Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis

The composition of the gastrointestinal microbiota influences systemic immune responses, but how this affects infectious disease pathogenesis and antibiotic therapy outcome is poorly understood. This question is rarely examined in humans due to the difficulty in dissociating the immunologic effects of antibiotic-induced pathogen clearance and microbiome alteration. Here, we analyze data from two longitudinal studies of tuberculosis (TB) therapy (35 and 20 individuals) and a cross sectional study from 55 healthy controls, in which we collected fecal samples (for microbiome analysis), sputum (for determination of Mycobacterium tuberculosis (Mtb) bacterial load), and peripheral blood (for transcriptomic analysis). We decouple microbiome effects from pathogen sterilization by comparing standard TB therapy with an experimental TB treatment that did not reduce Mtb bacterial load. Random forest regression to the microbiome-transcriptome-sputum data from the two longitudinal datasets reveals that renormalization of the TB inflammatory state is associated with Mtb pathogen clearance, increased abundance of Clusters IV and XIVa Clostridia, and decreased abundance of Bacilli and Proteobacteria. We find similar associations when applying machine learning to peripheral gene expression and microbiota profiling in the independent cohort of healthy individuals. Our findings indicate that antibiotic-induced reduction in pathogen burden and changes in the microbiome are independently associated with treatment-induced changes of the inflammatory response of active TB, and the response to antibiotic therapy may be a combined effect of pathogen killing and microbiome driven immunomodulation

An Antibiotic-Impacted Microbiota Compromises the Development of Colonic Regulatory T Cells and Predisposes to Dysregulated Immune Responses

Antibiotic exposure early in life and other practices impacting the vertical transmission and ordered assembly of a diverse and balanced gut microbiota are associated with a higher risk of immunological and metabolic disorders such as asthma and allergy, autoimmunity, obesity, and susceptibility to opportunistic infections. In this study, we used a model of perinatal exposure to the broad-spectrum antibiotic ampicillin to examine how the acquisition of a dysbiotic microbiota affects neonatal immune system development. We found that the resultant dysbiosis imprints in a manner that is irreversible after weaning, leading to specific and selective alteration of the colonic CD4$^{+}$ T-cell compartment. In contrast, colonic granulocyte and myeloid lineages and other mucosal T-cell compartments are unaffected. Among colonic CD4$^{+}$ T cells, we observed the most pronounced effects on neuropilin-negative, RORγt- and Foxp3-positive regulatory T cells, which are largely absent in antibiotic-exposed mice even as they reach adulthood. Immunomagnetically isolated dendritic cells from antibiotic-exposed mice fail to support the generation of Foxp3$^{+}$ regulatory T cells (Tregs) from naive T cells ex vivo The perinatally acquired dysbiotic microbiota predisposes to dysregulated effector T-cell responses to Citrobacter rodentium or ovalbumin challenge. The transfer of the antibiotic-impacted, but not healthy, fecal microbiota into germfree recipients recapitulates the selective loss of colonic neuropilin-negative, RORγt- and Foxp3-positive Tregs. The combined data indicate that the early-life acquisition of a dysbiotic microbiota has detrimental effects on the diversity and microbial community composition of offspring that persist into adulthood and predisposes to inappropriate T-cell responses that are linked to compromised immune tolerance.IMPORTANCE The assembly of microbial communities that populate all mucosal surfaces of the human body begins right after birth. This process is prone to disruption as newborns and young infants are increasingly exposed to antibiotics, both deliberately for therapeutic purposes, and as a consequence of transmaternal exposure. We show here using a model of ampicillin administration to lactating dams during their newborn offspring's early life that such exposures have consequences that persist into adulthood. Offspring acquire their mother's antibiotic-impacted microbiota, which compromises their ability to generate a colonic pool of CD4$^{+}$ T cells, particularly of colonic regulatory T cells. This Treg deficiency cannot be corrected by cohousing with normal mice later and is recapitulated by reconstitution of germfree mice with microbiota harvested from antibiotic-exposed donors. As a consequence of their dysbiosis, and possibly of their Treg deficiency, antibiotic-impacted offspring generate dysregulated Th1 responses to bacterial challenge infection and develop more severe symptoms of ovalbumin-induced anaphylaxis

