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

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Dynamic Regulation of JAK-STAT Signaling Through the Prolactin Receptor Predicted by Computational Modeling Ryland D bioRxiv preprint doi: https://doi.org/10.1101/2020.02.14.949321; this version posted February 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Dynamic regulation of JAK-STAT signaling through the prolactin receptor predicted by computational modeling Ryland D. Mortlock1, Senta K. Georgia2, and Stacey D. Finley1,3* 1Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 2Departments of Pediatrics and Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, Univer- sity of Southern California, Los Angeles, CA 3Departments of Biomedical Engineering and Biological Sciences, University of Southern California, Los Angeles, CA ABSTRACT high insulin demand, such as pregnancy or obesity, the body Introduction: Hormones signal through various receptors and responds by increasing beta cell mass in the pancreas. In fact, cascades of biochemical reactions to expand beta cell mass during studies have shown that over the approximately 20-day time pregnancy. Harnessing this phenomenon to treat beta cell dysfunc- course of pregnancy in mice, pancreatic beta cells both repli- tion requires quantitative understanding of the signaling at the molec- cate and grow in size, resulting in an increased beta cell ular level. This study explores how different regulatory elements im- mass.36 This phenomenon could potentially be harnessed to pact JAK-STAT signaling through the prolactin receptor in pancreatic increase the number of functioning beta cells in diabetes pa- beta cells. tients. Methods: A mechanistic computational model was constructed Beta cell compensation precedes insulin compensation to describe the key reactions and molecular species involved in JAK- and is driven by signaling through the prolactin recep- STAT signaling in response to the hormone prolactin. The effect of tor4,17,23,47 (PRLR), which is closely related to the growth hor- including and excluding different regulatory modules in the model mone receptor. Signaling by placental lactogens through structure was explored through ensemble modeling. A Bayesian ap- PRLR engages the JAK-STAT signaling cascade.34 Specifi- proach for likelihood estimation was used to parametrize the model to cally, Janus Kinase 2 (JAK2) is constitutively associated with experimental data from the literature. the PRLR7,15,38 and once the JAK2 kinase is activated, it re- Results: Receptor upregulation, combined with either inhibition cruits and phosphorylates Signal Transducer and Activator of by SOCS proteins, receptor internalization, or both, was required to Transcription 5 (STAT5). STAT5 regulates the expression of obtain STAT5 dynamics matching experimental results for INS-1 cells several target genes in the nucleus, including genes related treated with prolactin. Multiple model structures could fit the experi- to the cell cycle18,41 and apoptosis.19,24,46 Although initial dis- mental data, and key findings were conserved across model struc- coveries were made in rodent models, human prolactin has tures, including faster dimerization and nuclear import rates of been shown to protect human beta cells from apoptosis as STAT5B compared to STAT5A. The model was validated using ex- well.46 perimental data from rat primary beta cells not used in parameter es- In this work, we investigate the mechanisms by which the timation. Probing the fitted, validated model revealed possible strate- pregnancy-related hormone prolactin (PRL) drives JAK-STAT gies to modulate STAT5 signaling. signaling in pancreatic beta cells using a mathematical model Conclusions: JAK-STAT signaling must be tightly controlled to of the signaling pathway. Mathematical models have been obtain the biphasic response in STAT5 activation seen experimen- used to elucidate the balance between replication and apop- tally. Receptor up-regulation, combined with SOCS inhibition, recep- tosis in beta cells29, but no molecular-detailed computational tor internalization, or both is required to match experimental data. model exists for the beta cell response to pregnancy. Here, Modulating reactions upstream in the signaling can enhance STAT5 we use a systems biology approach to quantitatively analyze activation to increase beta cell mass. the beta cell response to hormone stimulation. We have con- structed a computational model, calibrated the model with ex- 1 INTRODUCTION perimental data, and validated it by predicting new data not Metabolic diseases impair the body’s ability to properly con- used for parameter estimation. During model construction, we vert nutrients into energy. Diabetes is a particularly harmful explored the effect of different model structures on the pre- metabolic disease that affects over 30 million people in the dicted signaling dynamics through an approach known as en- 25,31 United States alone.51 In cases of Type 1 diabetes, the