Biology subjects

Kirschner, D. E.

Publications and source records attributed to Kirschner, D. E..

9 recordsLinked to original sources

bioRxiv2026

Distinct mechanisms drive post-antibiotic Tuberculosis relapse post-cure versus post-treatment-failure

Tuberculosis (TB) remains a global health concern, as Mycobacterium tuberculosis (Mtb) currently infects a quarter of the worlds population. Though many TB patients sterilize infection with short-course treatment, regimens shorter than 4 months risk post-treatment, potentially facilitating drug resistance. Granulomas are spatially-heterogeneous hallmark immune structures that form during TB, comprising immune cells and caseum, necrotic tissue that can trap Mtb in a non-replicating state. Two mechanisms causing relapse have been hypothesized: persistence, where treatment kills all replicating Mtb, and relapse follows once Mtb trapped within caseum returns to a replicative niche; and threshold, where replicating Mtb remain alive below detectable levels. Typically, clinical relapse is described as TB recurrence <2 years after a misdiagnosis of cure upon treatment completion (MDxC). Comparatively, many experimental models cannot screen for cure and examine relapse [~]2-months after treatment completion. Capacity to untangle these considerations in vivo are limited. Here, we examine the impact of study design (e.g., cure screening) on mechanisms underpinning reported instances of relapse using our computational model capturing whole-host Mtb infection dynamics, HostSim. Simulations uncover rates of reported relapse depending on whether hosts are screened for cure upon treatment completion. If not screened, then relapse is likely driven by incomplete sterilization of replicating bacteria; whereas cure-screened relapse is most likely to be caused by gradual expansion of non-replicating Mtb from within caseum into cellular areas of a granuloma. This suggests that TB patients that relapse after MDxC may best be treated with caseum-penetrating antibiotics such as rifamycin-class antibiotics. ImportanceIncomplete treatment of TB leads to risks relapse, which may occur years later. The threat of relapse is the main reason TB treatment takes 4-9 months. Predictors of relapse are not well-defined given variability in technical definitions of relapse and nuances of study designs. Here, we simulate both clinical and experimental relapse studies, including multiple diagnostic tests and relapse definitions, using biologically-based computation. We find that two hypothesized types of relapse that each potentially require a different treatment strategy are simultaneously at play. Simulations suggest that relapse after a "cure" diagnosis (most clinical studies) is caused by reactivation of non-replicating bacteria hidden from treatment within necrotic granuloma tissue or other sites, whereas experiments that cannot test for cure post-treatment are likely to report relapse caused by incomplete sterilization of replicating Mtb. Predictions depend on the currently unclear relationship between bacterial burden and clinical symptoms.

systems biology↗

bioRxiv2025

The immunometabolic topography of tuberculosis granulomas governs cellular organization and bacterial control

Despite being heavily infiltrated by immune cells, tuberculosis (TB) granulomas often subvert the host response to Mycobacterium tuberculosis (Mtb) infection and support bacterial persistence. We previously discovered that human TB granulomas are enriched for immunosuppressive factors typically associated with tumor-immune evasion, raising the intriguing possibility that they promote tolerance to infection. In this study, our goal was to identify the prime drivers for establishing this tolerogenic niche and to determine if the magnitude of this response correlates with bacterial persistence. To do this, we conducted a multimodal spatial analysis of 52 granulomas from 16 non-human primates (NHP) who were infected with low dose Mtb for 9-12 weeks. Notably, each granulomas bacterial burden was individually quantified allowing us to directly ask how granuloma spatial structure and function relate to infection control. We found that a universal feature of TB granulomas was partitioning of the myeloid core into two distinct metabolic environments, one of which is hypoxic. This hypoxic environment associated with pathologic immune cell states, dysfunctional cellular organization of the granuloma, and a near-complete blockade of lymphocyte infiltration that would be required for a successful host response. The extent of these hypoxia-associated features correlated with worsened bacterial burden. We conclude that hypoxia governs immune cell state and organization within granulomas and is a potent driver of subverted immunity during TB.

