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Wessler T, Joslyn LR, Borish HJ, Gideon HP, Flynn JL, Kirschner DE, Linderman JJ. A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination. PLoS Comput Biol 2020; 16:e1007280. [PMID: 32433646 PMCID: PMC7239387 DOI: 10.1371/journal.pcbi.1007280] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 02/26/2020] [Indexed: 12/15/2022] Open
Abstract
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. Tuberculosis (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.
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Affiliation(s)
- Timothy Wessler
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Louis R. Joslyn
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - H. Jacob Borish
- Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Hannah P. Gideon
- Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - JoAnne L. Flynn
- Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Denise E. Kirschner
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail: (DEK); (JJL)
| | - Jennifer J. Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail: (DEK); (JJL)
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2
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Wagar LE, DiFazio RM, Davis MM. Advanced model systems and tools for basic and translational human immunology. Genome Med 2018; 10:73. [PMID: 30266097 PMCID: PMC6162943 DOI: 10.1186/s13073-018-0584-8] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 09/17/2018] [Indexed: 12/31/2022] Open
Abstract
There are fundamental differences between humans and the animals we typically use to study the immune system. We have learned much from genetically manipulated and inbred animal models, but instances in which these findings have been successfully translated to human immunity have been rare. Embracing the genetic and environmental diversity of humans can tell us about the fundamental biology of immune cell types and the elasticity of the immune system. Although people are much more immunologically diverse than conventionally housed animal models, tools and technologies are now available that permit high-throughput analysis of human samples, including both blood and tissues, which will give us deep insights into human immunity in health and disease. As we gain a more detailed picture of the human immune system, we can build more sophisticated models to better reflect this complexity, both enabling the discovery of new immunological mechanisms and facilitating translation into the clinic.
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Affiliation(s)
- Lisa E Wagar
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Robert M DiFazio
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Mark M Davis
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, 94305, USA.
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Torrelles JB, Schlesinger LS. Integrating Lung Physiology, Immunology, and Tuberculosis. Trends Microbiol 2017; 25:688-697. [PMID: 28366292 PMCID: PMC5522344 DOI: 10.1016/j.tim.2017.03.007] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Revised: 03/03/2017] [Accepted: 03/10/2017] [Indexed: 11/17/2022]
Abstract
Lungs are directly exposed to the air, have enormous surface area, and enable gas exchange in air-breathing animals. They are constantly 'attacked' by microbes from both outside and inside and thus possess a unique, highly regulated local immune defense system which efficiently allows for microbial clearance while minimizing damaging inflammatory responses. As a prototypic host-adapted airborne pathogen, Mycobacterium tuberculosis traverses the lung and has several 'interaction points' (IPs) which it must overcome to cause infection. These interactions are critical, not only from a pathogenesis perspective but also in considering the effectiveness of therapies and vaccines in the lungs. Here we discuss emerging views on immunologic interactions occurring in the lungs for M. tuberculosis and their impact on infection and persistence.
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Affiliation(s)
- Jordi B Torrelles
- Department of Microbial Infection and Immunity, College of Medicine, and the Center for Microbial Interface Biology, The Ohio State University, Columbus, OH 43210, USA.
| | - Larry S Schlesinger
- Department of Microbial Infection and Immunity, College of Medicine, and the Center for Microbial Interface Biology, The Ohio State University, Columbus, OH 43210, USA.
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Kirschner D, Pienaar E, Marino S, Linderman JJ. A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment. CURRENT OPINION IN SYSTEMS BIOLOGY 2017; 3:170-185. [PMID: 30714019 PMCID: PMC6354243 DOI: 10.1016/j.coisb.2017.05.014] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Tuberculosis (TB) is an ancient and deadly disease characterized by complex host-pathogen dynamics playing out over multiple time and length scales and physiological compartments. Computational modeling can be used to integrate various types of experimental data and suggest new hypotheses, mechanisms, and therapeutic approaches to TB. Here, we offer a first-time comprehensive review of work on within-host TB models that describe the immune response of the host to infection, including the formation of lung granulomas. The models include systems of ordinary and partial differential equations and agent-based models as well as hybrid and multi-scale models that are combinations of these. Many aspects of M. tuberculosis infection, including host dynamics in the lung (typical site of infection for TB), granuloma formation, roles of cytokine and chemokine dynamics, and bacterial nutrient availability have been explored. Finally, we survey applications of these within-host models to TB therapy and prevention and suggest future directions to impact this global disease.
