, recapitulating native islet architecture remains a key challenge for in vitro generation of islets from stem cells. In this review, we discuss work that has led to the current understanding of determinants of pancreatic islet architecture, and how this architecture is maintained or disrupted during tissue remodeling in response to normal and pathological metabolic changes. We further discuss both empirical and modeling data that highlight the importance of islet architecture for islet function.

IsletLab: an application to reconstruct and analyze islet architectures

The continuous interaction between experimental and theoretical work has proven to be extremely useful for the study of pancreatic cells and, recently, of pancreatic islets. This prolific interaction relies on the capability of implementing computational models and methods to derive quantitative data for the analysis and interpretation of experimental observations. In this addendum I introduce Isletlab, a multiplatform application developed to provide the research community with a user-friendly interface for the implementation of computational algorithms for the characterization and simulation of pancreatic islets.

Comparative analysis of reconstructed architectures from mice and human islets

Intra-islet communication via electrical, paracrine and autocrine signals, is highly dependent on the organization of cells within the islets and is key for an adequate response to changes in blood glucose and other stimuli. In spite of the fact that relevant structural differences between mouse and human islet architectures have been described, the functional implications of these differences remain only partially understood. In this work, aiming to contribute to a better understanding of the relationship between structural and functional properties of pancreatic islets, we reconstructed human and mice islets in order to perform a structural comparison based on both morphologic and network-derived metrics. According to our results, human islets constitute a more efficient network from a connectivity viewpoint, mainly due to the higher proportion of heterotypic contacts between islet cells in comparison to mice islets.

Reduced synchroneity of intra-islet Ca2+ oscillations in vivo in Robo-deficient β cells

The spatial architecture of the islets of Langerhans is hypothesized to facilitate synchronized insulin secretion among β cells, yet testing this in vivo in the intact pancreas is challenging. Robo βKO mice, in which the genes Robo1 and Robo2 are deleted selectively in β cells, provide a unique model of altered islet spatial architecture without loss of β cell differentiation or islet damage from diabetes. Combining Robo βKO mice with intravital microscopy, we show here that Robo βKO islets have reduced synchronized intra-islet Ca2+ oscillations among β cells in vivo. We provide evidence that this loss is not due to a β cell-intrinsic function of Robo, mis-expression or mis-localization of Cx36 gap junctions, or changes in islet vascularization or innervation, suggesting that the islet architecture itself is required for synchronized Ca2+ oscillations. These results have implications for understanding structure-function relationships in the islets during progression to diabetes as well as engineering islets from stem cells.

Machine Learning Algorithms, Applied to Intact Islets of Langerhans, Demonstrate Significantly Enhanced Insulin Staining at the Capillary Interface of Human Pancreatic β Cells

Pancreatic β cells secrete the hormone insulin into the bloodstream and are critical in the control of blood glucose concentrations. β cells are clustered in the micro-organs of the islets of Langerhans, which have a rich capillary network. Recent work has highlighted the intimate spatial connections between β cells and these capillaries, which lead to the targeting of insulin secretion to the region where the β cells contact the capillary basement membrane. In addition, β cells orientate with respect to the capillary contact point and many proteins are differentially distributed at the capillary interface compared with the rest of the cell. Here, we set out to develop an automated image analysis approach to identify individual β cells within intact islets and to determine if the distribution of insulin across the cells was polarised. Our results show that a U-Net machine learning algorithm correctly identified β cells and their orientation with respect to the capillaries. Using this information, we then quantified insulin distribution across the β cells to show enrichment at the capillary interface. We conclude that machine learning is a useful analytical tool to interrogate large image datasets and analyse sub-cellular organisation.

