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

Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series transcriptomic data

The discovery of upstream regulatory genes of a gene of interest still remains challenging. Here we applied a scalable computational method to unbiasedly predict candidate regulatory genes of critical transcription factors by searching the whole genome. We illustrated our approach with a case study on the master regulator FOXP3 of human primary regulatory T cells (Tregs). While target genes of FOXP3 have been identified, its upstream regulatory machinery still remains elusive. Our methodology selected five top-ranked candidates that were tested via proof-of-concept experiments. Following knockdown, three out of five candidates showed significant effects on the mRNA expression of FOXP3 across multiple donors. This provides insights into the regulatory mechanisms modulating FOXP3 transcriptional expression in Tregs. Overall, at the genome level this represents a high level of accuracy in predicting upstream regulatory genes of key genes of interest.

PASCAR: a multiscale framework to explore the design space of constitutive and inducible CAR T cells

CAR T cells are engineered to bind and destroy tumor cells by targeting overexpressed surface antigens. However, healthy cells expressing lower abundances of these antigens can also be lysed by CAR T cells. Various CAR T cell designs increase tumor cell elimination, whereas reducing damage to healthy cells. However, these efforts are costly and labor-intensive, constraining systematic exploration of potential hypotheses. We develop a protein abundance structured population dynamic model for CAR T cells (PASCAR), a framework that combines multiscale population dynamic models and multi-objective optimization approaches with data from cytometry and cytotoxicity assays to systematically explore the design space of constitutive and tunable CAR T cells. PASCAR can quantitatively describe in vitro and in vivo results for constitutive and inducible CAR T cells and can successfully predict experiments outside the training data. Our exploration of the CAR design space reveals that optimal CAR affinities in the intermediate range of dissociation constants effectively reduce healthy cell lysis, whereas maintaining high tumor cell-killing rates. Furthermore, our modeling offers guidance for optimizing CAR expressions in synthetic notch CAR T cells. PASCAR can be extended to other CAR immune cells.

An integrative systems biology view of host-pathogen interactions: The regulation of immunity and homeostasis is concomitant, flexible, and smart

The systemic bio-organization of humans and other mammals is essentially "preprogrammed", and the basic interacting units, the cells, can be crudely mapped into discrete sets of developmental lineages and maturation states. Over several decades, however, and focusing on the immune system, we and others invoked evidence - now overwhelming - suggesting dynamic acquisition of cellular properties and functions, through tuning, re-networking, chromatin remodeling, and adaptive differentiation. The genetically encoded "algorithms" that govern the integration of signals and the computation of new states are not fully understood but are believed to be "smart", designed to enable the cells and the system to discriminate meaningful perturbations from each other and from "noise". Cellular sensory and response properties are shaped in part by recurring temporal patterns, or features, of the signaling environment. We compared this phenomenon to associative brain learning. We proposed that interactive cell learning is subject to selective pressures geared to performance, allowing the response of immune cells to injury or infection to be progressively coordinated with that of other cell types across tissues and organs. This in turn is comparable to supervised brain learning. Guided by feedback from both the tissue itself and the neural system, resident or recruited antigen-specific and innate immune cells can eradicate a pathogen while simultaneously sustaining functional homeostasis. As informative memories of immune responses are imprinted both systemically and within the targeted tissues, it is desirable to enhance tissue preparedness by incorporating attenuated-pathogen vaccines and informed choice of tissue-centered immunomodulators in vaccination schemes. Fortunately, much of the "training" that a living system requires to survive and function in the face of disturbances from outside or within is already incorporated into its design, so it does not need to deep-learn how to face a new challenge each time from scratch. Instead, the system learns from experience how to efficiently select a built-in strategy, or a combination of those, and can then use tuning to refine its organization and responses. Efforts to identify and therapeutically augment such strategies can take advantage of existing integrative modeling approaches. One recently explored strategy is boosting the flux of uninfected cells into and throughout an infected tissue to rinse and replace the infected cells.

CD4+ T Cell Regulatory Network Underlies the Decrease in Th1 and the Increase in Anergic and Th17 Subsets in Severe COVID-19

In this model we use a dynamic and multistable Boolean regulatory network to provide a mechanistic explanation of the lymphopenia and dysregulation of CD4+ T cell subsets in COVID-19 and provide therapeutic targets. Using a previous model, the cytokine micro-environments found in mild, moderate, and severe COVID-19 with and without TGF-β and IL-10 was we simulated. It shows that as the severity of the disease increases, the number of antiviral Th1 cells decreases, while the the number of Th1-like regulatory and exhausted cells and the proportion between Th1 and Th1R cells increases. The addition of the regulatory cytokines TFG-β and IL-10 makes the Th1 attractor unstable and favors the Th17 and regulatory subsets. This is associated with the contradictory signals in the micro-environment that activate SOCS proteins that block the signaling pathways. Furthermore, it determined four possible therapeutic targets that increase the Th1 compartment in severe COVID-19: the activation of the IFN-γ pathway, or the inhibition of TGF-β or IL-10 pathways or SOCS1 protein; from these, inhibiting SOCS1 has the lowest number of predicted collateral effects. Finally, a tool is provided that allows simulations of specific cytokine environments and predictions of CD4 T cell subsets and possible interventions, as well as associated secondary effects.

Computational modeling of complex bioenergetic mechanisms that modulate CD4+ T cell effector and regulatory functions

We built a computational model of complex mechanisms at the intersection of immunity and metabolism that regulate CD4+ T cell effector and regulatory functions by using coupled ordinary differential equations. The model provides an improved understanding of how CD4+ T cells are shaping the immune response during Clostridioides difficile infection (CDI), and how they may be targeted pharmacologically to produce a more robust regulatory (Treg) response, which is associated with improved disease outcomes during CDI and other diseases. LANCL2 activation during CDI decreased the effector response, increased regulatory response, and elicited metabolic changes that favored Treg. Interestingly, LANCL2 activation provided greater immune and metabolic modulation compared to the addition of exogenous IL-2. Additionally, we identified gluconeogenesis via PEPCK-M as potentially responsible for increased immunosuppressive behavior in Treg cells. The model can perturb immune signaling and metabolism within a CD4+ T cell and obtain clinically relevant outcomes that help identify novel drug targets for infectious, autoimmune, metabolic, and neurodegenerative diseases.

Interferon-β acts directly on T cells to prolong allograft survival by enhancing regulatory T cell induction through Foxp3 acetylation

Type I interferons (IFNs) are pleiotropic cytokines with potent antiviral properties that also promote protective T cell and humoral immunity. Paradoxically, type I IFNs, including the widely expressed IFNβ, also have immunosuppressive properties, including promoting persistent viral infections and treating T-cell-driven, remitting-relapsing multiple sclerosis. Although associative evidence suggests that IFNβ mediates these immunosuppressive effects by impacting regulatory T (Treg) cells, mechanistic links remain elusive. Here, we found that IFNβ enhanced graft survival in a Treg-cell-dependent murine transplant model. Genetic conditional deletion models revealed that the extended allograft survival was Treg cell-mediated and required IFNβ signaling on T cells. Using an in silico computational model and analysis of human immune cells, we found that IFNβ directly promoted Treg cell induction via STAT1- and P300-dependent Foxp3 acetylation. These findings identify a mechanistic connection between the immunosuppressive effects of IFNβ and Treg cells, with therapeutic implications for transplantation, autoimmunity, and malignancy.