The Microbiome and Tuberculosis: Early Evidence for Cross Talk

Tuberculosis (TB) is an ancient infectious disease of humans that has been extensively studied both clinically and experimentally. Although susceptibility to Mycobacterium tuberculosis infection is clearly influenced by factors such as nutrition, immune status, and both mycobacterial and host genetics, the variable pathogenesis of TB in infected individuals remains poorly understood.Tuberculosis (TB) is an ancient infectious disease of humans that has been extensively studied both clinically and experimentally. Although susceptibility to Mycobacterium tuberculosis infection is clearly influenced by factors such as nutrition, immune status, and both mycobacterial and host genetics, the variable pathogenesis of TB in infected individuals remains poorly understood. During the past two decades, it has become clear that the microbiota—the trillion organisms that reside at mucosal surfaces within and on the body—can exert a major influence on disease outcome through its effects on host innate and adaptive immune function and metabolism. This new recognition of the potentially pleiotropic participation of the microbiome in immune responses has raised the possibility that the microbiota may influence M. tuberculosis infection and/or disease. Similarly, treatment of TB may alter the healthy steady-state composition and function of the microbiome, possibly affecting treatment outcome in addition to other host physiological parameters. Herein, we review emerging evidence for how the microbiota may influence the transition points in the life cycle of TB infection, including (i) resistance to initial infection, (ii) initial infection to latent tuberculosis (LTBI), (iii) LTBI to reactivated disease, and (iv) treatment to cure. A major goal of this review is to frame questions to guide future scientific and clinical studies in this largely unexplored but increasingly important area of TB research

Transcriptomic Biomarkers for Tuberculosis: Validation of as a Single mRNA Biomarker to Diagnose TB, Predict Disease Progression, and Monitor Treatment Response.

External validation in different cohorts is a key step in the translational development of new biomarkers. We previously described three host mRNA whose expression in peripheral blood is significantly higher (NPC2) or lower (DOCK9 and EPHA4) in individuals with TB compared to latent TB infection (LTBI) and controls. We have now conducted an independent validation of these genes by re-analyzing publicly available transcriptomic datasets from Brazil, China, Haiti, India, South Africa, and the United Kingdom. Comparisons between TB and control/LTBI showed significant differential expression of all three genes (NPC2high&nbsp;p < 0.01, DOCK9low&nbsp;p < 0.01, and EPHA4low&nbsp;p < 0.05). NPC2high had the highest mean area under the ROC curve (AUROC) for the differentiation of TB vs. controls (0.95) and LTBI (0.94). In addition, NPC2 accurately distinguished TB from the clinically similar conditions pneumonia (AUROC, 0.88), non-active sarcoidosis (0.87), and lung cancer (0.86), but not from active sarcoidosis (0.66). Interestingly, individuals progressing from LTBI to TB showed a constant increase in NPC2 expression with time when compared to non-progressors (p < 0.05), with a significant change closer to manifestation of active disease (≤3 months, p = 0.003). Moreover, NPC2 expression normalized with completion of anti-TB treatment. Taken together, these results validate NPC2 mRNA as a diagnostic host biomarker for active TB independent of host genetic background. Moreover, they reveal its potential to predict progression from latent to active infection and to indicate a response to anti-TB treatment

A pilot study of the Earable device to measure facial muscle and eye movement tasks among healthy volunteers.

