Katherine Wendelsdorf

Systems modeling of molecular mechanisms controlling cytokine-driven CD4+ T cell differentiation and phenotype plasticity.

Differentiation of CD4+ T cells into effector or regulatory phenotypes is tightly controlled by the cytokine milieu, complex intracellular signaling networks and numerous transcriptional regulators. We combined experimental approaches and computational modeling to investigate the mechanisms controlling differentiation and plasticity of CD4+ T cells in the gut of mice. Our computational model encompasses the major intracellular pathways involved in CD4+ T cell differentiation into T helper 1 (Th1), Th2, Th17 and induced regulatory T cells (iTreg). Our modeling efforts predicted a critical role for peroxisome proliferator-activated receptor gamma (PPARγ) in modulating plasticity between Th17 and iTreg cells. PPARγ regulates differentiation, activation and cytokine production, thereby controlling the induction of effector and regulatory responses, and is a promising therapeutic target for dysregulated immune responses and inflammation. Our modeling efforts predict that following PPARγ activation, Th17 cells undergo phenotype switch and become iTreg cells. This prediction was validated by results of adoptive transfer studies showing an increase of colonic iTreg and a decrease of Th17 cells in the gut mucosa of mice with colitis following pharmacological activation of PPARγ. Deletion of PPARγ in CD4+ T cells impaired mucosal iTreg and enhanced colitogenic Th17 responses in mice with CD4+ T cell-induced colitis. Thus, for the first time we provide novel molecular evidence in vivo demonstrating that PPARγ in addition to regulating CD4+ T cell differentiation also plays a major role controlling Th17 and iTreg plasticity in the gut mucosa.

Predictive computational modeling of the mucosal immune responses during Helicobacter pylori infection.

T helper (Th) cells play a major role in the immune response and pathology at the gastric mucosa during Helicobacter pylori infection. There is a limited mechanistic understanding regarding the contributions of CD4+ T cell subsets to gastritis development during H. pylori colonization. We used two computational approaches: ordinary differential equation (ODE)-based and agent-based modeling (ABM) to study the mechanisms underlying cellular immune responses to H. pylori and how CD4+ T cell subsets influenced initiation, progression and outcome of disease. To calibrate the model, in vivo experimentation was performed by infecting C57BL/6 mice intragastrically with H. pylori and assaying immune cell subsets in the stomach and gastric lymph nodes (GLN) on days 0, 7, 14, 30 and 60 post-infection. Our computational model reproduced the dynamics of effector and regulatory pathways in the gastric lamina propria (LP) in silico. Simulation results show the induction of a Th17 response and a dominant Th1 response, together with a regulatory response characterized by high levels of mucosal Treg) cells. We also investigated the potential role of peroxisome proliferator-activated receptor γ (PPARγ) activation on the modulation of host responses to H. pylori by using loss-of-function approaches. Specifically, in silico results showed a predominance of Th1 and Th17 cells in the stomach of the cell-specific PPARγ knockout system when compared to the wild-type simulation. Spatio-temporal, object-oriented ABM approaches suggested similar dynamics in induction of host responses showing analogous T cell distributions to ODE modeling and facilitated tracking lesion formation. In addition, sensitivity analysis predicted a crucial contribution of Th1 and Th17 effector responses as mediators of histopathological changes in the gastric mucosa during chronic stages of infection, which were experimentally validated in mice. These integrated immunoinformatics approaches characterized the induction of mucosal effector and regulatory pathways controlled by PPARγ during H. pylori infection affecting disease outcomes.

ENteric Immunity SImulator: A Tool for In Silico Study of Gastroenteric Infections

Clinical symptoms of microbial infection of the gastrointestinal (GI) tract are often exacerbated by inflammation induced pathology. Identifying novel avenues for treating and preventing such pathologies is necessary and complicated by the complexity of interacting immune pathways in the gut, where effector and inflammatory immune cells are regulated by anti-inflammatory or regulatory cells. Here we present new advances in the development of the ENteric Immunity SImulator (ENISI), a simulator of GI immune mechanisms in response to resident commensal bacteria as well as invading pathogens and the effect on the development of intestinal lesions. ENISI is a tool for identifying potential treatment strategies that reduce inflammation-induced damage and, at the same time, ensure pathogen removal by allowing one to test plausibility of in vitro observed behavior as explanations for observations in vivo, propose behaviors not yet tested in vitro that could explain these tissue-level observations, and conduct low-cost, preliminary experiments of proposed interventions/treatments. An example of such application is shown in which we simulate dysentery resulting from Brachyispira hyodysenteriae infection and identify aspects of the host immune pathways that lead to continued inflammation-induced tissue damage even after pathogen elimination.

High-Performance Interaction-Based Simulation of Gut Immunopathologies with ENteric Immunity Simulator (ENISI)

Here we present the ENteric Immunity Simulator (ENISI), a modeling system for the inflammatory and regulatory immune pathways triggered by microbe-immune cell interactions in the gut. With ENISI, immunologists and infectious disease experts can test and generate hypotheses for enteric disease pathology and propose interventions through experimental infection of an in silico gut. ENISI is an agent based simulator, in which individual cells move through the simulated tissues, and engage in context-dependent interactions with the other cells with which they are in contact. The scale of ENISI is unprecedented in this domain, with the ability to simulate 107 cells for 250 simulated days on 576 cores in one and a half hours, with the potential to scale to even larger hardware and problem sizes. In this paper we describe the ENISI simulator for modeling mucosal immune responses to gastrointestinal pathogens. We then demonstrate the utility of ENISI by recreating an experimental infection of a mouse with Helicobacter pylori 26695. The results identify specific processes by which bacterial virulence factors do and do not contribute to pathogenesis associated with H. pylori strain 26695. These modeling results inform general intervention strategies by indicating immunomodulatory mechanisms such as those used in inflammatory bowel disease may be more appropriate therapeutically than directly targeting specific microbial populations through vaccination or by using antimicrobials.

From Big Data Analytics and Network Inference to Systems Modeling

Informatics approaches that integrate high-throughput datasets across multicellular components and time points with predictive network modeling emerge as essential tools for understanding the organization, function, and dynamics of the immune system, and its relation to health and disease. Here we focus on integrative bioinformatics and network modeling techniques applied to large genome-wide transcriptional profiles enabled by the recent advancement of sequencing technologies. RNA sequencing (RNA-Seq) is widely used to characterize global changes in gene expression. In this chapter, we provide an introduction to RNA-Seq analysis, including its popular Galaxy implementation, cloud solutions, as well as applications to microbial transcriptomics (RNA Rocket). We also present a suite of new data analytic, network inference and supervised machine learning methods that can be integrated with the RNA-Seq pipeline toward comprehensive, predictive networks describing immune processes at the mechanistic level.

Use of Computational Modeling in Immunological Research

Abstract Computational Modeling in Immunological Research is emerging as a need to develop systems that allow for the storage, analysis, visualization, and interpretation of the massive amounts of big and complex data. These new computational methodologies are changing the way in which immunology research has been performed and will open new unforeseen avenues in immunology discovery. We have used computational modeling to investigate at the systems level the dynamics of the immune response to Helicobacter pylori and during inflammatory bowel disease (IBD). Helicobacter pylori is the causative agent of gastric and duodenal ulcers and gastric cancer. This bacterium, which is carried by half of the world’s population, can also behave as a beneficial commensal. IBD is an idiopathic disease caused by immune dysregulation at the gastrointestinal tract. We provide an overview of how the use of computational modeling has streamlined the process of hypothesis generation and provided a framework to perform analyses of the dynamical interactions between cell types participating in the immune response.