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Affiliation(s)
- Denise Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI
| | - Elsje Pienaar
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI
| | - Simeone Marino
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI
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Ciupe SM, Heffernan JM. In-host modeling. Infect Dis Model 2017; 2:188-202. [PMID: 29928736 PMCID: PMC6001971 DOI: 10.1016/j.idm.2017.04.002] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 04/24/2017] [Accepted: 04/26/2017] [Indexed: 01/14/2023] Open
Abstract
Understanding the mechanisms governing host-pathogen kinetics is important and can guide human interventions. In-host mathematical models, together with biological data, have been used in this endeavor. In this review, we present basic models used to describe acute and chronic pathogenic infections. We highlight the power of model predictions, the role of drug therapy, and advantage of considering the dynamics of immune responses. We also present the limitations of these models due in part to the trade-off between the complexity of the model and their predictive power, and the challenges a modeler faces in determining the appropriate formulation for a given problem.
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Affiliation(s)
- Stanca M. Ciupe
- Department of Mathematics, Virginia Tech, Blacksburg, VA, USA
| | - Jane M. Heffernan
- Centre for Disease Modelling, Department of Mathematics & Statistics, York University, Toronto, ON, Canada
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Korla K, Chandra N. A Systems Perspective of Signalling Networks in Host–Pathogen Interactions. J Indian Inst Sci 2017. [DOI: 10.1007/s41745-016-0017-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Linderman JJ, Cilfone NA, Pienaar E, Gong C, Kirschner DE. A multi-scale approach to designing therapeutics for tuberculosis. Integr Biol (Camb) 2015; 7:591-609. [PMID: 25924949 DOI: 10.1039/c4ib00295d] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Approximately one third of the world's population is infected with Mycobacterium tuberculosis. Limited information about how the immune system fights M. tuberculosis and what constitutes protection from the bacteria impact our ability to develop effective therapies for tuberculosis. We present an in vivo systems biology approach that integrates data from multiple model systems and over multiple length and time scales into a comprehensive multi-scale and multi-compartment view of the in vivo immune response to M. tuberculosis. We describe computational models that can be used to study (a) immunomodulation with the cytokines tumor necrosis factor and interleukin 10, (b) oral and inhaled antibiotics, and
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Affiliation(s)
- Jennifer J Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA.
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Pienaar E, Cilfone NA, Lin PL, Dartois V, Mattila JT, Butler JR, Flynn JL, Kirschner DE, Linderman JJ. A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment. J Theor Biol 2014; 367:166-179. [PMID: 25497475 DOI: 10.1016/j.jtbi.2014.11.021] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2014] [Revised: 10/17/2014] [Accepted: 11/20/2014] [Indexed: 12/31/2022]
Abstract
While active tuberculosis (TB) is a treatable disease, many complex factors prevent its global elimination. Part of the difficulty in developing optimal therapies is the large design space of antibiotic doses, regimens and combinations. Computational models that capture the spatial and temporal dynamics of antibiotics at the site of infection can aid in reducing the design space of costly and time-consuming animal pre-clinical and human clinical trials. The site of infection in TB is the granuloma, a collection of immune cells and bacteria that form in the lung, and new data suggest that penetration of drugs throughout granulomas is problematic. Here we integrate our computational model of granuloma formation and function with models for plasma pharmacokinetics, lung tissue pharmacokinetics and pharmacodynamics for two first line anti-TB antibiotics. The integrated model is calibrated to animal data. We make four predictions. First, antibiotics are frequently below effective concentrations inside granulomas, leading to bacterial growth between doses and contributing to the long treatment periods required for TB. Second, antibiotic concentration gradients form within granulomas, with lower concentrations toward their centers. Third, during antibiotic treatment, bacterial subpopulations are similar for INH and RIF treatment: mostly intracellular with extracellular bacteria located in areas non-permissive for replication (hypoxic areas), presenting a slowly increasing target population over time. Finally, we find that on an individual granuloma basis, pre-treatment infection severity (including bacterial burden, host cell activation and host cell death) is predictive of treatment outcome.
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Affiliation(s)
- Elsje Pienaar
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA; Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Nicholas A Cilfone
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Philana Ling Lin
- Department of Pediatrics, Children׳s Hospital of Pittsburgh of the University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Véronique Dartois
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, USA
| | - Joshua T Mattila
- Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - J Russell Butler
- Department of Health and Biomedical Sciences, Adventist University of Health Sciences, Orlando, FL, USA
| | - JoAnne L Flynn
- Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Denise E Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA.
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