American Institute of Mathematical Sciences

Advanced
2015, 12(3): 625-642. doi: 10.3934/mbe.2015.12.625

A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes

Chang Gong 1, , Jennifer J. Linderman 2, and  Denise Kirschner 3,

1. 

6775 Medical Science Building II, Ann Arbor, MI 48109-5620, United States
2. 

B28-G045W NCRC, Ann Arbor, MI 48109-5620, United States
3. 

6730 Medical Science Building II, Ann Arbor, MI 48109-5620, United States

Received  January 2015 Revised  January 2015 Published  February 2015

Granulomas play a centric role in tuberculosis (TB) infection progression. Multiple granulomas usually develop within a single host. These granulomas are not synchronized in size or bacteria load, and will follow different trajectories over time. How the fate of individual granulomas influence overall infection outcome at host scale is not understood, although computational models have been developed to predict single granuloma behavior. Here we present a within-host population model that tracks granulomas in two key organs during Mycobacteria tuberculosis (Mtb) infection: lung and lymph nodes (LN). We capture various time courses of TB progression, including latency and reactivation. The model predicts that there is no steady state; rather it is a continuous process of progressing to active disease over differing time periods. This is consistent with recently posed ideas suggesting that latent TB exists as a spectrum of states and not a single state. The model also predicts a dual role for granuloma development in LNs during Mtb infection: in early phases of infection granulomas suppress infection by providing additional antigens to the site of immune priming; however, this induces a more rapid reactivation at later stages by disrupting immune responses. We identify mechanisms that strongly correlate with better host-level outcomes, including elimination of uncontained lung granulomas by inducing low levels of lung tissue damage and inhibition of bacteria dissemination within the lung.
Keywords: within-host model., TB, granuloma, ODE model, infectious disease.
Mathematics Subject Classification: Primary: 97M60; Secondary: 92B0.
Citation: Chang Gong, Jennifer J. Linderman, Denise Kirschner. A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes. Mathematical Biosciences & Engineering, 2015, 12 (3) : 625-642. doi: 10.3934/mbe.2015.12.625
References:
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I. Y. Adamson, Drug-induced pulmonary fibrosis, Environmental health perspectives, 55 (1984), 25-36. Google Scholar

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C. E. Barry, H. I. Boshoff, V. Dartois, T. Dick, S. Ehrt, J. Flynn, D. Schnappinger, R. J. Wilkinson and D. Young, The spectrum of latent tuberculosis: Rethinking the biology and intervention strategies, Nature reviews. Microbiology, 7 (2009), 845-855. doi: 10.1038/nrmicro2236.  Google Scholar

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C. Castillo-Chavez and Z. Feng, To treat or not to treat: The case of tuberculosis, Journal of mathematical biology, 35 (1997), 629-656. doi: 10.1007/s002850050069.  Google Scholar

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A. A. Chackerian, J. M. Alt, T. V. Perera, C. C. Dascher and S. M. Behar, Dissemination of Mycobacterium tuberculosis Is Influenced by Host Factors and Precedes the Initiation of T-Cell Immunity, Infection and Immunity, 70 (2002), 4501-4509. doi: 10.1128/IAI.70.8.4501-4509.2002.  Google Scholar

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M. Fallahi-Sichani, M. A. Schaller, D. E. Kirschner, S. L. Kunkel and J. J. Linderman, Identification of key processes that control tumor necrosis factor availability in a tuberculosis granuloma, PLoS computational biology, 6 (2010), e1000778, 19pp. doi: 10.1371/journal.pcbi.1000778.  Google Scholar

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J. L. Flynn and J. Chan, Immunology of tuberculosis, Annual review of immunology, 19 (2001), 93-129. Google Scholar

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C. Gong, J. T. Mattila, M. Miller, J. L. Flynn, J. J. Linderman and D. Kirschner, Predicting lymph node output efficiency using systems biology, Journal of theoretical biology, 335 (2013), 169-184. doi: 10.1016/j.jtbi.2013.06.016.  Google Scholar

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[22]

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[23]

D. E. Kirschner, S. T. Chang, T. W. Riggs, N. Perry and J. J. Linderman, Toward a multiscale model of antigen presentation in immunity, Immunological reviews, 216 (2007), 93-118. Google Scholar

