A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes
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 overa...
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| Format: | Article |
| Language: | English |
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AIMS Press
2015-01-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2015.12.625 |
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| author | Chang Gong Jennifer J. Linderman Denise Kirschner |
| author_facet | Chang Gong Jennifer J. Linderman Denise Kirschner |
| author_sort | Chang Gong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3d54a4a55a10457dbf7096b4042cdd76 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-3d54a4a55a10457dbf7096b4042cdd762025-01-24T02:31:54ZengAIMS PressMathematical Biosciences and Engineering1551-00182015-01-0112362564210.3934/mbe.2015.12.625A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomesChang Gong0Jennifer J. Linderman1Denise Kirschner26775 Medical Science Building II, Ann Arbor, MI 48109-5620B28-G045W NCRC, Ann Arbor, MI 48109-56206730 Medical Science Building II, Ann Arbor, MI 48109-5620Granulomas 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.https://www.aimspress.com/article/doi/10.3934/mbe.2015.12.625within-host model.tbgranulomaode modelinfectious disease |
| spellingShingle | Chang Gong Jennifer J. Linderman Denise Kirschner A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes Mathematical Biosciences and Engineering within-host model. tb granuloma ode model infectious disease |
| title | A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes |
| title_full | A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes |
| title_fullStr | A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes |
| title_full_unstemmed | A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes |
| title_short | A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes |
| title_sort | population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes |
| topic | within-host model. tb granuloma ode model infectious disease |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2015.12.625 |
| work_keys_str_mv | AT changgong apopulationmodelcapturingdynamicsoftuberculosisgranulomaspredictshostinfectionoutcomes AT jenniferjlinderman apopulationmodelcapturingdynamicsoftuberculosisgranulomaspredictshostinfectionoutcomes AT denisekirschner apopulationmodelcapturingdynamicsoftuberculosisgranulomaspredictshostinfectionoutcomes AT changgong populationmodelcapturingdynamicsoftuberculosisgranulomaspredictshostinfectionoutcomes AT jenniferjlinderman populationmodelcapturingdynamicsoftuberculosisgranulomaspredictshostinfectionoutcomes AT denisekirschner populationmodelcapturingdynamicsoftuberculosisgranulomaspredictshostinfectionoutcomes |