Dynamic Regulation of JAK-STAT Signaling Through the Prolactin Receptor Predicted by Computational Modeling

Mortlock, Ryland D.; Georgia, Senta; Finley, Stacey D.

doi:10.1007/s12195-020-00647-8

Cited by 13 publications

(18 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Functional tests, such as intraperitoneal glucose tolerance test and insulin tolerance test, are needed to evaluate whether this increase in beta cell mass translates into differences in glucose sensitivity. Our group has used this work to develop a mechanical computational model to describe prolactin-mediated JAK-STAT signaling in pancreatic beta cells and reveal possible strategies to modulate STAT5 signaling [ 24 ]. Using such a model trained with experimental data could lead to the development of novel therapeutic strategies to expand beta cell mass.…”

Section: Discussionmentioning

confidence: 99%

Exogenous Lactogenic Signaling Stimulates Beta Cell Replication In Vivo and In Vitro

et al. 2022

Self Cite

View full text Add to dashboard Cite

As patients recently diagnosed with T1D and patients with T2D have residual beta cell mass, there is considerable effort in beta cell biology to understand the mechanisms that drive beta cell regeneration as a potential cellular therapy for expanding patients’ residual beta cell population. Both mouse and human studies have established that beta cell mass expansion occurs rapidly during pregnancy. To investigate the mechanisms of beta cell mass expansion during pregnancy, we developed a novel in vivo and in vitro models of pseudopregnancy. Our models demonstrate that pseudopregnancy promotes beta cell mass expansion in parous mice, and this expansion is driven by beta cell proliferation rather than hypertrophy. Importantly, estrogen, progesterone, and placental lactogen induce STAT5A signaling in the pseudopregnancy model, demonstrating that this model successfully recapitulates pregnancy-induced beta cell replication. We then created an in vitro model of pseudopregnancy and found that the combination of estrogen and placental lactogen induced beta cell replication in human islets and rat insulinoma cells. Therefore, beta cells both in vitro and in vivo increase proliferation when subjected to the pseudopregnancy cocktail compared to groups treated with estradiol or placental lactogen alone. The pseudopregnancy models described here may help inform novel methods of inducing beta cell replication in patients with diabetes.

show abstract

Section: Discussionmentioning

confidence: 99%

Exogenous Lactogenic Signaling Stimulates Beta Cell Replication In Vivo and In Vitro

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…This approach to uncertainty propagation is known as sampling-based uncertainty propagation [19] and is feasible because the simulation of dynamical systems biology models is computationally efficient. We refer the reader to [40, 84] for examples of sampling-based uncertainty propagation in systems biology.…”

Section: Methodsmentioning

confidence: 99%

Bayesian Parameter Estimation for Dynamical Models in Systems Biology

Linden

Krämer

Rangamani

2022

Preprint

View full text Add to dashboard Cite

Dynamical systems modeling, particularly via systems of ordinary differential equations, has been used to effectively capture the temporal behavior of different biochemical components in signal transduction networks. Despite the recent advances in experimental measurements, including sensor development and '-omics' studies that have helped populate protein-protein interaction networks in great detail, systems biology modeling lacks systematic methods to estimate kinetic parameters and quantify associated uncertainties. This is because of multiple reasons, including sparse and noisy experimental measurements, lack of detailed molecular mechanisms underlying the reactions, and missing biochemical interactions. Additionally, the inherent nonlinearities with respect to the states and parameters associated with the system of differential equations further compound the challenges of parameter estimation. In this study, we propose a comprehensive framework for Bayesian parameter estimation and complete quantification of the effects of uncertainties in the data and models. We apply these methods to a series of signaling models of increasing mathematical complexity. Systematic analysis of these dynamical systems showed that parameter estimation depends on data sparsity, noise level, and model structure, including the existence of multiple steady states. These results highlight how focused uncertainty quantification can enrich systems biology modeling and enable additional quantitative analyses for parameter estimation.

show abstract

“…Modeling framework. This study utilized an ordinary differential equation (ODE) model of the JAK2-STAT5 signaling pathway in pancreatic beta cells that we previously developed by Mortlock et al [13]. (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…Mathematical modeling has been applied to study different aspects of pancreatic beta cell signaling [11] and metabolism [12], providing insight into mechanisms underlying cellular behavior. We recently developed a novel computational model to represent the JAK-STAT signaling cascade [13]. The model was fitted to match experimental data of pancreatic beta cells from the INS-1 cell line treated with PRL.…”

Section: Introductionmentioning

confidence: 99%

Phosphatases are predicted to govern prolactin-mediated JAK-STAT signaling in pancreatic beta cells

Simoni

Huber

Georgia

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Patients with diabetes are unable to produce a sufficient amount of insulin to properly regulate their blood-glucose levels. One potential method of treating diabetes is to increase the number of insulin-secreting beta cells in the pancreas to enhance insulin secretion. It is known that during pregnancy, pancreatic beta cells proliferate in response to the pregnancy hormone, prolactin. Leveraging this proliferative response to prolactin may be a strategy to restore endogenous insulin production for patients with diabetes. To investigate this potential treatment, we previously developed a computational model to represent the prolactin-mediated JAK-STAT signaling pathway in pancreatic beta cells. However, this model does not account for variability in protein expression that naturally occurs between cells. Here, we applied the model to understand how heterogeneity affects the dynamics of JAK-STAT signaling. We simulated a sample of 10,000 heterogeneous cells with varying initial protein concentrations responding to prolactin stimulation. We used partial least squares regression to analyze the significance and role of each of the varied protein concentrations in producing the response of the cell. Our regression models predict that the concentrations of the cytosolic and nuclear phosphatases strongly influence the response of the cell. The model also predicts that increasing prolactin receptor strengthens negative feedback mediated by the inhibitor SOCS. These findings reveal biological targets that can potentially be used to modulate the proliferation of pancreatic beta cells to enhance insulin secretion and beta cell regeneration in the context of diabetes.

show abstract

Dynamic Regulation of JAK-STAT Signaling Through the Prolactin Receptor Predicted by Computational Modeling

Cited by 13 publications

References 54 publications

Exogenous Lactogenic Signaling Stimulates Beta Cell Replication In Vivo and In Vitro

Exogenous Lactogenic Signaling Stimulates Beta Cell Replication In Vivo and In Vitro

Bayesian Parameter Estimation for Dynamical Models in Systems Biology

Phosphatases are predicted to govern prolactin-mediated JAK-STAT signaling in pancreatic beta cells

Contact Info

Product

Resources

About