Systems Modeling of Interactions between Mucosal Immunity and the Gut Microbiome during Clostridium difficile Infection

Oral Omilancor Treatment Ameliorates Clostridioides difficile Infection During IBD Through Novel Immunoregulatory Mechanisms Mediated by LANCL2 Activation

BACKGROUND

Clostridioides difficile infection (CDI) is an opportunistic infection of the gastrointestinal tract, commonly associated with antibiotic administration, that afflicts almost 500 000 people yearly only in the United States. CDI incidence and recurrence is increased in inflammatory bowel disease (IBD) patients. Omilancor is an oral, once daily, first-in-class, gut-restricted, immunoregulatory therapeutic in clinical development for the treatment of IBD.

METHODS

Acute and recurrent murine models of CDI and the dextran sulfate sodium-induced concomitant model of IBD and CDI were utilized to determine the therapeutic efficacy of oral omilancor. To evaluate the protective effects against C. difficile toxins, in vitro studies with T84 cells were also conducted. 16S sequencing was employed to characterize microbiome composition.

RESULTS

Activation of the LANCL2 pathway by oral omilancor and its downstream host immunoregulatory changes decreased disease severity and inflammation in the acute and recurrence models of CDI and the concomitant model of IBD/CDI. Immunologically, omilancor treatment increased mucosal regulatory T cell and decreased pathogenic T helper 17 cell responses. These immunological changes resulted in increased abundance and diversity of tolerogenic gut commensal bacterial strains in omilancor-treated mice. Oral omilancor also resulted in accelerated C. difficile clearance in an antimicrobial-free manner. Furthermore, omilancor provided protection from toxin damage, while preventing the metabolic burst observed in intoxicated epithelial cells.

CONCLUSIONS

These data support the development of omilancor as a novel host-targeted, antimicrobial-free immunoregulatory therapeutic for the treatment of IBD patients with C. difficile-associated disease and pathology with the potential to address the unmet clinical needs of ulcerative colitis and Crohn's disease patients with concomitant CDI.

Modulation of colonic immunometabolic responses during Clostridioides difficile infection ameliorates disease severity and inflammation

Clostridioides difficile infection (CDI) is the leading cause of antibiotic-associated diarrhea, and its clinical symptoms can span from asymptomatic colonization to pseudomembranous colitis and even death. The current standard of care for CDI is antibiotic treatment to achieve bacterial clearance; however, 15 to 35% of patients experience recurrence after initial response to antibiotics. We have conducted a comprehensive, global colonic transcriptomics analysis of a 10-day study in mice to provide new insights on the local host response during CDI and identify novel host metabolic mechanisms with therapeutic potential. The analysis indicates major alterations of colonic gene expression kinetics at the acute infection stage, that are restored during the recovery phase. At the metabolic level, we observe a biphasic response pattern characterized by upregulated glycolytic metabolism during the peak of inflammation, while mitochondrial metabolism predominates during the recovery/healing stage. Inhibition of glycolysis via 2-Deoxy-D-glucose (2-DG) administration during CDI decreases disease severity, protects from mortality, and ameliorates colitis in vivo. Additionally, 2-DG also protects intestinal epithelial cells from C. difficile toxin damage, preventing loss of barrier integrity and secretion of proinflammatory mediators. These data postulate the pharmacological targeting of host immunometabolic pathways as novel treatment modalities for CDI.

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.

A Computational Model of Bacterial Population Dynamics in Gastrointestinal Yersinia enterocolitica Infections in Mice

Simple Summary

Computational modeling of bacterial infection is an attractive way to simulate infection scenarios. In the long-term, such models could be used to identify factors that make individuals more susceptible to infection, or how interference with bacterial growth influences the course of bacterial infection. This study used different mouse infection models (immunocompetent, lacking a microbiota, and immunodeficient models) to develop a basic mathematical model of a Yersinia enterocolitica gastrointestinal infection. We showed that our model can reflect our findings derived from mouse infections, and we demonstrated how crucial the exact knowledge about parameters influencing the population dynamics is. Still, we think that computational models will be of great value in the future; however, to foster the development of more complex models, we propose the broad implementation of the interdisciplinary training of mathematicians and biologists.

