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Helicobacter pylori is the dominant member of the gastric microbiota in over half of the human population of which 5–15% develop gastritis or gastric malignancies. Immune responses to H. pylori are characterized by mixed T helper cell, cytotoxic T cell and NK cell responses. The presence of Tregs is essential for the control of gastritis and together with regulatory CX3CR1+ mononuclear phagocytes and immune-evasion strategies they enable life-long persistence of H. pylori. This H. pylori-induced regulatory environment might contribute to its cross-protective effect in inflammatory bowel disease and obesity. Here we review host-microbe interactions, the development of pro- and anti-inflammatory immune responses and how the latter contribute to H. pylori's role as beneficial member of the gut microbiota. Furthermore, we present the integration of existing and new data into a computational/mathematical model and its use for the investigation of immunological mechanisms underlying initiation, progression and outcomes of H. pylori infection.

Modeling and simulations approaches have been widely used in computational biology, mathematics, bioinformatics and engineering to represent complex existing knowledge and to effectively generate novel hypotheses. While deterministic modeling strategies are widely used in computational biology, stochastic modeling techniques are not as popular due to a lack of user-friendly tools. This paper presents ENISI SDE, a novel web-based modeling tool with stochastic differential equations. ENISI SDE provides user-friendly web user interfaces to facilitate adoption by immunologists and computational biologists. This work provides three major contributions: (1) discussion of SDE as a generic approach for stochastic modeling in computational biology; (2) development of ENISI SDE, a web-based user-friendly SDE modeling tool that highly resembles regular ODE-based modeling; (3) applying ENISI SDE modeling tool through a use case for studying stochastic sources of cell heterogeneity in the context of CD4+ T cell differentiation. The CD4+ T cell differential ODE model has been published [8] and can be downloaded from biomodels.net. The case study reproduces a biological phenomenon that is not captured by the previously published ODE model and shows the effectiveness of SDE as a stochastic modeling approach in biology in general and immunology in particular and the power of ENISI SDE.

Therapeutic vaccines have been in development for decades with the goal of priming and boosting an immune response against specific antigens in cancer patients. More recently, vaccination with mutation-derived neoantigens has emerged as is an attractive approach to treat cancer patients. However, appropriately selecting immunogenic antigens and neoepitopes that successfully prime the immune system of cancer patents remains a challenge. In fact, despite extensive efforts throughout the years, many vaccines have proven to be ineffective in mediating a clinically relevant anti-tumor immune response in humans. With the advent of emerging new technologies, including the combination of next-generation sequencing and improved bioinformatics tools, a new era of vaccine development is underway. Hereto, we present a novel, highly sensitive, multiplex approach, known as Multiplexed Identification of T-cell Receptor Antigen specificity (MIRA), that combines high-throughput TCR repertoire sequencing with conventional immune monitoring techniques to assess T-cell specificity to large numbers of query antigens. MIRA maps antigen specificity to TCR sequence at an unprecedented sensitivity (1 in ~10 million T-cells) and scale (more than 400 antigens at a time). To validate this approach, we assessed the response of more than 10 million naïve human T-cells from each of 50 healthy donors against a panel of 366 neoepitopes. These include post-translationally modified peptides, derived from over 130 prevalent mutations in greater than 50 cancer indications. From three billion total input T-cells, we identified tens of thousands of TCRB sequences from responsive T-cell clones, of which at least one TCR was yielded for the majority of queried neoepitopes. The TCRs identified were skewed toward a small number of query antigens, with 12 neoepitopes accounting for over 50% of the TCRs identified. This suggests a higher precursor frequency of specific T-cells in the naïve repertoire or a higher immunogenicity of these epitopes or both. By performing MIRA experiments in parallel using transgenes versus peptides, we can further validate which of the neoepitopes were also presented via cells’ natural antigen presentation machinery. Of note, we also paired 52% of the TCRB sequences with their cognate TCRA subunit sequence thus permitting reconstitution of individual TCRs. Generating immunogenicity data to antigens of interest at scale facilitates an opportunity to better inform antigen selection in vaccine programs and improve vaccine design. Once a vaccine has been designed and administered to a patient, MIRA offers a unique solution to assess peptides based on their ability to elicit robust T responses. By creating a post-vaccination, patient-specific MIRA, a permanent record or signature of that patient’s response to the vaccine is created. This information can then be leveraged to identify early biomarkers of response to the vaccine and monitor this response longitudinally by simply using single chain TCRB deep sequencing from a small amount of a patient’s peripheral blood. Citation Format: Peter Ebert, Mark Klinger, Edward Osborne, Ruth Taniguchi, Joyce Hu, Tim Hayes, Sharon Benzeno, Adria Carbo, Melanie Laur, Erica Eggers, Harlan Robins. Optimization of cancer vaccine development by using Multiplexed Identification of T-cell Receptor Antigen specificity (MIRA) [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B115.

