Oxygen Modulates the Effectiveness of Granuloma Mediated Host Response to Mycobacterium tuberculosis: A Multiscale Computational Biology Approach

Mycobacterium tuberculosis associated granuloma formation can be viewed as a structural immune response that can contain and halt the spread of the pathogen. In several mammalian hosts, including non-human primates, Mtb granulomas are often hypoxic, although this has not been observed in wild type murine infection models. While a presumed consequence, the structural contribution of the granuloma to oxygen limitation and the concomitant impact on Mtb metabolic viability and persistence remains to be fully explored. We develop a multiscale computational model to test to what extent in vivo Mtb granulomas become hypoxic, and investigate the effects of hypoxia on host immune response efficacy and mycobacterial persistence. Our study integrates a physiological model of oxygen dynamics in the extracellular space of alveolar tissue, an agent-based model of cellular immune response, and a systems biology-based model of Mtb metabolic dynamics. Our theoretical studies suggest that the dynamics of granuloma organization mediates oxygen availability and illustrates the immunological contribution of this structural host response to infection outcome. Furthermore, our integrated model demonstrates the link between structural immune response and mechanistic drivers influencing Mtbs adaptation to its changing microenvironment and the qualitative infection outcome scenarios of clearance, containment, dissemination, and a newly observed theoretical outcome of transient containment. We observed hypoxic regions in the containment granuloma similar in size to granulomas found in mammalian in vivo models of Mtb infection. In the case of the containment outcome, our model uniquely demonstrates that immune response mediated hypoxic conditions help foster the shift down of bacteria through two stages of adaptation similar to thein vitro non-replicating persistence (NRP) observed in the Wayne model of Mtb dormancy. The adaptation in part contributes to the ability of Mtb to remain dormant for years after initial infection.

[1]  L D Loose,et al.  Characterization of Macrophage Dysfunction in Rodent Malaria , 1984, Journal of leukocyte biology.

[2]  Chen Hou,et al.  Reverse Engineering of Oxygen Transport in the Lung: Adaptation to Changing Demands and Resources through Space-Filling Networks , 2010, PLoS Comput. Biol..

[3]  D. Kirschner,et al.  A hybrid multi-compartment model of granuloma formation and T cell priming in tuberculosis. , 2011, Journal of theoretical biology.

[4]  Jose L. Segovia-Juarez,et al.  Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model. , 2004, Journal of theoretical biology.

[5]  J. D. B. MACDOUGALL,et al.  Diffusion Coefficient of Oxygen through Tissues , 1967, Nature.

[6]  Stephanie Forrest,et al.  Modeling Intercellular Interactions in Early Mycobacterium Infection , 2006, Bulletin of mathematical biology.

[7]  P. Barnes,et al.  Growth of Virulent and AvirulentMycobacterium tuberculosis Strains in Human Macrophages , 1998, Infection and Immunity.

[8]  Tudor I. Oprea,et al.  A systems chemical biology study of malate synthase and isocitrate lyase inhibition in Mycobacterium tuberculosis during active and NRP growth , 2013, Comput. Biol. Chem..

[9]  Mathematical Model of Oxygen Transport in Tuberculosis Granulomas , 2016, Annals of Biomedical Engineering.

[10]  James C Sacchettini,et al.  Biochemical and Structural Studies of Malate Synthase fromMycobacterium tuberculosis * , 2002, The Journal of Biological Chemistry.

[11]  J. Christian J. Ray,et al.  Synergy between Individual TNF-Dependent Functions Determines Granuloma Performance for Controlling Mycobacterium tuberculosis Infection1 , 2009, The Journal of Immunology.

[12]  A. Sagone,et al.  Oxygen dependence of human alveolar macrophage-mediated antibody-dependent cytotoxicity , 1982, Infection and immunity.

[13]  Amit Singh,et al.  Mycobacterium tuberculosis WhiB3 Maintains Redox Homeostasis by Regulating Virulence Lipid Anabolism to Modulate Macrophage Response , 2009, PLoS pathogens.

[14]  G. Schoolnik,et al.  Mycobacterium tuberculosis gene expression during adaptation to stationary phase and low-oxygen dormancy. , 2004, Tuberculosis.

[15]  D. Fuhrmann Encyclopedia Of International Sports Studies , 2016 .

[16]  L. Dexter,et al.  The pulmonary blood volume in man. , 1961, The Journal of clinical investigation.

[17]  A. Harris,et al.  Macrophage responses to hypoxia: relevance to disease mechanisms , 1999, Journal of leukocyte biology.

[18]  J. Hoogerheide,et al.  The Correlation of Bacterial Growth with Oxygen Consumption. , 1941, Journal of bacteriology.

[19]  S. Klamt,et al.  GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism , 2007, Genome Biology.

[20]  L. Wayne,et al.  An in vitro model for sequential study of shiftdown of Mycobacterium tuberculosis through two stages of nonreplicating persistence , 1996, Infection and immunity.

[21]  Elebeoba May,et al.  Circuit-Based Models of Biomolecular System Dynamics , 2011 .