Targeting Protein O-GlcNAcylation, a Link between Type 2 Diabetes Mellitus and Inflammatory Disease

Unresolved hyperglycaemia, a hallmark of type 2 diabetes mellitus (T2DM), is a well characterised manifestation of altered fuel homeostasis and our understanding of its role in the pathologic activation of the inflammatory system continues to grow. Metabolic disorders like T2DM trigger changes in the regulation of key cellular processes such as cell trafficking and proliferation, and manifest as chronic inflammatory disorders with severe long-term consequences. Activation of inflammatory pathways has recently emerged as a critical link between T2DM and inflammation. A substantial body of evidence has suggested that this is due in part to increased flux through the hexosamine biosynthetic pathway (HBP). The HBP, a unique nutrient-sensing metabolic pathway, produces the activated amino sugar UDP-GlcNAc which is a critical substrate for protein O-GlcNAcylation, a dynamic, reversible post-translational glycosylation of serine and threonine residues in target proteins. Protein O-GlcNAcylation impacts a range of cellular processes, including inflammation, metabolism, trafficking, and cytoskeletal organisation. As increased HBP flux culminates in increased protein O-GlcNAcylation, we propose that targeting O-GlcNAcylation may be a viable therapeutic strategy for the prevention and management of glucose-dependent pathologies with inflammatory components.

Continuous Modeling of T CD4 Lymphocyte Activation and Function

T CD4+ cells are central to the adaptive immune response against pathogens. Their activation is induced by the engagement of the T-cell receptor by antigens, and of co-stimulatory receptors by molecules also expressed on antigen presenting cells. Then, a complex network of intracellular events reinforce, diversify and regulate the initial signals, including dynamic metabolic processes that strongly influence both the activation state and the differentiation to effector cell phenotypes. The regulation of cell metabolism is controlled by the nutrient sensor adenosine monophosphate-activated protein kinase (AMPK), which drives the balance between oxidative phosphorylation (OXPHOS) and glycolysis. Herein, we put forward a 51-node continuous mathematical model that describes the temporal evolution of the early events of activation, integrating a circuit of metabolic regulation into the main routes of signaling. The model simulates the induction of anergy due to defective co-stimulation, the CTLA-4 checkpoint blockade, and the differentiation to effector phenotypes induced by external cytokines. It also describes the adjustment of the OXPHOS-glycolysis equilibrium by the action of AMPK as the effector function of the T cell develops. The development of a transient phase of increased OXPHOS before induction of a sustained glycolytic phase during differentiation to the Th1, Th2 and Th17 phenotypes is shown. In contrast, during Treg differentiation, glycolysis is subsequently reduced as cell metabolism is predominantly polarized towards OXPHOS. These observations are in agreement with experimental data suggesting that OXPHOS produces an ATP reservoir before glycolysis boosts the production of metabolites needed for protein synthesis, cell function, and growth.

Beyond the Finish Line: The Impact and Dynamics of Biomarkers in Physical Exercise-A Narrative Review

The research of biomarkers continues to emerge as a developing academic field which is attracting substantial interest. The study of biomarkers proves to be useful in developing and implementing new screening methods for a wide variety of diseases including in the sports area, whether for leisure activities or professional sports. Novel research has brought into question the immune system and the limitations it may impose on sports practicing. As the well-being of athletes is a priority, the state of their immune function offers valuable information regarding their health status and their ability to continue training. The assessment of various biomarkers may contribute to a more accurate risk stratification and subsequent prevention of some invalidating or even fatal pathologies such as the sudden cardiac death. Therefore, we have reviewed several studies that included sports-related pathology or specific morphofunctional alterations for which some immune biomarkers may represent an expression of the underlying mechanism. These include the defensins, immunoglobulin A (IgA), interleukin-6 (IL-6), the tumoral necrosis factor α (TNF-α), and the white blood cells (WBC) count. Similarly, also of significant interest are various endocrine biomarkers, such as cortisol and testosterone, as well as anabolic or catabolic markers, respectively. Literature data highlight that these values are greatly influenced not only by the duration, but also by the intensity of the physical exercise; moderate training sessions actually enhance the immune function of the body, while a significant increase in both duration and intensity of sports activity acts as a deleterious factor. Therefore, in this paper we aim to highlight the importance of biomarkers’ evaluation in connection with sports activities and a subsequent more adequate approach towards personalized training regimens.

Modeling the Th17 and Tregs Paradigm: Implications for Cancer Immunotherapy

CD4 + T cell differentiation is governed by gene regulatory and metabolic networks, with both networks being highly interconnected and able to adapt to external stimuli. Th17 and Tregs differentiation networks play a critical role in cancer, and their balance is affected by the tumor microenvironment (TME). Factors from the TME mediate recruitment and expansion of Th17 cells, but these cells can act with pro or anti-tumor immunity. Tregs cells are also involved in tumor development and progression by inhibiting antitumor immunity and promoting immunoevasion. Due to the complexity of the underlying molecular pathways, the modeling of biological systems has emerged as a promising solution for better understanding both CD4 + T cell differentiation and cancer cell behavior. In this review, we present a context-dependent vision of CD4 + T cell transcriptomic and metabolic network adaptability. We then discuss CD4 + T cell knowledge-based models to extract the regulatory elements of Th17 and Tregs differentiation in multiple CD4 + T cell levels. We highlight the importance of complementing these models with data from omics technologies such as transcriptomics and metabolomics, in order to better delineate existing Th17 and Tregs bifurcation mechanisms. We were able to recompilate promising regulatory components and mechanisms of Th17 and Tregs differentiation under normal conditions, which we then connected with biological evidence in the context of the TME to better understand CD4 + T cell behavior in cancer. From the integration of mechanistic models with omics data, the transcriptomic and metabolomic reprograming of Th17 and Tregs cells can be predicted in new models with potential clinical applications, with special relevance to cancer immunotherapy.

The role of GLS1-mediated glutaminolysis/2-HG/H3K4me3 and GSH/ROS signals in Th17 responses counteracted by PPARγ agonists

Background: Peroxisome proliferator-activated receptor gamma (PPARγ) has the ability to counter Th17 responses, but the full mechanisms remain elusive. Herein, we aimed to elucidate this process in view of cellular metabolism, especially glutaminolysis.

Methods: MTT, CCK-8, Annexin V-FITC/PI staining or trypan blue exclusion assays were used to analyze cytotoxicity. Flow cytometry and Q-PCR assays were applied to determine Th17 responses. The detection of metabolite levels using commercial kits and rate-limiting enzyme expression using western blotting assays was performed to illustrate the metabolic activity. ChIP assays were used to examine H3K4me3 modifications. Mouse models of dextran sulfate sodium (DSS)-induced colitis and house dust mite (HDM)/lipopolysaccharide (LPS)-induced asthma were established to confirm the mechanisms studied in vitro.

Results: The PPARγ agonists rosiglitazone and pioglitazone blocked glutaminolysis but not glycolysis under Th17-skewing conditions, as indicated by the detection of intracellular lactate and α-KG and the fluorescence ratios of BCECF-AM. The PPARγ agonists prevented the utilization of glutamine and thus directly limited Th17 responses even when Foxp3 was deficient. The mechanisms were ascribed to restricted conversion of glutamine to glutamate by reducing the expression of the rate-limiting enzyme GLS1, which was confirmed by GLS1 overexpression. Replenishment of α-KG and 2-HG but not succinate weakened the effects of PPARγ agonists, and α-KG-promoted Th17 responses were dampened by siIDH1/2. Inhibition of KDM5 but not KDM4/6 restrained the inhibitory effect of PPARγ agonists on IL-17A expression, and the H3K4me3 level in the promoter and CNS2 region of the il-17 gene locus down-regulated by PPARγ agonists was rescued by 2-HG and GLS1 overexpression. However, the limitation of PPARγ agonists on the mRNA expression of RORγt was unable to be stopped by 2-HG but was attributed to GSH/ROS signals subsequent to GLS1. The exact role of PPARγ was proved by GW9662 or PPARγ knockout, and the mechanisms for PPARγ-inhibited Th17 responses were further confirmed by GLS1 overexpression in vivo.

