Mutual repression between JNK/AP-1 and JAK/STAT stratifies senescent and proliferative cell behaviors during tissue regeneration.

Epithelial repair relies on the activation of stress signaling pathways to coordinate tissue repair. Their deregulation is implicated in chronic wound and cancer pathologies. Using TNF-α/Eiger-mediated inflammatory damage to Drosophila imaginal discs, we investigate how spatial patterns of signaling pathways and repair behaviors arise. We find that Eiger expression, which drives JNK/AP-1 signaling, transiently arrests proliferation of cells in the wound center and is associated with activation of a senescence program. This includes production of the mitogenic ligands of the Upd family, which allows JNK/AP-1-signaling cells to act as paracrine organizers of regeneration. Surprisingly, JNK/AP-1 cell-autonomously suppress activation of Upd signaling via Ptp61F and Socs36E, both negative regulators of JAK/STAT signaling. As mitogenic JAK/STAT signaling is suppressed in JNK/AP-1-signaling cells at the center of tissue damage, compensatory proliferation occurs by paracrine activation of JAK/STAT in the wound periphery. Mathematical modelling suggests that cell-autonomous mutual repression between JNK/AP-1 and JAK/STAT is at the core of a regulatory network essential to spatially separate JNK/AP-1 and JAK/STAT signaling into bistable spatial domains associated with distinct cellular tasks. Such spatial stratification is essential for proper tissue repair, as coactivation of JNK/AP-1 and JAK/STAT in the same cells creates conflicting signals for cell cycle progression, leading to excess apoptosis of senescently stalled JNK/AP-1-signaling cells that organize the spatial field. Finally, we demonstrate that bistable separation of JNK/AP-1 and JAK/STAT drives bistable separation of senescent signaling and proliferative behaviors not only upon tissue damage, but also in RasV12, scrib tumors. Revealing this previously uncharacterized regulatory network between JNK/AP-1, JAK/STAT, and associated cell behaviors has important implications for our conceptual understanding of tissue repair, chronic wound pathologies, and tumor microenvironments.

Photoconversion and nuclear trafficking cycles determine phytochrome A's response profile to far-red light.

Phytochrome A (phyA) is the only photoreceptor in plants, initiating responses in far-red light and, as such, essential for survival in canopy shade. Although the absorption and the ratio of active versus total phyA are maximal in red light, far-red light is the most efficient trigger of phyA-dependent responses. Using a joint experimental-theoretical approach, we unravel the mechanism underlying this shift of the phyA action peak from red to far-red light and show that it relies on specific molecular interactions rather than on intrinsic changes to phyA's spectral properties. According to our model, the dissociation rate of the phyA-FHY1/FHL nuclear import complex is a principle determinant of the phyA action peak. The findings suggest how higher plants acquired the ability to sense far-red light from an ancestral photoreceptor tuned to respond to red light.

Basal MET phosphorylation is an indicator of hepatocyte dysregulation in liver disease.

Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, metabolic dysfunction-associated steatotic liver disease (MASLD) is gaining importance as the disease can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in MASLD development, but molecular mechanisms responsible for disease progression remained unresolved. Here, we uncover in primary hepatocytes from a preclinical model fed with a Western diet (WD) an increased basal MET phosphorylation and a strong downregulation of the PI3K-AKT pathway. Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveal patient-specific variability in basal MET phosphorylation, which correlates with patient outcome after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.

Uncovering specific mechanisms across cell types in dynamical models.

Ordinary differential equations are frequently employed for mathematical modeling of biological systems. The identification of mechanisms that are specific to certain cell types is crucial for building useful models and to gain insights into the underlying biological processes. Regularization techniques have been proposed and applied to identify mechanisms specific to two cell types, e.g., healthy and cancer cells, including the LASSO (least absolute shrinkage and selection operator). However, when analyzing more than two cell types, these approaches are not consistent, and require the selection of a reference cell type, which can affect the results. To make the regularization approach applicable to identifying cell-type specific mechanisms in any number of cell types, we propose to incorporate the clustered LASSO into the framework of ordinary differential equation modeling by penalizing the pairwise differences of the logarithmized fold-change parameters encoding a specific mechanism in different cell types. The symmetry introduced by this approach renders the results independent of the reference cell type. We discuss the necessary adaptations of state-of-the-art numerical optimization techniques and the process of model selection for this method. We assess the performance with realistic biological models and synthetic data, and demonstrate that it outperforms existing approaches. Finally, we also exemplify its application to published biological models including experimental data, and link the results to independent biological measurements.