[22]  V. Mizrahi,et al.  Mycobacterium tuberculosis. , 2018, Trends in microbiology.

[23]  Steven J. Plimpton,et al.  A method for modeling oxygen diffusion in an agent-based model with application to host-pathogen infection , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[24]  Linda M. Wills,et al.  Reverse Engineering , 1996, Springer US.

[25]  B. Abomoelak,et al.  A Novel In Vitro Multiple-Stress Dormancy Model for Mycobacterium tuberculosis Generates a Lipid-Loaded, Drug-Tolerant, Dormant Pathogen , 2009, PloS one.

[26]  P. Scheurich,et al.  Tumor necrosis factor signaling , 2003, Cell Death and Differentiation.

[27]  John Chan,et al.  Differences in Reactivation of Tuberculosis Induced from Anti-TNF Treatments Are Based on Bioavailability in Granulomatous Tissue , 2007, PLoS Comput. Biol..

[28]  D. Kirschner,et al.  A methodology for performing global uncertainty and sensitivity analysis in systems biology. , 2008, Journal of theoretical biology.

[29]  H. Sauro Enzyme Kinetics for Systems Biology , 2012 .

[30]  J. Mills,et al.  Phagocytosis and ATP levels in alveolar macrophages during acute hypoxia. , 1992, American journal of respiratory cell and molecular biology.

[31]  Antje Chang,et al.  BRENDA, the enzyme information system in 2011 , 2010, Nucleic Acids Res..

[32]  Michael S. Eldred,et al.  DAKOTA , A Multilevel Parallel Object-Oriented Framework for Design Optimization , Parameter Estimation , Uncertainty Quantification , and Sensitivity Analysis Version 4 . 0 User ’ s Manual , 2006 .

[33]  P. J. Butterworth,et al.  Lehninger: principles of biochemistry (4th edn) D. L. Nelson and M. C. Cox, W. H. Freeman & Co., New York, 1119 pp (plus 17 pp glossary), ISBN 0‐7167‐4339‐6 (2004) , 2005 .

[34]  Shin-Il Kim,et al.  Mycobacterial granulomas: keys to a long-lasting host-pathogen relationship. , 2004, Clinical immunology.

[35]  S. G. Axline,et al.  Enzymatic basis for bioenergetic differences of alveolar versus peritoneal macrophages and enzyme regulation by molecular O2. , 1977, The Journal of clinical investigation.

[36]  Denise E. Kirschner,et al.  Multi-Scale Modeling Predicts a Balance of Tumor Necrosis Factor-α and Interleukin-10 Controls the Granuloma Environment during Mycobacterium tuberculosis Infection , 2013, PloS one.

[37]  R. Mann,et al.  Human Physiology , 1839, Nature.

[38]  James A. Raleigh,et al.  Tuberculous Granulomas Are Hypoxic in Guinea Pigs, Rabbits, and Nonhuman Primates , 2008, Infection and Immunity.

[39]  Simeone Marino,et al.  Variability in Tuberculosis Granuloma T Cell Responses Exists, but a Balance of Pro- and Anti-inflammatory Cytokines Is Associated with Sterilization , 2015, PLoS pathogens.

[40]  JoAnne L. Flynn,et al.  Sterilization of granulomas is common in both active and latent tuberculosis despite extensive within-host variability in bacterial killing , 2014 .

[41]  V. Singh,et al.  Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets , 2006, Theoretical Biology and Medical Modelling.

[42]  E E May,et al.  BioXyce: an engineering platform for the study of cellular systems. , 2009, IET systems biology.

[43]  Sophia Lefantzi,et al.  DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. , 2011 .

[44]  Z. Ivanovic Physiological, ex vivo cell oxygenation is necessary for a true insight into cytokine biology. , 2009, European cytokine network.

[45]  K Sembulingam,et al.  Essentials of Medical Physiology , 2006 .

[46]  Simeone Marino,et al.  Multiscale Computational Modeling Reveals a Critical Role for TNF-α Receptor 1 Dynamics in Tuberculosis Granuloma Formation , 2011, The Journal of Immunology.

[47]  J. Hamilton,et al.  Hypoxia Prolongs Monocyte/Macrophage Survival and Enhanced Glycolysis Is Associated with Their Maturation under Aerobic Conditions1 , 2009, The Journal of Immunology.

[48]  Nicholas Fisher,et al.  Chapter 17 Type II NADH: quinone oxidoreductases of Plasmodium falciparum and Mycobacterium tuberculosis kinetic and high-throughput assays. , 2009, Methods in enzymology.

[49]  M. Shiloh,et al.  Mycobacterium tuberculosis senses host-derived carbon monoxide during macrophage infection. , 2008, Cell host & microbe.

[50]  James E Gomez,et al.  M. tuberculosis persistence, latency, and drug tolerance. , 2004, Tuberculosis.

[51]  E. Weibel Understanding the limitation of O2 supply through comparative physiology. , 1999, Respiration physiology.