Timing matters in macrophage/CD4+ T cell interactions: an agent-based model comparing Mycobacterium tuberculosis host-pathogen interactions between latently infected and naïve individuals

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a significant health challenge. Clinical manifestations of TB exist across a spectrum with a majority of infected individuals remaining asymptomatic, commonly referred to as latent TB infection (LTBI). In vitro models have demonstrated that cells from individuals with LTBI can better control Mtb growth and form granuloma-like structures more quickly, compared to cells from uninfected (Mtb-naïve) individuals. These in vitro results agree with animal and clinical evidence that LTBI protects, to some degree, against reinfection. However, the mechanisms by which LTBI might offer protection against reinfection remain unclear, and quantifying the relative contributions of multiple control mechanisms is challenging using experimental methods alone. To complement in vitro models, we have developed an in silico agent-based model to help elucidate host responses that might contribute to protection against reinfection. Our simulations indicate that earlier contact between macrophages and CD4+ T cells leads to LTBI simulations having more activated CD4+ T cells and, in turn, more activated infected macrophages, all of which contribute to a decreased bacterial load early on. Our simulations also demonstrate that granuloma-like structures support this early macrophage activation in LTBI simulations. We find that differences between LTBI and Mtb-naïve simulations are driven by TNFα and IFNγ-associated mechanisms as well as macrophage phagocytosis and killing mechanisms. Together, our simulations show how important the timing of the first interactions between innate and adaptive immune cells is, how this impacts infection progression, and why cells from LTBI individuals might be faster to respond to reinfection.IMPORTANCETuberculosis (TB) remains a significant global health challenge, with millions of new infections and deaths annually. Despite extensive research, the mechanisms by which latent TB infection (LTBI) confers protection against reinfection remain unclear. In this study, we developed an in silico agent-based model to simulate early immune responses to Mycobacterium tuberculosis infection based on experimental in vitro infection of human donor cells. Our simulations reveal that early interactions between macrophages and CD4+ T cells, driven by TNFα and IFNγ, are critical for bacterial control and granuloma formation in LTBI. These findings offer new insights into the immune processes involved in TB, which could inform the development of targeted vaccines and host-directed therapies. By integrating experimental data with computational predictions, our research provides a robust framework for understanding TB immunity and guiding future interventions to mitigate the global TB burden.

Development and Analysis of Multiscale Models for Tuberculosis: From Molecules to Populations

Although infectious disease dynamics are often analyzed at the macro-scale, increasing numbers of drug-resistant infections highlight the importance of within-host modeling that simultaneously solves across multiple scales to effectively respond to epidemics. We review multiscale modeling approaches for complex, interconnected biological systems and discuss critical steps involved in building, analyzing, and applying such models within the discipline of model credibility. We also present our two tools: CaliPro, for calibrating multiscale models (MSMs) to datasets, and tunable resolution, for fine- and coarse-graining sub-models while retaining insights. We include as an example our work simulating infection with Mycobacterium tuberculosis to demonstrate modeling choices and how predictions are made to generate new insights and test interventions. We discuss some of the current challenges of incorporating novel datasets, rigorously training computational biologists, and increasing the reach of MSMs. We also offer several promising future research directions of incorporating within-host dynamics into applications ranging from combinatorial treatment to epidemic response.

A Multiscale Agent-Based Model for the Investigation of E. coli K12 Metabolic Response During Biofilm Formation

Bacterial biofilm formation is an organized collective response to biochemical cues that enables bacterial colonies to persist and withstand environmental insults. We developed a multiscale agent-based model that characterizes the intracellular, extracellular, and cellular scale interactions that modulate Escherichia coli MG1655 biofilm formation. Each bacterium's intracellular response and cellular state were represented as an outcome of interactions with the environment and neighboring bacteria. In the intracellular model, environment-driven gene expression and metabolism were captured using statistical regression and Michaelis-Menten kinetics, respectively. In the cellular model, growth, death, and type IV pili- and flagella-dependent movement were based on the bacteria's intracellular state. We implemented the extracellular model as a three-dimensional diffusion model used to describe glucose, oxygen, and autoinducer 2 gradients within the biofilm and bulk fluid. We validated the model by comparing simulation results to empirical quantitative biofilm profiles, gene expression, and metabolic concentrations. Using the model, we characterized and compared the temporal metabolic and gene expression profiles of sessile versus planktonic bacterial populations during biofilm formation and investigated correlations between gene expression and biofilm-associated metabolites and cellular scale phenotypes. Based on our in silico studies, planktonic bacteria had higher metabolite concentrations in the glycolysis and citric acid cycle pathways, with higher gene expression levels in flagella and lipopolysaccharide-associated genes. Conversely, sessile bacteria had higher metabolite concentrations in the autoinducer 2 pathway, with type IV pili, autoinducer 2 export, and cellular respiration genes upregulated in comparison with planktonic bacteria. Having demonstrated results consistent with in vitro static culture biofilm systems, our model enables examination of molecular phenomena within biofilms that are experimentally inaccessible and provides a framework for future exploration of how hypothesized molecular mechanisms impact bulk community behavior.

New concepts in understanding latent tuberculosis

PURPOSE OF REVIEW

Mycobacterium tuberculosis (M.tb), the etiologic agent of tuberculosis, is a prominent global health threat because of the enormous reservoir of subclinical latent tuberculosis infection (LTBI). Current diagnostic approaches are limited in their ability to predict reactivation risk and LTBI is recalcitrant to antibiotic treatment. The present review summarizes recent advances in our ability to detect, treat and model LTBI as well as our understanding of bacterial physiology during latency.

RECENT FINDINGS

T-cell subsets and circulating proteins have been identified which could serve as biomarkers for LTBI or indicators of reactivation risk. In addition, experimental and in-silico models have enabled discoveries regarding bacterial physiology during latency and the host immune response following infection with latent M.tb.

SUMMARY

Despite recent advances, much more research is needed to bolster our ability to detect, implement treatment and model LTBI. The present work is crucial for the eradication of this global problem.

Opening Pandora's Box: Mechanisms of Mycobacterium tuberculosis Resuscitation

Mycobacterium tuberculosis (Mtb) characteristically causes an asymptomatic infection. While this latent tuberculosis infection (LTBI) is not contagious, reactivation to active tuberculosis disease (TB) causes the patient to become infectious. A vaccine has existed for TB for a century, while drug treatments have been available for over 70 years; despite this, TB remains a major global health crisis. Understanding the factors which allow the bacillus to control responses to host stress and mechanisms leading to latency are critical for persistence. Similarly, molecular switches which respond to reactivation are important. Recently, research in the field has sought to focus on reactivation, employing system-wide approaches and animal models. Here, we describe the current work that has been done to elucidate the mechanisms of reactivation and stop reactivation in its tracks.