Exploratory Studies With BT-11: A Proposed Orally Active Therapeutic for Crohn's Disease.

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.

Multiscale modeling of mucosal immune responses.

BACKGROUND: Computational techniques are becoming increasingly powerful and modeling tools for biological systems are of greater needs. Biological systems are inherently multiscale, from molecules to tissues and from nano-seconds to a lifespan of several years or decades. ENISI MSM integrates multiple modeling technologies to understand immunological processes from signaling pathways within cells to lesion formation at the tissue level. This paper examines and summarizes the technical details of ENISI, from its initial version to its latest cutting-edge implementation. IMPLEMENTATION: Object-oriented programming approach is adopted to develop a suite of tools based on ENISI. Multiple modeling technologies are integrated to visualize tissues, cells as well as proteins; furthermore, performance matching between the scales is addressed. CONCLUSION: We used ENISI MSM for developing predictive multiscale models of the mucosal immune system during gut inflammation. Our modeling predictions dissect the mechanisms by which effector CD4+ T cell responses contribute to tissue damage in the gut mucosa following immune dysregulation.Computational modeling techniques are playing increasingly important roles in advancing a systems-level mechanistic understanding of biological processes. Computer simulations guide and underpin experimental and clinical efforts. This study presents ENteric Immune Simulator (ENISI), a multiscale modeling tool for modeling the mucosal immune responses. ENISI's modeling environment can simulate in silico experiments from molecular signaling pathways to tissue level events such as tissue lesion formation. ENISI's architecture integrates multiple modeling technologies including ABM (agent-based modeling), ODE (ordinary differential equations), SDE (stochastic modeling equations), and PDE (partial differential equations). This paper focuses on the implementation and developmental challenges of ENISI. A multiscale model of mucosal immune responses during colonic inflammation, including CD4+ T cell differentiation and tissue level cell-cell interactions was developed to illustrate the capabilities, power and scope of ENISI MSM.

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.

Helicobacter pylori infection in a pig model is dominated by Th1 and cytotoxic CD8+ T cell responses.

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.

Multimodal, broadly neutralizing antibodies against SARS-CoV-2 identified by high-throughput native pairing of BCRs from bulk B cells.

TruAB Discovery is an approach that integrates cellular immunology, high-throughput immunosequencing, bioinformatics, and computational biology in order to discover naturally occurring human antibodies for prophylactic or therapeutic use. We adapted our previously described pairSEQ technology to pair B cell receptor heavy and light chains of SARS-CoV-2 spike protein-binding antibodies derived from enriched antigen-specific memory B cells and bulk antibody-secreting cells. We identified approximately 60,000 productive, in-frame, paired antibody sequences, from which 2,093 antibodies were selected for functional evaluation based on abundance, isotype and patterns of somatic hypermutation. The exceptionally diverse antibodies included RBD-binders with broad neutralizing activity against SARS-CoV-2 variants, and S2-binders with broad specificity against betacoronaviruses and the ability to block membrane fusion. A subset of these RBD- and S2-binding antibodies demonstrated robust protection against challenge in hamster and mouse models. This high-throughput approach can accelerate discovery of diverse, multifunctional antibodies against any target of interest.

Abscisic acid regulates inflammation via ligand-binding domain-independent activation of peroxisome proliferator-activated receptor gamma.

Abscisic acid (ABA) has shown efficacy in the treatment of diabetes and inflammation; however, its molecular targets and the mechanisms of action underlying its immunomodulatory effects remain unclear. This study investigates the role of peroxisome proliferator-activated receptor γ (PPAR γ) and lanthionine synthetase C-like 2 (LANCL2) as molecular targets for ABA. We demonstrate that ABA increases PPAR γ reporter activity in RAW 264.7 macrophages and increases ppar γ expression in vivo, although it does not bind to the ligand-binding domain of PPAR γ. LANCL2 knockdown studies provide evidence that ABA-mediated activation of macrophage PPAR γ is dependent on lancl2 expression. Consistent with the association of LANCL2 with G proteins, we provide evidence that ABA increases cAMP accumulation in immune cells. ABA suppresses LPS-induced prostaglandin E(2) and MCP-1 production via a PPAR γ-dependent mechanism possibly involving activation of PPAR γ and suppression of NF-κB and nuclear factor of activated T cells. LPS challenge studies in PPAR γ-expressing and immune cell-specific PPAR γ null mice demonstrate that ABA down-regulates toll-like receptor 4 expression in macrophages and T cells in vivo through a PPAR γ-dependent mechanism. Global transcriptomic profiling and confirmatory quantitative RT-PCR suggest novel candidate targets and demonstrate that ABA treatment mitigates the effect of LPS on the expression of genes involved in inflammation, metabolism, and cell signaling, in part, through PPAR γ. In conclusion, ABA decreases LPS-mediated inflammation and regulates innate immune responses through a bifurcating pathway involving LANCL2 and an alternative, ligand-binding domain-independent mechanism of PPAR γ activation.

