Dataset Information

Proteomic Study of Tolerant S. aureus Strains after Drug Combination Treatment

ABSTRACT: Several groups have shown that through evolution experiments, tolerance evolved rapidly under cyclic antibiotic treatment which then promote the emergence of resistance. In other words, intermittent antibiotic exposure will “train” the bacteria to become tolerant to the drug, and then boost the chances for them to become fully resistant. This evolutionary trajectory of evolving tolerance and resistance not only occurs in vitro, but also in clinical patients with MRSA blood infections. Despite the fact that repetitive antibiotic treatment will lead to tolerance and resistance development in bacterial populations, the difference on the evolutionary path between those treated with a single drug and drug combination remains unaddressed. In this study, we monitored the development of tolerance in MRSA by treating them with either daptomycin alone for 2 weeks (Scheme 1), or daptomycin combined with rifampin for two weeks (Scheme 2), in a cyclic manner. In addition, we also investigate another scenario where we treat MRSA cells with daptomycin alone for one week, and subsequently treat them with daptomycin and rifampin combination in the second week (Scheme 3). At the end of the evolution experiment, we observed that the population from Scheme 1 developed tolerance towards daptomycin, the population from Scheme 2 developed both tolerance and resistance towards daptomycin, while the population from Scheme 3 developed tolerance after a week of treatment, but then the tolerance phenotype diminished at the end of the second week due to the drug combination treatment. We subjected them to whole genome sequencing and identified single point mutations that are responsible for the observed phenotype. Through proteomics, we compared the proteome profile of the population evolved from scheme 1 (S1D14), scheme 2 (S2D14) and scheme 3 (S3D14), and we observed that these three populations employ distinct mechanisms to survive antibiotic treatment, suggesting that the treatment strategy for these populations should be tailored depending on the treatment conditions.

INSTRUMENT(S): timsTOF Pro

ORGANISM(S): Staphylococcus Aureus

SUBMITTER: Jordy Evan Sulaiman

LAB HEAD: Henry Lam

PROVIDER: PXD021667 | Pride | 2021-07-27

REPOSITORIES: Pride

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Publications

Comparative proteomic investigation of multiple methicillin-resistant <i>Staphylococcus aureus</i> strains generated through adaptive laboratory evolution.

Sulaiman Jordy Evan JE Long Lexin L Wu Long L Qian Pei-Yuan PY Lam Henry H

iScience 20210806 9

Recent discoveries indicate that tolerance and resistance could rapidly evolve in bacterial populations under intermittent antibiotic treatment. In the present study, we applied antibiotic combinations in laboratory experiments to generate novel methicillin-resistant <i>Staphylococcus aureus</i> strains with distinct phenotypes (tolerance, resistance, and suppressed tolerance), and compared their proteome profiles to uncover the adaptation mechanisms. While the tolerant strains have very differe ...[more]

PMID: 34458699

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Project description:Kaiser2014 - Salmonella persistence after ciprofloxacin treatment The model describes the bacterial tolerance to antibiotics. Using a mouse model for Salmonella diarrhea, the authors have found that bacterial persistence occurs in the presence of the antibiotic ciprofloxacin because Salmonella can exist in two different states. One, the fast-growing population that spreads in the host's tissues and the other, slow-growing "persister" population that hide out inside dendritic cells of the host's immune system and cannot be attacked by the antibiotics. However, this can be killed by adding agents that directly stimulate the host's immune defense. This model is described in the article: Cecum lymph node dendritic cells harbor slow-growing bacteria phenotypically tolerant to antibiotic treatment. Kaiser P, Regoes RR, Dolowschiak T, Wotzka SY, Lengefeld J, Slack E, Grant AJ, Ackermann M, Hardt WD. PLoS Biol. 2014 Feb 18;12(2):e1001793. Abstract: In vivo, antibiotics are often much less efficient than ex vivo and relapses can occur. The reasons for poor in vivo activity are still not completely understood. We have studied the fluoroquinolone antibiotic ciprofloxacin in an animal model for complicated Salmonellosis. High-dose ciprofloxacin treatment efficiently reduced pathogen loads in feces and most organs. However, the cecum draining lymph node (cLN), the gut tissue, and the spleen retained surviving bacteria. In cLN, approximately 10%-20% of the bacteria remained viable. These phenotypically tolerant bacteria lodged mostly within CD103⁺CX₃CR1⁻CD11c⁺ dendritic cells, remained genetically susceptible to ciprofloxacin, were sufficient to reinitiate infection after the end of the therapy, and displayed an extremely slow growth rate, as shown by mathematical analysis of infections with mixed inocula and segregative plasmid experiments. The slow growth was sufficient to explain recalcitrance to antibiotics treatment. Therefore, slow-growing antibiotic-tolerant bacteria lodged within dendritic cells can explain poor in vivo antibiotic activity and relapse. Administration of LPS or CpG, known elicitors of innate immune defense, reduced the loads of tolerant bacteria. Thus, manipulating innate immunity may augment the in vivo activity of antibiotics. This model is hosted on BioModels Database and identified by: MODEL1312170001. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Dataset Information

Proteomic Study of Tolerant S. aureus Strains after Drug Combination Treatment

Publications

Comparative proteomic investigation of multiple methicillin-resistant <i>Staphylococcus aureus</i> strains generated through adaptive laboratory evolution.

Similar Datasets

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