Dataset Information

Mycobacterium tuberculosis Cell Wall Fragments Released upon Bacterial Contact with the Human Lung Mucosa Alter the Neutrophil Response to Infection.

ABSTRACT: In 2016, the World Health Organization reported that one person dies of tuberculosis (TB) every 21?s. A host environment that Mycobacterium tuberculosis (M.tb) finds during its route of infection is the lung mucosa bathing the alveolar space located in the deepest regions of the lungs. We published that human lung mucosa, or alveolar lining fluid (ALF), contains an array of hydrolytic enzymes that can significantly alter the M.tb surface during infection by cleaving off parts of its cell wall. This interaction results in two different outcomes: modifications on the M.tb cell wall surface and release of M.tb cell wall fragments into the environment. Typically, one of the first host immune cells at the site of M.tb infection is the neutrophil. Neutrophils can mount an extracellular and intracellular innate immune response to M.tb during infection. We hypothesized that exposure of neutrophils to ALF-induced M.tb released cell wall fragments would prime neutrophils to control M.tb infection better. Our results show that ALF fragments activate neutrophils leading to an increased production of inflammatory cytokines and oxidative radicals. However, neutrophil exposure to these fragments reduces production of chemoattractants (i.e., interleukin-8), and degranulation, with the subsequent reduction of myeloperoxidase release, and does not induce cytotoxicity. Unexpectedly, these ALF fragment-derived modulations in neutrophil activity do not further, either positively or negatively, contribute to the intracellular control of M.tb growth during infection. However, secreted products from neutrophils primed with ALF fragments are capable of regulating the activity of resting macrophages. These results indicate that ALF-induced M.tb fragments could further contribute to the control of M.tb growth and local killing by resident neutrophils by switching on the total oxidative response and limiting migration of neutrophils to the infection site.

SUBMITTER: Scordo JM

PROVIDER: S-EPMC5357626 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Mycobacterium tuberculosis Cell Wall Fragments Released upon Bacterial Contact with the Human Lung Mucosa Alter the Neutrophil Response to Infection.

Scordo Julia M JM Arcos Jesús J Kelley Holden V HV Diangelo Lauren L Sasindran Smitha J SJ Youngmin Ellie E Wewers Mark D MD Wang Shu-Hua SH Balada-Llasat Joan-Miquel JM Torrelles Jordi B JB

Frontiers in immunology 20170320

In 2016, the World Health Organization reported that one person dies of tuberculosis (TB) every 21 s. A host environment that Mycobacterium tuberculosis (M.tb) finds during its route of infection is the lung mucosa bathing the alveolar space located in the deepest regions of the lungs. We published that human lung mucosa, or alveolar lining fluid (ALF), contains an array of hydrolytic enzymes that can significantly alter the M.tb surface during infection by cleaving off part ...[more]

PMID: 28373877

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Project description:Pathogen genomic data are increasingly used to characterize global and local transmission patterns of important human pathogens and to inform public health interventions. Yet, there is no current consensus on how to measure genomic variation. To test the effect of the variant-identification approach on transmission inferences for Mycobacterium tuberculosis, we conducted an experiment in which five genomic epidemiology groups applied variant-identification pipelines to the same outbreak sequence data. We compared the variants identified by each group in addition to transmission and phylogenetic inferences made with each variant set. To measure the performance of commonly used variant-identification tools, we simulated an outbreak. We compared the performance of three mapping algorithms, five variant callers and two variant filters in recovering true outbreak variants. Finally, we investigated the effect of applying increasingly stringent filters on transmission inferences and phylogenies. We found that variant-calling approaches used by different groups do not recover consistent sets of variants, which can lead to conflicting transmission inferences. Further, performance in recovering true variation varied widely across approaches. While no single variant-identification approach outperforms others in both recovering true genome-wide and outbreak-level variation, variant-identification algorithms calibrated upon real sequence data or that incorporate local reassembly outperform others in recovering true pairwise differences between isolates. The choice of variant filters contributed to extensive differences across pipelines, and applying increasingly stringent filters rapidly eroded the accuracy of transmission inferences and quality of phylogenies reconstructed from outbreak variation. Commonly used approaches to identify M. tuberculosis genomic variation have variable performance, particularly when predicting potential transmission links from pairwise genetic distances. Phylogenetic reconstruction may be improved by less stringent variant filtering. Approaches that improve variant identification in repetitive, hypervariable regions, such as long-read assemblies, may improve transmission inference.

Project description:Genome-scale metabolic models of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, have been envisioned as a platform for drug discovery. By systematically probing the networks that underpin such models, the reactions that are essential for Mtb are identified. A majority of these reactions are catalyzed by enzymes and thus represent candidate drug targets to fight an Mtb infection. Nevertheless, this is complicated by the limited knowledge on the environment that Mtb encounters during infection. Modeling the behavior of the bacteria during infection requires knowledge of the so-called biomass reaction that represents bacterial biomass composition. This composition varies in different environments or bacterial growth phases. Accurate modeling of the metabolic state requires a precise biomass reaction for the described condition. In recent years, additional insights in the in-host environment occupied by Mtb have been gained as transcript abundance data of interacting host and pathogen have become available. Therefore, we used transcript abundance data and developed a straightforward and systematic method to obtain a condition-specific biomass reaction for Mtb during in vitro growth and during infection of its host. The method described herein is virtually free of any pre-set assumptions on uptake rates of nutrients, making it suitable for exploring environments with limited accessibility. The condition-specific biomass reaction represents the "metabolic objective" of Mtb in a given environment (in-host growth and growth on defined medium) at a specific time point, and as such allows modeling the bacterial metabolic state in these environments. Five different biomass reactions were used to predict nutrient uptake rates and gene essentiality. Predictions were subsequently compared to available experimental data. Our results show that nutrient uptake can accurately be predicted. Gene essentiality can also be predicted but accurate predictions remain difficult to obtain. In conclusion, a viable strategy to model Mtb metabolism in hard-to-access environments that is virtually free of pre-set assumptions is provided.

Dataset Information

Mycobacterium tuberculosis Cell Wall Fragments Released upon Bacterial Contact with the Human Lung Mucosa Alter the Neutrophil Response to Infection.

Publications

<i>Mycobacterium tuberculosis</i> Cell Wall Fragments Released upon Bacterial Contact with the Human Lung Mucosa Alter the Neutrophil Response to Infection.

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