[24]

P. L. Lin, T. Coleman, J. P. J. Carney, B. J. Lopresti, J. Tomko, D. Fillmore, V. Dartois, C. Scanga, L. J. Frye, Ch. Janssen, E. Klein, C. E. Barry and Joanne L Flynn, Radiologic responses in cynomolgous macaques for assessing tuberculosis chemotherapy regimens, Antimicrobial agents and chemotherapy, 57 (2013), 4237-4244. doi: 10.1128/AAC.00277-13.  Google Scholar

[25]

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show all references

References:
[1]

I. Y. Adamson, Drug-induced pulmonary fibrosis, Environmental health perspectives, 55 (1984), 25-36. Google Scholar

[2]

C. E. Barry, H. I. Boshoff, V. Dartois, T. Dick, S. Ehrt, J. Flynn, D. Schnappinger, R. J. Wilkinson and D. Young, The spectrum of latent tuberculosis: Rethinking the biology and intervention strategies, Nature reviews. Microbiology, 7 (2009), 845-855. doi: 10.1038/nrmicro2236.  Google Scholar

[3]

S. M. Blower, A. R. McLean, T. C. Porco, P. M. Small, P. C. Hopewell, M. A. Sanchez and A. R. Moss, The intrinsic transmission dynamics of tuberculosis epidemics, Nature Medicine, 1 (1995), 815-821. doi: 10.1038/nm0895-815.  Google Scholar

[4]

P.-J. Cardona, New insights on the nature of latent tuberculosis infection and its treatment, Inflammation & allergy drug targets, 6 (2007), 27-39. doi: 10.2174/187152807780077282.  Google Scholar

[5]

C. Castillo-Chávez and J. Aparicio, Mathematical modelling of tuberculosis epidemics, Mathematical Biosciences and Engineering, 6 (2009), 209-237. doi: 10.3934/mbe.2009.6.209.  Google Scholar

[6]

C. Castillo-Chavez and Z. Feng, To treat or not to treat: The case of tuberculosis, Journal of mathematical biology, 35 (1997), 629-656. doi: 10.1007/s002850050069.  Google Scholar

[7]

A. A. Chackerian, J. M. Alt, T. V. Perera, C. C. Dascher and S. M. Behar, Dissemination of Mycobacterium tuberculosis Is Influenced by Host Factors and Precedes the Initiation of T-Cell Immunity, Infection and Immunity, 70 (2002), 4501-4509. doi: 10.1128/IAI.70.8.4501-4509.2002.  Google Scholar

[8]

N. A. Cilfone, C. R. Perry, D. E. Kirschner and J. J. Linderman, Multi-scale modeling predicts a balance of tumor necrosis factor-$\alpha$ and interleukin-10 controls the granuloma environment during Mycobacterium tuberculosis infection, PloS one, 8 (2013), e68680. doi: 10.1371/journal.pone.0068680.  Google Scholar

[9]

M. T. Coleman, R. Y. Chen, M. Lee, P. L. Lin, L. E. Dodd, P. Maiello, L. E. Via, Y. Kim, G. Marriner, V. Dartois, C. Scanga, C. Janssen, J. Wang, E. Klein, S. N. Cho, C. E. Barry 3rd and J. L. Flynn, PET/CT imaging reveals a therapeutic response to oxazolidinones in macaques and humans with tuberculosis, Sci Transl Med, 6 (2014), p265ra167. doi: 10.1126/scitranslmed.3009500.  Google Scholar

[10]

J. A. Cooper, D. A. White and R. A. Matthay, Drug-induced pulmonary disease. Part 1: Cytotoxic drugs, The American review of respiratory disease, 133 (1986), 321-340. Google Scholar

[11]

E. L. Corbett, C. J. Watt, N. Walker, D. Maher, B. G. Williams, M. C. Raviglione and C. Dye, The growing burden of tuberculosis: Global trends and interactions with the HIV epidemic, Archives of internal medicine, 163 (2003), 1009-1021. doi: 10.1001/archinte.163.9.1009.  Google Scholar

[12]

M. H. Daba, K. E. El-Tahir, M. N. Al-Arifi and O. A. Gubara, Drug-induced pulmonary fibrosis,, 2004., ().   Google Scholar