Activation of PPARγ and δ by dietary punicic acid ameliorates intestinal inflammation in mice.

The goal of the present study was to elucidate the mechanisms of immunoregulation by which dietary punicic acid (PUA) prevents or ameliorates experimental inflammatory bowel disease (IBD). The expression of PPARγ and δ, their responsive genes and pro-inflammatory cytokines was assayed in the colonic mucosa. Immune cell-specific PPARγ null, PPARδ knockout and wild-type mice were treated with PUA and challenged with 2·5 % dextran sodium sulphate (DSS). The prophylactic efficacy of PUA was examined in an IL-10(-/-) model of IBD. The effect of PUA on the regulatory T-cell (Treg) compartment was also examined in mice with experimental IBD. PUA ameliorated spontaneous pan-enteritis in IL-10(-/-) mice and DSS colitis, up-regulated Foxp3 expression in Treg and suppressed TNF-α, but the loss of functional PPARγ or δ impaired these anti-inflammatory effects. At the cellular level, the macrophage-specific deletion of PPARγ caused a complete abrogation of the protective effect of PUA, whereas the deletion of PPARδ or intestinal epithelial cell-specific PPARγ decreased its anti-inflammatory efficacy. We provide in vivo molecular evidence demonstrating that PUA ameliorates experimental IBD by regulating macrophage and T-cell function through PPARγ- and δ-dependent mechanisms.

Abscisic Acid: A Novel Nutraceutical for Glycemic Control.

Abscisic acid is naturally present in fruits and vegetables, and it plays an important role in managing glucose homeostasis in humans. According to the latest U.S. dietary survey, about 92% of the population might have a deficient intake of ABA due to their deficient intake of fruits and vegetables. This review summarizes the in vitro, preclinical, mechanistic, and human translational findings obtained over the past 15 years in the study of the role of ABA in glycemic control. In 2007, dietary ABA was first reported to ameliorate glucose tolerance and obesity-related inflammation in mice. The most recent findings regarding the topic of ABA and its proposed receptor lanthionine synthetase C-like 2 in glycemic control and their interplay with insulin and glucagon-like peptide-1 suggest a major role for ABA in the physiological response to a glucose load in humans. Moreover, emerging evidence suggests that the ABA response might be dysfunctional in diabetic subjects. Follow on intervention studies in healthy individuals show that low-dose dietary ABA administration exerts a beneficial effect on the glycemia and insulinemia profiles after oral glucose load. These recent findings showing benefits in humans, together with extensive efficacy data in mouse models of diabetes and inflammatory disease, suggest the need for reference ABA values and its possible exploitation of the glycemia-lowering effects of ABA for preventative purposes. Larger clinical studies on healthy, prediabetic, and diabetic subjects are needed to determine whether addressing the widespread dietary ABA deficiency improves glucose control in humans.

Systems-wide analyses of mucosal immune responses to Helicobacter pylori at the interface between pathogenicity and symbiosis.

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.

An N,N-Bis(benzimidazolylpicolinoyl)piperazine (BT-11): A Novel Lanthionine Synthetase C-Like 2-Based Therapeutic for Inflammatory Bowel Disease.

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.

Modeling-Enabled Systems Nutritional Immunology.

This review highlights the fundamental role of nutrition in the maintenance of health, the immune response, and disease prevention. Emerging global mechanistic insights in the field of nutritional immunology cannot be gained through reductionist methods alone or by analyzing a single nutrient at a time. We propose to investigate nutritional immunology as a massively interacting system of interconnected multistage and multiscale networks that encompass hidden mechanisms by which nutrition, microbiome, metabolism, genetic predisposition, and the immune system interact to delineate health and disease. The review sets an unconventional path to apply complex science methodologies to nutritional immunology research, discovery, and development through "use cases" centered around the impact of nutrition on the gut microbiome and immune responses. Our systems nutritional immunology analyses, which include modeling and informatics methodologies in combination with pre-clinical and clinical studies, have the potential to discover emerging systems-wide properties at the interface of the immune system, nutrition, microbiome, and metabolism.

