Targeting intracellular nontuberculous mycobacteria and M. tuberculosis with a bactericidal enzymatic cocktail.

To address intracellular mycobacterial infections, we developed a cocktail of four enzymes that catalytically attack three layers of the mycobacterial envelope. This cocktail is delivered to macrophages, through a targeted liposome presented here as ENTX_001. Endolytix Cocktail 1 (EC1) leverages mycobacteriophage lysin enzymes LysA and LysB, while also including α-amylase and isoamylase for degradation of the mycobacterial envelope from outside of the cell. The LysA family of proteins from mycobacteriophages has been shown to cleave the peptidoglycan layer, whereas LysB is an esterase that hydrolyzes the linkage between arabinogalactan and mycolic acids of the mycomembrane. The challenge of gaining access to the substrates of LysA and LysB provided exogenously was addressed by adding amylase enzymes that degrade the extracellular capsule shown to be present in Mycobacterium tuberculosis. This enzybiotic approach avoids antimicrobial resistance, specific receptor-mediated binding, and intracellular DNA surveillance pathways that limit many bacteriophage applications. We show this cocktail of enzymes is bactericidal in vitro against both rapid- and slow-growing nontuberculous mycobacteria (NTM) as well as M. tuberculosis strains. The EC1 cocktail shows superior killing activity when compared to previously characterized LysB alone. EC1 is also powerfully synergistic with standard-of-care antibiotics. In addition to in vitro killing of NTM, ENTX_001 demonstrates the rescue of infected macrophages from necrotic death by Mycobacteroides abscessus and Mycobacterium avium. Here, we demonstrate shredding of mycobacterial cells by EC1 into cellular debris as a mechanism of bactericide.IMPORTANCEThe world needs entirely new forms of antibiotics as resistance to chemical antibiotics is a critical problem facing society. We addressed this need by developing a targeted enzyme therapy for a broad range of species and strains within mycobacteria and highly related genera including nontuberculous mycobacteria such as Mycobacteroides abscessus, Mycobacterium avium, Mycobacterium intracellulare, as well as Mycobacterium tuberculosis. One advantage of this approach is the ability to drive our lytic enzymes through encapsulation into macrophage-targeted liposomes resulting in attack of mycobacteria in the cells that harbor them where they hide from the adaptive immune system and grow. Furthermore, this approach shreds mycobacteria independent of cell physiology as the drug targets the mycobacterial envelope while sidestepping the host range limitations observed with phage therapy and resistance to chemical antibiotics.

A toolkit for enhanced reproducibility of RNASeq analysis for synthetic biologists.

Sequencing technologies, in particular RNASeq, have become critical tools in the design, build, test and learn cycle of synthetic biology. They provide a better understanding of synthetic designs, and they help identify ways to improve and select designs. While these data are beneficial to design, their collection and analysis is a complex, multistep process that has implications on both discovery and reproducibility of experiments. Additionally, tool parameters, experimental metadata, normalization of data and standardization of file formats present challenges that are computationally intensive. This calls for high-throughput pipelines expressly designed to handle the combinatorial and longitudinal nature of synthetic biology. In this paper, we present a pipeline to maximize the analytical reproducibility of RNASeq for synthetic biologists. We also explore the impact of reproducibility on the validation of machine learning models. We present the design of a pipeline that combines traditional RNASeq data processing tools with structured metadata tracking to allow for the exploration of the combinatorial design in a high-throughput and reproducible manner. We then demonstrate utility via two different experiments: a control comparison experiment and a machine learning model experiment. The first experiment compares datasets collected from identical biological controls across multiple days for two different organisms. It shows that a reproducible experimental protocol for one organism does not guarantee reproducibility in another. The second experiment quantifies the differences in experimental runs from multiple perspectives. It shows that the lack of reproducibility from these different perspectives can place an upper bound on the validation of machine learning models trained on RNASeq data. Graphical Abstract.

Polypharmacy-associated risk of hospitalisation among people ageing with and without HIV: an observational study.

