Kinetic and data-driven modeling of pancreatic ß-cell central carbon metabolism and insulin secretion.

Pancreatic ß-cells respond to increased extracellular glucose levels by initiating a metabolic shift. That change in metabolism is part of the process of glucose-stimulated insulin secretion and is of particular interest in the context of diabetes. However, we do not fully understand how the coordinated changes in metabolic pathways and metabolite products influence insulin secretion. In this work, we apply systems biology approaches to develop a detailed kinetic model of the intracellular central carbon metabolic pathways in pancreatic ß-cells upon stimulation with high levels of glucose. The model is calibrated to published metabolomics datasets for the INS1 823/13 cell line, accurately capturing the measured metabolite fold-changes. We first employed the calibrated mechanistic model to estimate the stimulated cell's fluxome. We then used the predicted network fluxes in a data-driven approach to build a partial least squares regression model. By developing the combined kinetic and data-driven modeling framework, we gain insights into the link between ß-cell metabolism and glucose-stimulated insulin secretion. The combined modeling framework was used to predict the effects of common anti-diabetic pharmacological interventions on metabolite levels, flux through the metabolic network, and insulin secretion. Our simulations reveal targets that can be modulated to enhance insulin secretion. The model is a promising tool to contextualize and extend the usefulness of metabolomics data and to predict dynamics and metabolite levels that are difficult to measure in vitro. In addition, the modeling framework can be applied to identify, explain, and assess novel and clinically-relevant interventions that may be particularly valuable in diabetes treatment.

Identification of a Proteomic Signature of Senescence in Primary Human Mammary Epithelial Cells.

Senescence is a permanent cell cycle arrest that occurs in response to cellular stress and promotes age-related disease. Because senescence differs greatly depending on cell type and senescence inducer, continued progress in the characterization of senescent cells is needed. Here, we analyzed primary human mammary epithelial cells (HMECs), a model system for aging and cancer, using mass spectrometry-based proteomics. By integrating data from replicative senescence, immortalization by telomerase reactivation, and quiescence, we identified a robust proteomic signature of HMEC senescence consisting of 34 upregulated and 10 downregulated proteins. This approach identified known senescence biomarkers including ß-galactosidase (GLB1) as well as novel senescence biomarkers including catechol O-methyltransferase (COMT), synaptic vesicle membrane protein VAT-1 homolog (VAT1), and plastin-1/3 (PLS1/PLS3). Gene ontology enrichment analysis demonstrated that senescent HMECs upregulated lysosomal proteins and downregulated RNA metabolic processes. In addition, a classification model based on our proteomic signature successfully discriminated proliferating and senescent HMECs at the transcriptional level. Finally, we found that the HMEC senescence signature was positively and negatively correlated with proteomic alterations in HMEC aging and breast cancer, respectively. Taken together, our results demonstrate the power of proteomics to identify cell type-specific signatures of senescence and advance the understanding of senescence in HMECs.

Bayesian metamodeling of complex biological systems across varying representations.

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 ß-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 ß-Cell Consortium.

AKT but not MYC promotes reactive oxygen species-mediated cell death in oxidative culture.

Oncogenes can create metabolic vulnerabilities in cancer cells. We tested how AKT (herein referring to AKT1) and MYC affect the ability of cells to shift between respiration and glycolysis. Using immortalized mammary epithelial cells, we discovered that constitutively active AKT, but not MYC, induced cell death in galactose culture, where cells rely on oxidative phosphorylation for energy generation. However, the negative effects of AKT were temporary, and AKT-expressing cells recommenced growth after ∼15 days in galactose. To identify the mechanisms regulating AKT-mediated cell death, we used metabolomics and found that AKT-expressing cells that were dying in galactose culture had upregulated glutathione metabolism. Proteomic profiling revealed that AKT-expressing cells dying in galactose also upregulated nonsense-mediated mRNA decay, a marker of sensitivity to oxidative stress. We therefore measured levels of reactive oxygen species (ROS) and discovered that galactose-induced ROS exclusively in cells expressing AKT. Furthermore, ROS were required for galactose-induced death of AKT-expressing cells. We then confirmed that galactose-induced ROS-mediated cell death in breast cancer cells with upregulated AKT signaling. These results demonstrate that AKT but not MYC restricts the flexibility of cancer cells to use oxidative phosphorylation.This article has an associated First Person interview with the first author of the paper.

Endogenous Cell Type-Specific Disrupted in Schizophrenia 1 Interactomes Reveal Protein Networks Associated With Neurodevelopmental Disorders.