ENISI SDE: A New Web-Based Tool for Modeling Stochastic Processes.

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.

Supervised learning methods in modeling of CD4+ T cell heterogeneity.

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.

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection.

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close "neighborhood" of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

Modeling-Enabled Characterization of Novel NLRX1 Ligands.

Nucleotide-binding domain and leucine-rich repeat containing (NLR) family are intracellular sentinels of cytosolic homeostasis that orchestrate immune and inflammatory responses in infectious and immune-mediated diseases. NLRX1 is a mitochondrial-associated NOD-like receptor involved in the modulation of immune and metabolic responses. This study utilizes molecular docking approaches to investigate the structure of NLRX1 and experimentally assesses binding to naturally occurring compounds from several natural product and lipid databases. Screening of compound libraries predicts targeting of NLRX1 by conjugated trienes, polyketides, prenol lipids, sterol lipids, and coenzyme A-containing fatty acids for activating the NLRX1 pathway. The ligands of NLRX1 were identified by docking punicic acid (PUA), eleostearic acid (ESA), and docosahexaenoic acid (DHA) to the C-terminal fragment of the human NLRX1 (cNLRX1). Their binding and that of positive control RNA to cNLRX1 were experimentally determined by surface plasmon resonance (SPR) spectroscopy. In addition, the ligand binding sites of cNLRX1 were predicted in silico and validated experimentally. Target mutagenesis studies demonstrate that mutation of 4 critical residues ASP677, PHE680, PHE681, and GLU684 to alanine resulted in diminished affinity of PUA, ESA, and DHA to NLRX1. Consistent with the regulatory actions of NLRX1 on the NF-κB pathway, treatment of bone marrow derived macrophages (BMDM)s with PUA and DHA suppressed NF-κB activity in a NLRX1 dependent mechanism. In addition, a series of pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the regulatory function of PUA on colitis is NLRX1 dependent. Thus, we identified novel small molecules that bind to NLRX1 and exert anti-inflammatory actions.

Computational modeling of heterogeneity and function of CD4+ T cells.

The immune system is composed of many different cell types and hundreds of intersecting molecular pathways and signals. This large biological complexity requires coordination between distinct pro-inflammatory and regulatory cell subsets to respond to infection while maintaining tissue homeostasis. CD4+ T cells play a central role in orchestrating immune responses and in maintaining a balance between pro- and anti- inflammatory responses. This tight balance between regulatory and effector reactions depends on the ability of CD4+ T cells to modulate distinct pathways within large molecular networks, since dysregulated CD4+ T cell responses may result in chronic inflammatory and autoimmune diseases. The CD4+ T cell differentiation process comprises an intricate interplay between cytokines, their receptors, adaptor molecules, signaling cascades and transcription factors that help delineate cell fate and function. Computational modeling can help to describe, simulate, analyze, and predict some of the behaviors in this complicated differentiation network. This review provides a comprehensive overview of existing computational immunology methods as well as novel strategies used to model immune responses with a particular focus on CD4+ T cell differentiation.

Systematic reverse engineering of network topologies: a case study of resettable bistable cellular responses.

A focused theme in systems biology is to uncover design principles of biological networks, that is, how specific network structures yield specific systems properties. For this purpose, we have previously developed a reverse engineering procedure to identify network topologies with high likelihood in generating desired systems properties. Our method searches the continuous parameter space of an assembly of network topologies, without enumerating individual network topologies separately as traditionally done in other reverse engineering procedures. Here we tested this CPSS (continuous parameter space search) method on a previously studied problem: the resettable bistability of an Rb-E2F gene network in regulating the quiescence-to-proliferation transition of mammalian cells. From a simplified Rb-E2F gene network, we identified network topologies responsible for generating resettable bistability. The CPSS-identified topologies are consistent with those reported in the previous study based on individual topology search (ITS), demonstrating the effectiveness of the CPSS approach. Since the CPSS and ITS searches are based on different mathematical formulations and different algorithms, the consistency of the results also helps cross-validate both approaches. A unique advantage of the CPSS approach lies in its applicability to biological networks with large numbers of nodes. To aid the application of the CPSS approach to the study of other biological systems, we have developed a computer package that is available in Information S1.

Systems modeling of the role of interleukin-21 in the maintenance of effector CD4+ T cell responses during chronic Helicobacter pylori infection.

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.

Dietary abscisic acid ameliorates influenza-virus-associated disease and pulmonary immunopathology through a PPARγ-dependent mechanism.

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.

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.