Summary Some type 1 diabetes (T1D) patients have been reported to exhibit T cell reactivity to wheat gluten. We tested the hypothesis that this T cell reactivity could be abolished by using prolyl-endopeptidase (PEP), an enzyme that cleaves peptide bonds after proline. Peripheral blood mononuclear cells (PBMCs) were isolated from T1D patients and healthy controls. PBMCs were stimulated with a peptic–tryptic digest of wheat gluten; a peptic–tryptic-PEP digest of wheat gluten; and a 13 amino acid peptide from wheat gluten. Fluorescent-labelled antibodies to CD3, CD4 and CD8 cell marker proteins were utilized to determine proliferative responses of CD3, CD4 and CD8 T cells. There were no significant differences in proliferative responses of CD3 or CD4 T cells to the wheat gluten antigens. A significantly higher proportion of CD8+ T cells from T1D patients proliferated in the presence of the 13 amino acid peptide than when challenged with the peptic–tryptic or the peptic–tryptic–PEP digests of wheat gluten. PEP treatment had no significant effect on CD8 T cell reactivity to the peptic–trytic digest of wheat gluten. Our results suggest that wheat gluten-derived peptides, containing ≤ 13 amino acids, may evoke T cell responses in T1D patients.

Helicobacter pylori infection is the leading cause for peptic ulcer disease and gastric adenocarcinoma. Mucosal T cell responses play an important role in mediating H. pylori -related gastric immunopathology. While induced regulatory T (iTreg) cells are required for chronic colonization without disease, T helper 1 (Th1) effector responses are associated with lower bacterial loads at the expense of gastric pathology. Pigs were inoculated with either H. pylori strain SS1 or J99. Phenotypic and functional changes in peripheral blood mononuclear cell (PBMC) populations were monitored weekly, and mucosal immune responses and bacterial loads were assessed up to 2 months postinfection. Both H. pylori strains elicited a Th1 response characterized by increased percentages of CD4 + Tbet + cells and elevated gamma interferon (IFN-γ) mRNA in PBMCs. A subset of CD8 + T cells expressing Tbet and CD16 increased following infection. Moreover, a significant increase in perforin and granzyme mRNA expression was observed in PBMCs of infected pigs, indicating a predominant cytotoxic immune response. Infiltration of B cells, myeloid cells, T cells expressing Treg- and Th17-associated transcription factors, and cytotoxic T cells was found in the gastric lamina propria of both infected groups. Interestingly, based on bacterial reisolation data, strain SS1 showed greater capacity to colonize and/or persist in the gastric mucosa than did strain J99. This novel pig model of infection closely mimics human gastric pathology and presents a suitable avenue for studying effector and regulatory responses toward H. pylori described in humans.