Likelihood-ratio test statistic for the finite-sample case in nonlinear ordinary differential equation models.

Likelihood ratios are frequently utilized as basis for statistical tests, for model selection criteria and for assessing parameter and prediction uncertainties, e.g. using the profile likelihood. However, translating these likelihood ratios into p-values or confidence intervals requires the exact form of the test statistic's distribution. The lack of knowledge about this distribution for nonlinear ordinary differential equation (ODE) models requires an approximation which assumes the so-called asymptotic setting, i.e. a sufficiently large amount of data. Since the amount of data from quantitative molecular biology is typically limited in applications, this finite-sample case regularly occurs for mechanistic models of dynamical systems, e.g. biochemical reaction networks or infectious disease models. Thus, it is unclear whether the standard approach of using statistical thresholds derived for the asymptotic large-sample setting in realistic applications results in valid conclusions. In this study, empirical likelihood ratios for parameters from 19 published nonlinear ODE benchmark models are investigated using a resampling approach for the original data designs. Their distributions are compared to the asymptotic approximation and statistical thresholds are checked for conservativeness. It turns out, that corrections of the likelihood ratios in such finite-sample applications are required in order to avoid anti-conservative results.

Optogenetic control of gene expression in plants in the presence of ambient white light.

Optogenetics is the genetic approach for controlling cellular processes with light. It provides spatiotemporal, quantitative and reversible control over biological signaling and metabolic processes, overcoming limitations of chemically inducible systems. However, optogenetics lags in plant research because ambient light required for growth leads to undesired system activation. We solved this issue by developing plant usable light-switch elements (PULSE), an optogenetic tool for reversibly controlling gene expression in plants under ambient light. PULSE combines a blue-light-regulated repressor with a red-light-inducible switch. Gene expression is only activated under red light and remains inactive under white light or in darkness. Supported by a quantitative mathematical model, we characterized PULSE in protoplasts and achieved high induction rates, and we combined it with CRISPR-Cas9-based technologies to target synthetic signaling and developmental pathways. We applied PULSE to control immune responses in plant leaves and generated Arabidopsis transgenic plants. PULSE opens broad experimental avenues in plant research and biotechnology.

Identification of Interleukin1ß as an Amplifier of Interferon alpha-induced Antiviral Responses.

The induction of an interferon-mediated response is the first line of defense against pathogens such as viruses. Yet, the dynamics and extent of interferon alpha (IFNα)-induced antiviral genes vary remarkably and comprise three expression clusters: early, intermediate and late. By mathematical modeling based on time-resolved quantitative data, we identified mRNA stability as well as a negative regulatory loop as key mechanisms endogenously controlling the expression dynamics of IFNα-induced antiviral genes in hepatocytes. Guided by the mathematical model, we uncovered that this regulatory loop is mediated by the transcription factor IRF2 and showed that knock-down of IRF2 results in enhanced expression of early, intermediate and late IFNα-induced antiviral genes. Co-stimulation experiments with different pro-inflammatory cytokines revealed that this amplified expression dynamics of the early, intermediate and late IFNα-induced antiviral genes can also be achieved by co-application of IFNα and interleukin1 beta (IL1ß). Consistently, we found that IL1ß enhances IFNα-mediated repression of viral replication. Conversely, we observed that in IL1ß receptor knock-out mice replication of viruses sensitive to IFNα is increased. Thus, IL1ß is capable to potentiate IFNα-induced antiviral responses and could be exploited to improve antiviral therapies.

PEtab-Interoperable specification of parameter estimation problems in systems biology.

Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been-so far-no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies.

Preventing COVID-19 outbreaks through surveillance testing in healthcare facilities: a modelling study.

BACKGROUND: Surveillance testing within healthcare facilities provides an opportunity to prevent severe outbreaks of coronavirus disease 2019 (COVID-19). However, the quantitative impact of different available surveillance strategies and their potential to decrease the frequency of outbreaks are not well-understood. METHODS: We establish an individual-based model representative of a mental health hospital yielding generalizable results. Attributes and features of this facility were derived from a prototypical hospital, which provides psychiatric, psychosomatic and psychotherapeutic treatment. We estimate the relative reduction of outbreak probability for three test strategies (entry test, once-weekly test and twice-weekly test) relative to a symptom-based baseline strategy. Based on our findings, we propose determinants of successful surveillance measures. RESULTS: Entry Testing reduced the outbreak probability by 26%, additionally testing once or twice weekly reduced the outbreak probability by 49% or 67% respectively. We found that fast diagnostic test results and adequate compliance of the clinic population are mandatory for conducting effective surveillance. The robustness of these results towards uncertainties is demonstrated via comprehensive sensitivity analyses. CONCLUSIONS: We conclude that active testing in mental health hospitals and similar facilities considerably reduces the number of COVID-19 outbreaks compared to symptom-based surveillance only.

Estimating chain length for time delays in dynamical systems using profile likelihood.

MOTIVATION: Apparent time delays in partly observed, biochemical reaction networks can be modelled by lumping a more complex reaction into a series of linear reactions often referred to as the linear chain trick. Since most delays in biochemical reactions are no true, hard delays but a consequence of complex unobserved processes, this approach often more closely represents the true system compared with delay differential equations. In this paper, we address the question of how to select the optimal number of additional equations, i.e. the chain length (CL). RESULTS: We derive a criterion based on parameter identifiability to infer CLs and compare this method to choosing the model with a CL that leads to the best fit in a maximum likelihood sense, which corresponds to optimizing the Bayesian information criterion. We evaluate performance with simulated data as well as with measured biological data for a model of JAK2/STAT5 signalling and access the influence of different model structures and data characteristics. Our analysis revealed that the proposed method features a superior performance when applied to biological models and data compared with choosing the model that maximizes the likelihood. AVAILABILITY AND IMPLEMENTATION: Models and data used for simulations are available at https://github.com/Data2Dynamics/d2d and http://jeti.uni-freiburg.de/PNAS_Swameye_Data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Disentangling molecular mechanisms regulating sensitization of interferon alpha signal transduction.

Tightly interlinked feedback regulators control the dynamics of intracellular responses elicited by the activation of signal transduction pathways. Interferon alpha (IFNα) orchestrates antiviral responses in hepatocytes, yet mechanisms that define pathway sensitization in response to prestimulation with different IFNα doses remained unresolved. We establish, based on quantitative measurements obtained for the hepatoma cell line Huh7.5, an ordinary differential equation model for IFNα signal transduction that comprises the feedback regulators STAT1, STAT2, IRF9, USP18, SOCS1, SOCS3, and IRF2. The model-based analysis shows that, mediated by the signaling proteins STAT2 and IRF9, prestimulation with a low IFNα dose hypersensitizes the pathway. In contrast, prestimulation with a high dose of IFNα leads to a dose-dependent desensitization, mediated by the negative regulators USP18 and SOCS1 that act at the receptor. The analysis of basal protein abundance in primary human hepatocytes reveals high heterogeneity in patient-specific amounts of STAT1, STAT2, IRF9, and USP18. The mathematical modeling approach shows that the basal amount of USP18 determines patient-specific pathway desensitization, while the abundance of STAT2 predicts the patient-specific IFNα signal response.

Cross-TCR Antagonism Revealed by Optogenetically Tuning the Half-Life of the TCR Ligand Binding.