Conclusion: PPARγ agonists repressed Th17 responses by counteracting GLS1-mediated glutaminolysis/2-HG/H3K4me3 and GSH/ROS signals, which is beneficial for Th17 cell-related immune dysregulation.

Goals in Nutrition Science 2020-2025

Five years ago, with the editorial board of Frontiers in Nutrition, we took a leap of faith to outline the Goals for Nutrition Science - the way we see it (1). Now, in 2020, we can put ourselves to the test and take a look back. Without a doubt we got it right with several of the key directions. To name a few, Sustainable Development Goals (SDGs) for Food and Nutrition are part of the global public agenda, and the SDGs contribute to the structuring of international science and research. Nutritional Science has become a critical element in strengthening work on the SDGs, and the development of appropriate methodologies is built on the groundwork of acquiring and analyzing big datasets. Investigation of the Human Microbiome is providing novel insight on the interrelationship between nutrition, the immune system and disease. Finally, with an advanced definition of the gut-brain-axis we are getting a glimpse into the potential for Nutrition and Brain Health. Various milestones have been achieved, and any look into the future will have to consider the lessons learned from Covid-19 and the sobering awareness about the frailty of our food systems in ensuring global food security. With a view into the coming 5 years from 2020 to 2025, the editorial board has taken a slightly different approach as compared to the previous Goals article. A mind map has been created to outline the key topics in nutrition science. Not surprisingly, when looking ahead, the majority of scientific investigation required will be in the areas of health and sustainability. Johannes le Coutre, Field Chief Editor, Frontiers in Nutrition.

Integrative computational approach identifies drug targets in CD4+ T-cell-mediated immune disorders

CD4⁺ T cells provide adaptive immunity against pathogens and abnormal cells, and they are also associated with various immune-related diseases. CD4⁺ T cells’ metabolism is dysregulated in these pathologies and represents an opportunity for drug discovery and development. Genome-scale metabolic modeling offers an opportunity to accelerate drug discovery by providing high-quality information about possible target space in the context of a modeled disease. Here, we develop genome-scale models of naïve, Th1, Th2, and Th17 CD4⁺ T-cell subtypes to map metabolic perturbations in rheumatoid arthritis, multiple sclerosis, and primary biliary cholangitis. We subjected these models to in silico simulations for drug response analysis of existing FDA-approved drugs and compounds. Integration of disease-specific differentially expressed genes with altered reactions in response to metabolic perturbations identified 68 drug targets for the three autoimmune diseases. In vitro experimental validation, together with literature-based evidence, showed that modulation of fifty percent of identified drug targets suppressed CD4⁺ T cells, further increasing their potential impact as therapeutic interventions. Our approach can be generalized in the context of other diseases, and the metabolic models can be further used to dissect CD4⁺ T-cell metabolism.

Exercise-induced downregulation of serum interleukin-6 and tumor necrosis factor-alpha in Egyptian handball players

Muscles of candidates work at various grades of intensity during handball exercises according to the pace of exercise. The movement pattern involves large number of contractions, feints, dodges and numerous changes in movements, all of which are highly responsible for changes in trainer's organs, including the immune system. In this study, inflammatory mediators involving interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) in serum of 18 Egyptian male handball players, selected from Tanta club handball under 21 year’s old team, were analyzed. The analysis was established on samples collected just before and immediately after intermediate reasonable exercise via enzyme linked immunosorbent assay (ELISA). Moreover, white blood cells (WBCs) count and other hematological markers including hemoglobin %, hematocrit value, and platelet count were assessed. Our results demonstrated a significant decrease in the levels of IL-6 and TNF-α after exercise compared to those before exercise. This was coupled with an increase in WBCs and platelets count. It is also noteworthy that there was a significant positive correlation between serum levels of IL-6 and TNF-α in the study subjects coupled with a significant negative correlation between IL-6 and WBCs after the exercise. Therefore, it is concluded that intermediate reasonable exercises result in decreased levels of IL-6 and TNF-α, which result in decreasing of the inflammation and help in healing and rapid recovery of muscles of the candidates.

A Dynamic Quantitative Systems Pharmacology Model of Inflammatory Bowel Disease: Part 1 - Model Framework

A mechanistic, multistate, mathematical model of inflammatory bowel disease (IBD) was developed by including key biological mechanisms in blood and gut, including cell differentiation, cytokine production, and clinical biomarkers. The model structure is consistent between healthy volunteers and IBD disease phenotype, with 24 parameters changed between diseases. Modular nature of the model allows for easy incorporation of new mechanisms or modification of existing interactions. Model simulations for steady-state levels of proteins and cells in the blood and gut using a population approach are consistent with published data. By simulating the response of two clinical biomarkers, C-reactive protein and fecal calprotectin, to parameter perturbations, the model explores hypotheses for possible treatment mechanisms. With additional experimental validation and addition of drug treatments, the model provides a platform to test hypothesis on treatment effects in IBD.

Circulating immune cell phenotype dynamics reflect the strength of tumor-immune cell interactions in patients during immunotherapy

The extent to which immune cell phenotypes in the peripheral blood reflect within-tumor immune activity prior to and early in cancer therapy is unclear. To address this question, we studied the population dynamics of tumor and immune cells, and immune phenotypic changes, using clinical tumor and immune cell measurements and single-cell genomic analyses. These samples were serially obtained from a cohort of advanced gastrointestinal cancer patients enrolled in a trial with chemotherapy and immunotherapy. Using an ecological population model, fitted to clinical tumor burden and immune cell abundance data from each patient, we find evidence of a strong tumor-circulating immune cell interaction in responder patients but not in those patients that progress on treatment. Upon initiation of therapy, immune cell abundance increased rapidly in responsive patients, and once the peak level is reached tumor burden decreases, similar to models of predator-prey interactions; these dynamic patterns were absent in nonresponder patients. To interrogate phenotype dynamics of circulating immune cells, we performed single-cell RNA sequencing at serial time points during treatment. These data show that peripheral immune cell phenotypes were linked to the increased strength of patients' tumor-immune cell interaction, including increased cytotoxic differentiation and strong activation of interferon signaling in peripheral T cells in responder patients. Joint modeling of clinical and genomic data highlights the interactions between tumor and immune cell populations and reveals how variation in patient responsiveness can be explained by differences in peripheral immune cell signaling and differentiation soon after the initiation of immunotherapy.

Interplay between Cytokine Circuitry and Transcriptional Regulation Shaping Helper T Cell Pathogenicity and Plasticity in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic disorder manifested as Crohn’s disease (CD) and ulcerative colitis (UC) characterized by intestinal inflammation and involves a dysregulated immune response against commensal microbiota through the activation of CD4 T helper cells. T helper cell differentiation to effector or regulatory phenotypes is controlled by cytokine networks and transcriptional regulators. Distinct polarized T helper cells are able to alter their phenotypes to adapt to diverse and fluctuating physiological environments. T helper cells exhibit intrinsic instability and flexibility to express cytokines of other lineages or transdifferentiate from one T helper cell type to another in response to various perturbations from physiological cytokine milieu as a means of promoting local immunity in response to injury or ensure tissue homeostasis. Furthermore, functional plasticity and diversity of T helper cells are associated with pathogenicity and are critical for immune homeostasis and prevention of autoimmunity. In this review, we provide deeper insights into the combinatorial extrinsic and intrinsic signals that control plasticity and transdifferentiation of T helper cells and also highlight the potential of exploiting the genetic reprogramming plasticity of T helper cells in the treatment of IBD.

