Bacterial antimicrobial resistance (AMR) poses a significant global public health challenge. The escalation of AMR is primarily attributed to the horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs), often facilitated by plasmids. This underscores the critical need for a comprehensive understanding of the resistance mechanisms and transmission dynamics of these plasmids. In this study, we utilized in vitro drug sensitivity testing, conjugation transfer assays, and whole-genome sequencing to investigate the resistance mechanism of an extensively drug-resistant (XDR) Pseudomonas aeruginosa clinical isolate, MAS152. We specifically focused on analyzing the drug-resistant plasmid pMAS152 it harbors and its potential for widespread dissemination. Bioinformatics analysis revealed that MAS152 carries a distinct IncpP-2A plasmid, pMAS152, characterized by a 44.8 kb multidrug resistance (MDR) region. This region houses a 16S rRNA methyltransferase (16S-RMTase) gene, rmtB, conferring high-level resistance to aminoglycoside antibiotics. Notably, this region also contains an extended-spectrum β-Lactamase (ESBL) gene, bla_PER-1, and an efflux pump operon, tmexCD-oprJ, which mediate resistance to β-Lactams and quinolone antibiotics, respectively. Such a combination of ARGs, unprecedented in reported plasmids, could significantly undermine the effectiveness of first-line antibiotics in treating P. aeruginosa infections. Investigation into the genetic environment of the MDR region suggests that Tn2 and IS91 elements may be instrumental in the horizontal transfer of rmtB. Additionally, a complex Class I integron with an ISCR1 structure, along with TnAs1, seems to facilitate the horizontal transfer of bla_PER-1. The conjugation transfer assay, coupled with the annotation of conjugation-related genes and phylogenetic analysis, indicates that the plasmid pMAS152 functions as a conjugative plasmid, with other genus Pseudomonas species as potential hosts. Our findings provide vital insights into the resistance mechanisms and transmission potential of the XDR P. aeruginosa isolate MAS152, underlining the urgent need for novel strategies to combat the spread of AMR. This study highlights the complex interplay of genetic elements contributing to antibiotic resistance and underscores the importance of continuous surveillance of emerging ARGs in clinical isolates.

Rapid and accurate identification of pathogenic bacteria phenotypic traits, including virulence, drug resistance, and metabolic activity, is essential for clinical diagnosis and infectious disease control. Traditional methods are time-consuming, highlighting the need for more efficient approaches. This study develops a single-cell Raman spectroscopy approach to detect multiple phenotypic traits of Staphylococcus aureus (S. aureus) as a proof of concept. We constructed a single-cell Raman spectral database encompassing 6240 spectra from 10 strains of S. aureus with diverse phenotypic traits and developed a convolutional neural network (CNN) to predict these phenotypes from the Raman spectra. The CNN model achieved 93.90%, 98.73%, and 98.66% accuracy in identifying enterotoxin-producing strains, methicillin-resistant S. aureus (MRSA), and growth stages, respectively. Characteristic Raman peaks for enterotoxin producers mainly appeared at 781, 939, 1161, 1337, 1451, and 1524 cm⁻¹, whereas MRSA primarily exhibited peaks at 723, 780, 939, 1095, 1162, 1340, 1451, 1523, and 1660 cm⁻¹. During culture, nucleic acid-related peaks weakened, lipid peaks increased, and protein peaks initially increased and subsequently decreased. This integration of Raman spectroscopy and machine learning demonstrates considerable potential for rapid bacterial phenotyping. Future research should expand to a wider range of bacterial species and phenotypes to enhance the diagnosis, prevention, and management of infectious diseases.

The discrepancy between the genotypic and phenotypic expression of enterotoxins in S. aureus had long been a significant challenge in toxin detection. However, the accurate and rapid application of Raman spectroscopy for the genotypic and phenotypic characterisation of S. aureus enterotoxins remains problematic. To address this, the present study utilised a single-cell Raman spectra database from 31 S. aureus isolates, acquired via a Raman laser tweezer system. When combined with convolutional neural network analysis, this approach achieved an average accuracy of 99.71% for identifying single-gene toxin types and 99.44% for multi-gene toxin types, with an average phenotypic identification accuracy of 98.71%. Notably, the phenotypic identification accuracy for the three strains carrying the sea and seb genes reached 100%, and the validation accuracy using unknown genotypes and phenotypes exceeded 85%. Furthermore, the CNN analysis identified characteristic spectral peaks for S. aureus enterotoxin genotypes at 1663–1665 cm⁻¹, 1570 cm⁻¹, and 1117–1119 cm⁻¹, corresponding to protein α-helices, guanine, and nucleic acid backbones respectively. Representative peaks for the phenotype were found at 1302–1314 cm⁻¹ and 912–923 cm⁻¹, corresponding to proteins/lipids and polysaccharides, respectively. Representative peaks for different virulence phenotypes carrying multiple enterotoxin genes were located at 1074–1076 cm⁻¹, 1253–1255 cm⁻¹, 1326 cm⁻¹, and 1327 cm⁻¹, corresponding to proteins, nucleic acids, and lipids, respectively. Furthermore, metabolomic analysis of three S. aureus strains (sea+seb+, sea+seb−, sea−seb+) revealed metabolic differences in fatty acids, purines, phenylalanine, and aspartic acid, consistent with the corresponding distinct Raman spectral peaks (1458, 1179, 1406–1409 cm⁻¹). Thus, this study employed S. aureus as a proof-of-concept, establishing for the first time a method combining Raman laser tweezers with convolutional neural networks for identifying S. aureus enterotoxin genotypes and phenotypes. It clarified the Raman spectral differential peaks and their corresponding biomarkers among five classical enterotoxin genotypes and phenotypic strains, providing a novel approach for accurate toxin typing and virulence characterisation.