Activation of T cells by agonistic peptide-MHC can be inhibited by antagonistic ones. However, the exact mechanism remains elusive. We used Jurkat cells expressing two different TCRs and tested whether stimulation of the endogenous TCR by agonistic anti-Vß8 antibodies can be modulated by ligand-binding to the second, optogenetic TCR. The latter TCR uses phytochrome B tetramers (PhyBt) as ligand, the binding half-life of which can be altered by light. We show that this half-life determined whether the PhyBt acted as a second agonist (long half-life), an antagonist (short half-life) or did not have any influence (very short half-life) on calcium influx. A mathematical model of this cross-antagonism shows that a mechanism based on an inhibitory signal generated by early recruitment of a phosphatase and an activating signal by later recruitment of a kinase explains the data.

Benchmark problems for dynamic modeling of intracellular processes.

MOTIVATION: Dynamic models are used in systems biology to study and understand cellular processes like gene regulation or signal transduction. Frequently, ordinary differential equation (ODE) models are used to model the time and dose dependency of the abundances of molecular compounds as well as interactions and translocations. A multitude of computational approaches, e.g. for parameter estimation or uncertainty analysis have been developed within recent years. However, many of these approaches lack proper testing in application settings because a comprehensive set of benchmark problems is yet missing. RESULTS: We present a collection of 20 benchmark problems in order to evaluate new and existing methodologies, where an ODE model with corresponding experimental data is referred to as problem. In addition to the equations of the dynamical system, the benchmark collection provides observation functions as well as assumptions about measurement noise distributions and parameters. The presented benchmark models comprise problems of different size, complexity and numerical demands. Important characteristics of the models and methodological requirements are summarized, estimated parameters are provided, and some example studies were performed for illustrating the capabilities of the presented benchmark collection. AVAILABILITY AND IMPLEMENTATION: The models are provided in several standardized formats, including an easy-to-use human readable form and machine-readable SBML files. The data is provided as Excel sheets. All files are available at https://github.com/Benchmarking-Initiative/Benchmark-Models, including step-by-step explanations and MATLAB code to process and simulate the models. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Extensions of ℓ1 regularization increase detection specificity for cell-type specific parameters in dynamic models.

BACKGROUND: Ordinary differential equation systems are frequently utilized to model biological systems and to infer knowledge about underlying properties. For instance, the development of drugs requires the knowledge to which extent malign cells differ from healthy ones to provide a specific treatment with least side effects. As these cell-type specific properties may stem from any part of biochemical cell processes, systematic quantitative approaches are necessary to identify the relevant potential drug targets. An ℓ1 regularization for the maximum likelihood parameter estimation proved to be successful, but falsely predicted cell-type dependent behaviour had to be corrected manually by using a Profile Likelihood approach. RESULTS: The choice of extended ℓ1 penalty functions significantly decreased the number of falsely detected cell-type specific parameters. Thus, the total accuracy of the prediction could be increased. This was tested on a realistic dynamical benchmark model used for the DREAM6 challenge. Among Elastic Net, Adaptive Lasso and a non-convex ℓq penalty, the latter one showed the best predictions whilst also requiring least computation time. All extended methods include a hyper-parameter in the regularization function. For an Erythropoietin (EPO) induced signalling pathway, the extended methods ℓq and Adaptive Lasso revealed an unpublished alternative parsimonious model when varying the respective hyper-parameters. CONCLUSIONS: Using ℓq or Adaptive Lasso with an a-priori choice for the hyper-parameter can lead to a more specific and accurate result than ℓ1. Scanning different hyper-parameters can yield additional pieces of information about the system.

Functional Proteomics of Breast Cancer Metabolism Identifies GLUL as Responder during Hypoxic Adaptation.

