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Temporal and Spatial Analyses of TB Granulomas to Predict Long-Term Outcomes

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Book cover Complex Systems and Computational Biology Approaches to Acute Inflammation

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Abstract

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), kills more individuals worldwide per year than any other infectious agent. As the hallmark of TB, lung granulomas are complex structures composed of immune cells that interact with and surround bacteria, infected cells, and a necrotic core. This interaction leads to diverse granuloma outcomes across time, ranging from bacterial sterilization to uncontrolled bacterial growth, as well as diverse spatial structures. At this time, there are no systematic quantitative methods to classify the formation, function, and spatial characteristics of granulomas. This type of analysis would enable better understanding and prediction of granuloma behaviors that have known associations with poor clinical outcomes for TB patients. Herein, we develop a temporal and spatial analysis framework for TB granulomas using a systems biology approach combining in silico granuloma modeling, geographic information systems, topological data analysis, and machine learning. We apply this framework to simulated granulomas to understand temporal granuloma dynamics, quantify granuloma spatial structure, and predict the relationship between granuloma structure and bacterial growth. As a proof-of-concept, we apply our in silico predictions to in vivo derived data to test our framework for future applications and as a personalized medicine intervention.

Louis R. Joslyn and Marissa Renardy are co-first authors.

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Abbreviations

ABM:

Agent-based model

BCG:

Bacillus Calmette–Guérin

CFU:

Colony forming units

GIS:

Geographic information systems

IFN:

Interferon

IHC:

Immunohistochemically

IL:

Interleukin

kNN:

k-nearest neighbors

LHS:

Latin hypercube sampling

MLR:

Multinomial logistic regression

Mtb:

Mycobacterium tuberculosis

NHP:

Nonhuman primate

PET-CT:

Positron emission tomography–computed tomography

SVM:

Support vector machines

TB:

Tuberculosis

TDA:

Topological data analysis

TGF-β:

Transforming growth factor beta

TNF:

Tumor necrosis factor

t-SNE:

t-Distributed stochastic neighbor embedding

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Acknowledgments

This research was supported by NIH grants R01AI123093 and U01 HL131072 awarded to D.E.K and JLF. Simulations also use resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. ACI-1053575 and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant MCB140228.

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Authors and Affiliations

  1. Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA

    Louis R. Joslyn, Marissa Renardy, Caleb Weissman & Denise E. Kirschner

  2. Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Nicole L. Grant & JoAnne L. Flynn

  3. Department of Biology, AdventHealth University, Orlando, FL, USA

    J. Russ Butler

Authors
  1. Louis R. Joslyn
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  2. Marissa Renardy
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  3. Caleb Weissman
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  4. Nicole L. Grant
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  5. JoAnne L. Flynn
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  6. J. Russ Butler
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  7. Denise E. Kirschner
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Corresponding author

Correspondence to Denise E. Kirschner .

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Editors and Affiliations

  1. Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA

    Yoram Vodovotz

  2. Department of Surgery, University of Vermont, Burlington, VT, USA

    Gary An

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Joslyn, L.R. et al. (2021). Temporal and Spatial Analyses of TB Granulomas to Predict Long-Term Outcomes. In: Vodovotz, Y., An, G. (eds) Complex Systems and Computational Biology Approaches to Acute Inflammation. Springer, Cham. https://doi.org/10.1007/978-3-030-56510-7_15

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