High-resolution computational modeling of immune responses in the gut

Background

Helicobacter pylori causes gastric cancer in 1–2% of cases but is also beneficial for protection against allergies and gastroesophageal diseases. An estimated 85% of H. pylori–colonized individuals experience no detrimental effects. To study the mechanisms promoting host tolerance to the bacterium in the gastrointestinal mucosa and systemic regulatory effects, we investigated the dynamics of immunoregulatory mechanisms triggered by H. pylori using a high-performance computing–driven ENteric Immunity SImulator multiscale model. Immune responses were simulated by integrating an agent-based model, ordinary, and partial differential equations.

Results

The outputs were analyzed using 2 sequential stages: the first used a partial rank correlation coefficient regression–based and the second a metamodel-based global sensitivity analysis. The influential parameters screened from the first stage were selected to be varied for the second stage. The outputs from both stages were combined as a training dataset to build a spatiotemporal metamodel. The Sobol indices measured time-varying impact of input parameters during initiation, peak, and chronic phases of infection. The study identified epithelial cell proliferation and epithelial cell death as key parameters that control infection outcomes. In silico validation showed that colonization with H. pylori decreased with a decrease in epithelial cell proliferation, which was linked to regulatory macrophages and tolerogenic dendritic cells.

Conclusions

The hybrid model of H. pylori infection identified epithelial cell proliferation as a key factor for successful colonization of the gastric niche and highlighted the role of tolerogenic dendritic cells and regulatory macrophages in modulating the host responses and shaping infection outcomes.

A case for the reuse and adaptation of mechanistic computational models to study transplant immunology

Computational mechanistic models constitute powerful tools for summarizing our knowledge in quantitative terms, providing mechanistic understanding, and generating new hypotheses. The present review emphasizes the advantages of reusing publicly available computational models as a way to capitalize on existing knowledge, reduce the number of parameters that need to be adjusted to experimental data, and facilitate hypothesis generation. Finally, it includes a step-by-step example of the reuse and adaptation of an existing model of immune responses to tuberculosis, tumor growth, and blood pathogens, to study donor-specific antibody (DSA) responses. This review aims to illustrate the benefit of leveraging the currently available computational models in immunology to accelerate the study of alloimmune responses, and to encourage modelers to share their models to further advance our understanding of transplant immunology.

Opportunities for improving cancer treatment using systems biology

Current cancer therapies target a limited set of tumor features, rather than considering the tumor as a whole. Systems biology aims to reveal therapeutic targets associated with a variety of facets in an individual's tumor, such as genetic heterogeneity and its evolution, cancer cell-autonomous phenotypes, and microenvironmental signaling. These disparate characteristics can be reconciled using mathematical modeling that incorporates concepts from ecology and evolution. This provides an opportunity to predict tumor growth and response to therapy, to tailor patient-specific approaches in real time or even prospectively. Importantly, as data regarding patient tumors is often available from only limited time points during treatment, systems-based approaches can address this limitation by interpolating longitudinal events within a principled framework. This review outlines areas in medicine that could benefit from systems biology approaches to deconvolve the complexity of cancer.

Interleukin-21 (IL-21) Downregulates Dendritic Cell Cytokine Responses to Helicobacter pylori and Modulates T Lymphocyte IL-17A Expression in Peyer's Patches during Infection

Interleukin-21 (IL-21), a cytokine produced by many subsets of activated immune cells, is critical for driving inflammation in several models. Using Helicobacter pylori infection as a model for chronic mucosal infection, we previously published that IL-21 is required for the development of gastritis in response to infection. Concomitant with protection from chronic inflammation, H. pylori-infected IL-21^-/- mice exhibited limited Th1 and Th17 responses in their gastric mucosa. Here we report that H. pylori-infected IL-21^-/- mice express significantly higher levels of IL-17A than H. pylori-infected wild-type (WT) mice in the Peyer's patches and mesenteric lymph nodes. This led us to hypothesize that IL-21 may indirectly regulate H. pylori-specific T cell responses by controlling dendritic cell (DC) functions in mucosa-associated lymphoid tissue. It was found that IL-21 treatment reduced the ability of dendritic cells to produce proinflammatory cytokines in response to H. pylori While H. pylori increased the expression of costimulatory proteins on DCs, IL-21 reduced the expression of CD40 in the presence of H. pylori Also, Th17 recall responses were intact when DCs were used as antigen-presenting cells in the presence of IL-21, but IL-21 did impact the ability of DCs to induce antigen-specific proliferation. These data suggest that IL-21, while proinflammatory in most settings, downregulates the proinflammatory cytokine microenvironment through modulating the cytokine expression of DCs, indirectly modifying IL-17A expression. Understanding how these proinflammatory cytokines are regulated will advance our understanding of how and why H. pylori infection may be tolerated in some individuals while it causes gastritis, ulcers, or cancer in others.

Th17 Cells in Helicobacter pylori Infection: a Dichotomy of Help and Harm

Helicobacter pylori is a Gram-negative bacterium that infects the gastric epithelia of its human host. Everyone who is colonized with these pathogenic bacteria can develop gastric inflammation, termed gastritis. Additionally, a small proportion of colonized people develop more adverse outcomes, including gastric ulcer disease, gastric adenocarcinoma, or gastric mucosa-associated lymphoid tissue lymphoma. The development of these adverse outcomes is dependent on the establishment of a chronic inflammatory response. The development and control of this chronic inflammatory response are significantly impacted by CD4⁺ T helper cell activity. Noteworthy, T helper 17 (Th17) cells, a proinflammatory subset of CD4⁺ T cells, produce several proinflammatory cytokines that activate innate immune cell antimicrobial activity, drive a pathogenic immune response, regulate B cell responses, and participate in wound healing. Therefore, this review was written to take an intricate look at the involvement of Th17 cells and their affiliated cytokines (interleukin-17A [IL-17A], IL-17F, IL-21, IL-22, and IL-26) in regulating the immune response to H. pylori colonization and carcinogenesis.

From Discrete to Continuous Modeling of Lymphocyte Development and Plasticity in Chronic Diseases

The molecular events leading to differentiation, development, and plasticity of lymphoid cells have been subject of intense research due to their key roles in multiple pathologies, such as lymphoproliferative disorders, tumor growth maintenance and chronic diseases. The emergent roles of lymphoid cells and the use of high-throughput technologies have led to an extensive accumulation of experimental data allowing the reconstruction of gene regulatory networks (GRN) by integrating biochemical signals provided by the microenvironment with transcriptional modules of lineage-specific genes. Computational modeling of GRN has been useful for the identification of molecular switches involved in lymphoid specification, prediction of microenvironment-dependent cell plasticity, and analyses of signaling events occurring downstream the activation of antigen recognition receptors. Among most common modeling strategies to analyze the dynamical behavior of GRN, discrete dynamic models are widely used for their capacity to capture molecular interactions when a limited knowledge of kinetic parameters is present. However, they are less powerful when modeling complex systems sensitive to biochemical gradients. To compensate it, discrete models may be transformed into regulatory networks that includes state variables and parameters varying within a continuous range. This approach is based on a system of differential equations dynamics with regulatory interactions described by fuzzy logic propositions. Here, we discuss the applicability of this method on modeling of development and plasticity processes of adaptive lymphocytes, and its potential implications in the study of pathological landscapes associated to chronic diseases.