The Pancreatic β-Cell: The Perfect Redox System

Pancreatic β-cell insulin secretion, which responds to various secretagogues and hormonal regulations, is reviewed here, emphasizing the fundamental redox signaling by NADPH oxidase 4- (NOX4-) mediated H₂O₂ production for glucose-stimulated insulin secretion (GSIS). There is a logical summation that integrates both metabolic plus redox homeostasis because the ATP-sensitive K⁺ channel (K_ATP) can only be closed when both ATP and H₂O₂ are elevated. Otherwise ATP would block K_ATP, while H₂O₂ would activate any of the redox-sensitive nonspecific calcium channels (NSCCs), such as TRPM2. Notably, a 100%-closed K_ATP ensemble is insufficient to reach the -50 mV threshold plasma membrane depolarization required for the activation of voltage-dependent Ca²⁺ channels. Open synergic NSCCs or Cl^- channels have to act simultaneously to reach this threshold. The resulting intermittent cytosolic Ca²⁺-increases lead to the pulsatile exocytosis of insulin granule vesicles (IGVs). The incretin (e.g., GLP-1) amplification of GSIS stems from receptor signaling leading to activating the phosphorylation of TRPM channels and effects on other channels to intensify integral Ca²⁺-influx (fortified by endoplasmic reticulum Ca²⁺). ATP plus H₂O₂ are also required for branched-chain ketoacids (BCKAs); and partly for fatty acids (FAs) to secrete insulin, while BCKA or FA β-oxidation provide redox signaling from mitochondria, which proceeds by H₂O₂ diffusion or hypothetical SH relay via peroxiredoxin "redox kiss" to target proteins.

Mata A, Godínez-Fernández JR. Reconstructing human pancreatic islet architectures using computational optimization

We outline a general methodology based on computational optimization and experimental data to reconstruct human pancreatic islet architectures. By using the nuclei coordinates of islet cells obtained through DAPI staining, cell types identified by immunostaining, and cell size distributions estimated from capacitance measurements, reconstructed islets composed of non-overlapping spherical cells were obtained through an iterative optimization procedure. In all cases, the reconstructed architectures included >99% of the experimental identified cells, each of them having a radius within the experimentally reported ranges. Given the wide use of mathematical modeling for the study of pancreatic cells, and recently, of cell-cell interactions within the pancreatic islets, the methodology here proposed, also capable of identifying cell-to-cell contacts, is aimed to provide with a framework for modeling and analyzing experimentally-based pancreatic islet architectures.

Determination of secretory granule maturation times in pancreatic islet β-cells by serial block-face electron microscopy

It is shown how serial block-face electron microscopy (SBEM) of insulin-secreting β-cells in wild-type mouse pancreatic islets of Langerhans can be used to determine maturation times of secretory granules. Although SBEM captures the β-cell structure at a snapshot in time, the observed ultrastructure can be considered representative of a dynamic equilibrium state of the cells since the pancreatic islets are maintained in culture in approximate homeostasis. It was found that 7.2 ± 1.2% (±st. dev.) of the β-cell volume is composed of secretory granule dense-cores exhibiting angular shapes surrounded by wide (typically ≳100 nm) electron-lucent halos. These organelles are identified as mature granules that store insulin for regulated release through the plasma membrane, with a release time of 96 ± 12 h, as previously obtained from pulsed ³⁵S-radiolabeling of cysteine and methionine. Analysis of β-cell 3D volumes reveals a subpopulation of secretory organelles without electron-lucent halos, identified as immature secretory granules. Another subpopulation of secretory granules is found with thin (typically ≲30 nm) electron-lucent halos, which are attributed to immature granules that are transforming from proinsulin to insulin by action of prohormone convertases. From the volume ratio of proinsulin in the immature granules to insulin in the mature granules, we estimate that the newly formed immature granules remain in morphologically-defined immature states for an average time of 135 ± 14 min, and the immature transforming granules for an average time of 130 ± 17 min.

