Mathematical analysis-based feasibility study of pre-emptive medicine for Staphylococcus aureus infectious disease: Early detection and antibiotic-free maintenance therapy

Hiroyuki Hamada, Satoshi Kurinomaru, Shogo Ohitokata, Yakun Yang, Kenta Asamura, Mari Deguchi, Yohei Motomura, Akiyuki Iwasaki, Tatsuya Sekiguchi, Taizo Hanai, Masahiro Okamoto

Research output: Contribution to journalArticle

Abstract

Global efforts are being made to achieve the clinical implementation of pre-emptive medicine for Staphylococcus aureus (S. aureus) infectious disease, which will realize both early detection at the pre-symptom stage and bacteriostatic therapy by antibiotic-free medicine in a future. Several research groups proposed the intercellular signal transduction factor (auto-inducing peptide: AIP) antibody, the synthesised AIP analogues and a cyclic depsipeptide with high constitutional similarity to AIP as a candidate of the pre-emptive medicine for S. aureus infectious disease. In this paper, to evaluate a validity of them, we mathematically explored both a pre-symptom associated with the pathogenic expression process of S. aureus and several therapeutic targets that delay or suppress the appearance of the pre-symptom. The stochastic mathematical analysis identified a peak of fluctuation in intracellular AgrD concentration as the pre-symptom. Moreover, employing parameter sensitivity analysis, the enhancement of binding inhibition between AgrC receptor and AIP was identified as effective therapeutic target. Based on these findings, we evaluated a feasibility of above-mentioned candidates, and concluded that the continuous application of AgrC receptor antagonists, such as the synthesised AIP analogues and a cyclic depsipeptide with high constitutional similarity to AIP, is useful as pre-emptive medicine for S. aureus infectious disease.

Original languageEnglish
Article number104238
JournalBioSystems
Volume198
DOIs
Publication statusPublished - Dec 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Applied Mathematics

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