BACKGROUND: Disrupted in schizophrenia 1 (DISC1) has been implicated in a number of psychiatric diseases along with neurodevelopmental phenotypes such as the proliferation and differentiation of neural progenitor cells. While there has been significant effort directed toward understanding the function of DISC1 through the determination of its protein-protein interactions within an in vitro setting, endogenous interactions involving DISC1 within a cell type-specific setting relevant to neural development remain unclear. METHODS: Using CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9) genome engineering technology, we inserted an endogenous 3X-FLAG tag at the C-terminus of the canonical DISC1 gene in human induced pluripotent stem cells (iPSCs). We further differentiated these cells and used affinity purification to determine protein-protein interactions involving DISC1 in iPSC-derived neural progenitor cells and astrocytes. RESULTS: We were able to determine 151 novel cell type-specific proteins present in DISC1 endogenous interactomes. The DISC1 interactomes can be clustered into several subcomplexes that suggest novel DISC1 cell-specific functions. In addition, the DISC1 interactome in iPSC-derived neural progenitor cells associates in a connected network containing proteins found to harbor de novo mutations in patients affected by schizophrenia and contains a subset of novel interactions that are known to harbor syndromic mutations in neurodevelopmental disorders. CONCLUSIONS: Endogenous DISC1 interactomes within iPSC-derived human neural progenitor cells and astrocytes are able to provide context to DISC1 function in a cell type-specific setting relevant to neural development and enables the integration of psychiatric disease risk factors within a set of defined molecular functions.

Computational Model of Chimeric Antigen Receptors Explains Site-Specific Phosphorylation Kinetics.

Chimeric antigen receptors (CARs) have recently been approved for the treatment of hematological malignancies, but our lack of understanding of the basic mechanisms that activate these proteins has made it difficult to optimize and control CAR-based therapies. In this study, we use phosphoproteomic mass spectrometry and mechanistic computational modeling to quantify the in vitro kinetics of individual tyrosine phosphorylation on a variety of CARs. We show that each of the 10 tyrosine sites on the CD28-CD3ζ CAR is phosphorylated by lymphocyte-specific protein-tyrosine kinase (LCK) with distinct kinetics. The addition of CD28 at the N-terminal of CD3ζ increases the overall rate of CD3ζ phosphorylation. Our computational model identifies that LCK phosphorylates CD3ζ through a mechanism of competitive inhibition. This model agrees with previously published data in the literature and predicts that phosphatases in this system interact with CD3ζ through a similar mechanism of competitive inhibition. This quantitative modeling framework can be used to better understand CAR signaling and T cell activation.

SH2-B beta expression in alveolar macrophages in BAL fluid of asthmatic guinea pigs and its role in NGF-TrkA-mediated asthma.

BACKGROUND AND OBJECTIVE: Nerve growth factor (NGF)/tyrosine kinase receptor A (TrkA) signalling may play an important role in the pathogenesis of asthma, and SH2-B beta, a TrkA-binding protein, modulates the NGF signalling pathway. In this study, SH2-B beta expression in alveolar macrophages (AM) in guinea pig BAL fluid and its role in asthma pathogenesis through the NGF-TrkA signalling pathway were investigated. METHODS: Guinea pigs were randomized into five groups: control, a model of asthma, anti-SH2-B beta antibody treatment, anti-NGF antibody treatment and anti-TrkA antibody treatment. The asthmatic model was established in guinea pigs by inhalation of ovalbumin. Specific anti-SH2-B beta, anti-NGF and anti-TrkA antibodies were administered and AM were isolated from BAL fluid to assess SH2-B beta expression using an immunofluorescence assay. SH2-B beta and TrkA protein expression were determined by western blotting, IL-1 beta and IL-4 levels in the BAL fluid supernatants were determined by ELISA, and pathological changes in the bronchi and lung tissues were examined by HE staining. RESULTS: Lymphocyte, eosinophil and total inflammatory cell numbers in BAL fluid were significantly higher in the asthma model group than in the other groups (P < 0.01). NGF expression in the asthma model group was significantly higher than that in the PBS control group (P < 0.01). SH2-B beta was expressed in AM of control animals and expression was significantly higher in the asthma model than in the other groups (P < 0.01). TrkA protein expression was significantly higher in the asthma model group than in the PBS group (P < 0.01), and treatment with anti-NGF antibody resulted in significant reduction of TrkA expression (P < 0.01). CONCLUSIONS: SH2-B beta is expressed in AM of normal guinea pigs, and SH2-B beta may participate in asthma pathogenesis through the NGF-TrkA signalling pathway.