Characterizing relationships between cell structures and functions requires mesoscale mapping of intact cells showing subcellular rearrangements following stimulation; however, current approaches are limited in this regard. Here, we report a unique application of soft x-ray tomography to generate three-dimensional reconstructions of whole pancreatic β cells at different time points following glucose-stimulated insulin secretion. Reconstructions following stimulation showed distinct insulin vesicle distribution patterns reflective of altered vesicle pool sizes as they travel through the secretory pathway. Our results show that glucose stimulation caused rapid changes in biochemical composition and/or density of insulin packing, increased mitochondrial volume, and closer proximity of insulin vesicles to mitochondria. Costimulation with exendin-4 (a glucagon-like peptide-1 receptor agonist) prolonged these effects and increased insulin packaging efficiency and vesicle maturation. This study provides unique perspectives on the coordinated structural reorganization and interactions of organelles that dictate cell responses.

BackgroundProteinaceous bioactive substances and pharmaceuticals are most conveniently administered orally. However, the facing problems are the side effects of proteolytic degradation and denaturation in the gastrointestinal tract. In recent years, lactic acid bacteria (LAB) have been verified to be a promising delivery vector for susceptible functional proteins and drugs. KiSS-1 peptide, a cancer suppressor, plays a critical role in inhibiting cancer metastasis and its activity has been confirmed by direct administration. However, whether this peptide can be functionally expressed in LAB and exert activity on cancer cells, thus providing a potential alternative administration manner in the future, has not been demonstrated.ResultsA recombinant Lactococcus lactis strain NZ9000-401-kiss1 harboring a plasmid containing the gene of the tumor metastasis-inhibiting peptide KiSS1 was constructed. After optimization of the nisin induction conditions, the recombinant strain efficiently secreted KiSS1 with a maximum detectable amount of 27.9 μg/ml in Dulbecco’s Modified Eagle medium. The 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide and would healing assays, respectively, indicated that the secreted KiSS1 peptide remarkably inhibited HT-29 cell proliferation and migration. Furthermore, the expressed KiSS1 was shown to induce HT-29 cell morphological changes, apoptosis and reduce the expression of matrix metalloproteinase 9 (MMP-9) at both mRNA and protein levels.ConclusionsA recombinant L. lactis NZ9000-401-kiss1 successfully expressing the human kiss1 was constructed. The secreted KiSS1 peptide inhibited human HT-29 cells’ proliferation and migration probably by invoking, or mediating the cell-apoptosis pathway and by down regulating MMP-9 expression, respectively. Our results suggest that L. lactis is an ideal cell factory for secretory expression of tumor metastasis-inhibiting peptide KiSS1, and the KiSS1-producing L. lactis strain may serve as a new tool for cancer therapy in the future.

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Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide and conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are 1) converted to a standardized statistical representation relying on probabilistic graphical models, 2) coupled by modeling their mutual relations with the physical world, and 3) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic β-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic β-Cell Consortium.

Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide-and-conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are (i) converted to a standardized statistical representation relying on Probabilistic Graphical Models, (ii) coupled by modeling their mutual relations with the physical world, and (iii) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic β-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic β-Cell Consortium.