Accelerating early anti-tuberculosis drug discovery by creating mycobacterial indicator strains that predict mode of action
Smith, Trever C.
van Gemert, Mae
Aldridge, Bree B.
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
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AbstractDue to the rise of drug resistant forms of tuberculosis there is an urgent need for novel antibiotics to effectively combat these cases and shorten treatment regimens. Recently, drug screens using whole cell analyses have been shown to be successful. However, current high-throughput screens focus mostly on stricto sensu life-death screening that give little qualitative information. In doing so, promising compound scaffolds or non-optimized compounds that fail to reach inhibitory concentrations are missed. To accelerate early TB drug discovery, we performed RNA sequencing on Mycobacterium tuberculosis and Mycobacterium marinum to map the stress responses that follow upon exposure to sub-inhibitory concentrations of antibiotics with known targets: ciprofloxacin, ethambutol, isoniazid, streptomycin and rifampicin. The resulting dataset comprises the first overview of transcriptional stress responses of mycobacteria to different antibiotics. We show that antibiotics can be distinguished based on their specific transcriptional stress fingerprint. Notably, this fingerprint was more distinctive in M. marinum. We decided to use this to our advantage and continue with this model organism. A selection of diverse antibiotic stress genes was used to construct stress reporters. In total, three functional reporters were constructed to respond to DNA damage, cell wall damage and ribosomal inhibition. Subsequently, these reporter strains were used to screen a small anti-TB compound library to predict the mode of action. In doing so, we could identify the putative mode of action for three novel compounds, which confirms our approach.
CitationBoot M, Commandeur S, Subudhi AK, Bahira M, Smith TC, et al. (2018) Accelerating early anti-tuberculosis drug discovery by creating mycobacterial indicator strains that predict mode of action. Antimicrobial Agents and Chemotherapy: AAC.00083–18. Available: http://dx.doi.org/10.1128/aac.00083-18.
SponsorsThe authors would like to express their gratitude to Christina M.J.E. Vandenbroucke-Grauls for helpful discussions. We would like to thank Francois Rustenburg and Coen Kuijl for technical assistance. The research leading to these results has received funding from the Innovative Medicines Initiative Joint Undertaking under grant agreement n°115337, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in-kind contribution. The grant was awarded to WB. This work was additionally supported by NIH grants (DP2LM011952 to BBA and T32 AI 7329-23). This work was supported by the Netherlands Organization for Scientific Research (NWO) through a VENI grant (016.Veni.171.090) awarded to ASp. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors declare that they have no conflicts of interest with the contents of this article.
PublisherAmerican Society for Microbiology
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