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Volume 168, Issue 12

An efficient CRISPR interference-based prediction method for synergistic/additive effects of novel combinations of anti-tuberculosis drugs Open Access

Abstract

Tuberculosis (TB) is treated by chemotherapy with multiple anti-TB drugs for a long period, spanning 6 months even in a standard course. In perspective, to prevent the emergence of antimicrobial resistance, novel drugs that act synergistically or additively in combination with major anti-TB drugs and, if possible, shorten the duration of TB therapy are needed. However, their combinatorial effect cannot be predicted until the lead identification phase of the drug development. Clustered regularly interspaced short palindromic repeats interference (CRISPRi) is a powerful genetic tool that enables high-throughput screening of novel drug targets. The development of anti-TB drugs promises to be accelerated by CRISPRi. This study determined whether CRISPRi could be applicable for predictive screening of the combinatorial effect between major anti-TB drugs and an inhibitor of a novel target. In the checkerboard assay, isoniazid killed synergistically or additively in combinations with rifampicin or ethambutol, respectively. The susceptibility to rifampicin and ethambutol was increased by knockdown of , which encodes a target molecule of isoniazid. Additionally, knockdown of , which encodes a target molecule of rifampicin, increased the susceptibility to isoniazid and ethambutol, which act synergistically with rifampicin in the checkerboard assay. Moreover, CRISPRi could successfully predict the synergistic action of cyclomarin A, a novel TB drug candidate, with isoniazid or rifampicin. These results demonstrate that CRISPRi is a useful tool not only for drug target exploration but also for screening the combinatorial effects of novel combinations of anti-TB drugs. This study provides a rationale for anti-TB drug development using CRISPRi.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): anti-tuberculosis drug , combination effect , CRISPR interference and Mycobacterium smegmatis
Funding
This study was supported by the:
  • Japan Agency for Medical Research and Development (Award 20fk0108089)
    • Principle Award Recipient: SohkichiMatsumoto
  • GlaxoSmithKline Japan (Award A-65)
    • Principle Award Recipient: TakehiroYamaguchi
  • Japan Society for the Promotion of Science (Award 20H03483)
    • Principle Award Recipient: SohkichiMatsumoto
  • Japan Society for the Promotion of Science (Award 19K07536)
    • Principle Award Recipient: YurikoOzeki
  • Japan Society for the Promotion of Science (Award 19K16651)
    • Principle Award Recipient: TakehiroYamaguchi
© 2022 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2022-12-19
2024-04-16
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An efficient CRISPR interference-based prediction method for synergistic/additive effects of novel combinations of anti-tuberculosis drugs
Microbiology 168, 001285 (2022); https://doi.org/10.1099/mic.0.001285
/content/journal/micro/10.1099/mic.0.001285
/content/journal/micro/10.1099/mic.0.001285
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