O-GlcNAcylation in immunity and inflammation: An intricate system (Review)

Chronic, low‑grade inflammation associated with obesity and diabetes result from the infiltration of adipose and vascular tissue by immune cells and contributes to cardiovascular complications. Despite an incomplete understanding of the mechanistic underpinnings of immune cell differentiation and inflammation, O‑GlcNAcylation, the addition of O‑linked N‑acetylglucosamine (O‑GlcNAc) to cytoplasmic, nuclear and mitochondrial proteins by the two cycling enzymes, the O‑linked N‑acetylglucosamine transferase (OGT) and the O‑GlcNAcase (OGA), may contribute to fine‑tune immunity and inflammation in both physiological and pathological conditions. Early studies have indicated that O‑GlcNAcylation of proteins play a pro‑inflammatory role in diabetes and insulin resistance, whereas subsequent studies have demonstrated that this post‑translational modification could also be protective against acute injuries. These studies suggest that diverse types of insults result in dynamic changes to O‑GlcNAcylation patterns, which fluctuate with cellular metabolism to promote or inhibit inflammation. In this review, the current understanding of O‑GlcNAcylation and its adaptive modulation in immune and inflammatory responses is summarized.

T Cell Subsets' Distribution in Elite Karate Athletes as a Response to Physical Effort

Background

During karate fight muscles work at a very high intensity, and their contractions are extremely strong. The movement pattern contains a great number of feints, dodges, frequent changes in movements’ tempo and direction, hits and kicks, all of which is highly stressful for athlete’s organism, including the immune system.

Methods

T lymphocyte subsets’ distribution and selected cytokines in peripheral blood of three elite karate athletes aged 30 years old (range 21–31 years) with minimum 15 years of training experience were analysed in two experiments: at the beginning of the preparatory phase (a progressive test until exhaustion; an analysis of immune system’s selected parameters and cardiorespiratory fitness measures, including VO₂max, VE, AT, MVV, MET, Rf), and during the start-up period (Karate Championships; an analysis of selected parameters of the immune system).

Results

Maximal effort caused an increase in total lymphocyte percentage (p<0.05). A decrease in Th cells in recovery (p<0.05 compared to post-exercise), and an increase in Th naïve cells in recovery (p<0.05) were observed. A significant increase in CD8⁺ central memory cells (p<0.05) was found only after the progressive test, and no changes in both central and effector memory subsets of CD4⁺ cells during the first experiment. An increase (p<0.05) in Treg and Th1 and a decrease (p<0.05) in Th2 cells’ distribution during recovery time were found. Additionally, changes (p<0.05) in TNF-α, IL-6, IL-8, IL-10 and IL-12p70 were observed.

Conclusion

Post-effort disorder in immune balance activated compensation pathways involving CD4⁺ cells. Treg and Th1 cells seem to be subsets of key importance involved in the anabolic effect of physical effort, at least among karate athletes.

Analysis of selected T cell subsets in peripheral blood after exhaustive effort among elite soccer players

Introduction

Physical exercise induces developing of naïve T lymphocyte subsets into polarised effector ones by immune system. The aim of the study was to examine the influence of exhaustive effort on the selected Th cell subsets and inflammation markers among soccer players.

Materials and methods

Fourteen soccer players aged 18 (16-21) years old performed the progressive efficiency test on mechanical treadmill. Th and Tc memory lymphocytes’ subsets and selected cytokine concentrations pre-exercise, post-exercise and in recovery were analysed by flow cytometry.

Results

Physical effort induced changes in Th cell percentages. Increase in recovery Treg and Th17 cell subsets’ percentages in comparison to pre-exercise values were observed (10.98 (9.83-14.07) vs. 3.95 (3.15-5.53), P < 0.001 in autumn; 10.58 (7.54-12.67) vs. 4.83 (3.73-6.81), P < 0.010 in spring for Treg and 29.21 (26.34-32.16) vs. 21.64 (18.48-25.76) in autumn; 27.15 (24.60-29.16) vs. 17.43 (15.83-19.77) in spring, both P < 0.010; for Th17, respectively). Increases in Th1 cell percentages post-exercise (31.86 (28.72-33.72) in autumn, 25.60 (21.50-29.19) in spring, both P < 0.010) and in recovery (34.64 (31.21-38.20) in autumn; 26.68 (25.17-28.07) in spring, both P < 0.001) compared to pre-exercise (22.70 (21.21-26.74) for autumn and 15.64 (14.38-19.63) for spring, respectively) were found. Interestingly, no changes in Th2 cell subsets were found. Post-exercise and recovery changes in IL-6, IL-8, TNF-α and IL-10 were also observed.

Conclusions

It seems that the given effort in the progressive test induced an anabolic effect being related not only with cytokine profile but also with CD4⁺ T cells’ differentiation and peripheral distribution.

Dynamic optimal experimental design yields marginal improvement over steady-state results for computational maximisation of regulatory T-cell induction in ex vivo culture

The isolation of T cells, followed by differentiation into Regulatory T cells (Tregs), and re‐transplantation into the body has been proposed as a therapeutic option for inflammatory bowel disease. A key requirement for making this a viable therapeutic option is the generation of a large population of Tregs. However, cytokines in the local microenvironment can impact the yield of Tregs during differentiation. As such, experimental design is an essential part of evaluating the importance of different cytokine concentrations for Treg differentiation. However, currently only single, constant concentrations of the cytokines have been investigated. This work addresses this point by performing experimental design in silico which seeks to maximize the predicted induction of Tregs relative to Th17 cells, by selecting an optimal input function for the concentrations of TGF‐β, IL‐2, IL‐6, and IL‐23. While this approach sounds promising, the results show that only marginal improvements in the concentration of Tregs can be achieved for dynamic cytokine profiles as compared to optimal constant concentrations. Since constant concentrations are easier to implement in experiments, it is recommended for this particular system to keep the concentrations constant where IL‐6 should be kept low and high concentrations of TGF‐β, IL‐2, and IL‐23 should be used.

Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology

Immune responses to pathogens are complex and not well understood in many diseases, and this is especially true for infections by persistent pathogens. One mechanism that allows for long-term control of infection while also preventing an over-zealous inflammatory response from causing extensive tissue damage is for the immune system to balance pro- and anti-inflammatory cells and signals. This balance is dynamic and the immune system responds to cues from both host and pathogen, maintaining a steady state across multiple scales through continuous feedback. Identifying the signals, cells, cytokines, and other immune response factors that mediate this balance over time has been difficult using traditional research strategies. Computational modeling studies based on data from traditional systems can identify how this balance contributes to immunity. Here we provide evidence from both experimental and mathematical/computational studies to support the concept of a dynamic balance operating during persistent and other infection scenarios. We focus mainly on tuberculosis, currently the leading cause of death due to infectious disease in the world, and also provide evidence for other infections. A better understanding of the dynamically balanced immune response can help shape treatment strategies that utilize both drugs and host-directed therapies.

Role of Cytokine Combinations on CD4+ T Cell Differentiation, Partial Polarization, and Plasticity: Continuous Network Modeling Approach

Purpose: We put forward a theoretical and dynamical approach for the semi-quantitative analysis of CD4+ T cell differentiation, the process by which cells with different functions are derived from activated CD4+ T naïve lymphocytes in the presence of particular cytokine microenvironments. We explore the system-level mechanisms that underlie CD4+ T plasticity-the conversion of polarized cells to phenotypes different from those originally induced. Methods: In this paper, we extend a previous study based on a Boolean network to a continuous framework. The network includes transcription factors, signaling pathways, as well as autocrine and exogenous cytokines, with interaction rules derived using fuzzy logic. Results: This approach allows us to assess the effect of relative differences in the concentrations and combinations of exogenous and endogenous cytokines, as well as of the expression levels of diverse transcription factors. We found either abrupt or gradual differentiation patterns between observed phenotypes depending on critical concentrations of single or multiple environmental cytokines. Plastic changes induced by environmental cytokines were observed in conditions of partial phenotype polarization in the T helper 1 to T helper 2 transition. On the other hand, the T helper 17 to induced regulatory T-cells transition was highly dependent on cytokine concentrations, with TGFβ playing a prime role. Conclusion: The present approach is useful to further understand the system-level mechanisms underlying observed patterns of CD4+ T differentiation and response to changing immunological challenges.