BACKGROUND: Polypharmacy, defined as use of five or more medications concurrently, is associated with adverse health outcomes and people ageing with HIV might be at greater risk than similar uninfected individuals. We aimed to determine whether known pairwise drug interactions (KPDIs) were associated with risk of admission to hospital (hereafter referred to as hospitalisation) and medication count among people ageing with and without HIV after accounting for physiological frailty. METHODS: In this observational study, we collected individual-level data for participants of the Veterans Aging Cohort Study (VACS) with HIV on antiretroviral therapy (ART) and with supressed HIV-1 RNA and people without HIV who were receiving at least one prescription medication, based on active medications in the 2009 fiscal year (ie, Oct 1, 2008, to Sept 30, 2009). We identified KPDIs among these patients by linking prescription fill and refill data with data from DrugBank (version 5.0.11). We collected data on all-cause mortality and hospitalisations between Oct 1, 2009, and March 31, 2019. We compared KPDI counts using random selection and actual patterns of use across medication counts from two to 12. We created a weighted KPDI Index on the basis of the average association of each KPDI with mortality among people ageing without HIV and used nested Cox models stratified by HIV status to estimate the association between medication count and hospitalisation, with incremental adjustments for demographics, physiological frailty, and KPDI Index. FINDINGS: We collected data for 9186 people ageing with HIV and 37 930 individuals without HIV. 45 913 (97·4%) of 47 116 patients were men and the sample was predominantly aged 50-64 years (30 413 [64·6%]). Compared with a random sample of medications, real-world pattern of medication counts and combinations were associated with five-to-six times more KPDIs (eg, for a combination of six medications, KPDI count was 1·09 in the random sample, 5·49 in the HIV-negative population, and 7·13 in the HIV-positive population). For each additional observed medication, people ageing with HIV had approximately 2·94 additional KPDIs and comparators had approximately 2·67 additional KPDIs. Adjustment for demographics, physiological frailty, and KPDI Index reduced the association between medication count and risk of hospitalisation for people ageing with HIV (hazard ratio 1·08 [95% CI 1·07-1·09] reduced to 1·06 [1·05-1·07]) and those without HIV (1·08 [1·07-1·08] reduced to 1·04 [1·03-1·05]). INTERPRETATION: For each additional medication, people ageing with HIV have more drug-drug interactions than those without HIV. Adjusting for known non-ART drug-drug interactions, each additional non-ART medication confers excess risk of hospitalisation for people ageing with HIV. Randomised trials will be needed to determine whether reducing these interactions improves outcomes. FUNDING: National Institutes of Health, National Institute on Alcohol Abuse and Alcoholism, Department of Veterans Affairs Health Services Research & Development, and Office of Research and Development.

A k-mer based approach for classifying viruses without taxonomy identifies viral associations in human autism and plant microbiomes.

Viruses are an underrepresented taxa in the study and identification of microbiome constituents; however, they play an essential role in health, microbiome regulation, and transfer of genetic material. Only a few thousand viruses have been isolated, sequenced, and assigned a taxonomy, which limits the ability to identify and quantify viruses in the microbiome. Additionally, the vast diversity of viruses represents a challenge for classification, not only in constructing a viral taxonomy, but also in identifying similarities between a virus' genotype and its phenotype. However, the diversity of viral sequences can be leveraged to classify their sequences in metagenomic and metatranscriptomic samples, even if they do not have a taxonomy. To identify and quantify viruses in transcriptomic and genomic samples, we developed a dynamic programming algorithm for creating a classification tree out of 715,672 metagenome viruses. To create the classification tree, we clustered proportional similarity scores generated from the k-mer profiles of each of the metagenome viruses to create a database of metagenomic viruses. The resulting Kraken2 database of the metagenomic viruses can be found here: https://www.osti.gov/biblio/1615774 and is compatible with Kraken2. We then integrated the viral classification database with databases created with genomes from NCBI for use with ParaKraken (a parallelized version of Kraken provided in Supplemental Zip 1), a metagenomic/transcriptomic classifier. To illustrate the breadth of our utility for classifying metagenome viruses, we analyzed data from a plant metagenome study identifying genotypic and compartment specific differences between two Populus genotypes in three different compartments. We also identified a significant increase in abundance of eight viral sequences in post mortem brains in a human metatranscriptome study comparing Autism Spectrum Disorder patients and controls. We also show the potential accuracy for classifying viruses by utilizing both the JGI and NCBI viral databases to identify the uniqueness of viral sequences. Finally, we validate the accuracy of viral classification with NCBI databases containing viruses with taxonomy to identify pathogenic viruses in known COVID-19 and cassava brown streak virus infection samples. Our method represents the compulsory first step in better understanding the role of viruses in the microbiome by allowing for a more complete identification of sequences without taxonomy. Better classification of viruses will improve identifying associations between viruses and their hosts as well as viruses and other microbiome members. Despite the lack of taxonomy, this database of metagenomic viruses can be used with any tool that utilizes a taxonomy, such as Kraken, for accurate classification of viruses.