Hypoxia as well as metabolism are central hallmarks of cancer, and hypoxia-inducible factors (HIFs) and metabolic effectors are crucial elements in oxygen-compromised tumor environments. Knowledge of changes in the expression of metabolic proteins in response to HIF function could provide mechanistic insights into adaptation to hypoxic stress, tumorigenesis, and disease progression. We analyzed time-resolved alterations in metabolism-associated protein levels in response to different oxygen potentials across breast cancer cell lines. Effects on the cellular metabolism of both HIF-dependent and -independent processes were analyzed by reverse-phase protein array profiling and a custom statistical model. We revealed a strong induction of glucose transporter 1 (GLUT1) and lactate dehydrogenase A (LDHA) as well as reduced glutamate-ammonia ligase (GLUL) protein levels across all cell lines tested as consistent changes upon hypoxia induction. Low GLUL protein levels were correlated with aggressive molecular subtypes in breast cancer patient data sets and also with hypoxic tumor regions in a xenograft mouse tumor model. Moreover, low GLUL expression was associated with poor survival in breast cancer patients and with high HIF-1α-expressing patient subgroups. Our data reveal time-resolved changes in the regulation of metabolic proteins under oxygen-deprived conditions and elucidate GLUL as a strong responder to HIFs and the hypoxic environment.

USP18 lack in microglia causes destructive interferonopathy of the mouse brain.

Microglia are tissue macrophages of the central nervous system (CNS) that control tissue homeostasis. Microglia dysregulation is thought to be causal for a group of neuropsychiatric, neurodegenerative and neuroinflammatory diseases, called "microgliopathies". However, how the intracellular stimulation machinery in microglia is controlled is poorly understood. Here, we identified the ubiquitin-specific protease (Usp) 18 in white matter microglia that essentially contributes to microglial quiescence. We further found that microglial Usp18 negatively regulates the activation of Stat1 and concomitant induction of interferon-induced genes, thereby terminating IFN signaling. The Usp18-mediated control was independent from its catalytic activity but instead required the interaction with Ifnar2. Additionally, the absence of Ifnar1 restored microglial activation, indicating a tonic IFN signal which needs to be negatively controlled by Usp18 under non-diseased conditions. These results identify Usp18 as a critical negative regulator of microglia activation and demonstrate a protective role of Usp18 for microglia function by regulating the Ifnar pathway. The findings establish Usp18 as a new molecule preventing destructive microgliopathy.

IL-1ß-induced and p38MAPK-dependent activation of the mitogen-activated protein kinase-activated protein kinase 2 (MK2) in hepatocytes: Signal transduction with robust and concentration-independent signal amplification.

The IL-1ß induced activation of the p38MAPK/MAPK-activated protein kinase 2 (MK2) pathway in hepatocytes is important for control of the acute phase response and regulation of liver regeneration. Many aspects of the regulatory relevance of this pathway have been investigated in immune cells in the context of inflammation. However, very little is known about concentration-dependent activation kinetics and signal propagation in hepatocytes and the role of MK2. We established a mathematical model for IL-1ß-induced activation of the p38MAPK/MK2 pathway in hepatocytes that was calibrated to quantitative data on time- and IL-1ß concentration-dependent phosphorylation of p38MAPK and MK2 in primary mouse hepatocytes. This analysis showed that, in hepatocytes, signal transduction from IL-1ß via p38MAPK to MK2 is characterized by strong signal amplification. Quantification of p38MAPK and MK2 revealed that, in hepatocytes, at maximum, 11.3% of p38MAPK molecules and 36.5% of MK2 molecules are activated in response to IL-1ß. The mathematical model was experimentally validated by employing phosphatase inhibitors and the p38MAPK inhibitor SB203580. Model simulations predicted an IC50 of 1-1.2 µm for SB203580 in hepatocytes. In silico analyses and experimental validation demonstrated that the kinase activity of p38MAPK determines signal amplitude, whereas phosphatase activity affects both signal amplitude and duration. p38MAPK and MK2 concentrations and responsiveness toward IL-1ß were quantitatively compared between hepatocytes and macrophages. In macrophages, the absolute p38MAPK and MK2 concentration was significantly higher. Finally, in line with experimental observations, the mathematical model predicted a significantly higher half-maximal effective concentration for IL-1ß-induced pathway activation in macrophages compared with hepatocytes, underscoring the importance of cell type-specific differences in pathway regulation.

Protein abundance of AKT and ERK pathway components governs cell type-specific regulation of proliferation.

