The Experts below are selected from a list of 3519 Experts worldwide ranked by ideXlab platform
Jennifer J Linderman - One of the best experts on this subject based on the ideXlab platform.
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a computational model tracks whole Lung mycobacterium tuberculosis infection and predicts factors that inhibit dissemination
PLOS Computational Biology, 2020Co-Authors: Timothy Wessler, Louis R Joslyn, Jacob H Borish, Hannah P Gideon, Joanne L Flynn, Denise E Kirschner, Jennifer J LindermanAbstract:Mycobacterium tuberculosis (Mtb), the causative infectious agent of tuberculosis (TB), kills more individuals per year than any other infectious agent. Granulomas, the hallmark of Mtb infection, are complex structures that form in Lungs, composed of immune cells surrounding bacteria, infected cells, and a caseous necrotic core. While Granulomas serve to physically contain and immunologically restrain bacteria growth, some Granulomas are unable to control Mtb growth, leading to bacteria and infected cells leaving the Granuloma and disseminating, either resulting in additional Granuloma formation (local or non-local) or spread to airways or lymph nodes. Dissemination is associated with development of active TB. It is challenging to experimentally address specific mechanisms driving dissemination from TB Lung Granulomas. Herein, we develop a novel hybrid multi-scale computational model, MultiGran, that tracks Mtb infection within multiple Granulomas in an entire Lung. MultiGran follows cells, cytokines, and bacterial populations within each Lung Granuloma throughout the course of infection and is calibrated to multiple non-human primate (NHP) cellular, Granuloma, and whole-Lung datasets. We show that MultiGran can recapitulate patterns of in vivo local and non-local dissemination, predict likelihood of dissemination, and predict a crucial role for multifunctional CD8+ T cells and macrophage dynamics for preventing dissemination.
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a computational model tracks whole Lung mycobacterium tuberculosis infection and predicts factors that inhibit dissemination
bioRxiv, 2019Co-Authors: Timothy Wessler, Louis R Joslyn, Hannah P Gideon, Joanne L Flynn, Denise E Kirschner, H J Borish, Jennifer J LindermanAbstract:Abstract Mycobacterium tuberculosis (Mtb), the causative infectious agent of tuberculosis (TB), kills more individuals per year than any other infectious agent. Granulomas, the hallmark of Mtb infection, are complex structures that form in Lungs, composed of immune cells surrounding bacteria, infected cells, and a caseous necrotic core. While Granulomas serve to physically contain and immunologically restrain bacteria growth, some Granulomas are unable to control Mtb growth, leading to bacteria and infected cells leaving the Granuloma and disseminating, either resulting in additional Granuloma formation (local or non-local) or spread to airways or lymph nodes. Dissemination is associated with development of active TB. It is challenging to experimentally address specific mechanisms driving dissemination from TB Lung Granulomas. Herein, we develop a novel hybrid multi-scale computational model, MultiGran, that tracks Mtb infection within multiple Granulomas in an entire Lung. MultiGran follows cells, cytokines, and bacterial populations within each Lung Granuloma throughout the course of infection and is calibrated to multiple non-human primate (NHP) cellular, Granuloma, and whole-Lung datasets. We show that MultiGran can recapitulate patterns of in vivo local and non-local dissemination, predict likelihood of dissemination, and predict a crucial role for multifunctional CD8+ T cells and macrophage dynamics for preventing dissemination. Author Summary Tuberculosis (TB) is caused by infection with Mycobacterium tuberculosis (Mtb) and kills 3 people per minute worldwide. Granulomas, spherical structures composed of immune cells surrounding bacteria, are the hallmark of Mtb infection and sometimes fail to contain the bacteria and disseminate, leading to further Granuloma growth within the Lung environment. To date, the mechanisms that determine Granuloma dissemination events have not been characterized. We present a computational multi-scale model of Granuloma formation and dissemination within primate Lungs. Our computational model is calibrated to multiple experimental datasets across the cellular, Granuloma, and whole-Lung scales of non-human primates. We match to both individual Granuloma and Granuloma-population datasets, predict likelihood of dissemination events, and predict a critical role for multifunctional CD8+ T cells and macrophage-bacteria interactions to prevent infection dissemination.
Martin Rao - One of the best experts on this subject based on the ideXlab platform.
