Clinical pilot of bacterial transcriptional profiling as a combined genotypic and phenotypic antimicrobial susceptibility test

Antimicrobial resistance is a growing health threat, but standard methods for determining antibiotic susceptibility are slow and can delay optimal treatment, which is especially consequential in severe infections such as bacteremia. Novel approaches for rapid susceptibility profiling have emerged that characterize either bacterial response to antibiotics (phenotype) or detect specific resistance genes (genotype). en typic and enotypic through NA detection (GoPhAST-R) is a novel assay, performed directly on positive blood cultures, that integrates rapid transcriptional response profiling with the detection of key resistance gene transcripts, thereby providing simultaneous data on both phenotype and genotype. Here, we performed the first clinical pilot of GoPhAST-R on 42 positive blood cultures: 26 growing , 15 growing , and 1 with both. An aliquot of each positive blood culture was exposed to nine different antibiotics, lysed, and underwent rapid transcriptional profiling on the NanoString platform; results were analyzed using an in-house susceptibility classification algorithm. GoPhAST-R achieved 95% overall agreement with standard antimicrobial susceptibility testing methods, with the highest agreement for beta-lactams (98%) and the lowest for fluoroquinolones (88%). Epidemic resistance genes including the extended spectrum beta-lactamase and the carbapenemase were also detected within the population. This study demonstrates the clinical feasibility of using transcriptional response profiling for rapid resistance determination, although further validation with larger and more diverse bacterial populations will be essential in future work. GoPhAST-R represents a promising new approach for rapid and comprehensive antibiotic susceptibility testing in clinical settings.IMPORTANCEExposure to antibiotics causes differential transcriptional signatures in susceptible vs resistant bacteria. These differences can be leveraged to rapidly predict resistance profiles of and in clinically positive blood cultures.