2016
DOI: 10.1128/iai.01438-15
|View full text |Cite
|
Sign up to set email alerts
|

Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions

Abstract: cGranulomas are a hallmark of tuberculosis. Inside granulomas, the pathogen Mycobacterium tuberculosis may enter a metabolically inactive state that is less susceptible to antibiotics. Understanding M. tuberculosis metabolism within granulomas could contribute to reducing the lengthy treatment required for tuberculosis and provide additional targets for new drugs. Two key adaptations of M. tuberculosis are a nonreplicating phenotype and accumulation of lipid inclusions in response to hypoxic conditions. To exp… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
50
0

Year Published

2016
2016
2023
2023

Publication Types

Select...
6
1
1

Relationship

2
6

Authors

Journals

citations
Cited by 46 publications
(50 citation statements)
references
References 111 publications
(170 reference statements)
0
50
0
Order By: Relevance
“…Subsequent iterations contain macrophages (M1 and M2) and T cells (CD4+, CD8+, and Tregs) as agents that can have multiple states and phenotypes (eg, infected, activated, etc. ), and bacteria represented as either continuous functions or as agents in the extra‐ or intracellular environment . Probabilistic interactions between immune cells and bacterial populations are described by a well‐defined set of rules that are continually updated with new biological datasets.…”
Section: Computational Model Of Granuloma Formation and Functionmentioning
confidence: 99%
See 1 more Smart Citation
“…Subsequent iterations contain macrophages (M1 and M2) and T cells (CD4+, CD8+, and Tregs) as agents that can have multiple states and phenotypes (eg, infected, activated, etc. ), and bacteria represented as either continuous functions or as agents in the extra‐ or intracellular environment . Probabilistic interactions between immune cells and bacterial populations are described by a well‐defined set of rules that are continually updated with new biological datasets.…”
Section: Computational Model Of Granuloma Formation and Functionmentioning
confidence: 99%
“…), 89 and bacteria represented as either continuous functions or as agents in the extra-or intracellular environment. 102 Probabilistic interactions between immune cells and bacterial populations are described by a well-defined set of rules that are continually updated with new biological datasets.…”
Section: Computational Model Of G R Anuloma Formation and Fun C Tionmentioning
confidence: 99%
“…As described in the previous section, the Pamer laboratory has advanced mechanistic knowledge for C. difficile infection. Denise Kirschner's laboratory has advanced mechanistic understanding of tuberculosis and generated robust validated models of immune responses to tuberculosis that predict health outcomes across multiple temporal and spatial scales (Cilfone, Pienaar, Thurber, Kirschner, & Linderman, 2015;Kirschner & Linderman, 2009;Marino et al, 2016;Marino, Linderman, & Kirschner, 2011;Pienaar, Matern, Linderman, Bader, & Kirschner, 2016). However, such bodies of mechanistic data are lacking for most other pathogens, particularly for biothreat agents and foodborne and waterborne pathogens, as well as many opportunistic pathogens and pathobionts that cause illness in dysbiotic, not healthy, hosts.…”
Section: Opening a Path Forward For Nextgen Microbial Risk Analysismentioning
confidence: 99%
“…At this time, we assume a constant background oxygen level arising from airways and focus on oxygen diffusion from the vascular system. Since it has been observed that when a vessel is surrounded by caseous material, its perfusion and diffusion capabilities are impaired (Datta et al, 2015;Pienaar et al, 2016), we have incorporated this by considering a lower diffusion and supply rate in the granuloma structure as compared to the normal vessels, i.e.…”
Section: Oxygen Dynamicsmentioning
confidence: 99%
“…In (Pienaar et al, 2016) the authors map metabolite and genescale perturbations , finding that slowly replicating phenotypes of M. tuberculosis preserve the bacterial population in vivo by continuously adapting to dynamic granuloma microenvironments. (Sershen et al, 2016) also combines a physiological model of oxygen dynamics, an agent-based model of cellular immune response and a systems-based model of M.tb metabolic dynamics.…”
Section: Introductionmentioning
confidence: 99%