Neurotransmitter networks in mouse prefrontal cortex are reconfigured by isoflurane anesthesia.

This study quantified eight small-molecule neurotransmitters collected simultaneously from prefrontal cortex of C57BL/6J mice (n = 23) during wakefulness and during isoflurane anesthesia (1.3%). Using isoflurane anesthesia as an independent variable enabled evaluation of the hypothesis that isoflurane anesthesia differentially alters concentrations of multiple neurotransmitters and their interactions. Machine learning was applied to reveal higher order interactions among neurotransmitters. Using a between-subjects design, microdialysis was performed during wakefulness and during anesthesia. Concentrations (nM) of acetylcholine, adenosine, dopamine, GABA, glutamate, histamine, norepinephrine, and serotonin in the dialysis samples are reported (means ± SD). Relative to wakefulness, acetylcholine concentration was lower during isoflurane anesthesia (1.254 ± 1.118 vs. 0.401 ± 0.134, P = 0.009), and concentrations of adenosine (29.456 ± 29.756 vs. 101.321 ± 38.603, P < 0.001), dopamine (0.0578 ± 0.0384 vs. 0.113 ± 0.084, P = 0.036), and norepinephrine (0.126 ± 0.080 vs. 0.219 ± 0.066, P = 0.010) were higher during anesthesia. Isoflurane reconfigured neurotransmitter interactions in prefrontal cortex, and the state of isoflurane anesthesia was reliably predicted by prefrontal cortex concentrations of adenosine, norepinephrine, and acetylcholine. A novel finding to emerge from machine learning analyses is that neurotransmitter concentration profiles in mouse prefrontal cortex undergo functional reconfiguration during isoflurane anesthesia. Adenosine, norepinephrine, and acetylcholine showed high feature importance, supporting the interpretation that interactions among these three transmitters may play a key role in modulating levels of cortical and behavioral arousal.NEW & NOTEWORTHY This study discovered that interactions between neurotransmitters in mouse prefrontal cortex were altered during isoflurane anesthesia relative to wakefulness. Machine learning further demonstrated that, relative to wakefulness, higher order interactions among neurotransmitters were disrupted during isoflurane administration. These findings extend to the neurochemical domain the concept that anesthetic-induced loss of wakefulness results from a disruption of neural network connectivity.

Plant Host-Associated Mechanisms for Microbial Selection.

Plants serve as host to numerous microorganisms. The members of these microbial communities interact among each other and with the plant, and there is increasing evidence to suggest that the microbial community may promote plant growth, improve drought tolerance, facilitate pathogen defense and even assist in environmental remediation. Therefore, it is important to better understand the mechanisms that influence the composition and structure of microbial communities, and what role the host may play in the recruitment and control of its microbiome. In particular, there is a growing body of research to suggest that plant defense systems not only provide a layer of protection against pathogens but may also actively manage the composition of the overall microbiome. In this review, we provide an overview of the current research into mechanisms employed by the plant host to select for and control its microbiome. We specifically review recent research that expands upon the role of keystone microbial species, phytohormones, and abiotic stress, and in how they relate to plant driven dynamic microbial structuring.

Epigenetic effects of paternal cocaine on reward stimulus behavior and accumbens gene expression in mice.

