Limitations in predicting reduced susceptibility to third generation cephalosporins in Escherichia coli based on whole genome sequence data

• Published in: PloS one Vol. 18; no. 11; p. e0295233
• Main Authors: Heydecke, Anna, Yin, Hong, Tano, Eva, Sütterlin, Susanne
• Format: Journal Article
• Language: English
• Published: San Francisco, CA USA Public Library of Science 30.11.2023; Public Library of Science (PLoS)
• Subjects: Analysis; Bacterial infections; Beta lactamases; Bioinformatics; Bioinformatik; Biology and Life Sciences; Cephaloridine; Cephalosporins; Diagnosis; DNA sequencing; Escherichia coli; Genomes; Genomics; Health aspects; Information management; Medicine and Health Sciences; Microbiology; Mikrobiologi; Moxalactam; Nucleotide sequencing; Research and Analysis Methods; Risk factors; Sweden
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0295233

Prediction of antibiotic resistance from whole genome sequence (WGS) data has been proposed. However, the performance of WGS data analysis for this matter may be influenced by the resistance mechanism's biology. This study compared traditional antimicrobial susceptibility testing with whole genome sequencing for identification of extended-spectrum beta-lactamases (ESBL) in a collection of 419 Escherichia coli isolates. BLASTn-based prediction and read mapping with srst2 gave matching results, and in 381/419 (91%) isolates WGS was congruent with phenotypic testing. Incongruent results were grouped by potential explanations into biological-related and sequence analysis-related results. Biological-related explanations included weak ESBL-enzyme activity (n = 4), inconclusive phenotypic ESBL-testing (n = 4), potential loss of plasmid during subculturing (n = 7), and other resistance mechanisms than ESBL-enzymes (n = 2). Sequence analysis-related explanations were cut-off dependency for read depth (n = 5), too stringent (n = 3) and too loose cut-off for nucleotide identity and coverage (n = 13), respectively. The results reveal limitations of both traditional antibiotic susceptibility testing and sequence-based resistance prediction and highlight the need for evidence-based standards in sequence analysis.