BHLHE40 Regulates Myeloid Cell Polarization through IL-10-Dependent and -Independent Mechanisms.

Better understanding of the host responses to Mycobacterium tuberculosis infections is required to prevent tuberculosis and develop new therapeutic interventions. The host transcription factor BHLHE40 is essential for controlling M. tuberculosis infection, in part by repressing Il10 expression, where excess IL-10 contributes to the early susceptibility of Bhlhe40-/- mice to M. tuberculosis infection. Deletion of Bhlhe40 in lung macrophages and dendritic cells is sufficient to increase the susceptibility of mice to M. tuberculosis infection, but how BHLHE40 impacts macrophage and dendritic cell responses to M. tuberculosis is unknown. In this study, we report that BHLHE40 is required in myeloid cells exposed to GM-CSF, an abundant cytokine in the lung, to promote the expression of genes associated with a proinflammatory state and better control of M. tuberculosis infection. Loss of Bhlhe40 expression in murine bone marrow-derived myeloid cells cultured in the presence of GM-CSF results in lower levels of proinflammatory associated signaling molecules IL-1ß, IL-6, IL-12, TNF-α, inducible NO synthase, IL-2, KC, and RANTES, as well as higher levels of the anti-inflammatory-associated molecules MCP-1 and IL-10 following exposure to heat-killed M. tuberculosis. Deletion of Il10 in Bhlhe40-/- myeloid cells restored some, but not all, proinflammatory signals, demonstrating that BHLHE40 promotes proinflammatory responses via both IL-10-dependent and -independent mechanisms. In addition, we show that macrophages and neutrophils within the lungs of M. tuberculosis-infected Bhlhe40-/- mice exhibit defects in inducible NO synthase production compared with infected wild-type mice, supporting that BHLHE40 promotes proinflammatory responses in innate immune cells, which may contribute to the essential role for BHLHE40 during M. tuberculosis infection in vivo.

MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning.

BACKGROUND: Diverse microbiome communities drive biogeochemical processes and evolution of animals in their ecosystems. Many microbiome projects have demonstrated the power of using metagenomics to understand the structures and factors influencing the function of the microbiomes in their environments. In order to characterize the effects from microbiome composition for human health, diseases, and even ecosystems, one must first understand the relationship of microbes and their environment in different samples. Running machine learning model with metagenomic sequencing data is encouraged for this purpose, but it is not an easy task to make an appropriate machine learning model for all diverse metagenomic datasets. RESULTS: We introduce MegaR, an R Shiny package and web application, to build an unbiased machine learning model effortlessly with interactive visual analysis. The MegaR employs taxonomic profiles from either whole metagenome sequencing or 16S rRNA sequencing data to develop machine learning models and classify the samples into two or more categories. It provides various options for model fine tuning throughout the analysis pipeline such as data processing, multiple machine learning techniques, model validation, and unknown sample prediction that can be used to achieve the highest prediction accuracy possible for any given dataset while still maintaining a user-friendly experience. CONCLUSIONS: Metagenomic sample classification and phenotype prediction is important particularly when it applies to a diagnostic method for identifying and predicting microbe-related human diseases. MegaR provides various interactive visualizations for user to build an accurate machine-learning model without difficulty. Unknown sample prediction with a properly trained model using MegaR will enhance researchers to identify the sample property in a fast turnaround time.

Inducing vulnerability to InhA inhibition restores isoniazid susceptibility in drug-resistant Mycobacterium tuberculosis.

Of the approximately 10 million cases of Mycobacterium tuberculosis (Mtb) infections each year, over 10% are resistant to the frontline antibiotic isoniazid (INH). INH resistance is predominantly caused by mutations that decrease the activity of the bacterial enzyme KatG, which mediates the conversion of the pro-drug INH to its active form INH-NAD. We previously discovered an inhibitor of Mtb respiration, C10, that enhances the bactericidal activity of INH, prevents the emergence of INH-resistant mutants, and re-sensitizes a collection of INH-resistant mutants to INH through an unknown mechanism. To investigate the mechanism of action of C10, we exploited the toxicity of high concentrations of C10 to select for resistant mutants. We discovered two mutations that confer resistance to the disruption of energy metabolism and allow for the growth of Mtb in high C10 concentrations, indicating that growth inhibition by C10 is associated with inhibition of respiration. Using these mutants as well as direct inhibitors of the Mtb electron transport chain, we provide evidence that inhibition of energy metabolism by C10 is neither sufficient nor necessary to potentiate killing by INH. Instead, we find that C10 acts downstream of INH-NAD synthesis, causing Mtb to become particularly sensitive to inhibition of the INH-NAD target, InhA, without changing the concentration of INH-NAD or the activity of InhA, the two predominant mechanisms of potentiating INH. Our studies revealed that there exists a vulnerability in Mtb that can be exploited to render Mtb sensitive to otherwise subinhibitory concentrations of InhA inhibitor.IMPORTANCEIsoniazid (INH) is a critical frontline antibiotic to treat Mycobacterium tuberculosis (Mtb) infections. INH efficacy is limited by its suboptimal penetration of the Mtb-containing lesion and by the prevalence of clinical INH resistance. We previously discovered a compound, C10, that enhances the bactericidal activity of INH, prevents the emergence of INH-resistant mutants, and re-sensitizes a set of INH-resistant mutants to INH. Resistance is typically mediated by katG mutations that decrease the activation of INH, which is required for INH to inhibit the essential enzyme InhA. Our current work demonstrates that C10 re-sensitizes INH-resistant katG-hypomorphs without enhancing the activation of INH. We furthermore show that C10 causes Mtb to become particularly vulnerable to InhA inhibition without compromising InhA activity on its own. Therefore, C10 represents a novel strategy to curtail the development of INH resistance and to sensitize Mtb to sub-lethal doses of INH, such as those achieved at the infection site.