A Mechanistic Computational Model Reveals That Plasticity of CD4+ T Cell Differentiation Is a Function of Cytokine Composition and Dosage

CD4⁺ T cells provide cell-mediated immunity in response to various antigens. During an immune response, naïve CD4⁺ T cells differentiate into specialized effector T helper (Th1, Th2, and Th17) cells and induced regulatory (iTreg) cells based on a cytokine milieu. In recent studies, complex phenotypes resembling more than one classical T cell lineage have been experimentally observed. Herein, we sought to characterize the capacity of T cell differentiation in response to the complex extracellular environment. We constructed a comprehensive mechanistic (logical) computational model of the signal transduction that regulates T cell differentiation. The model's dynamics were characterized and analyzed under 511 different environmental conditions. Under these conditions, the model predicted the classical as well as the novel complex (mixed) T cell phenotypes that can co-express transcription factors (TFs) related to multiple differentiated T cell lineages. Analyses of the model suggest that the lineage decision is regulated by both compositions and dosage of signals that constitute the extracellular environment. In this regard, we first characterized the specific patterns of extracellular environments that result in novel T cell phenotypes. Next, we predicted the inputs that can regulate the transition between the canonical and complex T cell phenotypes in a dose-dependent manner. Finally, we predicted the optimal levels of inputs that can simultaneously maximize the activity of multiple lineage-specifying TFs and that can drive a phenotype toward one of the co-expressed TFs. In conclusion, our study provides new insights into the plasticity of CD4⁺ T cell differentiation, and also acts as a tool to design testable hypotheses for the generation of complex T cell phenotypes by various input combinations and dosages.

Simulation of Stimulation: Cytokine Dosage and Cell Cycle Crosstalk Driving Timing-Dependent T Cell Differentiation

Triggering an appropriate protective response against invading agents is crucial to the effectiveness of human innate and adaptive immunity. Pathogen recognition and elimination requires integration of a myriad of signals from many different immune cells. For example, T cell functioning is not qualitatively, but quantitatively determined by cellular and humoral signals. Tipping the balance of signals, such that one of these is favored or gains advantage on another one, may impact the plasticity of T cells. This may lead to switching their phenotypes and, ultimately, modulating the balance between proliferating and memory T cells to sustain an appropriate immune response. We hypothesize that, similar to other intracellular processes such as the cell cycle, the process of T cell differentiation is the result of: (i) pleiotropy (pattern) and (ii) magnitude (dosage/concentration) of input signals, as well as (iii) their timing and duration. That is, a flexible, yet robust immune response upon recognition of the pathogen may result from the integration of signals at the right dosage and timing. To investigate and understand how system's properties such as T cell plasticity and T cell-mediated robust response arise from the interplay between these signals, the use of experimental toolboxes that modulate immune proteins may be explored. Currently available methodologies to engineer T cells and a recently devised strategy to measure protein dosage may be employed to precisely determine, for example, the expression of transcription factors responsible for T cell differentiation into various subtypes. Thus, the immune response may be systematically investigated quantitatively. Here, we provide a perspective of how pattern, dosage and timing of specific signals, called interleukins, may influence T cell activation and differentiation during the course of the immune response. We further propose that interleukins alone cannot explain the phenotype variability observed in T cells. Specifically, we provide evidence that the dosage of intercellular components of both the immune system and the cell cycle regulating cell proliferation may contribute to T cell activation, differentiation, as well as T cell memory formation and maintenance. Altogether, we envision that a qualitative (pattern) and quantitative (dosage) crosstalk between the extracellular milieu and intracellular proteins leads to T cell plasticity and robustness. The understanding of this complex interplay is crucial to predict and prevent scenarios where tipping the balance of signals may be compromised, such as in autoimmunity.

A systems pharmacology model for inflammatory bowel disease

Motivation

The literature on complex diseases is abundant but not always quantitative. This is particularly so for Inflammatory Bowel Disease (IBD), where many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. We propose the elaboration and validation of a logic network for IBD able to capture the information available in the literature that will facilitate the identification/validation of therapeutic targets.

Results

In this article, we propose a logic model for Inflammatory Bowel Disease (IBD) which consists of 43 nodes and 298 qualitative interactions. The model presented is able to describe the pathogenic mechanisms of the disorder and qualitatively describes the characteristic chronic inflammation. A perturbation analysis performed on the IBD network indicates that the model is robust. Also, as described in clinical trials, a simulation of anti-TNFα, anti-IL2 and Granulocyte and Monocyte Apheresis showed a decrease in the Metalloproteinases node (MMPs), which means a decrease in tissue damage. In contrast, as clinical trials have demonstrated, a simulation of anti-IL17 and anti-IFNγ or IL10 overexpression therapy did not show any major change in MMPs expression, as corresponds to a failed therapy. The model proved to be a promising in silico tool for the evaluation of potential therapeutic targets, the identification of new IBD biomarkers, the integration of IBD polymorphisms to anticipate responders and non-responders and can be reduced and transformed in quantitative model/s.

Modulation of Immune Signaling and Metabolism Highlights Host and Fungal Transcriptional Responses in Mouse Models of Invasive Pulmonary Aspergillosis

Incidences of invasive pulmonary aspergillosis, an infection caused predominantly by Aspergillus fumigatus, have increased due to the growing number of immunocompromised individuals. While A. fumigatus is reliant upon deficiencies in the host to facilitate invasive disease, the distinct mechanisms that govern the host-pathogen interaction remain enigmatic, particularly in the context of distinct immune modulating therapies. To gain insights into these mechanisms, RNA-Seq technology was utilized to sequence RNA derived from lungs of 2 clinically relevant, but immunologically distinct murine models of IPA on days 2 and 3 post inoculation when infection is established and active disease present. Our findings identify notable differences in host gene expression between the chemotherapeutic and steroid models at the interface of immunity and metabolism. RT-qPCR verified model specific and nonspecific expression of 23 immune-associated genes. Deep sequencing facilitated identification of highly expressed fungal genes. We utilized sequence similarity and gene expression to categorize the A. fumigatus putative in vivo secretome. RT-qPCR suggests model specific gene expression for nine putative fungal secreted proteins. Our analysis identifies contrasting responses by the host and fungus from day 2 to 3 between the two models. These differences may help tailor the identification, development, and deployment of host- and/or fungal-targeted therapeutics.

Informatics for Nutritional Genetics and Genomics

While traditional nutrition science is focusing on nourishing population, modern nutrition is aiming at benefiting individual people. The goal of modern nutritional research is to promote health, prevent diseases, and improve performance. With the development of modern technologies like bioinformatics, metabolomics, and molecular genetics, this goal is becoming more attainable. In this chapter, we will discuss the new concepts and technologies especially in informatics and molecular genetics and genomics, and how they have been implemented to change the nutrition science and lead to the emergence of new branches like nutrigenomics, nutrigenetics, and nutritional metabolomics.

Empirical modeling of T cell activation predicts interplay of host cytokines and bacterial indole

Adoptive transfer of anti-inflammatory FOXP3+ Tregs has gained attention as a new therapeutic strategy for auto-inflammatory disorders such as Inflammatory Bowel Disease. The isolated cells are conditioned in vitro to obtain a sufficient number of anti-inflammatory FOXP3+ Tregs that can be reintroduced into the patient to potentially reduce the pathologic inflammatory response. Previous evidence suggests that microbiota metabolites can potentially condition cells during the in vitro expansion/differentiation step. However, the number of combinations of cytokines and metabolites that can be varied is large, preventing a purely experimental investigation which would determine optimal cell therapeutic outcomes. To address this problem, a combined experimental and modeling approached is investigated here: an artificial neural network model was trained to predict the steady-state T cell population phenotype after differentiation with a variety of host cytokines and the microbial metabolite indole. This artificial neural network model was able to both reliably predict the phenotype of these T cell populations and also uncover unexpected conditions for optimal Treg differentiation that were subsequently verified experimentally. Biotechnol. Bioeng. 2017;114: 2660-2667. © 2017 Wiley Periodicals, Inc.

