Escherichia coli OxyS RNA triggers cephalothin resistance by modulating the expression of CRP-associated genes

Cho, Hyejin; Kim, Kwang-sun

doi:10.1016/j.bbrc.2018.10.084

Cited by 4 publications

(5 citation statements)

References 35 publications

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Section: Resultsmentioning

confidence: 99%

“…In any case, the DADNP complexes discovered contained a wide set of ADs, including PEN (ampicillin, meropenem, and imipenem), TETRA (tetracycline), MACRO (gentamicin, vancomycin, and rifampicin), QUIN (ofloxacin and ciprofloxacin), aminoglycoside (tobramycin, kanamycin, and streptomycin), amphenicols (chloramphenicol), lipopeptide (daptomycin), and polypeptide (polymyxin B). 34,[78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97] We may infer that this experimental collection of DADNP complex preclinical experiments contains a significant degree of structural and biological variety. Our initial collection of AD and NP assays vs. MN used to assemble putative DADNP complexes was sampled intentinally with significant structural The study of the interaction of the physical properties of conjugated nanoparticles with antibacterial drugs vs. the various metabolic networks of microorganisms could have an impact on the reduction of costs and environmental impacts.…”

Section: Discussionmentioning

confidence: 99%

“…In the introduction, we mentioned the importance of predicting which bacteria strains with different MN may lead to MDR strains. 89 MDR strain surveillance is defined as the task of making a follow up of the new MDR strains as soon as they appear. 90,91 Once the IFPTML-LDA model was trained we can use it to map perturbations on MN of mutant strains vs. DADNP systems.…”

Section: Comparison Of Ifptml With Previous Modelsmentioning

confidence: 99%

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Towards rational nanomaterial design by predicting drug–nanoparticle system interaction vs. bacterial metabolic networks

Gonzalez-Diaz

Rasulev

Dieugez-Santana

2022

Environ. Sci.: Nano

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Comparison Of Ifptml With Previous Modelsmentioning

confidence: 99%

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Towards rational nanomaterial design by predicting drug–nanoparticle system interaction vs. bacterial metabolic networks

Gonzalez-Diaz

Rasulev

Dieugez-Santana

2022

Environ. Sci.: Nano

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“…One major area where transcriptional data can contribute to antimicrobial resistance understanding is in connecting genotypic antimicrobial resistance data to phenotypic antimicrobial resistance results 170 . In cases where antimicrobial resistance genes are present but no resistance phenotype is found or in cases where no obvious antimicrobial resistance genes are present but antimicrobial resistance is confirmed, transcriptomic data may bridge the knowledge gap and point to novel antimicrobial resistance genes 169,[171][172][173] . transcriptomics also has the potential to identify combinatorial gene effects resulting in antimicrobial resistance 174 .…”

Section: Transcriptomicsmentioning

confidence: 99%

Sequencing-based methods and resources to study antimicrobial resistance

2019

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Antimicrobial resistance extracts high morbidity, mortality and economic costs yearly by rendering bacteria immune to antibiotics. Identifying and understanding antimicrobial resistance are imperative for clinical practice to treat resistant infections and for public health efforts to limit the spread of resistance. Technologies such as next-generation sequencing are expanding our abilities to detect and study antimicrobial resistance. This Review provides a detailed overview of antimicrobial resistance identification and characterization methods, from traditional antimicrobial susceptibility testing to recent deep-learning methods. We focus on sequencing-based resistance discovery and discuss tools and databases used in antimicrobial resistance studies.Antimicrobials are small molecules that can inhibit or kill bacteria. These small molecules are commonly used as therapeutics for bacterial infections, but some bacteria can grow and survive despite antimicrobial pressures, a property known as antimicrobial resistance. In clinical settings, resistant bacterial infections decrease available treatment options and increase morbidity and mortality compared with those caused by susceptible bacteria 1-5 . Resistance is observed against nearly all antimicrobials (FIG. 1a,b), including so-called last-*

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“…The oxidative stress-induced sRNA of Escherichia coli is one of the most well-studied sRNAs. More than two decades of research on OxyS have shown that it is involved in format metabolism, regulation of stationary phase, and resistance to cephalothin ( Zhang et al 1998 ; Argaman and Altuvia 2000 ; Cho and Kim 2018 ). Moreover, we found that this sRNA plays a role in protecting cells against the damaging effects of mutagens such as hydrogen peroxide and alkylating agents ( Altuvia et al 1997 ), while overexpression of OxyS has been found to be toxic.…”

Section: Introductionmentioning

confidence: 99%

Balanced cell division is secured by two different regulatory sites in OxyS RNA

Elgrably-Weiss,

Hussain,

Georg

et al. 2023

RNA

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The hydrogen peroxide-induced small RNA OxyS has been proposed to originate from the 3’ UTR of a peroxide mRNA. Unexpectedly, phylogenetic OxyS targetome predictions indicate that most OxyS targets belong to the category of “cell cycle” including cell division and cell elongation. Previously, we reported that E. coli OxyS inhibits cell division by repressing expression of the essential transcription termination factor nusG, thereby leading to expression of KilR protein which interferes with the function of the major cell division protein FtsZ. By interfering with cell division OxyS brings about cell cycle arrest thus, allowing DNA damage repair. Cell division and cell elongation are opposing functions to the extent that inhibition of cell division requires a parallel inhibition of cell elongation for the cells to survive. In this study, we report that in addition to cell division, OxyS inhibits mepS encoding an essential peptidoglycan endopeptidase, responsible for cell elongation. Our study indicates that cell cycle arrest and balancing between cell division and cell elongation are important and conserved functions of the oxidative stress-induced sRNA OxyS.

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Escherichia coli OxyS RNA triggers cephalothin resistance by modulating the expression of CRP-associated genes

Cited by 4 publications

References 35 publications

Towards rational nanomaterial design by predicting drug–nanoparticle system interaction vs. bacterial metabolic networks

Towards rational nanomaterial design by predicting drug–nanoparticle system interaction vs. bacterial metabolic networks

Sequencing-based methods and resources to study antimicrobial resistance

Balanced cell division is secured by two different regulatory sites in OxyS RNA

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