Protein Biomarker Discovery for Methicillin-Sensitive, Heterogeneous Vancomycin-Intermediate and Vancomycin-Intermediate Staphylococcus aureus Strains Using Label-Free Data-Independent Acquisition Proteomics

Prevalence, biofilm formation, and mass spectrometric characterization of linezolid-resistant Staphylococcus capitis isolated from a tertiary hospital in China

OBJECTIVES

Linezolid-resistant Staphylococcus capitis (LRSC) has become a new challenge for clinical anti-infective therapy. The present study aimed to investigate the trends of LRSC prevalence in a tertiary hospital of China 2017-2020. The resistance mechanisms, virulence genes, biofilm formation, and mass spectrometric characteristics of LRSC isolates were also analysed.

METHODS

This study retrospectively analysed the antibiotic resistance trends of coagulase negative staphylococci (CoNS) isolated from clinical samples collected between 2017-2020. Antimicrobial resistance profiles were tested by micro-broth dilution and the E-test method. Antimicrobial resistance genes and virulence genes were detected by polymerase chain reaction, and dru-typing sequences were obtained by Sanger sequencing. Crystal violet staining in 96-well plates was used to detect biofilm formation ability. Mass spectrometric characterization of LRSC was analysed by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) coupled with ClinProTools.

RESULTS

The linezolid resistance rate in 3575 CoNS clinical strains was 1.6%, wherein the great majority of was LRSC (91.1%, n = 51/56), with a resistant rate of 15.5% (n = 51/328) in all S. capitis isolates. In this study, 48 out of the 51 LRSC strains and 54 of 277 linezolid-susceptible S. capitis (LSSC) strains were enrolled. G2576T, C2104T, T2130A, C2163T, and T2319C mutations in the 23S rRNA V region and acquisition of cfr were the main linezolid resistant mechanisms in LRSC. The biofilm-forming ability of LRSC was more potent than LSSC, with a higher detection rate of bap (P < 0.05). Eleven mass spectrometric peaks of interest were identified by using MALDI-TOF MS and ClinProTools, which were differently distributed between LRSC and LSSC strains, with the area under the receiver operating characteristic curve of more than 0.8, especially for 5465.37 m/z.

CONCLUSIONS

Linezolid resistance was mediated by mutations in the 23S rRNA V region and presence of the cfr gene in LRSC strains. LRSC strains have stronger biofilm-forming ability than LSSC strains, which maybe associated with the adhesion-related gene of bap. Further, linezolid-resistant and linezolid-susceptible S. capitis could be rapidly identified with mass spectrometric characterization. To the best of our knowledge, this study is the first to document the biofilm formation ability of LRSC and the potential usefulness of MALDI-TOF MS for the discrimination of LRSC and LSSC.

Quantitative proteomics reveals reduction in central carbon and energy metabolisms contributes to gentamicin resistance in Staphylococcus aureus

The emergence of antibiotic resistance greatly increases the difficulty of treating bacterial infections. In order to develop effective treatments, the underlying mechanisms of antibiotic resistance must be understood. In this study, Staphylococcus aureus ATCC6538 strain was passaged in medium with and without gentamicin and obtained lab-evolved gentamicin-resistant S. aureus (R_GEN) and gentamicin-sensitive S. aureus (S_GEN) strains, respectively. Data-Independent Acquisition (DIA)-based proteomics approach was applied to compare the two strains. A total of 1426 proteins were identified, of which 462 were significantly different: 126 were upregulated and 336 were downregulated in R_GEN compared to S_GEN. Further analysis found that reduced protein biosynthesis was a characteristic feature in R_GEN, related to metabolic suppression. The most differentially expressed proteins were involved in metabolic pathways. In R_GEN, central carbon metabolism was dysregulated and energy metabolism decreased. After verification, it was found that the levels of NADH, ATP, and reactive oxygen species (ROS) decreased, and superoxide dismutase and catalase activities increased. These findings suggest that inhibition of central carbon and energy metabolic pathways may play an important role in the resistance of S. aureus to gentamicin, and that gentamicin resistance is associated with oxidative stress. Significance: The overuse and misuse of antibiotics have led to bacterial antibiotic resistance, which is a serious threat to human health. Understanding the mechanisms of antibiotic resistance will help better control these antibiotic-resistant pathogens in the future. The present study characterized the differential proteome of gentamicin-resistant Staphylococcus aureus using the most advanced DIA-based proteomics technology. Many of the differential expressed proteins were related to metabolism, specifically, reduced central carbon and energy metabolism. Lower levels of NADH, ROS, and ATP were detected as a consequence of the reduced metabolism. These results reveal that downregulation of protein expression affecting central carbon and energy metabolisms may play an important role in the resistance of S. aureus to gentamicin.

Proteomic Investigation over the Antimicrobial Photodynamic Therapy Mediated by Rose Bengal Against Staphylococcus aureus

In order, to understand the antimicrobial action of photodynamic therapy and how this technique can contribute to its application in the control of pathogens. The objective of the study was to employ a proteomic approach to investigate the protein profile of S. aureus after antimicrobial photodynamic therapy mediated by rose bengal (RB-aPDT). S. aureus was treated with RB (10 nmol/l) and illuminated with green LED (0.17 J/cm² ) for cell viability evaluation. Afterward, proteomic analysis was employed for protein identification and bioinformatic tools to classify the differentially expressed proteins. The reduction of S. aureus after photoinactivation was ~2.5 log CFU/ml. A total of 12 proteins (four up-regulated and eight down-regulated), correspond exclusively to alteration by RB-aPDT. Functionally these proteins are distributed in protein binding, structural constituent of ribosome, proton transmembrane transporter activity, and ATPase activity. The effects of photodamage include alterations of levels of several proteins resulting in an activated stress response, altered membrane potential, and effects on energy metabolism. These 12 proteins required the presence of both light and RB suggesting a unique response to photodynamic effects. The information about this technique contributes valuable insights into bacterial mechanisms and the mode of action of photodynamic therapy.

Rapid Identification of Methicillin-Resistant Staphylococcus aureus Using MALDI-TOF MS and Machine Learning from over 20,000 Clinical Isolates

Rapidly identifying methicillin-resistant Staphylococcus aureus (MRSA) with high integration in the current workflow is critical in clinical practices. We proposed a matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI–TOF MS)-based machine learning model for rapid MRSA prediction. The model was evaluated on a prospective test and four external clinical sites. For the data set comprising 20,359 clinical isolates, the area under the receiver operating curve of the classification model was 0.78 to 0.88. These results were further interpreted using shapely additive explanations and presented using the pseudogel method. The important MRSA feature, m/z 6,590 to 6,599, was identified as a UPF0337 protein SACOL1680 with a lower binding affinity or no docking results compared with UPF0337 protein SA1452, which is mainly detected in methicillin-susceptible S. aureus. Our MALDI–TOF MS-based machine learning model for rapid MRSA identification can be easily integrated into the current clinical workflows and can further support physicians in prescribing proper antibiotic treatments.

IMPORTANCE Over 20,000 clinical MSSA and MRSA isolates were collected to build a machine learning (ML) model to identify MSSA/MRSA and their markers. This model was tested across four external clinical sites to ensure the model’s usability. We report the first discovery and validation of MRSA markers on the largest scale of clinical MSSA and MRSA isolates collected to date, covering five different clinical sites. Our developed approach for the rapid identification of MSSA and MRSA can be highly integrated into the current workflows.