The CD4+ T cell regulatory network mediates inflammatory responses during acute hyperinsulinemia: a simulation study

Background

Obesity is frequently linked to insulin resistance, high insulin levels, chronic inflammation, and alterations in the behaviour of CD4+ T cells. Despite the biomedical importance of this condition, the system-level mechanisms that alter CD4+ T cell differentiation and plasticity are not well understood.

Results

We model how hyperinsulinemia alters the dynamics of the CD4+ T regulatory network, and this, in turn, modulates cell differentiation and plasticity. Different polarizing microenvironments are simulated under basal and high levels of insulin to assess impacts on cell-fate attainment and robustness in response to transient perturbations. In the presence of high levels of insulin Th1 and Th17 become more stable to transient perturbations, and their basin sizes are augmented, Tr1 cells become less stable or disappear, while TGFβ producing cells remain unaltered. Hence, the model provides a dynamic system-level framework and explanation to further understand the documented and apparently paradoxical role of TGFβ in both inflammation and regulation of immune responses, as well as the emergence of the adipose Treg phenotype. Furthermore, our simulations provide new predictions on the impact of the microenvironment in the coexistence of the different cell types, suggesting that in pro-Th1, pro-Th2 and pro-Th17 environments effector and regulatory cells can coexist, but that high levels of insulin severely diminish regulatory cells, especially in a pro-Th17 environment.

Conclusions

This work provides a first step towards a system-level formal and dynamic framework to integrate further experimental data in the study of complex inflammatory diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0436-y) contains supplementary material, which is available to authorized users.

NLRX1 Regulates Effector and Metabolic Functions of CD4+ T Cells

Nucleotide oligomerization domain-like receptor X1 (NLRX1) has been implicated in viral response, cancer progression, and inflammatory disorders; however, its role as a dual modulator of CD4⁺ T cell function and metabolism has not been defined. The loss of NLRX1 results in increased disease severity, populations of Th1 and Th17 cells, and inflammatory markers (IFN-γ, TNF-α, and IL-17) in mice with dextran sodium sulfate-induced colitis. To further characterize this phenotype, we used in vitro CD4⁺ T cell-differentiation assays and show that NLRX1-deficient T cells have a greater ability to differentiate into an inflammatory phenotype and possess greater proliferation rates. Further, NLRX1^-/- cells have a decreased responsiveness to immune checkpoint pathways and greater rates of lactate dehydrogenase activity. When metabolic effects of the knockout are impaired, NLRX1-deficient cells do not display significant differences in differentiation or proliferation. To confirm the role of NLRX1 specifically in T cells, we used an adoptive-transfer model of colitis. Rag2^-/- mice receiving NLRX1^-/- naive or effector T cells experienced increased disease activity and effector T cell populations, whereas no differences were observed between groups receiving wild-type or NLRX1^-/- regulatory T cells. Metabolic effects of NLRX1 deficiency are observed in a CD4-specific knockout of NLRX1 within a Citrobacter rodentium model of colitis. The aerobic glycolytic preference in NLRX1^-/- effector T cells is combined with a decreased sensitivity to immunosuppressive checkpoint pathways to provide greater proliferative capabilities and an inflammatory phenotype bias leading to increased disease severity.

Modeling the Mechanisms by Which HIV-Associated Immunosuppression Influences HPV Persistence at the Oral Mucosa

Human immunodeficiency virus (HIV)-infected patients are at an increased risk of co-infection with human papilloma virus (HPV), and subsequent malignancies such as oral cancer. To determine the role of HIV-associated immune suppression on HPV persistence and pathogenesis, and to investigate the mechanisms underlying the modulation of HPV infection and oral cancer by HIV, we developed a mathematical model of HIV/HPV co-infection. Our model captures known immunological and molecular features such as impaired HPV-specific effector T helper 1 (Th1) cell responses, and enhanced HPV infection due to HIV. We used the model to determine HPV prognosis in the presence of HIV infection, and identified conditions under which HIV infection alters HPV persistence in the oral mucosa system. The model predicts that conditions leading to HPV persistence during HIV/HPV co-infection are the permissive immune environment created by HIV and molecular interactions between the two viruses. The model also determines when HPV infection continues to persist in the short run in a co-infected patient undergoing antiretroviral therapy. Lastly, the model predicts that, under efficacious antiretroviral treatment, HPV infections will decrease in the long run due to the restoration of CD4+ T cell numbers and protective immune responses.

The Functional Stability of FOXP3 and RORγt in Treg and Th17 and Their Therapeutic Applications

The balance of CD4⁺CD25⁺FOXP3⁺ regulatory T cells (Tregs) and effector T cells plays a key role in maintaining immune homeostasis, while the imbalance of them is related to many inflammatory diseases in both human and mice. Here we discuss about the plasticity of Tregs and Th17 cells, and the related human diseases resulted from the imbalance of them. Further, we will focus on the mechanisms regulating the plasticity between Tregs and Th17 cells and the potential therapeutic strategies by targeting regulators of the expression and activity of FOXP3 and RORγt or regulators of Treg/Th17 balance in autoimmune diseases, allergy, infection, and cancer.

Modeling the Role of Lanthionine Synthetase C-Like 2 (LANCL2) in the Modulation of Immune Responses to Helicobacter pylori Infection

Immune responses to Helicobacter pylori are orchestrated through complex balances of host-bacterial interactions, including inflammatory and regulatory immune responses across scales that can lead to the development of the gastric disease or the promotion of beneficial systemic effects. While inflammation in response to the bacterium has been reasonably characterized, the regulatory pathways that contribute to preventing inflammatory events during H. pylori infection are incompletely understood. To aid in this effort, we have generated a computational model incorporating recent developments in the understanding of H. pylori-host interactions. Sensitivity analysis of this model reveals that a regulatory macrophage population is critical in maintaining high H. pylori colonization without the generation of an inflammatory response. To address how this myeloid cell subset arises, we developed a second model describing an intracellular signaling network for the differentiation of macrophages. Modeling studies predicted that LANCL2 is a central regulator of inflammatory and effector pathways and its activation promotes regulatory responses characterized by IL-10 production while suppressing effector responses. The predicted impairment of regulatory macrophage differentiation by the loss of LANCL2 was simulated based on multiscale linkages between the tissue-level gastric mucosa and the intracellular models. The simulated deletion of LANCL2 resulted in a greater clearance of H. pylori, but also greater IFNγ responses and damage to the epithelium. The model predictions were validated within a mouse model of H. pylori colonization in wild-type (WT), LANCL2 whole body KO and myeloid-specific LANCL2-/- (LANCL2^Myeloid) mice, which displayed similar decreases in H. pylori burden, CX3CR1+ IL-10-producing macrophages, and type 1 regulatory (Tr1) T cells. This study shows the importance of LANCL2 in the induction of regulatory responses in macrophages and T cells during H. pylori infection.

Translating nutritional immunology into drug development for inflammatory bowel disease

PURPOSE OF REVIEW

To highlight recent advances in the understanding of nutritional immunology and in the development of novel therapeutics for inflammatory bowel disease (IBD).

RECENT FINDINGS

We highlight the variety of factors that contribute to the interaction of the immune system and nutrition including the microbiome and the nervous system stimulation of the gut. We describe the potential for therapeutic development in IBD. Further, we review the cellular metabolic effects on immune activation and promising therapeutic targets. Finally, we show how the progression of understanding the role of lanthionine synthetase C-like 2 has encompassed both nutritional and therapeutic advances and led to the development of novel oral small molecule therapeutics for IBD.