Type I IFN signaling in the absence of IRGM1 promotes M. tuberculosis replication in immune cells by suppressing T cell responses.

Polymorphisms in the IRGM gene are associated with susceptibility to tuberculosis in humans. A murine ortholog of Irgm, Irgm1, is also essential for controlling Mycobacterium tuberculosis (Mtb) infection in mice. Multiple processes have been associated with IRGM1 activity that could impact the host response to Mtb infection, including roles in autophagy-mediated pathogen clearance and expansion of activated T cells. However, what IRGM1-mediated pathway is necessary to control Mtb infection in vivo and the mechanistic basis for this control remains unknown. We dissected the contribution of IRGM1 to immune control of Mtb pathogenesis in vivo and found that Irgm1 deletion leads to higher levels of IRGM3-dependent type I interferon signaling. The increased type I interferon signaling precludes T cell expansion during Mtb infection. The absence of Mtb-specific T cell expansion in Irgm1-/- mice results in uncontrolled Mtb infection in neutrophils and alveolar macrophages, which directly contributes to susceptibility to infection. Together, our studies reveal that IRGM1 is required to promote T cell-mediated control of Mtb infection in neutrophils, which is essential for the survival of Mtb-infected mice. These studies also uncover new ways type I interferon signaling can impact TH1 immune responses.

Discovery, Synthesis, and Optimization of 1,2,4-Triazolyl Pyridines Targeting Mycobacterium tuberculosis.

The rise in multidrug resistant tuberculosis cases underscores the urgent need to develop new treatment strategies for tuberculosis. Herein, we report the discovery and synthesis of a new series of compounds containing a 3-thio-1,2,4-triazole moiety that show inhibition of Mycobacterium tuberculosis (Mtb) growth and survival. Structure-activity relationship studies led us to identify several potent analogs displaying low micromolar to nanomolar inhibitory activity, specifically against Mtb. The potent analogs demonstrated no cytotoxicity in mammalian cells at over 100 times the effective concentration required in Mtb and were bactericidal against Mtb during infection of macrophages. In the exploratory ADME investigations, we observed suboptimal ADME characteristics, which prompted us to identify potential metabolic liabilities for further optimization. Our preliminary investigations into the mechanism of action suggest that this series is not engaging the promiscuous targets that arise from many phenotypic screens. We selected for resistant mutants with the nanomolar potent nitro-containing compound 20 and identified resistant isolates with mutations in genes required for coenzyme F420 biosynthesis and the nitroreductase Ddn. This suggests that the aromatic nitro-1,2,4-triazolyl pyridines are activated by F420-dependent Ddn activity, similar to the nitro-containing TB drug pretomanid. We were able to circumvent the requirement for F420-dependent Ddn activity using compounds that contained non-nitro groups, identifying a key feature to be modified to avoid this predominant resistance mechanism. These studies provide the foundation for the development of a new class of 1,2,4-triazole compounds for the treatment of tuberculosis.

Type I IFN signaling in the absence of IRGM1 promotes M. tuberculosis replication in immune cells by suppressing T cell responses.

Polymorphisms in the IRGM gene are associated with susceptibility to tuberculosis in humans. A murine ortholog of Irgm, Irgm1, is also essential for controlling Mycobacterium tuberculosis (Mtb) infection in mice. Multiple processes have been associated with IRGM1 activity that could impact the host response to Mtb infection, including roles in autophagy-mediated pathogen clearance and expansion of activated T cells. However, what IRGM1-mediated pathway is necessary to control Mtb infection in vivo and the mechanistic basis for this control remains unknown. We dissected the contribution of IRGM1 to immune control of Mtb pathogenesis in vivo and found that Irgm1 deletion leads to higher levels of IRGM3-dependent type I interferon signaling. The increased type I interferon signaling precludes T cell expansion during Mtb infection. The absence of Mtb-specific T cell expansion in Irgm1-/- mice results in uncontrolled Mtb infection in neutrophils and alveolar macrophages, which directly contributes to susceptibility to infection. Together, our studies reveal that IRGM1 is required to promote T cell-mediated control of Mtb infection in neutrophils, which is essential for the survival of Mtb-infected mice. These studies also uncover new ways type I interferon signaling can impact TH1 immune responses.

MegaD: Deep Learning for Rapid and Accurate Disease Status Prediction of Metagenomic Samples.

The diversity within different microbiome communities that drive biogeochemical processes influences many different phenotypes. Analyses of these communities and their diversity by countless microbiome projects have revealed an important role of metagenomics in understanding the complex relation between microbes and their environments. This relationship can be understood in the context of microbiome composition of specific known environments. These compositions can then be used as a template for predicting the status of similar environments. Machine learning has been applied as a key component to this predictive task. Several analysis tools have already been published utilizing machine learning methods for metagenomic analysis. Despite the previously proposed machine learning models, the performance of deep neural networks is still under-researched. Given the nature of metagenomic data, deep neural networks could provide a strong boost to growth in the prediction accuracy in metagenomic analysis applications. To meet this urgent demand, we present a deep learning based tool that utilizes a deep neural network implementation for phenotypic prediction of unknown metagenomic samples. (1) First, our tool takes as input taxonomic profiles from 16S or WGS sequencing data. (2) Second, given the samples, our tool builds a model based on a deep neural network by computing multi-level classification. (3) Lastly, given the model, our tool classifies an unknown sample with its unlabeled taxonomic profile. In the benchmark experiments, we deduced that an analysis method facilitating a deep neural network such as our tool can show promising results in increasing the prediction accuracy on several samples compared to other machine learning models.