A novel approach for tuberculosis diagnosis using exosomal DNA and droplet digital PCR
CLINICAL MICROBIOLOGY AND INFECTION
Authors: Cho, S. M.; Shin, S.; Kim, Y.; Song, W.; Hong, S. G.; Jeong, S. H.; Kang, M. S.; Lee, K. A.
Objectives: The rapid diagnosis of tuberculosis (TB) is important for patient treatment and infection control. Current molecular diagnostic techniques for TB have insufficient sensitivity to detect samples with low bacterial loads. The sensitivity of molecular testing depends on not only the performance of the assay technique but also the nucleic acid extraction method. Here, we present a novel approach using exosomal DNA (exoDNA) and droplet digital PCR (ddPCR) platforms to detect Mycobacterium tuberculosis DNA in clinical samples. Methods: The ddPCR platform targeting IS6110 was evaluated in parallel using total DNA and exoDNA. The clinical performance of ddPCR method was assessed with 190 respiratory samples from patients with suspected pulmonary TB. Results: Compared with mycobacterial culture, sensitivity and specificity of ddPCR were 61.5% (95% CI 44.6-76.6%) and 98.0% (95% CI 94.3-99.6%) using total DNA, and 76.9% (95% CI 60.7-88.9%) and 98.0% (95% CI 94.3-99.6%) using exoDNA, respectively. Among 15 culture -positive specimens with low con- centrations of target molecules (2 similar to 99 positive droplets with exoDNA), only 53.3% (8/15), 46.7% (7/15), and 26.7% (4/15) of cases were detected using ddPCR with total DNA, real-time PCR with exoDNA, and real-time PCR with total DNA, respectively. Discussion: Our platform using ddPCR and exoDNA has the potential to provide sensitive and accurate methodology for TB diagnosis. S.M. Cho, Clin Microbiol Infect 2020;26:942.e1 - 942.e5
Arrayed CRISPRi and quantitative imaging describe the morphotypic landscape of essential mycobacterial genes
Authors: de Wet, Timothy J.; Winkler, Kristy R.; Mhlanga, Musa; Mizrahi, Valerie; Warner, Digby F.
Mycobacterium tuberculosis possesses a large number of genes of unknown or predicted function, undermining fundamental understanding of pathogenicity and drug susceptibility. To address this challenge, we developed a high-throughput functional genomics approach combining inducible CRISPR-interference and image-based analyses of morphological features and sub-cellular chromosomal localizations in the related non-pathogen, M. smegmatis. Applying automated imaging and analysis to 263 essential gene knockdown mutants in an arrayed library, we derive robust, quantitative descriptions of bacillary morphologies consequent on gene silencing. Leveraging statistical-learning, we demonstrate that functionally related genes cluster by morphotypic similarity and that this information can be used to inform investigations of gene function. Exploiting this observation, we infer the existence of a mycobacterial restriction-modification system, and identify filamentation as a defining mycobacterial response to histidine starvation. Our results support the application of large-scale image-based analyses for mycobacterial functional genomics, simultaneously establishing the utility of this approach for drug mechanism-of-action studies.