Abstract
As whole genome sequencing is becoming more accessible and affordable for clinical microbiological diagnostics, the reliability of genotypic antimicrobial resistance (AMR) prediction from sequencing data is an important issue to address. Computational AMR prediction can be performed at multiple levels. The first-level approach, such as simple AMR search relies heavily on the quality of the information fed into the database. However, AMR due to mutations are often undetected, since this is not included in the database or poorly documented. Using co-trimoxazole (trimethoprim-sulfamethoxazole) resistance in Staphylococcus aureus, we compared single-level and multi-level analysis to investigate the strengths and weaknesses of both approaches. The results revealed that a single mutation in the AMR gene on the nucleotide level may produce false positive results, which could have been detected if protein sequence analysis would have been performed. For AMR predictions based on chromosomal mutations, such as the folP gene of S. aureus, natural genetic variations should be taken into account to differentiate between variants linked to genetic lineage (MLST) and not over-estimate the potential resistant variants. Our study showed that careful analysis of the whole genome data and additional criterion such as lineage-independent mutations may be useful for identification of mutations leading to phenotypic resistance. Furthermore, the creation of reliable database for point mutations is needed to fully automatized AMR prediction.
Highlights
The reliable detection and prediction of antimicrobial resistance is an on-going issue in the era of antimicrobial resistance (AMR) with significant clinical implications
242 S. aureus isolates were analyzed in this study
Screening for known TMP AMR genes from the draft genome resulted in 62 dfrG genes, but only 61 correlated with the phenotypical TMP resistance (Figure 1A)
Summary
The reliable detection and prediction of antimicrobial resistance is an on-going issue in the era of antimicrobial resistance (AMR) with significant clinical implications. In silico Prediction of SXT Resistance in S. aureus advances in whole genome sequencing have brought major breakthroughs for genome-based AMR prediction, this method is not yet completely accurate (Ellington et al, 2017; Hendriksen et al, 2019). Data analysis can be performed on multiple levels, such as nucleotide alignment, protein sequence alignment and with multiple levels of resolution; read or assembly based. Instead of identifying the presence or absence of a particular gene, the analysis would have to differentiate the nonsynonymous polymorphism to extrapolate the protein sequences and discriminate between susceptible and resistant isolates. Misclassification of resistant isolates as susceptible isolates, i.e., false negative, would have profound clinical implications and should be avoided
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