Abstract
Elizabethkingia meningoseptica (EM) is a multi-drug-resistant bacterium of global concern for its role in nosocomial infection and is generally resistant to aminoglycoside antibiotics. In the whole genome of an EM strain (FMS-007), an aminoglycoside-6-adenyl transferase gene (ant(6)FMS-007) was predicted. This study aimed to characterize the biochemical function of ANT(6)FMS-007 and analyze the relationship between genotype and phenotype of ant(6) in clinical EM isolates, so as to provide evidence for clinical precision drug use. This study could establish a method for the verification of known or unknown functionally resistant genes. A total of 42 EM clinical isolates were collected from clinical departments during 2015-2023. The phenotype of aminoglycoside antibiotics was analyzed by broth microdilution (BMD) and Kirby-Bauer (K-B) methods. The whole-length ant(6) from EM clinical isolates was analyzed by polymerase chain reaction (PCR) and sequencing. The biochemical function of predictive ANT(6)FMS-007 from the FMS-007 whole genome was identified by 3D plate experiment and mass spectrometry analysis. Candidate active sites were predicted by multi-species sequence alignment and molecular docking, and other important sites were identified in the comparison of ant(6) genotypes and phenotypes of EM clinical isolates. Drug susceptibility test was used to verify the function of these sites. The predictive ANT(6)FMS-007 protein could inactivate STR by modifying STR with ATP to form STR-AMP. Four active sites (Asp-38, Asp-42, Lys-95, and Lys-213) of ANT(6)FMS-007 were identified. Thirty-one EM clinical isolates (74%) carried the ant(6) gene. Eight EM clinical isolates containing the ant(6) gene had MIC values (<=32μg/mL) lower by at least 16-fold than FMS-007 (512μg/mL) for STR, and N59H and K204Q were the common mutations in the ant(6) gene. This assay verified the biochemical function of the predictive gene ant(6)FMS-007 and could provide an alternative method to study resistant gene function in multi-drug-resistant bacteria. The inconsistency between genotype and phenotype of resistant genes indicated that the combination of resistance gene detection and functional analysis could better provide precision medicine for clinical use.
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