Dataset Information

Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning.

ABSTRACT: Here, we report a label-free surface-enhanced Raman scattering (SERS) method for the rapid and accurate identification of methicillin-susceptible Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA) based on aptamer-guided AgNP enhancement and convolutional neural network (CNN) classification. Sixty clinical isolates of Staphylococcus aureus (S. aureus), comprising 30 strains of MSSA and 30 strains of MRSA were used to build the CNN classification model. The developed method exhibited 100% identification accuracy for MSSA and MRSA, and is thus a promising tool for the rapid detection of drug-sensitive and drug-resistant bacterial strains.

SUBMITTER: Wang S

PROVIDER: S-EPMC9042729 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning.

Wang Shu S Dong Hao H Shen Wanzhu W Yang Yong Y Li Zhigang Z Liu Yong Y Wang Chongwen C Gu Bing B Zhang Long L

RSC advances 20211025 55

Here, we report a label-free surface-enhanced Raman scattering (SERS) method for the rapid and accurate identification of methicillin-susceptible Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA) based on aptamer-guided AgNP enhancement and convolutional neural network (CNN) classification. Sixty clinical isolates of Staphylococcus aureus (S. aureus), comprising 30 strains of MSSA and 30 strains of MRSA were used to build the CNN clas ...[more]

PMID: 35494737

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Project description:BackgroundBloodstream infections due to Staphylococcus aureus cause significant patient morbidity and mortality worldwide. Of major concern is the emergence and spread of methicillin-resistant S. aureus (MRSA) in bloodstream infections, which are associated with therapeutic failure and increased mortality.MethodsWe generated high quality draft genomes from 323 S. aureus blood culture isolates from patients diagnosed with bloodstream infection at the Dartmouth-Hitchcock Medical Center, New Hampshire, USA in 2010-2018.ResultsIn silico detection of antimicrobial resistance genes revealed that 133/323 isolates (41.18%) carry horizontally acquired genes conferring resistance to at least three antimicrobial classes, with resistance determinants for aminoglycosides, beta-lactams and macrolides being the most prevalent. The most common resistance genes were blaZ and mecA, which were found in 262/323 (81.11%) and 104/323 (32.20%) isolates, respectively. Majority of the MRSA (102/105 isolates or 97.14%) identified using in vitro screening were related to two clonal complexes (CC) 5 and 8. The two CCs emerged in the New Hampshire population at separate times. We estimated that the time to the most recent common ancestor of CC5 was 1973 (95% highest posterior density (HPD) intervals: 1966-1979) and 1946 for CC8 (95% HPD intervals: 1924-1959). The effective population size of CC8 increased until the late 1960s when it started to level off until late 2000s. The levelling off of CC8 in 1968 coincided with the acquisition of SCCmec Type IV in majority of the strains. The plateau in CC8 also coincided with the acceleration in the population growth of CC5 carrying SCCmec Type II in the early 1970s, which eventually leveled off in the early 1990s. Lastly, we found evidence for frequent recombination in the two clones during their recent clonal expansion, which has likely contributed to their success in the population.ConclusionsWe conclude that the S. aureus population was shaped mainly by the clonal expansion, recombination and co-dominance of two major MRSA clones in the last five decades in New Hampshire, USA. These results have important implications on the development of effective and robust strategies for intervention, control and treatment of life-threatening bloodstream infections.

Project description:Rapid detection and differentiation of methicillin-resistant Staphylococcus aureus (MRSA) are critical for the early diagnosis of difficult-to-treat nosocomial and community acquired clinical infections and improved epidemiological surveillance. We developed a microfluidics chip coupled with surface enhanced Raman scattering (SERS) spectroscopy (532 nm) "lab-on-a-chip" system to rapidly detect and differentiate methicillin-sensitive S. aureus (MSSA) and MRSA using clinical isolates from China and the United States. A total of 21 MSSA isolates and 37 MRSA isolates recovered from infected humans were first analyzed by using polymerase chain reaction (PCR) and multilocus sequence typing (MLST). The mecA gene, which refers resistant to methicillin, was detected in all the MRSA isolates, and different allelic profiles were identified assigning isolates as either previously identified or novel clones. A total of 17 400 SERS spectra of the 58 S. aureus isolates were collected within 3.5 h using this optofluidic platform. Intra- and interlaboratory spectral reproducibility yielded a differentiation index value of 3.43-4.06 and demonstrated the feasibility of using this optofluidic system at different laboratories for bacterial identification. A global SERS-based dendrogram model for MRSA and MSSA identification and differentiation to the strain level was established and cross-validated (Simpson index of diversity of 0.989) and had an average recognition rate of 95% for S. aureus isolates associated with a recent outbreak in China. SERS typing correlated well with MLST indicating that it has high sensitivity and selectivity and would be suitable for determining the origin and possible spread of MRSA. A SERS-based partial least-squares regression model could quantify the actual concentration of a specific MRSA isolate in a bacterial mixture at levels from 5% to 100% (regression coefficient, >0.98; residual prediction deviation, >10.05). This optofluidic platform has advantages over traditional genotyping for ultrafast, automated, and reliable detection and epidemiological surveillance of bacterial infections.

Dataset Information

Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning.

Publications

Rapid SERS identification of methicillin-susceptible and methicillin-resistant <i>Staphylococcus aureus via</i> aptamer recognition and deep learning.

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