immunology↗

bioRxiv2023

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

AbstractAlthough 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.

systems biology↗

bioRxiv2022

Optimizing tuberculosis treatment efficacy: comparing the standard regimen with Moxifloxacin-containing regimens

Tuberculosis (TB) continues to be one of the deadliest infectious diseases in the world, causing ~1.5 million deaths every year. The World Health Organization initiated an End TB Strategy that aims to reduce TB-related deaths in 2035 by 95%. Recent research goals have focused on discovering more effective and more patient-friendly antibiotic drug regimens to increase patient compliance and decrease emergence of resistant TB. Moxifloxacin is one promising antibiotic that may improve the current standard regimen by shortening treatment time. Clinical trials and in vivo mouse studies suggest that regimens containing moxifloxacin have better bactericidal activity. However, testing every possible combination regimen with moxifloxacin either in vivo or clinically is not feasible due to experimental and clinical limitations. To identify better regimens more systematically, we simulated pharmacokinetics/pharmacodynamics of various regimens (with and without moxifloxacin) to evaluate efficacies, and then compared our predictions to both clinical trials and nonhuman primate studies performed herein. We used GranSim, our well-established hybrid agent-based model that simulates granuloma formation and antibiotic treatment, for this task. In addition, we established a multiple-objective optimization pipeline using GranSim to discover optimized regimens based on treatment objectives of interest, i.e., minimizing total drug dosage and lowering time needed to sterilize granulomas. Our approach can efficiently test many regimens and successfully identify optimal regimens to inform pre-clinical studies or clinical trials and ultimately accelerate the TB regimen discovery process. Author summaryTuberculosis (TB) is a top global health concern and treatment for TB requires multiple antibiotics taken for long periods of time, which is challenging for TB patients. Therefore, identifying regimens that are more effective and more patient-friendly than the standard treatment is urgently needed. It is also known that non-compliance leads to the development of drug resistant TB. In this work, we pair computational and experimental models to predict new regimens for the treatment of TB that optimize how fast bacteria are cleared using minimal dosage. We apply novel approaches to this goal and validate our predictions using a non-human primate model. Our findings suggest that systems pharmacological modeling should be employed as a method to narrow the design space for drug regimens for tuberculosis and other diseases as well.

pharmacology and toxicology↗

bioRxiv2022

Concomitant immunity to M. tuberculosis infection

Some persistent infections provide a level of immunity that protects against reinfection with the same pathogen, a process referred to as concomitant immunity. To explore the phenomenon of concomitant immunity during Mycobacterium tuberculosis infection, we utilized HostSim, a previously published virtual host model of the immune response following Mtb infection. By simulating reinfection scenarios and comparing with data from non-human primate studies, we predict that the durability of a concomitant immune response against Mtb is intrinsically tied to levels of tissue resident memory T cells (Trms) during primary infection, with a secondary but important role for circulating Mtb-specific T cells. Further, we compare HostSim reinfection experiments to observational TB studies from the pre-antibiotic era to predict that the upper bound of the lifespan of resident memory T cells in human lung tissue is likely 2-3 years. To the authors knowledge, this is the first estimate of resident memory T-cell lifespan in humans. Our findings are a first step towards demonstrating the important role of Trms in preventing disease and suggest that the induction of lung Trms is likely critical for vaccine success.

immunology↗

bioRxiv2022

T cell transcription factor expression evolves as adaptive immunity matures in granulomas from Mycobacterium tuberculosis-infected cynomolgus macaques