Abstract

The complex interplay of a pathogen with its virulence and fitness factors, the host’s immune response, and the endogenous microbiome determine the course and outcome of gastrointestinal infection. The expansion of a pathogen within the gastrointestinal tract implies an increased risk of developing severe systemic infections, especially in dysbiotic or immunocompromised individuals. We developed a mechanistic computational model that calculates and simulates such scenarios, based on an ordinary differential equation system, to explain the bacterial population dynamics during gastrointestinal infection. For implementing the model and estimating its parameters, oral mouse infection experiments with the enteropathogen, Yersinia enterocolitica (Ye), were carried out. Our model accounts for specific pathogen characteristics and is intended to reflect scenarios where colonization resistance, mediated by the endogenous microbiome, is lacking, or where the immune response is partially impaired. Fitting our data from experimental mouse infections, we can justify our model setup and deduce cues for further model improvement. The model is freely available, in SBML format, from the BioModels Database under the accession number MODEL2002070001.

Computational approaches to understanding Clostridioides difficile metabolism and virulence

The progress of infection by Clostridioides difficile is strongly influenced by metabolic cues it encounters as it colonizes the gastrointestinal tract. Both colonization and regulation of virulence have a multi-factorial interaction between host, microbiome, and gene expression cascades. While these connections with metabolism have been understood for some time, many mechanisms of control have remained difficult to directly assay due to high metabolic variability among C. difficile isolates and difficult genetic systems. Computational systems offer a means to interrogate structure of complex or noisy datasets and generate useful, tractable hypotheses to be tested in the laboratory. Recently, in silico techniques have provided powerful insights into metabolic elements of C. difficile infection ranging from virulence regulation to interactions with the gut microbiota. In this review, we introduce and provide context to the methods of computational modeling that have been applied to C. difficile metabolism and virulence thus far. The techniques discussed here have laid the foundation for future multi-scale efforts aimed at understanding the complex interplay of metabolic activity between pathogen, host, and surrounding microbial community in the regulation of C. difficile pathogenesis.

Loss of Interleukin-10 (IL-10) Signaling Promotes IL-22-Dependent Host Defenses against Acute Clostridioides difficile Infection

Infection with the bacterial pathogen Clostridioides difficile causes severe damage to the intestinal epithelium that elicits a robust inflammatory response. Markers of intestinal inflammation accurately predict clinical disease, however, the extent to which host-derived proinflammatory mediators drive pathogenesis versus promote host protective mechanisms remains elusive. In this report, we employed Il10-/- mice as a model of spontaneous colitis to examine the impact of constitutive intestinal immune activation, independent of infection, on C. difficile disease pathogenesis. Upon C. difficile challenge, Il10-/- mice exhibited significantly decreased morbidity and mortality compared to littermate Il10 heterozygote (Il10HET) control mice, despite a comparable C. difficile burden, innate immune response, and microbiota composition following infection. Similarly, antibody-mediated blockade of interleukin-10 (IL-10) signaling in wild-type C57BL/6 mice conveyed a survival advantage if initiated 3 weeks prior to infection. In contrast, no advantage was observed if blockade was initiated on the day of infection, suggesting that the constitutive activation of inflammatory defense pathways prior to infection mediated host protection. IL-22, a cytokine critical in mounting a protective response against C. difficile infection, was elevated in the intestine of uninfected, antibiotic-treated Il10-/- mice, and genetic ablation of the IL-22 signaling pathway in Il10-/- mice negated the survival advantage following C. difficile challenge. Collectively, these data demonstrate that constitutive loss of IL-10 signaling, via genetic ablation or antibody blockade, enhances IL-22-dependent host defense mechanisms to limit C. difficile pathogenesis.

The adaptive immune response to Clostridioides difficile: A tricky balance between immunoprotection and immunopathogenesis

Clostridioides difficile (C. difficile) is the major cause of hospital-acquired gastrointestinal infections in individuals following antibiotics treatment. The pathogenesis of C. difficile infection (CDI) is mediated mainly by the production of toxins that induce tissue damage and host inflammatory responses. While innate immunity is well characterized in human and animal models of CDI, adaptive immune responses remain poorly understood. In this review, the current understanding of adaptive immunity is summarized and its influence on pathogenesis and disease outcome is discussed. The perspectives on what we believe to be the main pending questions and the focus of future research are also provided. There is no doubt that the innate immune response provides a first line of defense to CDI. But, is the adaptive immune response a friend or a foe? Probably it depends on the course of the disease. Adaptive immunity is essential for pathogen eradication, but may also trigger uncontrolled or pathological inflammation. Most of the understanding of the role of T cells is based on findings from experimental models. While they are a very valuable tool for research studies, more studies in human are needed to translate these findings into human disease. Another main challenge is to unravel the role of the different T cell populations on protection or induction of immunopathogenesis.