The anti-inflammatory phytohormone abscisic acid (ABA) modulates immune and inflammatory responses in mouse models of colitis and obesity. ABA has been identified as a ligand of lanthionine synthetase C-like 2, a novel therapeutic target upstream of the peroxisome proliferator-activated receptor γ (PPARγ) pathway. The goal of this study was to investigate the immune modulatory mechanisms underlying the anti-inflammatory efficacy of ABA against influenza-associated pulmonary inflammation. Wild-type (WT) and conditional knockout mice with defective PPARγ expression in lung epithelial and hematopoietic cells (cKO) treated orally with or without ABA (100 mg/kg diet) were challenged with influenza A/Udorn (H3N2) to assess ABA's impact in disease, lung lesions and gene expression. Dietary ABA ameliorated disease activity and lung inflammatory pathology, accelerated recovery and increased survival in WT mice. ABA suppressed leukocyte infiltration and monocyte chemotactic protein 1 mRNA expression in WT mice through PPARγ since this effect was abrogated in cKO mice. ABA ameliorated disease when administered therapeutically on the same day of the infection to WT but not mice lacking PPARγ in myeloid cells. We also show that ABA's greater impact is between days 7 and 10 postchallenge when it regulates the expression of genes involved in resolution, like 5-lipoxygenase and other members of the 5-lipoxygenase pathway. Furthermore, ABA significantly increased the expression of the immunoregulatory cytokine interleukin-10 in WT mice. Our results show that ABA, given preventively or therapeutically, ameliorates influenza-virus-induced pathology by activating PPARγ in pulmonary immune cells, suppressing initial proinflammatory responses and promoting resolution.

Lanthionine synthetase C-like 2 (LANCL2), a novel therapeutic target for inflammatory and autoimmune diseases and diabetes, exerts anti-inflammatory and insulin-sensitizing effects. This study reports the first LANCL2-based therapeutics for inflammatory bowel disease (IBD). Analogues of 1 (ABA) and 2 (NSC61610) were screened by molecular docking, then synthesized and analyzed for binding to LANCL2 by surface plasmon resonance. Piperazine-1,4-diylbis(6-benzo[d]imidazole-2-yl)pyridine-2-yl)methanone, 7, was identified as the lead LANCL2-binding compound for treating IBD. The oral treatment with 7 (8 mg/kg/d) in a mouse model of IBD resulted in lowering the disease activity index, decreasing colonic inflammatory lesions by 4-fold, and suppressing inflammatory markers (e.g., TNF-α, and interferon-γ) in the gut. Furthermore, studies in LANCL2-/- mice demonstrated that loss of LANCL2 abrogated beneficial actions of 7, suggesting high selectivity for the target. In conclusion, 7 merits continued development as a LANCL2-based, first-in-class orally active therapeutic for IBD.

Lanthionine synthetase cyclase-like receptor 2 (LANCL2) is a novel therapeutic target for Crohn’s disease (CD). BT-11 is a small molecule that binds LANCL2, is orally active, and has demonstrated therapeutic efficacy in 3 validated mouse models of colitis at doses as low as 8 mg/kg/d. Exploratory experiments evaluated BT-11 in male Harlan Sprague Dawley rats with a single oral dose of 500 mg/kg and 80 mg/kg/d for 14 days (n = 10 rats dosed/group). Treated and control rats were observed for behavioral detriments, and blood and tissues were collected for clinical pathology and histopathological examination. A functional observational battery demonstrated no differences between treated and control groups over multiple times of observation for quantal, categorical, and continuous end points, including posture, in cage activity, approach, response to touch, weight, grip strength, body temperature, and time on a rotarod. Histopathological examination of the brain, kidney, liver, adrenal gland, testes, stomach, small and large intestines, duodenum, pancreas, heart, lungs, spleen, thymus, and rib found no significant differences between the groups. Plasma enzymes associated with liver function were transiently elevated 2 to 4 days after the 500 mg/kg single dose but returned to normal values by 8 days and were not observed at any time in rats given 80 mg/kg/d for 14 days. One hour after oral administration of a single dose of 80 mg/kg, BT-11 had a maximal concentration of 21 ng/mL; the half-life was 3 hours. These experimental results demonstrated that BT-11 is well tolerated in rats, and, with further testing, may hold promise as an orally active therapeutic for CD.

Ordinary differential equations (ODEs) are a system of equations used to describe changes of quantity or concentration of different species with respect to time. The mathematical formalism has been used successfully in an array of different fields, from social to natural sciences and biochemistry. Mathematical models build on the basis of ODEs are frequently used to describe dynamical phenomena as well as evolution. There are different tools (such as Matlab or Mathematica) that can be used to analytically solve a system of differential equation; however, in cases where an analytical solution cannot be found due to the complexity of the system, a numerical solution can be estimated. We have developed a user-friendly tool for ODE modeling called COmplex PAthway SImulator (COPASI) and MIEP adapted its use to ODE-based immune modeling.