Tripartite cell networks for glucose homeostasis

Controlling the excess and shortage of energy is a fundamental task for living organisms. Diabetes is a representative metabolic disease caused by the malfunction of energy homeostasis. The islets of Langerhans in the pancreas release long-range messengers, hormones, into the blood to regulate the homeostasis of the primary energy fuel, glucose. The hormone and glucose levels in the blood show rhythmic oscillations with a characteristic period of 5-10 min, and the functional roles of the oscillations are not clear. Each islet has [Formula: see text] and [Formula: see text] cells that secrete glucagon and insulin, respectively. These two counter-regulatory hormones appear sufficient to increase and decrease glucose levels. However, pancreatic islets have a third cell type, [Formula: see text] cells, which secrete somatostatin. The three cell populations have a unique spatial organization in islets, and they interact to perturb their hormone secretions. The mini-organs of islets are scattered throughout the exocrine pancreas. Considering that the human pancreas contains approximately a million islets, the coordination of hormone secretion from the multiple sources of islets and cells within the islets should have a significant effect on human physiology. In this review, we introduce the hierarchical organization of tripartite cell networks, and recent biophysical modeling to systematically understand the oscillations and interactions of [Formula: see text], [Formula: see text], and [Formula: see text] cells. Furthermore, we discuss the functional roles and clinical implications of hormonal oscillations and their phase coordination for the diagnosis of type II diabetes.

The Difference δ-Cells Make in Glucose Control

The role of beta and α-cells to glucose control are established, but the physiological role of δ-cells is poorly understood. Delta-cells are ideally positioned within pancreatic islets to modulate insulin and glucagon secretion at their source. We review the evidence for a negative feedback loop between delta and β-cells that determines the blood glucose set point and suggest that local δ-cell-mediated feedback stabilizes glycemic control.

Design principles of the paradoxical feedback between pancreatic alpha and beta cells

Mammalian glucose homeostasis is controlled by the antagonistic hormones insulin and glucagon, secreted by pancreatic beta and alpha cells respectively. These two cell types are adjacently located in the islets of Langerhans and affect each others’ secretions in a paradoxical manner: while insulin inhibits glucagon secretion from alpha cells, glucagon seems to stimulate insulin secretion from beta cells. Here we ask what are the design principles of this negative feedback loop. We systematically simulate the dynamics of all possible islet inter-cellular connectivity patterns and analyze different performance criteria. We find that the observed circuit dampens overshoots of blood glucose levels after reversion of glucose drops. This feature is related to the temporal delay in the rise of insulin concentrations in peripheral tissues, compared to the immediate hormone action on the liver. In addition, we find that the circuit facilitates coordinate secretion of both hormones in response to protein meals. Our study highlights the advantages of a paradoxical paracrine feedback loop in maintaining metabolic homeostasis.

Local complexity predicts global synchronization of hierarchically networked oscillators

We study the global synchronization of hierarchically-organized Stuart-Landau oscillators, where each subsystem consists of three oscillators with activity-dependent couplings. We considered all possible coupling signs between the three oscillators, and found that they can generate different numbers of phase attractors depending on the network motif. Here, the subsystems are coupled through mean activities of total oscillators. Under weak inter-subsystem couplings, we demonstrate that the synchronization between subsystems is highly correlated with the number of attractors in uncoupled subsystems. Among the network motifs, perfect anti-symmetric ones are unique to generate both single and multiple attractors depending on the activities of oscillators. The flexible local complexity can make global synchronization controllable.

A Local Counter-Regulatory Motif Modulates the Global Phase of Hormonal Oscillations

Counter-regulatory elements maintain dynamic equilibrium ubiquitously in living systems. The most prominent example, which is critical to mammalian survival, is that of pancreatic α and β cells producing glucagon and insulin for glucose homeostasis. These cells are not found in a single gland but are dispersed in multiple micro-organs known as the islets of Langerhans. Within an islet, these two reciprocal cell types interact with each other and with an additional cell type: the δ cell. By testing all possible motifs governing the interactions of these three cell types, we found that a unique set of positive/negative intra-islet interactions between different islet cell types functions not only to reduce the superficially wasteful zero-sum action of glucagon and insulin but also to enhance/suppress the synchronization of hormone secretions between islets under high/normal glucose conditions. This anti-symmetric interaction motif confers effective controllability for network (de)synchronization.