Computational profiling of the gut-brain axis: microflora dysbiosis insights to neurological disorders

Almost 2500 years after Hippocrates' observations on health and its direct association to the gastrointestinal tract, a paradigm shift has recently occurred, making the gut and its symbionts (bacteria, fungi, archaea and viruses) a point of convergence for studies. It is nowadays well established that the gut microflora's compositional diversity regulates via its genes (the microbiome) the host's health and provides preliminary insights into disease progression and regulation. The microbiome's involvement is evident in immunological and physiological studies that link changes in its biodiversity to its contributions to the host's phenotype but also in neurological investigations, substantiating the aptly named gut-brain axis. The definitive mechanisms of this last bidirectional interaction will be our main focus because it presents researchers with a new conundrum. In this review, we prospect current literature for computational analysis methodologies that accommodate the need for better understanding of the microbiome-gut-brain interactions and neurological disorder onset and progression, through cross-disciplinary systems biology applications. We will present bioinformatics tools used in exploring these synergies that help build and interpret microbial 16S ribosomal RNA data sets, produced by shotgun and high-throughput sequencing of healthy and neurological disorder samples stored in biological databases. These approaches provide alternative means for researchers to form hypotheses to their inquests faster, cheaper and swith precision. The goal of these studies relies on the integration of combined metagenomics and metabolomics assessments. An accurate characterization of the microbiome and its functionality can support new diagnostic, prognostic and therapeutic strategies for neurological disorders, customized for each individual host.

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.

Risk and Outcomes of Clostridium difficile Infection With Chronic Pancreatitis

OBJECTIVES

Chronic pancreatitis (CP) is associated with high rates of recurrent hospitalizations, which predisposes to Clostridium difficile infection (CDI). We investigate the burden of CDI in CP.

METHODS

We identified records of patients with CP from the Nationwide Inpatient Sample (NIS) 2012-2014 and estimated the impact of CDI on their outcomes. We calculated the adjusted odds ratio (AOR) of CP on having CDI (NIS 2014). From NIS 2007-2014, we plotted the trends of CDI and its interaction with CP.

RESULTS

From 2012 to 2014, 886 (2.72%) of the 32,614 CP patients had concomitant CDI, which was associated with poorer outcomes: acute kidney injury (AOR, 2.57 [95% confidence interval {CI}, 2.11-3.13]), length of stay (13.3 vs 7.4 days), and charges (US $127,496 vs US $72,767), but not mortality (AOR, 0.93 [95% CI, 0.28-3.05]). In 2014, CP was associated with an increased risk of CDI (crude odds ratio, 2.10 [95% CI, 1.95-2.26]), which persisted after multivariate adjustment (AOR, 2.03 [95% CI, 1.87-2.19]). From 2007 to 2014, the annual prevalence of CDI was 106.4 cases per 10,000 hospitalizations, increasing from 2007 (95.5/10,000) to 2014 (118.4/10,000), with a 3.7 times higher annual rate of increase among CP versus no-CP patients (13.4/10,000 vs 3.7/10,000 population/year).

CONCLUSIONS

Chronic pancreatitis patients have high burden of CDI and may benefit from CDI prophylaxis.

Host-microbe interaction in the gastrointestinal tract

The gastrointestinal tract is a highly complex organ in which multiple dynamic physiological processes are tightly coordinated while interacting with a dense and extremely diverse microbial population. From establishment in early life, through to host-microbe symbiosis in adulthood, the gut microbiota plays a vital role in our development and health. The effect of the microbiota on gut development and physiology is highlighted by anatomical and functional changes in germ-free mice, affecting the gut epithelium, immune system and enteric nervous system. Microbial colonisation promotes competent innate and acquired mucosal immune systems, epithelial renewal, barrier integrity, and mucosal vascularisation and innervation. Interacting or shared signalling pathways across different physiological systems of the gut could explain how all these changes are coordinated during postnatal colonisation, or after the introduction of microbiota into germ-free models. The application of cell-based in-vitro experimental systems and mathematical modelling can shed light on the molecular and signalling pathways which regulate the development and maintenance of homeostasis in the gut and beyond.

