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1.
Sci Transl Med ; 11(515)2019 10 23.
Article in English | MEDLINE | ID: mdl-31645455

ABSTRACT

Improved tuberculosis (TB) prevention and control depend critically on the development of a simple, readily accessible rapid triage test to stratify TB risk. We hypothesized that a blood protein-based host response signature for active TB (ATB) could distinguish it from other TB-like disease (OTD) in adult patients with persistent cough, thereby providing a foundation for a point-of-care (POC) triage test for ATB. Three adult cohorts consisting of ATB suspects were recruited. A bead-based immunoassay and machine learning algorithms identified a panel of four host blood proteins, interleukin-6 (IL-6), IL-8, IL-18, and vascular endothelial growth factor (VEGF), that distinguished ATB from OTD. An ultrasensitive POC-amenable single-molecule array (Simoa) panel was configured, and the ATB diagnostic algorithm underwent blind validation in an independent, multinational cohort in which ATB was distinguished from OTD with receiver operator characteristic-area under the curve (ROC-AUC) of 0.80 [95% confidence interval (CI), 0.75 to 0.85], 80% sensitivity (95% CI, 73 to 85%), and 65% specificity (95% CI, 57 to 71%). When host antibodies against TB antigen Ag85B were added to the panel, performance improved to 86% sensitivity and 69% specificity. A blood-based host response panel consisting of four proteins and antibodies to one TB antigen can help to differentiate ATB from other causes of persistent cough in patients with and without HIV infection from Africa, Asia, and South America. Performance characteristics approach World Health Organization (WHO) target product profile accuracy requirements and may provide the foundation for an urgently needed blood-based POC TB triage test.


Subject(s)
Cough/diagnosis , Triage/methods , Tuberculosis, Pulmonary/diagnosis , Antibodies, Bacterial/analysis , Cough/microbiology , Cough/pathology , Humans , Machine Learning , Point-of-Care Systems , Tuberculosis, Pulmonary/microbiology , Tuberculosis, Pulmonary/pathology
2.
PLoS Genet ; 11(11): e1005641, 2015 Nov.
Article in English | MEDLINE | ID: mdl-26536359

ABSTRACT

RNA-seq technologies have provided significant insight into the transcription networks of mycobacteria. However, such studies provide no definitive information on the translational landscape. Here, we use a combination of high-throughput transcriptome and proteome-profiling approaches to more rigorously understand protein expression in two mycobacterial species. RNA-seq and ribosome profiling in Mycobacterium smegmatis, and transcription start site (TSS) mapping and N-terminal peptide mass spectrometry in Mycobacterium tuberculosis, provide complementary, empirical datasets to examine the congruence of transcription and translation in the Mycobacterium genus. We find that nearly one-quarter of mycobacterial transcripts are leaderless, lacking a 5' untranslated region (UTR) and Shine-Dalgarno ribosome-binding site. Our data indicate that leaderless translation is a major feature of mycobacterial genomes and is comparably robust to leadered initiation. Using translational reporters to systematically probe the cis-sequence requirements of leaderless translation initiation in mycobacteria, we find that an ATG or GTG at the mRNA 5' end is both necessary and sufficient. This criterion, together with our ribosome occupancy data, suggests that mycobacteria encode hundreds of small, unannotated proteins at the 5' ends of transcripts. The conservation of small proteins in both mycobacterial species tested suggests that some play important roles in mycobacterial physiology. Our translational-reporter system further indicates that mycobacterial leadered translation initiation requires a Shine Dalgarno site in the 5' UTR and that ATG, GTG, TTG, and ATT codons can robustly initiate translation. Our combined approaches provide the first comprehensive view of mycobacterial gene structures and their non-canonical mechanisms of protein expression.


Subject(s)
Mycobacterium/genetics , RNA, Messenger/genetics , Genes, Bacterial , Mycobacterium/metabolism , Ribosomes/metabolism , Sequence Analysis, RNA
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