SUMMARY

Nutritional immunology and drug development research centered around immunoregulatory pathways can provide safer and more effective drugs while accelerating the path to cures.

Mucosal Interactions between Genetics, Diet, and Microbiome in Inflammatory Bowel Disease

Numerous reviews have discussed gut microbiota composition changes during inflammatory bowel diseases (IBD), particularly Crohn’s disease (CD). However, most studies address the observed effects by focusing on studying the univariate connection between disease and dietary-induced alterations to gut microbiota composition. The possibility that these effects may reflect a number of other interconnected (i.e., pantropic) mechanisms, activated in parallel, particularly concerning various bacterial metabolites, is in the process of being elucidated. Progress seems, however, hampered by various difficult-to-study factors interacting at the mucosal level. Here, we highlight some of such factors that merit consideration, namely: (1) the contribution of host genetics and diet in altering gut microbiome, and in turn, the crosstalk among secondary metabolic pathways; (2) the interdependence between the amount of dietary fat, the fatty acid composition, the effects of timing and route of administration on gut microbiota community, and the impact of microbiota-derived fatty acids; (3) the effect of diet on bile acid composition, and the modulator role of bile acids on the gut microbiota; (4) the impact of endogenous and exogenous intestinal micronutrients and metabolites; and (5) the need to consider food associated toxins and chemicals, which can introduce confounding immune modulating elements (e.g., antioxidant and phytochemicals in oils and proteins). These concepts, which are not mutually exclusive, are herein illustrated paying special emphasis on physiologically inter-related processes.

Bistability analyses of CD4+ T follicular helper and regulatory cells during Helicobacter pylori infection

T follicular helper (Tfh) cells are a highly plastic subset of CD4+ T cells specialized in providing B cell help and promoting inflammatory and effector responses during infectious and immune-mediate diseases. Helicobacter pylori is the dominant member of the gastric microbiota and exerts both beneficial and harmful effects on the host. Chronic inflammation in the context of H. pylori has been linked to an upregulation in T helper (Th)1 and Th17 CD4+ T cell phenotypes, controlled in part by the cytokine, interleukin-21. This study investigates the differentiation and regulation of Tfh cells, major producers of IL-21, in the immune response to H. pylori challenge. To better understand the conditions influencing the promotion and inhibition of a chronically elevated Tfh population, we used top-down and bottom-up approaches to develop computational models of Tfh and T follicular regulatory (Tfr) cell differentiation. Stability analysis was used to characterize the presence of two bi-stable steady states in the calibrated Tfh/Tfr models. Stochastic simulation was used to illustrate the ability of the parameter set to dictate two distinct behavioral patterns. Furthermore, sensitivity analysis helped identify the importance of various parameters on the establishment of Tfh and Tfr cell populations. The core network model was expanded into a more comprehensive and predictive model by including cytokine production and signaling pathways. From the expanded network, the interaction between TGFB-Induced Factor Homeobox 1 (Tgif1) and the retinoid X receptor (RXR) was displayed to exert control over the determination of the Tfh response. Model simulations predict that Tgif1 and RXR respectively induce and curtail Tfh responses. This computational hypothesis was validated experimentally by assaying Tgif1, RXR and Tfh in stomachs of mice infected with H. pylori.

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.

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.

Analyzing Th17 cell differentiation dynamics using a novel integrative modeling framework for time-course RNA sequencing data

Background

The differentiation of naive CD 4⁺ helper T (Th) cells into effector Th17 cells is steered by extracellular cytokines that activate and control the lineage specific transcriptional program. While the inducing cytokine signals and core transcription factors driving the differentiation towards Th17 lineage are well known, detailed mechanistic interactions between the key components are poorly understood.

Results

We develop an integrative modeling framework which combines RNA sequencing data with mathematical modeling and enables us to construct a mechanistic model for the core Th17 regulatory network in a data-driven manner.

Conclusions

Our results show significant evidence, for instance, for inhibitory mechanisms between the transcription factors and reveal a previously unknown dependency between the dosage of the inducing cytokine TGF β and the expression of the master regulator of competing (induced) regulatory T cell lineage. Further, our experimental validation approves this dependency in Th17 polarizing conditions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0223-6) contains supplementary material, which is available to authorized users.

Mathematical Modeling of Early Cellular Innate and Adaptive Immune Responses to Ischemia/Reperfusion Injury and Solid Organ Allotransplantation

A mathematical model of the early inflammatory response in transplantation is formulated with ordinary differential equations. We first consider the inflammatory events associated only with the initial surgical procedure and the subsequent ischemia/reperfusion (I/R) events that cause tissue damage to the host as well as the donor graft. These events release damage-associated molecular pattern molecules (DAMPs), thereby initiating an acute inflammatory response. In simulations of this model, resolution of inflammation depends on the severity of the tissue damage caused by these events and the patient's (co)-morbidities. We augment a portion of a previously published mathematical model of acute inflammation with the inflammatory effects of T cells in the absence of antigenic allograft mismatch (but with DAMP release proportional to the degree of graft damage prior to transplant). Finally, we include the antigenic mismatch of the graft, which leads to the stimulation of potent memory T cell responses, leading to further DAMP release from the graft and concomitant increase in allograft damage. Regulatory mechanisms are also included at the final stage. Our simulations suggest that surgical injury and I/R-induced graft damage can be well-tolerated by the recipient when each is present alone, but that their combination (along with antigenic mismatch) may lead to acute rejection, as seen clinically in a subset of patients. An emergent phenomenon from our simulations is that low-level DAMP release can tolerize the recipient to a mismatched allograft, whereas different restimulation regimens resulted in an exaggerated rejection response, in agreement with published studies. We suggest that mechanistic mathematical models might serve as an adjunct for patient- or sub-group-specific predictions, simulated clinical studies, and rational design of immunosuppression.

Modeling the Regulatory Mechanisms by Which NLRX1 Modulates Innate Immune Responses to Helicobacter pylori Infection

Helicobacter pylori colonizes half of the world’s population as the dominant member of the gastric microbiota resulting in a lifelong chronic infection. Host responses toward the bacterium can result in asymptomatic, pathogenic or even favorable health outcomes; however, mechanisms underlying the dual role of H. pylori as a commensal versus pathogenic organism are not well characterized. Recent evidence suggests mononuclear phagocytes are largely involved in shaping dominant immunity during infection mediating the balance between host tolerance and succumbing to overt disease. We combined computational modeling, bioinformatics and experimental validation in order to investigate interactions between macrophages and intracellular H. pylori. Global transcriptomic analysis on bone marrow-derived macrophages (BMDM) in a gentamycin protection assay at six time points unveiled the presence of three sequential host response waves: an early transient regulatory gene module followed by sustained and late effector responses. Kinetic behaviors of pattern recognition receptors (PRRs) are linked to differential expression of spatiotemporal response waves and function to induce effector immunity through extracellular and intracellular detection of H. pylori. We report that bacterial interaction with the host intracellular environment caused significant suppression of regulatory NLRC3 and NLRX1 in a pattern inverse to early regulatory responses. To further delineate complex immune responses and pathway crosstalk between effector and regulatory PRRs, we built a computational model calibrated using time-series RNAseq data. Our validated computational hypotheses are that: 1) NLRX1 expression regulates bacterial burden in macrophages; and 2) early host response cytokines down-regulate NLRX1 expression through a negative feedback circuit. This paper applies modeling approaches to characterize the regulatory role of NLRX1 in mechanisms of host tolerance employed by macrophages to respond to and/or to co-exist with intracellular H. pylori.

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.