Paternal cocaine use causes phenotypic alterations in offspring behavior and associated neural processing. In rodents, changes in first generation (F1) offspring include drug reward behavior, circadian timing, and anxiety responses. This study, utilizing a murine (C57BL/6J) oral cocaine model, examines the effects of paternal cocaine exposure on fundamental characteristics of offspring reward responses, including: 1) the extent of cocaine-induced effects after different durations of sire drug withdrawal; 2) sex- and drug-dependent differences in F1 reward preference; 3) effects on second generation (F2) cocaine preference; and 4) corresponding changes in reward area (nucleus accumbens) mRNA expression. We demonstrate that paternal cocaine intake over a single ˜40-day spermatogenic cycle significantly decreased cocaine (but not ethanol or sucrose) preference in a sex-specific manner in F1 mice from sires mated 24 h after drug withdrawal. However, F1 offspring of sires bred 4 months after withdrawal did not exhibit altered cocaine preference. Altered cocaine preference also was not observed in F2's. RNASeq analyses of F1 accumbens tissue revealed changes in gene expression in male offspring of cocaine-exposed sires, including many genes not previously linked to cocaine addiction. Enrichment analyses highlight genes linked to CNS development, synaptic signaling, extracellular matrix, and immune function. Expression correlation analyses identified a novel target, Fam19a4, that may negatively regulate many genes in the accumbens, including genes already identified in addiction. Collectively, these results reveal that paternal cocaine effects in F1 offspring may involve temporally limited epigenetic germline effects and identify new genetic targets for addiction research.

Defining the genetic components of callus formation: A GWAS approach.

A characteristic feature of plant cells is the ability to form callus from parenchyma cells in response to biotic and abiotic stimuli. Tissue culture propagation of recalcitrant plant species and genetic engineering for desired phenotypes typically depends on efficient in vitro callus generation. Callus formation is under genetic regulation, and consequently, a molecular understanding of this process underlies successful generation for propagation materials and/or introduction of genetic elements in experimental or industrial applications. Herein, we identified 11 genetic loci significantly associated with callus formation in Populus trichocarpa using a genome-wide association study (GWAS) approach. Eight of the 11 significant gene associations were consistent across biological replications, exceeding a chromosome-wide-log10 (p) = 4.46 [p = 3.47E-05] Bonferroni-adjusted significance threshold. These eight genes were used as hub genes in a high-resolution co-expression network analysis to gain insight into the genome-wide basis of callus formation. A network of positively and negatively co-expressed genes, including several transcription factors, was identified. As proof-of-principle, a transient protoplast assay confirmed the negative regulation of a Chloroplast Nucleoid DNA-binding-related gene (Potri.018G014800) by the LEC2 transcription factor. Many of the candidate genes and co-expressed genes were 1) linked to cell division and cell cycling in plants and 2) showed homology to tumor and cancer-related genes in humans. The GWAS approach based on a high-resolution marker set, and the ability to manipulate targets genes in vitro, provided a catalog of high-confidence genes linked to callus formation that can serve as an important resource for successful manipulation of model and non-model plant species, and likewise, suggests a robust method of discovering common homologous functions across organisms.

Quantitative proteome profile of water deficit stress responses in eastern cottonwood (Populus deltoides) leaves.

Drought stress is a recurring feature of world climate and the single most important factor influencing agricultural yield worldwide. Plants display highly variable, species-specific responses to drought and these responses are multifaceted, requiring physiological and morphological changes influenced by genetic and molecular mechanisms. Moreover, the reproducibility of water deficit studies is very cumbersome, which significantly impedes research on drought tolerance, because how a plant responds is highly influenced by the timing, duration, and intensity of the water deficit. Despite progress in the identification of drought-related mechanisms in many plants, the molecular basis of drought resistance remains to be fully understood in trees, particularly in poplar species because their wide geographic distribution results in varying tolerances to drought. Herein, we aimed to better understand this complex phenomenon in eastern cottonwood (Populus deltoides) by performing a detailed contrast of the proteome changes between two different water deficit experiments to identify functional intersections and divergences in proteome responses. We investigated plants subjected to cyclic water deficit and compared these responses to plants subjected to prolonged acute water deficit. In total, we identified 108,012 peptide sequences across both experiments that provided insight into the quantitative state of 22,737 Populus gene models and 8,199 functional protein groups in response to drought. Together, these datasets provide the most comprehensive insight into proteome drought responses in poplar to date and a direct proteome comparison between short period dehydration shock and cyclic, post-drought re-watering. Overall, this investigation provides novel insights into drought avoidance mechanisms that are distinct from progressive drought stress. Additionally, we identified proteins that have been associated as drought-relevant in previous studies. Importantly, we highlight the RD26 transcription factor as a gene regulated at both the transcript and protein level, regardless of species and drought condition, and, thus, represents a key, universal drought marker for Populus species.