Signaling through the AKT and ERK pathways controls cell proliferation. However, the integrated regulation of this multistep process, involving signal processing, cell growth and cell cycle progression, is poorly understood. Here, we study different hematopoietic cell types, in which AKT and ERK signaling is triggered by erythropoietin (Epo). Although these cell types share the molecular network topology for pro-proliferative Epo signaling, they exhibit distinct proliferative responses. Iterating quantitative experiments and mathematical modeling, we identify two molecular sources for cell type-specific proliferation. First, cell type-specific protein abundance patterns cause differential signal flow along the AKT and ERK pathways. Second, downstream regulators of both pathways have differential effects on proliferation, suggesting that protein synthesis is rate-limiting for faster cycling cells while slower cell cycles are controlled at the G1-S progression. The integrated mathematical model of Epo-driven proliferation explains cell type-specific effects of targeted AKT and ERK inhibitors and faithfully predicts, based on the protein abundance, anti-proliferative effects of inhibitors in primary human erythroid progenitor cells. Our findings suggest that the effectiveness of targeted cancer therapy might become predictable from protein abundance.

L1 regularization facilitates detection of cell type-specific parameters in dynamical systems.

MOTIVATION: A major goal of drug development is to selectively target certain cell types. Cellular decisions influenced by drugs are often dependent on the dynamic processing of information. Selective responses can be achieved by differences between the involved cell types at levels of receptor, signaling, gene regulation or further downstream. Therefore, a systematic approach to detect and quantify cell type-specific parameters in dynamical systems becomes necessary. RESULTS: Here, we demonstrate that a combination of nonlinear modeling with L1 regularization is capable of detecting cell type-specific parameters. To adapt the least-squares numerical optimization routine to L1 regularization, sub-gradient strategies as well as truncation of proposed optimization steps were implemented. Likelihood-ratio tests were used to determine the optimal regularization strength resulting in a sparse solution in terms of a minimal number of cell type-specific parameters that is in agreement with the data. By applying our implementation to a realistic dynamical benchmark model of the DREAM6 challenge we were able to recover parameter differences with an accuracy of 78%. Within the subset of detected differences, 91% were in agreement with their true value. Furthermore, we found that the results could be improved using the profile likelihood. In conclusion, the approach constitutes a general method to infer an overarching model with a minimum number of individual parameters for the particular models. AVAILABILITY AND IMPLEMENTATION: A MATLAB implementation is provided within the freely available, open-source modeling environment Data2Dynamics. Source code for all examples is provided online at http://www.data2dynamics.org/ CONTACT: bernhard.steiert@fdm.uni-freiburg.de.

Fast integration-based prediction bands for ordinary differential equation models.

MOTIVATION: To gain a deeper understanding of biological processes and their relevance in disease, mathematical models are built upon experimental data. Uncertainty in the data leads to uncertainties of the model's parameters and in turn to uncertainties of predictions. Mechanistic dynamic models of biochemical networks are frequently based on nonlinear differential equation systems and feature a large number of parameters, sparse observations of the model components and lack of information in the available data. Due to the curse of dimensionality, classical and sampling approaches propagating parameter uncertainties to predictions are hardly feasible and insufficient. However, for experimental design and to discriminate between competing models, prediction and confidence bands are essential. To circumvent the hurdles of the former methods, an approach to calculate a profile likelihood on arbitrary observations for a specific time point has been introduced, which provides accurate confidence and prediction intervals for nonlinear models and is computationally feasible for high-dimensional models. RESULTS: In this article, reliable and smooth point-wise prediction and confidence bands to assess the model's uncertainty on the whole time-course are achieved via explicit integration with elaborate correction mechanisms. The corresponding system of ordinary differential equations is derived and tested on three established models for cellular signalling. An efficiency analysis is performed to illustrate the computational benefit compared with repeated profile likelihood calculations at multiple time points. AVAILABILITY AND IMPLEMENTATION: The integration framework and the examples used in this article are provided with the software package Data2Dynamics, which is based on MATLAB and freely available at http://www.data2dynamics.org CONTACT: helge.hass@fdm.uni-freiburg.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.