[13]

O. Diekmann, J. A. P. Heesterbeek and M. G. Roberts, The construction of next-generation matrices for compartmental epidemic models, Journal of the Royal Society, Interface / the Royal Society, 7 (2010), 873-885. doi: 10.1098/rsif.2009.0386.  Google Scholar

[14]

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[15]

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[16]

M. Fallahi-Sichani, D. E. Kirschner and J. J. Linderman, NF-$\kappa$B Signaling Dynamics Play a Key Role in Infection Control in Tuberculosis, Frontiers in physiology, 2012. doi: 10.3389/fphys.2012.00170.  Google Scholar

[17]

M. Fallahi-Sichani, M. A. Schaller, D. E. Kirschner, S. L. Kunkel and J. J. Linderman, Identification of key processes that control tumor necrosis factor availability in a tuberculosis granuloma, PLoS computational biology, 6 (2010), e1000778, 19pp. doi: 10.1371/journal.pcbi.1000778.  Google Scholar

[18]

Z. Feng, C. Castillo-Chavez and A. F. Capurro, A model for tuberculosis with exogenous reinfection, Theoretical population biology, 57 (2000), 235-247. doi: 10.1006/tpbi.2000.1451.  Google Scholar

[19]

J. L. Flynn and J. Chan, Immunology of tuberculosis, Annual review of immunology, 19 (2001), 93-129. Google Scholar

[20]

C. Gong, J. T. Mattila, M. Miller, J. L. Flynn, J. J. Linderman and D. Kirschner, Predicting lymph node output efficiency using systems biology, Journal of theoretical biology, 335 (2013), 169-184. doi: 10.1016/j.jtbi.2013.06.016.  Google Scholar

[21]

G. Guzzetta, M. Ajelli, Z. Yang, S. Merler, C. Furlanello and D. Kirschner, Modeling socio-demography to capture tuberculosis transmission dynamics in a low burden setting, Journal of theoretical biology, 289 (2011), 197-205. doi: 10.1016/j.jtbi.2011.08.032.  Google Scholar

[22]

D. Kirschner, Dynamics of co-infection with M. Tuberculosis and HIV-1, Theoretical population biology, 55 (1999), 94-109. Google Scholar

[23]

D. E. Kirschner, S. T. Chang, T. W. Riggs, N. Perry and J. J. Linderman, Toward a multiscale model of antigen presentation in immunity, Immunological reviews, 216 (2007), 93-118. Google Scholar

[24]

P. L. Lin, T. Coleman, J. P. J. Carney, B. J. Lopresti, J. Tomko, D. Fillmore, V. Dartois, C. Scanga, L. J. Frye, Ch. Janssen, E. Klein, C. E. Barry and Joanne L Flynn, Radiologic responses in cynomolgous macaques for assessing tuberculosis chemotherapy regimens, Antimicrobial agents and chemotherapy, 57 (2013), 4237-4244. doi: 10.1128/AAC.00277-13.  Google Scholar

[25]

P. L. Lin, C. B. Ford, M. T. Coleman, A. J. Myers, R. Gawande, T. Ioerger, J. Sacchettini, S. M. Fortune and J. L. Flynn, Sterilization of granulomas is common in active and latent tuberculosis despite within-host variability in bacterial killing, Nature medicine, 20 (2014), 75-79. doi: 10.1038/nm.3412.  Google Scholar

[26]

P. L. Lin, M. Rodgers, L. Smith, M. Bigbee, A. Myers, C. Bigbee, I. Chiosea, S. V. Capuano, C. Fuhrman, E. Klein and J. L. Flynn, Quantitative comparison of active and latent tuberculosis in the cynomolgus macaque model, Infection and immunity, 77 (2009), 4631-4642. doi: 10.1128/IAI.00592-09.  Google Scholar

[27]

J. J. Linderman, T. Riggs, M. Pande, M. Miller, S. Marino and D. E. Kirschner, Characterizing the dynamics of CD4+ T cell priming within a lymph node, Journal of immunology (Baltimore, Md. : 1950), 184 (2010), 2873-2885. doi: 10.4049/jimmunol.0903117.  Google Scholar