The Earable device is a behind-the-ear wearable originally developed to measure cognitive function. Since Earable measures electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG), it may also have the potential to objectively quantify facial muscle and eye movement activities relevant in the assessment of neuromuscular disorders. As an initial step to developing a digital assessment in neuromuscular disorders, a pilot study was conducted to determine whether the Earable device could be utilized to objectively measure facial muscle and eye movements intended to be representative of Performance Outcome Assessments, (PerfOs) with tasks designed to model clinical PerfOs, referred to as mock-PerfO activities. The specific aims of this study were: To determine whether the Earable raw EMG, EOG, and EEG signals could be processed to extract features describing these waveforms; To determine Earable feature data quality, test re-test reliability, and statistical properties; To determine whether features derived from Earable could be used to determine the difference between various facial muscle and eye movement activities; and, To determine what features and feature types are important for mock-PerfO activity level classification. A total of N = 10 healthy volunteers participated in the study. Each study participant performed 16 mock-PerfOs activities, including talking, chewing, swallowing, eye closure, gazing in different directions, puffing cheeks, chewing an apple, and making various facial expressions. Each activity was repeated four times in the morning and four times at night. A total of 161 summary features were extracted from the EEG, EMG, and EOG bio-sensor data. Feature vectors were used as input to machine learning models to classify the mock-PerfO activities, and model performance was evaluated on a held-out test set. Additionally, a convolutional neural network (CNN) was used to classify low-level representations of the raw bio-sensor data for each task, and model performance was correspondingly evaluated and compared directly to feature classification performance. The model's prediction accuracy on the Earable device's classification ability was quantitatively assessed. Study results indicate that Earable can potentially quantify different aspects of facial and eye movements and may be used to differentiate mock-PerfO activities. Specially, Earable was found to differentiate talking, chewing, and swallowing tasks from other tasks with observed F1 scores >0.9. While EMG features contribute to classification accuracy for all tasks, EOG features are important for classifying gaze tasks. Finally, we found that analysis with summary features outperformed a CNN for activity classification. We believe Earable may be used to measure cranial muscle activity relevant for neuromuscular disorder assessment. Classification performance of mock-PerfO activities with summary features enables a strategy for detecting disease-specific signals relative to controls, as well as the monitoring of intra-subject treatment responses. Further testing is needed to evaluate the Earable device in clinical populations and clinical development settings

A pilot study of the Earable device to measure facial muscle and eye movement tasks among healthy volunteers

Many neuromuscular disorders impair function of cranial nerve enervated muscles. Clinical assessment of cranial muscle function has several limitations. Clinician rating of symptoms suffers from inter-rater variation, qualitative or semi-quantitative scoring, and limited ability to capture infrequent or fluctuating symptoms. Patient-reported outcomes are limited by recall bias and poor precision. Current tools to measure orofacial and oculomotor function are cumbersome, difficult to implement, and non-portable. Here, we show how Earable, a wearable device, can discriminate certain cranial muscle activities such as chewing, talking, and swallowing. We demonstrate using data from a pilot study of 10 healthy participants how Earable can be used to measure features from EMG, EEG, and EOG waveforms from subjects performing mock Performance Outcome Assessments (mock-PerfOs), utilized widely in clinical research. Our analysis pipeline provides a framework for how to computationally process and statistically rank features from the Earable device. Finally, we demonstrate that Earable data may be used to classify these activities. Our results, conducted in a pilot study of healthy participants, enable a more comprehensive strategy for the design, development, and analysis of wearable sensor data for investigating clinical populations. Additionally, the results from this study support further evaluation of Earable or similar devices as tools to objectively measure cranial muscle activity in the context of a clinical research setting. Future work will be conducted in clinical disease populations, with a focus on detecting disease signatures, as well as monitoring intra-subject treatment responses. Readily available quantitative metrics from wearable sensor devices like Earable support strategies for the development of novel digital endpoints, a hallmark goal of clinical research

A pilot study of the Earable device to measure facial muscle and eye movement tasks among healthy volunteers