NLRX1 Modulates Immunometabolic Mechanisms Controlling the Host-Gut Microbiota Interactions during Inflammatory Bowel Disease

Interactions among the gut microbiome, dysregulated immune responses, and genetic factors contribute to the pathogenesis of inflammatory bowel disease (IBD). Nlrx1^−/− mice have exacerbated disease severity, colonic lesions, and increased inflammatory markers. Global transcriptomic analyses demonstrate enhanced mucosal antimicrobial defense response, chemokine and cytokine expression, and epithelial cell metabolism in colitic Nlrx1^−/− mice compared to wild-type (WT) mice. Cell-specificity studies using cre-lox mice demonstrate that the loss of NLRX1 in intestinal epithelial cells (IEC) recapitulate the increased sensitivity to DSS colitis observed in whole body Nlrx1^−/− mice. Further, organoid cultures of Nlrx1^−/− and WT epithelial cells confirm the altered patterns of proliferation, amino acid metabolism, and tight junction expression. These differences in IEC behavior can impact the composition of the microbiome. Microbiome analyses demonstrate that colitogenic bacterial taxa such as Veillonella and Clostridiales are increased in abundance in Nlrx1^−/− mice and in WT mice co-housed with Nlrx1^−/− mice. The transfer of an Nlrx1^−/−-associated gut microbiome through co-housing worsens disease in WT mice confirming the contributions of the microbiome to the Nlrx1^−/− phenotype. To validate NLRX1 effects on IEC metabolism mediate gut–microbiome interactions, restoration of WT glutamine metabolic profiles through either exogenous glutamine supplementation or administration of 6-diazo-5-oxo-l-norleucine abrogates differences in inflammation, microbiome, and overall disease severity in Nlrx1^−/− mice. The influence NLRX1 deficiency on SIRT1-mediated effects is identified to be an upstream controller of the Nlrx1^−/− phenotype in intestinal epithelial cell function and metabolism. The altered IEC function and metabolisms leads to changes in barrier permeability and microbiome interactions, in turn, promoting greater translocation and inflammation and resulting in an increased disease severity. In conclusion, NLRX1 is an immunoregulatory molecule and a candidate modulator of the interplay between mucosal inflammation, metabolism, and the gut microbiome during IBD.

Agents and networks to model the dynamic interactions of intracellular transport

Cell biology is increasingly evolving to become a more formal and quantitative science. The field of intracellular transport is no exception. However, it is extremely challenging to formulate mathematical and computational models for processes that involve dynamic structures that continuously change their shape, position and composition, leading to information transfer and functional outcomes. The two major strategies employed to represent intracellular trafficking are based on "ordinary differential equations" and "agent-" based modeling. Both approaches have advantages and drawbacks. Combinations of both modeling strategies have promising characteristics to generate meaningful simulations for intracellular transport and allow the formulation of new hypotheses and provide new insights. In the near future, cell biologists will encounter and hopefully overcome the challenge of translating descriptive cartoon representations of biological systems into mathematical network models.

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.

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.

The promises of quantitative systems pharmacology modelling for drug development

Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

Local and Long-Distance Calling: Conversations between the Gut Microbiota and Intra- and Extra-Gastrointestinal Tract Infections

Preservation of health from infectious diseases depends upon both mucosal and systemic immunity via the collaborative effort of innate and adaptive immune responses. The proficiency of host immunity stems from robust defense mechanisms—physical barriers and specialized immune cells—and a failure of these mechanisms leads to pathology. Intriguingly, immunocompetence to pathogens can be shaped by the gut microbiome as recent publications highlight a dynamic interplay between the gut microbiome and host susceptibility to infection. Modulation of host immunity to enteric pathogens has long been studied where gut bacteria shape multiple facts of both innate and adaptive immunity. Conversely, the impact of gut commensals on host immunity to extra-gastrointestinal (GI) tract infections has only recently been recognized. In this context, the gut microbiome can augment host immunity to extra-GI tract bacterial, viral, and parasitic pathogens. This review explores the research that affords insight into the role of the gut microbiome in various infectious diseases, with a particular emphasis on extra-GI tract infections. A better understanding of the link between the gut microbiome and infectious disease will be critical for improving global health in the years ahead.

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.