In today’s Big Data era, computing tools and techniques help revolutionize immunological research in a meaningful way that is also shifting paradigm from top-down or bottom-up approaches to a middle-out approach. The latter is based on conceptualizing models and tools at the level that provides the richest data and connecting that to higher or lower levels for comprehensive integrated systems. In essence, building large multiscale models are an emerging trend mainly because these comprehensive models can be useful in providing great opportunities for model-driven knowledge discoveries and hypothesis generation that are clinically and pharmacologically relevant. In this chapter, we will highlight the technological advances that drive cutting-edge immune-informatics practices in a systems immunology research cycle. We present our next-generation multiscale modeling (MSM) technology, ENteric Immune Simulator (ENISI) MSM, a tool that integrates ordinary differential equation, partial differential equation, and agent-based modeling technologies and four spatiotemporal scales. ENISI MSM is a tool designed for modeling the mucosal immune responses that can simulate 10 8 –10 9 cells in high-performance simulations.

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.

The immune system is highly complex and dynamic, encompassing hierarchical interactions with dimensions ranging from nanometers to meters and time scales from nanoseconds to years. Infectious and immune-mediated diseases involve changes spanning many spatiotemporal scales, but are generally studied in compartmentalized single-scale settings. Computational and mathematical modeling provides an avenue for integrating and standardizing diverse and complex data and knowledge to comprehensively understand unforeseen mechanisms across spatiotemporal scales. Models can be used to guide experimental designs of wet-lab experiments that will most efficiently narrow the range of mechanisms to be explored, to determine those time points at which data will best distinguish between alternative hypotheses concerning the time course of wet-lab experiments or clinical studies, to generalize from in vitro results to in vivo, from wild-type to knockout animals, and from one animal model to another. This chapter describes the modeling process and tools.

Computational modeling techniques and tools are playing increasingly important roles in advancing a system-level mechanistic understanding of biological processes. The in silico experimentation can help shape and guide experimental and clinical efforts. There are an array of tools that can be used to accelerate creation of mathematical models and therefore facilitate generation of in silico simulations. In this chapter, we will briefly review some of these tools and provide hands-on modeling examples. Finally, the chapter proposes an examination of the relationship between model complexity, reliability and knowledge discovery, and model-driven hypothesis generation.

Crohn's disease (CD) is one of the two most prevailing clinical manifestations of inflammatory bowel disease (IBD), a disease that afflicts 1.4 million Americans and 4 million people worldwide. The current treatment paradigm for IBD includes medications with limited efficacy and significant side effects. Thus, there is an unmet clinical need for safer and more effective CD therapeutics. Drug discovery and development is a lengthy and costly process, especially in clinical trial stages requiring billions of dollars to advance new products to market. To accelerate the path to cures, we have developed a novel integrated approach for creating a synthetic patient population and testing the efficacy of novel therapeutics for CD in large clinical cohorts in silico. By using supervised machine learning methods, thousands of virtual patients were created based on clinical and immunological data of CD patients. A nutritional intervention, conjugated linoleic acid, a Phase IIa therapeutic targeting mothers against decapentaplegic homolog 7 (SMAD7), GED-0301, a novel preclinical stage lanthionine synthetase cyclase-like 2 (LANCL2) therapeutic, and a placebo were administered to 10,000 virtual patients in silico. Efficacy of these treatments on CD was evaluated by analyzing predicted changes of Crohn's Disease Activity Index (CDAI) scores and correlations with immunological variables. Our study shows that targeting LANCL2, a novel therapeutic target for inflammation and diabetes, significantly ameliorates disease activity of CD patients with an average drop of 126 points of CDAI for severe cases. This is the first study to design and implement an in silico Phase III clinical trial for CD to investigate response to treatment in terms of changes in pharmacodynamic immunological marker profiles (ie, TNF-α and IFN-γ) and health outcomes.