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a global health concern, yearly resulting in 10 million new cases of active TB. Immunologic investigation of lung granulomas is essential for understanding host control of bacterial replication. We identified and compared the pathological, cellular, and functional differences in granulomas at 4, 12, and 20 weeks post-infection in Chinese cynomolgus macaques. Original granulomas differed in transcription factor expression within adaptive lymphocytes with those at 12 weeks showing higher frequencies of CD8+T-bet+ T cells, while increases in CD4+T-bet+ T cells were observed at 20 weeks post-infection. The appearance of T-bet+ adaptive T cells at 12 and 20 weeks was coincident with a reduction in bacterial burden, suggesting their critical role in Mtb control. This study highlights the evolution of T cell responses within lung granulomas, suggesting that vaccines promoting the development and migration of T-bet+ T cells would enhance mycobacterial control.

immunology↗

bioRxiv2021

A virtual host model of Mycobacterium tuberculosis infection identifies early immune events as predictive of infection outcomes

Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), is one of the worlds deadliest infectious diseases and remains a significant global health burden. TB disease and pathology can present clinically across a spectrum of outcomes, ranging from total sterilization of infection to active disease. Much remains unknown about the biology that drives an individual towards various clinical outcomes as it is challenging to experimentally address specific mechanisms driving clinical outcomes. Furthermore, it is unknown whether numbers of immune cells in the blood accurately reflect ongoing events during infection within human lungs. Herein, we utilize a systems biology approach by developing a whole-host model of the immune response to Mtb across multiple physiologic and time scales. This model, called HostSim, tracks events at the cellular, granuloma, organ, and host scale and represents the first whole-host, multi-scale model of the immune response following Mtb infection. We show that this model can capture various aspects of human and non-human primate TB disease and predict that biomarkers in the blood may only faithfully represent events in the lung at early time points after infection. We posit that HostSim, as a first step toward personalized digital twins in TB research, offers a powerful computational tool that can be used in concert with experimental approaches to understand and predict events about various aspects of TB disease and therapeutics.

systems biology↗

bioRxiv2020

A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs

Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.

systems biology↗

bioRxiv2019

A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination

Mycobacterium tuberculosis (Mtb), the causative infectious agent of tuberculosis (TB), kills more individuals per year than any other infectious agent. Granulomas, the hallmark of Mtb infection, are complex structures that form in lungs, composed of immune cells surrounding bacteria, infected cells, and a caseous necrotic core. While granulomas serve to physically contain and immunologically restrain bacteria growth, some granulomas are unable to control Mtb growth, leading to bacteria and infected cells leaving the granuloma and disseminating, either resulting in additional granuloma formation (local or non-local) or spread to airways or lymph nodes. Dissemination is associated with development of active TB. It is challenging to experimentally address specific mechanisms driving dissemination from TB lung granulomas. Herein, we develop a novel hybrid multi-scale computational model, MultiGran, that tracks Mtb infection within multiple granulomas in an entire lung. MultiGran follows cells, cytokines, and bacterial populations within each lung granuloma throughout the course of infection and is calibrated to multiple non-human primate (NHP) cellular, granuloma, and whole-lung datasets. We show that MultiGran can recapitulate patterns of in vivo local and non-local dissemination, predict likelihood of dissemination, and predict a crucial role for multifunctional CD8+ T cells and macrophage dynamics for preventing dissemination.\n\nAuthor SummaryTuberculosis (TB) is caused by infection with Mycobacterium tuberculosis (Mtb) and kills 3 people per minute worldwide. Granulomas, spherical structures composed of immune cells surrounding bacteria, are the hallmark of Mtb infection and sometimes fail to contain the bacteria and disseminate, leading to further granuloma growth within the lung environment. To date, the mechanisms that determine granuloma dissemination events have not been characterized. We present a computational multi-scale model of granuloma formation and dissemination within primate lungs. Our computational model is calibrated to multiple experimental datasets across the cellular, granuloma, and whole-lung scales of non-human primates. We match to both individual granuloma and granuloma-population datasets, predict likelihood of dissemination events, and predict a critical role for multifunctional CD8+ T cells and macrophage-bacteria interactions to prevent infection dissemination.

immunology↗