Benjamin J. Garcia

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.

Genome Sequencing of Mycobacterium abscessus Isolates from Patients in the United States and Comparisons to Globally Diverse Clinical Strains

ABSTRACT Nontuberculous mycobacterial infections caused by Mycobacterium abscessus are responsible for a range of disease manifestations from pulmonary to skin infections and are notoriously difficult to treat, due to innate resistance to many antibiotics. Previous population studies of clinical M. abscessus isolates utilized multilocus sequence typing or pulsed-field gel electrophoresis, but high-resolution examinations of genetic diversity at the whole-genome level have not been well characterized, particularly among clinical isolates derived in the United States. We performed whole-genome sequencing of 11 clinical M. abscessus isolates derived from eight U.S. patients with pulmonary nontuberculous mycobacterial infections, compared them to 30 globally diverse clinical isolates, and investigated intrapatient genomic diversity and evolution. Phylogenomic analyses revealed a cluster of closely related U.S. and Western European M. abscessus subsp. abscessus isolates that are genetically distinct from other European isolates and all Asian isolates. Large-scale variation analyses suggested genome content differences of 0.3 to 8.3%, relative to the reference strain ATCC 19977T. Longitudinally sampled isolates showed very few single-nucleotide polymorphisms and correlated genomic deletion patterns, suggesting homogeneous infection populations. Our study explores the genomic diversity of clinical M. abscessus strains from multiple continents and provides insight into the genome plasticity of an opportunistic pathogen.

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

ABSTRACT 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.

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.

A minimum variance method for genome-wide data-driven normalization of quantitative real-time polymerase chain reaction expression data

Advances in multiplex qRT-PCR have enabled increasingly accurate and robust quantification of RNA, even at lower concentrations, facilitating RNA expression profiling in clinical and environmental samples. Here we describe a data-driven qRT-PCR normalization method, the minimum variance method, and evaluate it on clinically derived Mycobacterium tuberculosis samples with variable transcript detection percentages. For moderate to significant amounts of nondetection (∼50%), our minimum variance method consistently produces the lowest false discovery rates compared to commonly used data-driven normalization methods.

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 TB among US patients: A case-control study with systematic cross-classifier evaluation

ABSTRACT 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.

Draft Genome Sequence of Mycobacterium chelonae Type Strain ATCC 35752

ABSTRACT 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.

MycoBASE: expanding the functional annotation coverage of mycobacterial genomes

BackgroundCentral 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.DescriptionHere 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.ConclusionImproved 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.

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 Lebeks 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.