Computational modeling tools have increasingly important roles in our understanding of biological processes. Computer simulations guide experimental and clinical efforts in an unprecedented rate. This study presents ENteric Immunity Simulator - multiscale modeling (MSM) platform developed for high performance computing (HPC). ENISI MSMv2 is designed for modeling mucosal immune responses. The system scales to 109 agents in HPC simulations. This is an important step towards building large scale information processing representations of immune responses that integrate multiple modeling technologies and spatiotemporal scales ranging from nanoseconds to years and from molecules to systems. Our HPC-driven ENISI MSM platform combines the study of molecular pathways controlling T cell differentiation and tissue level interactions between cells to characterize novel mechanisms of immunoregulation at the gut mucosa.

Background Modeling of the immune system – a highly non-linear and complex system – requires practical and efficient data analytic approaches. The immune system is composed of heterogeneous cell populations and hundreds of cell types, such as neutrophils, eosinophils, macrophages, dendritic cells, T cells, and B cells. Each cell type is highly diverse and can be further differentiated into subsets with unique and overlapping functions. For example, CD4+ T cells can be differentiated into Th1, Th2, Th17, Th9, Th22, Treg, Tfh, as well as Tr1. Each subset plays different roles in the immune system. To study molecular mechanisms of cell differentiation, computational systems biology approaches can be used to represent these processes; however, the latter often requires building complex intracellular signaling models with a large number of equations to accurately represent intracellular pathways and biochemical reactions. Furthermore, studying the immune system entails integration of complex processes which occur at different time and space scales. Methods This study presents and compares four supervised learning methods for modeling CD4+ T cell differentiation: Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Linear Regression (LR). Application of supervised learning methods could reduce the complexity of Ordinary Differential Equations (ODEs)-based intracellular models by only focusing on the input and output cytokine concentrations. In addition, this modeling framework can be efficiently integrated into multiscale models. Results Our results demonstrate that ANN and RF outperform the other two methods. Furthermore, ANN and RF have comparable performance when applied to in silico data with and without added noise. The trained models were also able to reproduce dynamic behavior when applied to experimental data; in four out of five cases, model predictions based on ANN and RF correctly predicted the outcome of the system. Finally, the running time of different methods was compared, which confirms that ANN is considerably faster than RF. Conclusions Using machine learning as opposed to ODE-based method reduces the computational complexity of the system and allows one to gain a deeper understanding of the complex interplay between the different related entities. Published version

Background: CD4+ T cells play important roles in orchestrating immune responses, maintaining homeostasis and offering a functional control of immune responses. Depending on the cytokine milieu, a naive CD4+ T cell differentiates into different phenotypes. The T helper 17 (Th17) phenotype remains controversial since it has been identified as an inductor of inflammation, but it contributes to the regulatory arm of the immune response. With the increased availability of high-throughput datasets, data-driven computational modeling built upon a robust conceptual base becomes a powerful tool to build predictive, counterintuitive, dynamic gene expression networks. Results: We used a publicly available microarray dataset generated by Yosef et al [1]. to construct a dynamic model of Th17 differentiation. Following data treatment to tailor the data to our 2,000+ genes of interest, our data-driven modeling pipeline used the Ingenuity Pathway Analysis (IPA) platform to infer a proof-of-concept modeling network composed of 67 genes involved in induction, function, and maintenance of Th17 differentiation. In vitro time-course mRNA expression data was used to calibrate the network using COPASI. Computational simulations highlighted the potential role of IL-24 in shaping IL-17A producing responses, and local sensitivity analysis demonstrated that IL-24 is negatively correlated with FOXP3 expression, further regulating the balance between FOXP3 and ROR?t. Moreover, NLRP3 was identified as another potential therapeutic target to treat Th17-driven pathologies. Local sensitivity analyses on NLRP3 highlighted the positive correlation of NLRP3 with IP-10, GM-CSF, IFN?, and IL-17A. Conclusions: We established a modeling pipeline for constructing and calibrating data-driven models of Th17 differentiation. Our results highlighted the role of IL-24, SGK1, and NLRP3 as key modulators of Th17 cell differentiation. This modeling pipeline is an example of a modeling environment for building computational models with deterministic and stochastic features to generate new mechanistic immunological knowledge and to identify novel therapeutic targets for human diseases.