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Latent TB Infection (LTBI) – Mycobacterium tuberculosis pathogenesis and the dynamics of the Granuloma battleground
'Elsevier BV', 2019Co-Authors: Martin Rao, Giuseppe Ippolito, Sayoki Mfinanga, Francine Ntoumi, Dorothy Yeboah-manu, Cristina Vilaplana, Alimuddin Zumla, Markus MaeurerAbstract:Latent tuberculosis infection (LTBI) is established in over 90% of persons infected with Mycobacterium tuberculosis (Mtb), from whom new active TB cases will arise. Understanding the spatio-temporal dynamics of host immune responses in LTBI Granulomas is essential to designing effective post-exposure therapies that inhibit progression to TB. Information arising from cancer studies and other modalities – where local chronic inflammation leads to immunopathology – can help provide insights into the biological pathways at play in LTBI Granulomas. Translational studies using patient material as well as LTBI+ donor-derived tissue samples are instrumental in understanding the various components of Granuloma dynamics, immunological landscapes therein and how this could help to identify therapeutic targets. Deep sequencing technologies may aid to decipher the genetic changes in Lung Granuloma and blood samples from LTBI+ individuals associated with progression to active TB disease. This may lead to advancement of development of targeted Host-Directed Therapies (HDTs) and their evaluation as adjunct TB therapies for improving treatment outcomes for LTBI and pulmonary TB. Keywords: latent tuberculosis infection, Granuloma, mutations, immune landscape, host-directed therapie
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macrophage arginase 1 controls bacterial growth and pathology in hypoxic tuberculosis Granulomas
Proceedings of the National Academy of Sciences of the United States of America, 2014Co-Authors: Maria A Duquecorrea, Martin Rao, Anja A Kuhl, Paulo C Rodriguez, Ulrike Zedler, Sandra Schommerleitner, January Weiner, Robert Hurwitz, Joseph E Qualls, George A KosmiadiAbstract:Lung Granulomas develop upon Mycobacterium tuberculosis (Mtb) infection as a hallmark of human tuberculosis (TB). They are structured aggregates consisting mainly of Mtb-infected and -uninfected macrophages and Mtb-specific T cells. The production of NO by Granuloma macrophages expressing nitric oxide synthase-2 (NOS2) via l-arginine and oxygen is a key protective mechanism against mycobacteria. Despite this protection, TB Granulomas are often hypoxic, and bacterial killing via NOS2 in these conditions is likely suboptimal. Arginase-1 (Arg1) also metabolizes l-arginine but does not require oxygen as a substrate and has been shown to regulate NOS2 via substrate competition. However, in other infectious diseases in which Granulomas occur, such as leishmaniasis and schistosomiasis, Arg1 plays additional roles such as T-cell regulation and tissue repair that are independent of NOS2 suppression. To address whether Arg1 could perform similar functions in hypoxic regions of TB Granulomas, we used a TB murine Granuloma model in which NOS2 is absent. Abrogation of Arg1 expression in macrophages in this setting resulted in exacerbated Lung Granuloma pathology and bacterial burden. Arg1 expression in hypoxic Granuloma regions correlated with decreased T-cell proliferation, suggesting that Arg1 regulation of T-cell immunity is involved in disease control. Our data argue that Arg1 plays a central role in the control of TB when NOS2 is rendered ineffective by hypoxia.
Stefan H E Kaufmann - One of the best experts on this subject based on the ideXlab platform.
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fx11 limits mycobacterium tuberculosis growth and potentiates bactericidal activity of isoniazid through host directed activity
Disease Models & Mechanisms, 2020Co-Authors: Gopinath Krishnamoorthy, January Weiner, Peggy Kaiser, Ulrike Abu Abed, Pedro Mouraalves, Volker Brinkmann, Stefan H E KaufmannAbstract:ABSTRACT Lactate dehydrogenase A (LDHA) mediates interconversion of pyruvate and lactate, and increased lactate turnover is exhibited by malignant and infected immune cells. Hypoxic Lung Granuloma in Mycobacterium tuberculosis-infected animals present elevated levels of Ldha and lactate. Such alterations in the metabolic milieu could influence the outcome of host-M. tuberculosis interactions. Given the central role of LDHA for tumorigenicity, targeting lactate metabolism is a promising approach for cancer therapy. Here, we sought to determine the importance of LDHA for tuberculosis (TB) disease progression and its potential as a target for host-directed therapy. To this end, we orally administered FX11, a known small-molecule NADH-competitive LDHA inhibitor, to M. tuberculosis-infected C57BL/6J mice and Nos2−/− mice with hypoxic necrotizing Lung TB lesions. FX11 did not inhibit M. tuberculosis growth in aerobic/hypoxic liquid culture, but modestly reduced the pulmonary bacterial burden in C57BL/6J mice. Intriguingly, FX11 administration limited M. tuberculosis replication and onset of necrotic Lung lesions in Nos2−/− mice. In this model, isoniazid (INH) monotherapy has been known to exhibit biphasic killing kinetics owing to the probable selection of an INH-tolerant bacterial subpopulation. However, adjunct FX11 treatment corrected this adverse effect and resulted in sustained bactericidal activity of INH against M. tuberculosis. As a limitation, LDHA inhibition as an underlying cause of FX11-mediated effect could not be established as the on-target effect of FX11 in vivo was unconfirmed. Nevertheless, this proof-of-concept study encourages further investigation on the underlying mechanisms of LDHA inhibition and its significance in TB pathogenesis.