Significant under expression of the DosR regulon in M. tuberculosis complex lineage 6 in sputum.

Mycobacterium africanum lineage (L) 6 is an important pathogen in West Africa, causing up to 40% of pulmonary tuberculosis (TB). The biology underlying the clinical differences between M. africanum and M. tuberculosis sensu stricto remains poorly understood. We performed ex vivo expression of 2179 genes of the most geographically dispersed cause of human TB, M. tuberculosis L4 and the geographically restricted, M. africanum L6 directly from sputa of 11 HIV-negative TB patients from The Gambia who had not started treatment. The DosR regulon was the most significantly decreased category in L6 relative to L4. Further, we identified nonsynonymous mutations in major DosR regulon genes of 44 L6 genomes of TB patients from The Gambia and Ghana. Using Lebek's test, we assessed differences in oxygen requirements for growth. L4 grew only at the aerobic surface while L6 grew throughout the medium. In the host, the DosR regulon is critical for M. tuberculosis in adaptation to oxygen limitation. However, M. africanum L6 appears to have adapted to growth under hypoxic conditions or to different biological niches. The observed under expression of DosR in L6 fits with the genomic changes in DosR genes, microaerobic growth and the association with extrapulmonary disease.

Sputum is a surrogate for bronchoalveolar lavage for monitoring Mycobacterium tuberculosis transcriptional profiles in TB patients.

Pathogen-targeted transcriptional profiling in human sputum may elucidate the physiologic state of Mycobacterium tuberculosis (M. tuberculosis) during infection and treatment. However, whether M. tuberculosis transcription in sputum recapitulates transcription in the lung is uncertain. We therefore compared M. tuberculosis transcription in human sputum and bronchoalveolar lavage (BAL) samples from 11 HIV-negative South African patients with pulmonary tuberculosis. We additionally compared these clinical samples with in vitro log phase aerobic growth and hypoxic non-replicating persistence (NRP-2). Of 2179 M. tuberculosis transcripts assayed in sputum and BAL via multiplex RT-PCR, 194 (8.9%) had a p-value <0.05, but none were significant after correction for multiple testing. Categorical enrichment analysis indicated that expression of the hypoxia-responsive DosR regulon was higher in BAL than in sputum. M. tuberculosis transcription in BAL and sputum was distinct from both aerobic growth and NRP-2, with a range of 396-1020 transcripts significantly differentially expressed after multiple testing correction. Collectively, our results indicate that M. tuberculosis transcription in sputum approximates M. tuberculosis transcription in the lung. Minor differences between M. tuberculosis transcription in BAL and sputum suggested lower oxygen concentrations or higher nitric oxide concentrations in BAL. M. tuberculosis-targeted transcriptional profiling of sputa may be a powerful tool for understanding M. tuberculosis pathogenesis and monitoring treatment responses in vivo.

Adaptation of Mycobacterium tuberculosis to Impaired Host Immunity in HIV-Infected Patients.

