Using RT qPCR for quantifying Mycobacteria marinum from in vitro and in vivo samples
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Mycobacterium marinum, the causative agent of fish tuberculosis, is rarely a human pathogen causing a chronic skin infection. It is now wildely used as a model system in animal models, especially in zebra fish model, to study the pathology of tuberculosis and as a means of screening new anti-tuberculosis agent. To facilitate such research, quantifying the viable count of M. marinum bacteria is a crucial step. The main approach used currently is still by counting the number of colony forming units (cfu), a method that has been in place for almost 100 years. Though this method well established, understood and relatively easy to perform, it is time-consuming and labor-intensive. The result can be compromised by failure to grow effectively and the relationship between count and actual numbers is confused by clumping of the bacteria where a single colony is made from multiple organisms. More importantly, this method is not able to detect live but not cultivable bacteria, and there is increasing evidence that mycobacteria readily enter a "dormant" state which confounds the relationship between bacterial number in the host and the number detected in a cfu assay. DNA based PCR methods detect both living and dead organisms but here we describe a method, which utilizes species specific Taq-Man assay and RT-qPCR technology for quantifying the viable M. marinum bacterial load by detecting 16S ribosomal RNA (16S rRNA).
Xaio , H & Gillespie , S H 2018 , Using RT qPCR for quantifying Mycobacteria marinum from in vitro and in vivo samples . in S Gillespie (ed.) , Antibiotic Resistance Protocols . Methods in Molecular Biology , vol. 1736 , Humana Press/Springer , New York, NY , pp. 137-145 . https://doi.org/10.1007/978-1-4939-7638-6_13
Antibiotic Resistance Protocols
© 2018, Springer Science+Business Media, LLC. This work has been made available online by kind permission of the publisher. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1007/978-1-4939-7638-6_13