Computational modeling of the immune system requires practical and efficient data analytical approaches. The immune system is composed of heterogeneous cell populations and hundreds of cell types. Each cell type is highly diverse and can be further differentiated into subsets with unique and overlapping functions. Computational systems biology approaches can be used to represent and study molecular mechanisms of cell differentiation. However, such systematic modeling efforts require the building of complex intracellular signaling models with a large number of equations to accurately represent intracellular pathways and biochemical reactions. It also requires the integration of complex processes that occur at different scales of spatiotemporal magnitude. Application of supervised learning methods, such as artificial neural network (ANN), can reduce the complexity of ordinary differential equation (ODE)–based models of intracellular networks by focusing on the input and output cytokines. In addition, this modeling framework can be efficiently integrated into multiscale tissue-level models of the immune system.

Biological systems span several orders of magnitude in space and time from intracellular pathways to tissue-level processes. Many studies focus on molecular level events while other studies focus on cellular level and tissue level interactions. The immune system is highly complex and dynamic, encompassing hierarchical interactions with dimensions ranging from nanometers to meters and time scales from nanoseconds to years. To comprehensively model immunological processes computationally, multi-scale models are needed. However, the lack of multi-scale modeling tools can be a deterrent to advance the understanding of the immune system across scales. In this paper, we developed an object-oriented multi-scale modeling (MSM) platform, ENISI MSM, that integrates agent-based modeling (ABM), ordinary-differential equations (ODE), and partial differential equations (PDE) models. To our best knowledge, this is the first such multi-scale modeling platform that is capable of integrating ODE, PDE, and ABM models together. The tool is developed in Java and is object-oriented. For example, the agents are objects and the ODE and PDE solvers are also objects. ENISI MSM also provides user-friendly interfaces and visualizations. We developed a multi-scale CD4+ T cell differentiation model in the context of gut inflammatory and showed the effectiveness of ENISI MSM.

The development of gastritis during Helicobacter pylori infection is dependent on an activated adaptive immune response orchestrated by T helper (Th) cells. However, the relative contributions of the Th1 and Th17 subsets to gastritis and control of infection are still under investigation. To investigate the role of interleukin-21 (IL-21) in the gastric mucosa during H. pylori infection, we combined mathematical modeling of CD4+ T cell differentiation with in vivo mechanistic studies. We infected IL-21-deficient and wild-type mice with H. pylori strain SS1 and assessed colonization, gastric inflammation, cellular infiltration, and cytokine profiles. Chronically H. pylori-infected IL-21-deficient mice had higher H. pylori colonization, significantly less gastritis, and reduced expression of proinflammatory cytokines and chemokines compared to these parameters in infected wild-type littermates. These in vivo data were used to calibrate an H. pylori infection-dependent, CD4+ T cell-specific computational model, which then described the mechanism by which IL-21 activates the production of interferon gamma (IFN-γ) and IL-17 during chronic H. pylori infection. The model predicted activated expression of T-bet and RORγt and the phosphorylation of STAT3 and STAT1 and suggested a potential role of IL-21 in the modulation of IL-10. Driven by our modeling-derived predictions, we found reduced levels of CD4+ splenocyte-specific tbx21 and rorc expression, reduced phosphorylation of STAT1 and STAT3, and an increase in CD4+ T cell-specific IL-10 expression in H. pylori-infected IL-21-deficient mice. Our results indicate that IL-21 regulates Th1 and Th17 effector responses during chronic H. pylori infection in a STAT1- and STAT3-dependent manner, therefore playing a major role controlling H. pylori infection and gastritis., IMPORTANCE Helicobacter pylori is the dominant member of the gastric microbiota in more than 50% of the world’s population. H. pylori colonization has been implicated in gastritis and gastric cancer, as infection with H. pylori is the single most common risk factor for gastric cancer. Current data suggest that, in addition to bacterial virulence factors, the magnitude and types of immune responses influence the outcome of colonization and chronic infection. This study uses a combined computational and experimental approach to investigate how IL-21, a proinflammatory T cell-derived cytokine, maintains the chronic proinflammatory T cell immune response driving chronic gastritis during H. pylori infection. This research will also provide insight into a myriad of other infectious and immune disorders in which IL-21 is increasingly recognized to play a central role. The use of IL-21-related therapies may provide treatment options for individuals chronically colonized with H. pylori as an alternative to aggressive antibiotics.

