Staphylococcus aureus whole genome sequence-based susceptibility and resistance prediction using a clinically...

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• Antimicrobial resistance in Staphylococcus aureus poses a significant public health threat.
• Rapid and accurate prediction of antimicrobial susceptibility is crucial for effective treatment.
• Whole genome sequencing (WGS) offers a powerful tool for genotypic antimicrobial resistance prediction.

Purpose of the Study:

• To evaluate the performance of automated bioinformatics tools for predicting antimicrobial susceptibility in Staphylococcus aureus using WGS data.
• To compare the accuracy of WGS-based predictions against traditional phenotypic susceptibility testing.

Main Methods:

• Analysis of WGS data from 102 Staphylococcus aureus blood culture isolates.
• Utilized graphical user interface-based automated analytical tools from Next Gen Diagnostics and 1928 Diagnostics.
• Compared genotypic predictions with phenotypic susceptibility testing results for various antibiotics.

Main Results:

• High concordance observed for oxacillin, vancomycin, and mupirocin predictions with both platforms.
• Minor discrepancies noted for clindamycin, minocycline, and trimethoprim-sulfamethoxazole.
• Next Gen Diagnostics showed 9 discrepancies (8 clindamycin, 1 minocycline) out of 916 combinations.
• 1928 Diagnostics showed 13 discrepancies (9 clindamycin, 3 trimethoprim-sulfamethoxazole, 1 rifampin) out of 612 combinations.

Conclusions:

• Automated bioinformatics tools demonstrate acceptable performance for predicting antimicrobial susceptibility and resistance in Staphylococcus aureus.
• These WGS-based prediction tools show potential for clinical application, with specific antibiotics requiring careful consideration.
• Further validation and refinement of bioinformatics pipelines are warranted for optimal diagnostic accuracy.