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inhibition of host lactate dehydrogenase a by a small molecule limits mycobacterium tuberculosis growth and potentiates bactericidal activity of isoniazid
bioRxiv, 2019Co-Authors: Gopinath Krishnamoorthy, January Weiner, Peggy Kaiser, Ulrike Abu Abed, Pedro Mouraalves, Volker Brinkmann, Stefan H E KaufmannAbstract:ABSTRACT Lactate dehydrogenase A (LDHA) mediates interconversion of pyruvate and lactate. Increased lactate turnover is shared by malignant and immune cells. Hypoxic Lung Granuloma in Mycobacterium tuberculosis-infected animals present elevated levels of Ldha and lactate. Such alteration in metabolic milieu could influence the outcome of interactions between M. tuberculosis and its infected immune cells. Given the central role of LDHA for tumorigenicity, targeting lactate metabolism is a promising approach for cancer therapy. Here, we sought to determine the importance of LDHA for Tuberculosis (TB) disease progression and its potential as a host-directed therapeutic target. To this end, we administered FX11, a small-molecule NADH-competitive LDHA inhibitor, to M. tuberculosis infected C57BL/6J mice and Nos2−/− mice with hypoxic necrotizing Lung TB lesions mimicking human pathology more closely. FX11 did not inhibit M. tuberculosis growth in aerobic/hypoxic liquid culture, but modestly reduced the pulmonary bacterial burden in C57BL/6J mice. Intriguingly, FX11 administration limited M. tuberculosis replication and onset of necrotic lesions in Nos2−/− mice. In this model, Isoniazid (INH) monotherapy has been known to exhibit biphasic killing kinetics owing to the probable selection of an INH-tolerant subpopulation. This adverse effect was corrected by adjunct FX11 treatment and augmented the INH-derived bactericidal effect against M. tuberculosis. Our findings therefore support LDHA as a potential target for host-directed adjunctive TB therapy and encourage further investigations into the underlying mechanism. IMPORTANCE Tuberculosis (TB) continues to be a global health threat of critical dimension. Standard TB drug treatment is prolonged and cumbersome. Inappropriate treatment or non-compliance results in emergence of drug-resistant Mycobacterium tuberculosis strains (MDR-TB) that render current treatment options ineffective. Targeting the host immune system as adjunct therapy to augment bacterial clearance is attractive as it is also expected to be effective against MDR-TB. Here, we provide evidence that pharmaceutical blockade of host lactate dehydrogenase A (LDHA) by a small-molecule limits M. tuberculosis growth and reduces pathology. Notably, LDHA inhibition potentiates the effect of Isoniazid, a first-line anti-TB drug. Hence, its implications of our findings for short-term TB treatment are profound. In sum, our findings establish murine LDHA as a potential target for host-directed TB therapy.
Christopher W K Lam - One of the best experts on this subject based on the ideXlab platform.
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sophora flavescens protects against mycobacterial trehalose dimycolate induced Lung Granuloma by inhibiting inflammation and infiltration of macrophages
Scientific Reports, 2018Co-Authors: Dehua Liu, Ben Chunglap Chan, Ling Cheng, Miranda Sinman Tsang, Jing Zhu, Chunwai Wong, Delong Jiao, Helen Yautsz Chan, Pingchung Leung, Christopher W K LamAbstract:The immune system responds to Mycobacterium tuberculosis (MTB) infection by forming Granulomas to quarantine the bacteria from spreading. Granuloma-mediated inflammation is a cause of Lung destruction and disease transmission. Sophora flavescens (SF) has been demonstrated to exhibit bactericidal activities against MTB. However, its immune modulatory activities on MTB-mediated Granulomatous inflammation have not been reported. In the present study, we found that flavonoids from Sophora flavescens (FSF) significantly suppressed the pro-inflammatory mediators released from mouse Lung alveolar macrophages (MH-S) upon stimulation by trehalose dimycolate (TDM), the most abundant lipoglycan on MTB surface. Moreover, FSF reduced adhesion molecule (LFA-1) expression on MH-S cells after TDM stimulation. Furthermore, FSF treatment on TDM-activated Lung epithelial (MLE-12) cells significantly downregulated macrophage chemoattractant protein (MCP-1/CCL2) expression, which in turn reduced the in vitro migration of MH-S to MLE-12 cells. In addition, FSF increased the clearance of mycobacterium bacteria (Mycobacterium aurum) in macrophages. FSF mainly affected the Mincle-Syk-Erk signaling pathway in TDM-activated MH-S cells. In TDM-induced mouse Granulomas model, oral administration with FSF significantly suppressed Lung Granulomas formation and inflammation. These findings collectively implicated an anti-inflammatory role of FSF on MTB-mediated Granulomatous inflammation, thereby providing evidence of FSF as an efficacious adjunct treatment during mycobacterial infection.