body semble modeling. After validating the model, we analyzed is unable to produce insulin, a key hormone that regulates the how changes in kinetic parameters and initial values can lead transport of glucose from the blood to the cells where it is used to greater STAT5 activation. to produce energy. Patients with Type 2 or gestational diabe- In particular, mathematical modeling is used to explore tes can produce some insulin, but not enough to properly reg- the effects of various regulatory mechanisms that control sig- ulate blood glucose levels. Recent advances in the study of naling. Experimental data shows that when insulin-secreting pancreatic beta cells, the cells that produce and secrete insu- cells of the INS-1 cell line are treated with a constant concen- lin, have shed light on the body’s ability to adapt in response tration of PRL in vitro, the amount of phosphorylated STAT5 10,11 to changes in metabolic demand.36 For example, in cases of has multiple peaks within six hours of stimulation. The *Correspondence: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.14.949321; this version posted February 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Mortlock et al. presence of these peaks is influenced by Suppressors of Cy- dimerize in the cytosol and are transported into the nucleus. Three tokine Signaling (SOCS) genes, which are transcribed in re- phosphatases are included in the model, which serve to attenuate the sponse to STAT signaling and exert negative feedback on the signaling after initial ligand binding: SH2 domain-containing tyrosine system. Modeling the cytokine IFN- g in liver cells, Yamada et phosphatase 2 (SHP-2) dephosphorylates the activated receptor-JAK al. found that the presence of a nuclear phosphatase, in addi- complex, and phosphatases in the cytosol and nucleus (termed PPX and PPN, respectively) dephosphorylate STAT5 species48. The phos- tion to SOCS negative feedback, are sufficient to cause a de- phatase action is a form of negative feedback shown to be necessary crease in phosphorylated STAT after the initial peak, leading for attenuation of STAT activation.48 pSTAT molecules are shuttled to multiple peaks in phosphorylated STAT dimer in the nu- out of the nucleus when they are not dimerized with another molecule. cleus.48 In addition, JAK-STAT signaling through the prolactin The phosphorylated STAT5 dimer promotes transcription of several receptor (PRLR) has been shown to include positive feedback target genes once in the nucleus. Specifically, we include suppressors as nuclear STAT5 promotes transcription of PRLR of cytokine signaling (SOCS), the prolactin receptor, and the anti- mRNA.20,27,33,35 We hypothesize that positive feedback could apoptotic protein Bcl-xL as STAT5 targets. It has been shown that play a role in explaining the initial peak, subsequent decline, suppressors of cytokine signaling (SOCS) proteins bind competitively to the receptor JAK complexes and also target the receptors for ubiq- then prolonged activation of STAT5 activity in INS-1 cells dis- 7,49 covered by Brelje and colleagues. Although these regulatory uitination-based degradation. These mechanisms were incorpo- rated in the model rather than the non-competitive binding used by mechanisms significantly influence beta cell signaling, no Yamada, et al.48 model to our knowledge explores the interplay between SOCS STAT5 dimers promote transcription of mRNA for the prolactin feedback and positive regulation of PRL signaling. Therefore, receptor. This has been shown in vitro in INS-1 cells20 and in vivo we built upon the work of Yamada and colleagues to create a during pregnancy in mice. This positive feedback mechanism may computational model of JAK-STAT signaling in pancreatic play a role in the islet response to pregnancy20 and has not been ex- beta cells through PRLR. We applied the model to investigate plored computationally before. The phosphorylated STAT5 dimer in the influence of these regulation schemes, individually and in the nucleus also promotes transcription of cell-cycle genes such as 18,41 combination, and found that model structures that include cyclin D proteins and anti-apoptotic species such as Bcl-family 19,46 both positive and negative regulation produce multiple peaks proteins . We included a module for the STAT5-mediated tran- in STAT5 phosphorylation within a tight range of parameter scription and translation of the response protein Bcl-xL. A full list of reactions is included in the supplementary File S1. MATLAB was used values. By fitting to experimental data using a Bayesian ap- to carry out model simulations, and statistical analyses of the simu- proach for likelihood estimation of parameter values, we show lated results were performed using R statistical computing lan- that the model can simultaneously determine the rates of guage.45 All code including MATLAB files, SBML files, and R scripts STAT5 phosphorylation and nuclear translocation.
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