FtsH Sensitizes Methicillin-Resistant Staphylococcus aureus to β-Lactam Antibiotics by Degrading YpfP, a Lipoteichoic Acid Synthesis Enzyme

Yeo, Won-Sik; Jeong, Bohyun; Ullah, Nimat; Shah, Majid Ali; Ali, Amjad; Kim, Kyeong Kyu; Bae, Taeok

doi:10.3390/antibiotics10101198

Cited by 9 publications

(9 citation statements)

References 58 publications

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“…Particularly, methicillin resistance is becoming more common in S. aureus, posing a growing public health threat with substantial mortality and morbidity. The methicillin-resistant S. aureus (MRSA) produces a low-affinity penicillin-binding protein (PBP2a), which enables it to confer resistance to almost all beta-lactam antibiotics [3,4]. The PBP2a is encoded by the gene mecA, which is carried by a large mobile genetic element known as staphylococcal cassette chromosome mec (SCCmec) [5].…”

Section: Introductionmentioning

confidence: 99%

Comparative Genomic Analysis of a Panton–Valentine Leukocidin-Positive ST22 Community-Acquired Methicillin-Resistant Staphylococcus aureus from Pakistan

et al. 2022

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Staphylococcus aureus (S. aureus) ST22 is considered a clinically important clone because an epidemic strain EMRSA-15 belongs to ST22, and several outbreaks of this clone have been documented worldwide. We performed genomic analysis of an S. aureus strain Lr2 ST22 from Pakistan and determined comparative analysis with other ST22 strains. The genomic data show that Lr2 belongs to spa-type t2986 and harbors staphylococcal cassette chromosome mec (SCCmec) type IVa(2B), one complete plasmid, and seven prophages or prophage-like elements. The strain harbors several prophage-associated virulence factors, including Panton–Valentine leukocidin (PVL) and toxic shock syndrome toxin (TSST). The single nucleotide polymorphism (SNPs)-based phylogenetic relationship inferred from whole genome and core genome revealed that strain Lr2 exhibits the nearest identities to a South African community-acquired methicillin-resistant S. aureus (CA-MRSA) ST22 strain and makes a separate clade with an Indian CA-MRSA ST22 strain. Although most ST22 strains carry blaZ, mecA, and mutations in gyrA, the Lr2 strain does not have the blaZ gene but, unlike other ST22 strains, carries the antibiotic resistance genes erm(C) and aac(6′)-Ie-aph(2″)-Ia. Among ST22 strains analyzed, only the strain Lr2 possesses both PVL and TSST genes. The functional annotation of genes unique to Lr2 revealed that mobilome is the third-largest Cluster of Orthologous Genes (COGs) category, which encodes genes associated with prophages and transposons. This possibly makes methicillin-resistant S. aureus (MRSA) Lr2 ST22 strain highly virulent, and this study would improve the knowledge of MRSA ST22 strains in Pakistan. However, further studies are needed on a large collection of MRSA to comprehend the genomic epidemiology and evolution of this clone in Pakistan.

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Section: Introductionmentioning

confidence: 99%

Comparative Genomic Analysis of a Panton–Valentine Leukocidin-Positive ST22 Community-Acquired Methicillin-Resistant Staphylococcus aureus from Pakistan

et al. 2022

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“…The knockout mutation in ftsH has been demonstrated to increase thickness of cell walls. 78 FtsH was drastically upregulated by 3.8-fold and 5.1-fold respectively after 8 h and 24 h of treatment. In addition to that, the proteins GuaA, Spx, ThyA and YbeY were found to be significantly upregulated.…”

Section: Resultsmentioning

confidence: 96%

Antimicrobial Activity of Graphene Oxide Contributes to Alteration of Key Stress-Related and Membrane Bound Proteins

