Making microbial genomics work for clinical and public health microbiology

Antibiotic resistance genotype, phenotype, and clinical outcomes in patients with Gram-negative infections at Rabin Medical Center in Israel

Antibiotic resistance is a major cause of morbidity and mortality. However, a better understanding of the relationship between bacterial genetic markers, phenotypic resistance, and clinical outcomes is needed. We performed whole-genome sequencing on five medically important pathogens (Acinetobacter baumannii, Enterobacter cloacae, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa) to investigate how resistance genes impact patient outcomes. A total of 168 isolates from 162 patients with Gram-negative infections admitted to Beilinson Hospital at Rabin Medical Center in Israel were included for final analysis. Genomes were analyzed for resistance determinants and correlated with microbiologic and clinical data. Thirty-day mortality from time of culture was 26.5% (43/162). Twenty-nine patients had carbapenem-resistant isolates (29/168, 17.2%), while 63 patients had multidrug-resistant isolates (63/168, 37.5%). Albumin levels were inversely associated with mortality and length of stay, while arrival from a healthcare facility and cancer chemotherapy predicted having a multidrug-resistant isolate. Sequencing revealed possible patient-to-patient transmission events. blaCTX-M-15 was associated with multidrug-resistance in E. coli (OR = 3.888, P = 0.023) on multivariate analysis. Increased blaOXA-72 copy number was associated with carbapenem-resistance in A. baumannii (P = 0.003) and meropenem minimum inhibitory concentration (P = 0.005), yet carbapenem-resistant isolates retained sensitivity to cefiderocol and sulbactam-durlobactam. RJX84154 was associated with multidrug-resistance across all pathogens (P = 0.0018) and in E. coli (P = 0.0024). Low albumin levels were associated with mortality and length of stay in this sample population. blaCTX-M-15 was correlated with multidrug-resistance in E. coli, and blaOXA-72 depth predicted meropenem minimum inhibitory concentration in A. baumannii. RJX84154 may play a role in multidrug-resistance.

IMPORTANCE

While there have been several studies that attempt to find clinical predictors of outcomes in patients hospitalized with bacterial infections, less has been done to combine clinical data with genomic mechanisms of antibiotic resistance. This study focused on a hospitalized patient population in Israel with infections due to medically important bacterial pathogens as a way to build a framework that would unite clinical data with both bacterial antibiotic susceptibility and genomic data. Merging both clinical and genomic data allowed us to find both bacterial and clinical factors that impact certain clinical outcomes. As genome sequencing of bacteria becomes both rapid and commonplace, near real-time monitoring of resistance determinants could help to optimize clinical care and potentially improve outcomes in these patients.

Suboptimal bioinformatic predictions of antimicrobial resistance from whole-genome sequences in multidrug-resistant Corynebacterium isolates

Herein, we combined different bioinformatics tools and databases (BV-BRC, ResFinder, RAST, and KmerResistance) to perform a prediction of antimicrobial resistance (AMR) in the genomic sequences of 107 Corynebacterium striatum isolates for which trustable antimicrobial susceptibility (AST) phenotypes could be retrieved. Then, the reliabilities of the AMR predictions were evaluated by different metrics: area under the ROC curve (AUC); Major Error Rates (MERs) and Very Major Error Rates (VMERs); Matthews Correlation Coefficient (MCC); F1-Score; and Accuracy. Out of 15 genes that were reliably detected in the C. striatum isolates, only tetW yielded predictive values for tetracycline resistance that were acceptable considering Food and Drug Administration (FDA)'s criteria for quality (MER < 3.0% and VMER with a 95% C.I. ≤1.5-≤7.5); this was accompanied by a MCC score higher than 0.9 for this gene. Noteworthy, our results indicate that other commonly used metrics (AUC, F1-score, and Accuracy) may render overoptimistic evaluations of AMR-prediction reliabilities on imbalanced datasets. Accordingly, out of 10 genes tested by PCR on additional multidrug-resistant Corynebacterium spp. isolates (n = 18), the tetW gene rendered the best agreement values with AST profiles (94.11%). Overall, our results indicate that genome-based AMR prediction can still be challenging for MDR clinical isolates of emerging Corynebacterium spp.