Timothy Wessler - One of the best experts on this subject based on the ideXlab platform.
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a computational model tracks whole Lung mycobacterium tuberculosis infection and predicts factors that inhibit dissemination
PLOS Computational Biology, 2020Co-Authors: Timothy Wessler, Louis R Joslyn, Jacob H Borish, Hannah P Gideon, Joanne L Flynn, Denise E Kirschner, Jennifer J LindermanAbstract:Mycobacterium tuberculosis (Mtb), the causative infectious agent of tuberculosis (TB), kills more individuals per year than any other infectious agent. Granulomas, the hallmark of Mtb infection, are complex structures that form in Lungs, composed of immune cells surrounding bacteria, infected cells, and a caseous necrotic core. While Granulomas serve to physically contain and immunologically restrain bacteria growth, some Granulomas are unable to control Mtb growth, leading to bacteria and infected cells leaving the Granuloma and disseminating, either resulting in additional Granuloma formation (local or non-local) or spread to airways or lymph nodes. Dissemination is associated with development of active TB. It is challenging to experimentally address specific mechanisms driving dissemination from TB Lung Granulomas. Herein, we develop a novel hybrid multi-scale computational model, MultiGran, that tracks Mtb infection within multiple Granulomas in an entire Lung. MultiGran follows cells, cytokines, and bacterial populations within each Lung Granuloma throughout the course of infection and is calibrated to multiple non-human primate (NHP) cellular, Granuloma, and whole-Lung datasets. We show that MultiGran can recapitulate patterns of in vivo local and non-local dissemination, predict likelihood of dissemination, and predict a crucial role for multifunctional CD8+ T cells and macrophage dynamics for preventing dissemination.
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a computational model tracks whole Lung mycobacterium tuberculosis infection and predicts factors that inhibit dissemination
bioRxiv, 2019Co-Authors: Timothy Wessler, Louis R Joslyn, Hannah P Gideon, Joanne L Flynn, Denise E Kirschner, H J Borish, Jennifer J LindermanAbstract:Abstract Mycobacterium tuberculosis (Mtb), the causative infectious agent of tuberculosis (TB), kills more individuals per year than any other infectious agent. Granulomas, the hallmark of Mtb infection, are complex structures that form in Lungs, composed of immune cells surrounding bacteria, infected cells, and a caseous necrotic core. While Granulomas serve to physically contain and immunologically restrain bacteria growth, some Granulomas are unable to control Mtb growth, leading to bacteria and infected cells leaving the Granuloma and disseminating, either resulting in additional Granuloma formation (local or non-local) or spread to airways or lymph nodes. Dissemination is associated with development of active TB. It is challenging to experimentally address specific mechanisms driving dissemination from TB Lung Granulomas. Herein, we develop a novel hybrid multi-scale computational model, MultiGran, that tracks Mtb infection within multiple Granulomas in an entire Lung. MultiGran follows cells, cytokines, and bacterial populations within each Lung Granuloma throughout the course of infection and is calibrated to multiple non-human primate (NHP) cellular, Granuloma, and whole-Lung datasets. We show that MultiGran can recapitulate patterns of in vivo local and non-local dissemination, predict likelihood of dissemination, and predict a crucial role for multifunctional CD8+ T cells and macrophage dynamics for preventing dissemination. Author Summary Tuberculosis (TB) is caused by infection with Mycobacterium tuberculosis (Mtb) and kills 3 people per minute worldwide. Granulomas, spherical structures composed of immune cells surrounding bacteria, are the hallmark of Mtb infection and sometimes fail to contain the bacteria and disseminate, leading to further Granuloma growth within the Lung environment. To date, the mechanisms that determine Granuloma dissemination events have not been characterized. We present a computational multi-scale model of Granuloma formation and dissemination within primate Lungs. Our computational model is calibrated to multiple experimental datasets across the cellular, Granuloma, and whole-Lung scales of non-human primates. We match to both individual Granuloma and Granuloma-population datasets, predict likelihood of dissemination events, and predict a critical role for multifunctional CD8+ T cells and macrophage-bacteria interactions to prevent infection dissemination.