[28]

G. Magombedze, W. Garira and E. Mwenje, Modelling the human immune response mechanisms to mycobacterium tuberculosis infection in the lungs, Mathematical biosciences and engineering : MBE, 3 (2006), 661-682. doi: 10.3934/mbe.2006.3.661.  Google Scholar

[29]

G. Magombedze and N. Mulder, A mathematical representation of the development of Mycobacterium tuberculosis active, latent and dormant stages, Journal of theoretical biology, 292 (2012), 44-59. doi: 10.1016/j.jtbi.2011.09.025.  Google Scholar

[30]

S. Marino, M. El-Kebir and D. Kirschner, A hybrid multi-compartment model of granuloma formation and T cell priming in tuberculosis, Journal of theoretical biology, 280 (2011), 50-62. doi: 10.1016/j.jtbi.2011.03.022.  Google Scholar

[31]

S. Marino, I. B. Hogue, C. J. Ray and D. E. Kirschner, A methodology for performing global uncertainty and sensitivity analysis in systems biology, Journal of theoretical biology, 254 (2008), 178-196. doi: 10.1016/j.jtbi.2008.04.011.  Google Scholar

[32]

S. Marino and D. E. Kirschner, The human immune response to Mycobacterium tuberculosis in lung and lymph node, Journal of theoretical biology, 227 (2004), 463-486. doi: 10.1016/j.jtbi.2003.11.023.  Google Scholar

[33]

S. Marino, J. J. Linderman and D. E. Kirschner, A multifaceted approach to modeling the immune response in tuberculosis, Wiley interdisciplinary reviews. Systems biology and medicine, 3 (2011), 479-489. doi: 10.1002/wsbm.131.  Google Scholar

[34]

F. A. Milner, M. Iannelli and Z. Feng, A Two-Strain Tuberculosis Model with Age of Infection, SIAM Journal on Applied Mathematics, 62 (2002), 1634-1656. doi: 10.1137/S003613990038205X.  Google Scholar

[35]

B. M. Murphy, B. H. Singer, S. Anderson and D. Kirschner, Comparing epidemic tuberculosis in demographically distinct heterogeneous populations, Mathematical Biosciences, 180 (2002), 161-185. doi: 10.1016/S0025-5564(02)00133-5.  Google Scholar

[36]

B. M. Murphy, B. H. Singer and D. Kirschner, On treatment of tuberculosis in heterogeneous populations, Journal of Theoretical Biology, 223 (2003), 391-404. doi: 10.1016/S0022-5193(03)00038-9.  Google Scholar

[37]

A. O'Garra, P. S. Redford, F. W. McNab, C. I. Bloom, R. J. Wilkinson and M. P. R. Berry, The immune response in tuberculosis, Annual review of immunology, 31 (2013), 475-527. doi: 10.1146/annurev-immunol-032712-095939.  Google Scholar

[38]

W. H. Organization, Global Tuberculosis Report 2013,, 2013., ().   Google Scholar

[39]

R. Pabst, J. Westermann and H. J. Rothkotter, Immunoarchitecture of regenerated splenic and lymph node transplants, Int Rev Cytol, 128 (1991), 215-260. doi: 10.1016/S0074-7696(08)60500-8.  Google Scholar

[40]

T. H. Petersen, E. A. Calle, L. Zhao, E. J. Lee, L. Gui, M. B. Raredon, K. Gavrilov, T. Yi, Z. W. Zhuang, C. Breuer, E. Herzog and L. E. Niklason, Tissue-engineered lungs for in vivo implantation, Science, 329 (2010), 538-541. doi: 10.1126/science.1189345.  Google Scholar

[41]

L. Ramakrishnan, Revisiting the role of the granuloma in tuberculosis, Nature reviews. Immunology, 12 (2012), 352-366. doi: 10.1038/nri3211.  Google Scholar

[42]

J. Rengarajan, B. R. Bloom and E. J. Rubin, Genome-wide requirements for Mycobacterium tuberculosis adaptation and survival in macrophages, Proceedings of the National Academy of Sciences of the United States of America, 102 (2005), 8327-8332. doi: 10.1073/pnas.0503272102.  Google Scholar

[43]

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