Fighting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

Abstract

The growing importance of antibiotic resistance on clinical outcomes and cost of care underscores the need for optimization of current diagnostics. For a number of bacterial species antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and gene expression profiles, we generated predictive models and identified biomarkers of susceptibility or resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8-0.9) or very high (>0.9) sensitivity and predictive values, where the relative contribution of the different categories of biomarkers strongly depended on the antibiotic. For all drugs except for ciprofloxacin, gene expression information substantially improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers.The implementation of a molecular susceptibility test system in routine clinical microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

Parallel evolutionary paths to produce more than one Pseudomonas aeruginosa biofilm phenotype

“…Environment-dependent divergence of transcriptional profiles of clinical P. aeruginosa isolates We next recorded the transcriptional profiles of a subgroup of 77 clinical P. aeruginosa isolates under planktonic (in a previous study 42 ) and biofilm growth conditions (this study) ( Fig. 2a).…”

“…Furthermore, transcriptional profiles under planktonic conditions have been recorded for all 414 strains in the frame of a previous study. 42 In this study, we performed transcriptional profiling under biofilm growth conditions for a subset of 77 strains (Supplementary Table 1). All clinical strains used in this study were collected in clinical microbiology laboratories, in private practice laboratories, or were provided by strain collection curators.…”

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

“…In addition to classic and highly 62 standardized phenotypic testing of resistance, several methods of resistance predic-63 tion have been developed. Most novel methods use a genetic or genomic approach, 64 although transcriptomic approaches have been investigated as well (6), (7), (8). An im-65 portant factor in the choice of the resistance prediction method is the microorganism 66 under study.…”

“…To date, these methods have been restricted mostly to assign bacteria to 74 binary categories, i.e. susceptible or non-susceptible (12), (13), (14), (8), (15), (16), (17). 75 However, clinical breakpoints used to define susceptible and non-susceptible categories 76 can change and such binary categories do not allow following more subtle changes in 77 susceptibility in time.…”

Improved inference and prediction of bacterial genotype-phenotype associations using pangenome-spanning regressions

“…With enough independent observations, these methods can automatically predict the phenotype of new isolates, and potentially tell us something about the underlying genetic mechanisms. A deluge of recent papers have applied general predictive models to such datasets, most showing high accuracy (3)(4)(5)(6)(7)(8) , though some commentaries have been more cautious in their conclusions (9,10) .…”