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.KÂ 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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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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DOI: https://doi.org/10.1007/978-3-030-56510-7_15
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