The immune system is composed of heterogeneous cell populations and it includes several hundreds of distinct cells types such as neutrohils, eosinophils, basophils, macrophages, dendritic cells, CD4+ and CD8+ T cells, γδ T cells, mast cells, and B cells and each main cell type can be further differentiated into subsets with unique and overlapping functions. For example CD4+ T cells can be differentiated into T helper (Th)1, Th2, Thl7, and regulatory T cell (Treg) subsets. To study molecular mechanisms of cell differentiation, Systems Biology Markup Language (SBML) based Ordinary Differential Equation (ODE) models can be used for representing such processes. These intracellular signaling models often require many equations to accurately represent intracellular pathways and biochemical reactions. Another challenge in studying the immune system and immune responses is the need for integration of complex processes that occur at different time and space scales (i.e., populations, whole organism, tissue level, cellular and molecular) through multi-scale modeling. This study presents two novel neural network models for modeling CD4+ T cell differentiation and immune cell subset classification. The first model reduces the complex ODE intracellular model by focusing on the input and output cytokines and the second model establishes an automated subset classification based on molecular patterns expressed in immune cells. After training, the first model achieves small prediction errors of cytokine concentrations and the second model achieves 98% prediction rate for subset classification. Neural network algorithm and models have been widely used for many data mining tasks such as classification and pattern recognition. However, to the best of our knowledge this study is the first one applying the neural network algorithm for immune cell differentiation and subset classification. In addition, these novel neural network models can be computationally efficiently integrated into multi-scale models with limited computational costs.

Stochasticity is part of the nature of many biological processes and an important aspect in modeling and simulations of computational biology. Gillespie's algorithm was developed for modeling stochasticity of chemical reactions and has been broadly applied to stochastic modeling in computational biology. The Gillespie's algorithm accounts for the particle effect in chemical reactions. However, many biological processes including cellular and molecular immunological mechanisms have many other sources of stochasticity such as cell movement, ligand binding, or unaccounted variation in experimental settings. In this paper, we propose stochastic differential equations (SDE) being used as a generic stochastic modeling technique for systems immunology. SDE has been widely used in statistics and economics areas; but only a few isolated studies in computational biology are found using SDE to model stochastic behaviors of cells and molecules. In addition, to the best of our knowledge, there is no user-friendly SDE-based modeling tool available for computational biologists. This paper presents ENISI SDE, a web-based user-friendly stochastic modeling tool for computational biologists. This work provides three major contributions: (1) we discuss SDE and propose it as a generic approach for stochastic modeling in computational biology; (2) we develop ENISI SDE, a web-based user-friendly SDE modeling tool that only requires little extra effort beyond regular ODE-based modeling; (3) we use the model SDE modeling tool to study stochastic sources of cell heterogeneity in the context of a CD4+ T Cell differentiation process. The case study clearly shows the effectiveness of SDE as a stochastic modeling approach in biology in general and immunology in particular and the power of the SDE modeling tool we developed.

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 $10^7$ 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.