The Earable device is a behind-the-ear wearable originally developed to measure cognitive function. Since Earable measures electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG), it may also have the potential to objectively quantify facial muscle and eye movement activities relevant in the assessment of neuromuscular disorders. As an initial step to developing a digital assessment in neuromuscular disorders, a pilot study was conducted to determine whether the Earable device could be utilized to objectively measure facial muscle and eye movements intended to be representative of Performance Outcome Assessments, (PerfOs) with tasks designed to model clinical PerfOs, referred to as mock-PerfO activities. The specific aims of this study were: To determine whether the Earable raw EMG, EOG, and EEG signals could be processed to extract features describing these waveforms; To determine Earable feature data quality, test re-test reliability, and statistical properties; To determine whether features derived from Earable could be used to determine the difference between various facial muscle and eye movement activities; and, To determine what features and feature types are important for mock-PerfO activity level classification. A total of N = 10 healthy volunteers participated in the study. Each study participant performed 16 mock-PerfOs activities, including talking, chewing, swallowing, eye closure, gazing in different directions, puffing cheeks, chewing an apple, and making various facial expressions. Each activity was repeated four times in the morning and four times at night. A total of 161 summary features were extracted from the EEG, EMG, and EOG bio-sensor data. Feature vectors were used as input to machine learning models to classify the mock-PerfO activities, and model performance was evaluated on a held-out test set. Additionally, a convolutional neural network (CNN) was used to classify low-level representations of the raw bio-sensor data for each task, and model performance was correspondingly evaluated and compared directly to feature classification performance. The model’s prediction accuracy on the Earable device’s classification ability was quantitatively assessed. Study results indicate that Earable can potentially quantify different aspects of facial and eye movements and may be used to differentiate mock-PerfO activities. Specially, Earable was found to differentiate talking, chewing, and swallowing tasks from other tasks with observed F1 scores >0.9. While EMG features contribute to classification accuracy for all tasks, EOG features are important for classifying gaze tasks. Finally, we found that analysis with summary features outperformed a CNN for activity classification. We believe Earable may be used to measure cranial muscle activity relevant for neuromuscular disorder assessment. Classification performance of mock-PerfO activities with summary features enables a strategy for detecting disease-specific signals relative to controls, as well as the monitoring of intra-subject treatment responses. Further testing is needed to evaluate the Earable device in clinical populations and clinical development settings. Author summary Many neuromuscular disorders impair function of cranial nerve enervated muscles. Clinical assessment of cranial muscle function has several limitations. Clinician rating of symptoms suffers from inter-rater variation, qualitative or semi-quantitative scoring, and limited ability to capture infrequent or fluctuating symptoms. Patient-reported outcomes are limited by recall bias and poor precision. Current tools to measure orofacial and oculomotor function are cumbersome, difficult to implement, and non-portable. Here, we show how Earable, a wearable device, can discriminate certain cranial muscle activities such as chewing, talking, and swallowing. We demonstrate using data from a pilot study how Earable can be used to measure features from EMG, EEG, and EOG waveforms from subjects wearing the device while performing mock Performance Outcome Assessments (PerfOs), utilized widely in clinical research. Our analysis pipeline provides a framework for how to computationally process and statistically rank features from the Earable device. Our results, conducted in a pilot study of healthy participants, enable a more comprehensive strategy for the design, development, and analysis of wearable sensor data for investigating clinical populations. Understanding how to derive clinically meaningful quantitative metrics from wearable sensor devices is required for the development of novel digital endpoints, a hallmark goal of clinical research

Mycobacterial Mutagenesis and Drug Resistance Are Controlled by Phosphorylation- and Cardiolipin-Mediated Inhibition of the RecA Coprotease

Infection with Mycobacterium tuberculosis continues to cause substantial human mortality, in part because of the emergence of antimicrobial resistance. Antimicrobial resistance in tuberculosis is solely the result of chromosomal mutations that modify drug activators or targets, yet the mechanisms controlling the mycobacterial DNA-damage response (DDR) remain incompletely defined. Here, we identify RecA serine 207 as a multifunctional signaling hub that controls the DDR in mycobacteria. RecA S207 is phosphorylated after DNA damage, which suppresses the emergence of antibiotic resistance by selectively inhibiting the LexA coprotease function of RecA without affecting its ATPase or strand exchange functions. Additionally, RecA associates with the cytoplasmic membrane during the mycobacterial DDR, where cardiolipin can specifically inhibit the LexA coprotease function of unmodified, but not S207 phosphorylated, RecA. These findings reveal that RecA S207 controls mutagenesis and antibiotic resistance in mycobacteria through phosphorylation and cardiolipin-mediated inhibition of RecA coprotease function

Thermal Isomerizations of <i>cis</i>,<i>anti</i>,<i>cis</i>-Tricyclo[7.4.0.0^2,8]tridec-10-ene

<i>cis</i>,<i>anti</i>,<i>cis</i>-Tricyclo­[7.4.0.0^2,8]­tridec-10-ene (<b>13TCT</b>) undergoes [1,3] sigmatropic rearrangements at 315 °C in the gas phase to the <i>si</i> product <b>1</b> and to the <i>sr</i> product <b>2</b> with <i>si</i>/<i>sr</i> = 2.1. The dominant thermal isomerization process, however, is epimerization at C8 to afford product <b>3</b>. That stereomutation at C8 occurs 50% faster than the <i>si</i> and <i>sr</i> shifts combined