BACKGROUND: It is unknown whether immunosuppression influences the physiologic state of Mycobacterium tuberculosis in vivo. We evaluated the impact of host immunity by comparing M. tuberculosis and human gene transcription in sputum between human immunodeficiency virus (HIV)-infected and uninfected patients with tuberculosis. METHODS: We collected sputum specimens before treatment from Gambians and Ugandans with pulmonary tuberculosis, revealed by positive results of acid-fast bacillus smears. We quantified expression of 2179 M. tuberculosis genes and 234 human immune genes via quantitative reverse transcription-polymerase chain reaction. We summarized genes from key functional categories with significantly increased or decreased expression. RESULTS: A total of 24 of 65 patients with tuberculosis were HIV infected. M. tuberculosis DosR regulon genes were less highly expressed among HIV-infected patients with tuberculosis than among HIV-uninfected patients with tuberculosis (Gambia, P < .0001; Uganda, P = .037). In profiling of human genes from the same sputa, HIV-infected patients had 3.4-fold lower expression of IFNG (P = .005), 4.9-fold higher expression of ARG1 (P = .0006), and 3.4-fold higher expression of IL10 (P = .0002) than in HIV-uninfected patients with tuberculosis. CONCLUSIONS: M. tuberculosis in HIV-infected patients had lower expression of the DosR regulon, a critical metabolic and immunomodulatory switch induced by NO, carbon monoxide, and hypoxia. Our human data suggest that decreased DosR expression may result from alternative pathway activation of macrophages, with consequent decreased NO expression and/or by poor granuloma formation with consequent decreased hypoxic stress.

Network Analysis of Human Genes Influencing Susceptibility to Mycobacterial Infections.

Tuberculosis and nontuberculous mycobacterial infections constitute a high burden of pulmonary disease in humans, resulting in over 1.5 million deaths per year. Building on the premise that genetic factors influence the instance, progression, and defense of infectious disease, we undertook a systems biology approach to investigate relationships among genetic factors that may play a role in increased susceptibility or control of mycobacterial infections. We combined literature and database mining with network analysis and pathway enrichment analysis to examine genes, pathways, and networks, involved in the human response to Mycobacterium tuberculosis and nontuberculous mycobacterial infections. This approach allowed us to examine functional relationships among reported genes, and to identify novel genes and enriched pathways that may play a role in mycobacterial susceptibility or control. Our findings suggest that the primary pathways and genes influencing mycobacterial infection control involve an interplay between innate and adaptive immune proteins and pathways. Signaling pathways involved in autoimmune disease were significantly enriched as revealed in our networks. Mycobacterial disease susceptibility networks were also examined within the context of gene-chemical relationships, in order to identify putative drugs and nutrients with potential beneficial immunomodulatory or anti-mycobacterial effects.

Blood Transcriptional Biomarkers for Active Tuberculosis among Patients in the United States: a Case-Control Study with Systematic Cross-Classifier Evaluation.

UNLABELLED: Blood transcriptional signatures are promising for tuberculosis (TB) diagnosis but have not been evaluated among U.S. PATIENTS: To be used clinically, transcriptional classifiers need reproducible accuracy in diverse populations that vary in genetic composition, disease spectrum and severity, and comorbidities. In a prospective case-control study, we identified novel transcriptional classifiers for active TB among U.S. patients and systematically compared their accuracy to classifiers from published studies. Blood samples from HIV-uninfected U.S. adults with active TB, pneumonia, or latent TB infection underwent whole-transcriptome microarray. We used support vector machines to classify disease state based on transcriptional patterns. We externally validated our classifiers using data from sub-Saharan African cohorts and evaluated previously published transcriptional classifiers in our population. Our classifier distinguishing active TB from pneumonia had an area under the concentration-time curve (AUC) of 96.5% (95.4% to 97.6%) among U.S. patients, but the AUC was lower (90.6% [89.6% to 91.7%]) in HIV-uninfected Sub-Saharan Africans. Previously published comparable classifiers had AUC values of 90.0% (87.7% to 92.3%) and 82.9% (80.8% to 85.1%) when tested in U.S. PATIENTS: Our classifier distinguishing active TB from latent TB had AUC values of 95.9% (95.2% to 96.6%) among U.S. patients and 95.3% (94.7% to 96.0%) among Sub-Saharan Africans. Previously published comparable classifiers had AUC values of 98.0% (97.4% to 98.7%) and 94.8% (92.9% to 96.8%) when tested in U.S. PATIENTS: Blood transcriptional classifiers accurately detected active TB among U.S. adults. The accuracy of classifiers for active TB versus that of other diseases decreased when tested in new populations with different disease controls, suggesting additional studies are required to enhance generalizability. Classifiers that distinguish active TB from latent TB are accurate and generalizable across populations and can be explored as screening assays.

