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.

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.

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.

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 β-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.