Background There is an inverse secular trend between the incidence of obesity and gastric colonization with Helicobacter pylori, a bacterium that can affect the secretion of gastric hormones that relate to energy homeostasis. H. pylori strains that carry the cag pathogenicity island (PAI) interact more intimately with gastric epithelial cells and trigger more extensive host responses than cag− strains. We hypothesized that gastric colonization with H. pylori strains differing in cag PAI status exert distinct effects on metabolic and inflammatory phenotypes. Methodology/Principal Findings To test this hypothesis, we examined metabolic and inflammatory markers in db/db mice and mice with diet-induced obesity experimentally infected with isogenic forms of H. pylori strain 26695: the cag PAI wild-type and its cag PAI mutant strain 99–305. H. pylori colonization decreased fasting blood glucose levels, increased levels of leptin, improved glucose tolerance, and suppressed weight gain. A response found in both wild-type and mutant H. pylori strain-infected mice included decreased white adipose tissue macrophages (ATM) and increased adipose tissue regulatory T cells (Treg) cells. Gene expression analyses demonstrated upregulation of gastric PPAR γ-responsive genes (i.e., CD36 and FABP4) in H. pylori-infected mice. The loss of PPAR γ in immune and epithelial cells in mice impaired the ability of H. pylori to favorably modulate glucose homeostasis and ATM infiltration during high fat feeding. Conclusions/Significance Gastric infection with some commensal strains of H. pylori ameliorates glucose homeostasis in mice through a PPAR γ-dependent mechanism and modulates macrophage and Treg cell infiltration into the abdominal white adipose tissue.

Clostridium difficile is an anaerobic bacterium that has re-emerged as a facultative pathogen and can cause nosocomial diarrhea, colitis or even death. Peroxisome proliferator-activated receptor (PPAR) γ has been implicated in the prevention of inflammation in autoimmune and infectious diseases; however, its role in the immunoregulatory mechanisms modulating host responses to C. difficile and its toxins remains largely unknown. To characterize the role of PPARγ in C. difficile-associated disease (CDAD), immunity and gut pathology, we used a mouse model of C. difficile infection in wild-type and T cell-specific PPARγ null mice. The loss of PPARγ in T cells increased disease activity and colonic inflammatory lesions following C. difficile infection. Colonic expression of IL-17 was upregulated and IL-10 downregulated in colons of T cell-specific PPARγ null mice. Also, both the loss of PPARγ in T cells and C. difficile infection favored Th17 responses in spleen and colonic lamina propria of mice with CDAD. MicroRNA (miRNA)-sequencing analysis and RT-PCR validation indicated that miR-146b was significantly overexpressed and nuclear receptor co-activator 4 (NCOA4) suppressed in colons of C. difficile-infected mice. We next developed a computational model that predicts the upregulation of miR-146b, downregulation of the PPARγ co-activator NCOA4, and PPARγ, leading to upregulation of IL-17. Oral treatment of C. difficile-infected mice with the PPARγ agonist pioglitazone ameliorated colitis and suppressed pro-inflammatory gene expression. In conclusion, our data indicates that miRNA-146b and PPARγ activation may be implicated in the regulation of Th17 responses and colitis in C. difficile-infected mice.

BACKGROUND: Inflammatory bowel disease (IBD) therapies are modestly successful and associated with significant side effects. Thus, the investigation of novel approaches to prevent colitis is important. Probiotic bacteria can produce immunoregulatory metabolites in vitro such as conjugated linoleic acid (CLA), a polyunsaturated fatty acid with potent anti-inflammatory effects. This study aimed to investigate the cellular and molecular mechanisms underlying the anti-inflammatory efficacy of probiotic bacteria using a mouse model of colitis. METHODOLOGY/PRINCIPAL FINDINGS: The immune modulatory mechanisms of VSL#3 probiotic bacteria and CLA were investigated in a mouse model of DSS colitis. Colonic specimens were collected for histopathology, gene expression and flow cytometry analyses. Immune cell subsets in the mesenteric lymph nodes (MLN), spleen, blood and colonic lamina propria cells were phenotypically and functionally characterized. Fecal samples and colonic contents were collected to determine the effect of VSL#3 and CLA on gut microbial diversity and CLA production. CLA and VSL#3 treatment ameliorated colitis and decreased colonic bacterial diversity, a finding that correlated with decreased gut pathology. Colonic CLA concentrations were increased in response to probiotic bacterial treatment, but without systemic distribution in blood. VSL#3 and CLA decreased macrophage accumulation in the MLN of mice with DSS colitis. The loss of PPAR γ in myeloid cells abrogated the protective effect of probiotic bacteria and CLA in mice with DSS colitis. CONCLUSIONS/SIGNIFICANCE: Probiotic bacteria modulate gut microbial diversity and favor local production of CLA in the colon that targets myeloid cell PPAR γ to suppress colitis.