Ravikumar

Mijaković

Pandit

2022

IJN

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Antibacterial activity of graphene oxide (GO) has been extensively studied, wherein penetration of the bacterial cell membrane and oxidative stress are considered to play a major role in the bactericidal activity of GO. However, the specific mechanism responsible for the antibacterial activity of GO remains largely unknown. Hence, the goal of this study was to explore the mode of action of GO, via an in-depth proteomic analysis of the targeted bacteria. Methods: Staphylococcus aureus was grown in the presence of GO and samples were collected at different growth phases to examine the cell viability and to analyze the changes in protein expression. Antimicrobial efficiency of GO was tested by assessing bacterial viability, live/dead staining and scanning electron microscopy. The intracellular reactive oxygen species (ROS) induced by GO treatment were examined by fluorescence microscopy. Label-free quantitative proteomics analysis was performed to examine the differentially regulated proteins in S. aureus after GO treatment. Results: GO treatment was observed to reduce S. aureus viability, from 50 ± 17% after 4 h, to 93 ± 2% after 24 h. The live/dead staining confirmed this progressive antimicrobial effect of GO. SEM images revealed the wrapping of bacterial cells and their morphological disruption by means of pore formation due to GO insertion. GO treatment was observed to generate intracellular ROS, correlating to the loss of cell viability. The proteomics analysis revealed alteration in the expression of cell membrane, oxidative stress response, general stress response, and virulence-associated proteins in GO-treated bacterial cells. The time-dependent bactericidal activity of GO correlated with a higher number of differentially regulated proteins involved in the above.-mentioned processes. Conclusion:The obtained results suggest that the time-dependent bactericidal effect of GO is attributed to its wrapping/trapping ability, ROS production and due to physical disruption of the cell membrane.

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“…In particular, mutations in ftsH and ftsH -controlling genes in P. aeruginosa increase sensitivity to tobramycin (45). In Staphylococcus aureus, FtsH is involved in bacterial virulence, stress resistance and β -lactam resistance (46). In E. coli , some isolates resistant to vancomycin and erythromycin also carry mutations in ftsH (47).…”

Section: Resultsmentioning

confidence: 99%

Machine learning and phylogenetic analysis allow for predicting antibiotic resistance inM. tuberculosis

Yurtseven,

Buyanova,

Agrawal

et al. 2023

Preprint

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Antimicrobial resistance (AMR) poses a significant global health threat, and an accurate prediction of bacterial resistance patterns is critical for effective treatment and control strategies. In recent years, machine learning (ML) approaches have emerged as powerful tools for analyzing large-scale bacterial AMR data. However, ML methods often ignore evolutionary relationships among bacterial strains, which can greatly impact performance of the ML methods, especially if resistance-associated features are attempted to be detected. Genome-wide association studies (GWAS) methods like linear mixed models accounts for the evolutionary relationships in bacteria, but they uncover only highly significant variants which have already been reported in literature. In this work, we introduce a novel phylogeny-related parallelism score (PRPS), which measures whether a certain feature is correlated with the population structure of a set of samples. We demonstrate that PRPS can be used, in combination with SVM- and random forest-based models, to reduce the number of features in the analysis, while simultaneously increasing models' performance. We applied our pipeline to publicly available AMR data from PATRIC database for Mycobacterium tuberculosis against six common antibiotics. Using our pipeline, we re-discovered known resistance-associated mutations as well as new candidate mutations which can be related to resistance and not previously reported in the literature.

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FtsH Sensitizes Methicillin-Resistant Staphylococcus aureus to β-Lactam Antibiotics by Degrading YpfP, a Lipoteichoic Acid Synthesis Enzyme

Cited by 9 publications

References 58 publications

Comparative Genomic Analysis of a Panton–Valentine Leukocidin-Positive ST22 Community-Acquired Methicillin-Resistant Staphylococcus aureus from Pakistan

Comparative Genomic Analysis of a Panton–Valentine Leukocidin-Positive ST22 Community-Acquired Methicillin-Resistant Staphylococcus aureus from Pakistan

Antimicrobial Activity of Graphene Oxide Contributes to Alteration of Key Stress-Related and Membrane Bound Proteins

Machine learning and phylogenetic analysis allow for predicting antibiotic resistance inM. tuberculosis

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