MycoBASE: expanding the functional annotation coverage of mycobacterial genomes.

BACKGROUND: Central to most omic scale experiments is the interpretation and examination of resulting gene lists corresponding to differentially expressed, regulated, or observed gene or protein sets. Complicating interpretation is a lack of functional annotation assigned to a large percentage of many microbial genomes. This is particularly noticeable in mycobacterial genomes, which are significantly divergent from many of the microbial model species used for gene and protein functional characterization, but which are extremely important clinically. Mycobacterial species, ranging from M. tuberculosis to M. abscessus, are responsible for deadly infectious diseases that kill over 1.5 million people each year across the world. A better understanding of the coding capacity of mycobacterial genomes is therefore necessary to shed increasing light on putative mechanisms of virulence, pathogenesis, and functional adaptations. DESCRIPTION: Here we describe the improved functional annotation coverage of 11 important mycobacterial genomes, many involved in human diseases including tuberculosis, leprosy, and nontuberculous mycobacterial (NTM) infections. Of the 11 mycobacterial genomes, we provide 9899 new functional annotations, compared to NCBI and TBDB annotations, for genes previously characterized as genes of unknown function, hypothetical, and hypothetical conserved proteins. Functional annotations are available at our newly developed web resource MycoBASE (Mycobacterial Annotation Server) at strong.ucdenver.edu/mycobase. CONCLUSION: Improved annotations allow for better understanding and interpretation of genomic and transcriptomic experiments, including analyzing the functional implications of insertions, deletions, and mutations, inferring the function of understudied genes, and determining functional changes resulting from differential expression studies. MycoBASE provides a valuable resource for mycobacterial researchers, through improved and searchable functional annotations and functional enrichment strategies. MycoBASE will be continually supported and updated to include new genomes, enabling a powerful resource to aid the quest to better understand these important pathogenic and environmental species.

Draft Genome Sequence of Mycobacterium chelonae Type Strain ATCC 35752.

Mycobacterium chelonae is a rapidly growing opportunistic nontuberculous mycobacterial (NTM) species that causes infections in humans and other hosts. Here, we report the draft genome sequence of Mycobacterium chelonae type strain ATCC 35752, consisting of 4.89 Mbp, 63.96% G+C content, 4,489 protein-coding genes, 48 tRNAs, and 3 rRNA genes.

Transcriptional Adaptation of Drug-tolerant Mycobacterium tuberculosis During Treatment of Human Tuberculosis.

BACKGROUND: Treatment initiation rapidly kills most drug-susceptible Mycobacterium tuberculosis, but a bacterial subpopulation tolerates prolonged drug exposure. We evaluated drug-tolerant bacilli in human sputum by comparing messenger RNA (mRNA) expression of drug-tolerant bacilli that survive the early bactericidal phase with treatment-naive bacilli. METHODS: M. tuberculosis gene expression was quantified via reverse-transcription polymerase chain reaction in serial sputa from 17 Ugandans treated for drug-susceptible pulmonary tuberculosis. RESULTS: Within 4 days, bacterial mRNA abundance declined >98%, indicating rapid killing. Thereafter, the rate of decline slowed >94%, indicating drug tolerance. After 14 days, 16S ribosomal RNA transcripts/genome declined 96%, indicating slow growth. Drug-tolerant bacilli displayed marked downregulation of genes associated with growth, metabolism, and lipid synthesis and upregulation in stress responses and key regulatory categories-including stress-associated sigma factors, transcription factors, and toxin-antitoxin genes. Drug efflux pumps were upregulated. The isoniazid stress signature was induced by initial drug exposure, then disappeared after 4 days. CONCLUSIONS: Transcriptional patterns suggest that drug-tolerant bacilli in sputum are in a slow-growing, metabolically and synthetically downregulated state. Absence of the isoniazid stress signature in drug-tolerant bacilli indicates that physiological state influences drug responsiveness in vivo. These results identify novel drug targets that should aid in development of novel shorter tuberculosis treatment regimens.