Dataset Information

A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates.

ABSTRACT: Variation in the genome of Pseudomonas aeruginosa, an important pathogen, can have dramatic impacts on the bacterium's ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based on their genomic content. We applied a machine learning approach to a genetically and phenotypically diverse collection of 115 clinical P. aeruginosa isolates using genomic information and corresponding virulence phenotypes in a mouse model of bacteremia. We defined the accessory genome of these isolates through the presence or absence of accessory genomic elements (AGEs), sequences present in some strains but not others. Machine learning models trained using AGEs were predictive of virulence, with a mean nested cross-validation accuracy of 75% using the random forest algorithm. However, individual AGEs did not have a large influence on the algorithm's performance, suggesting instead that virulence predictions are derived from a diffuse genomic signature. These results were validated with an independent test set of 25 P. aeruginosa isolates whose virulence was predicted with 72% accuracy. Machine learning models trained using core genome single-nucleotide variants and whole-genome k-mers also predicted virulence. Our findings are a proof of concept for the use of bacterial genomes to predict pathogenicity in P. aeruginosa and highlight the potential of this approach for predicting patient outcomes.IMPORTANCE Pseudomonas aeruginosa is a clinically important Gram-negative opportunistic pathogen. P. aeruginosa shows a large degree of genomic heterogeneity both through variation in sequences found throughout the species (core genome) and through the presence or absence of sequences in different isolates (accessory genome). P. aeruginosa isolates also differ markedly in their ability to cause disease. In this study, we used machine learning to predict the virulence level of P. aeruginosa isolates in a mouse bacteremia model based on genomic content. We show that both the accessory and core genomes are predictive of virulence. This study provides a machine learning framework to investigate relationships between bacterial genomes and complex phenotypes such as virulence.

SUBMITTER: Pincus NB

PROVIDER: S-EPMC7448275 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background:Pseudomonas aeruginosa is ubiquitous, has intrinsic antibiotic resistance mechanisms, and is associated with serious hospital-associated infections. It has evolved from being a burn wound infection into a major nosocomial threat. In this study, we compared and correlated the antimicrobial resistance, virulence traits and clonal relatedness between clinical and fresh water environmental isolates of P. aeruginosa. Methods:219 P. aeruginosa isolates were studied: (a) 105 clinical isolates from 1977 to 1985 (n = 52) and 2015 (n = 53), and (b) 114 environmental isolates from different fresh water sources. All isolates were subjected to ERIC-PCR typing, antimicrobial susceptibility testing and virulence factor genes screening. Results:Clinical and environmental isolates of P. aeruginosa were genetically heterogenous, with only four clinical isolates showing 100% identical ERIC-PCR patterns to seven environmental isolates. Most of the clinical and environmental isolates were sensitive to almost all of the antipseudomonal drugs, except for ticarcillin/clavulanic acid. Increased resistant isolates was seen in 2015 compared to that of the archived isolates; four MDR strains were detected and all were retrieved in 2015. All clinical isolates retrieved from 1977 to 1985 were susceptible to ceftazidime and ciprofloxacin; but in comparison, the clinical isolates recovered in 2015 exhibited 9.4% resistance to ceftazidime and 5.7% to ciprofloxacin; a rise in resistance to imipenem (3.8% to 7.5%), piperacillin (9.6% to 11.3%) and amikacin (1.9% to 5.7%) and a slight drop in resistance rates to piperacillin/tazobactam (7.7% to 7.5%), ticarcillin/clavulanic acid (19.2% to 18.9%), meropenem (15.4% to 7.5%), doripenem (11.5% to 7.5%), gentamicin (7.7% to 7.5%) and netilmicin (7.7% to 7.5%). Environmental isolates were resistant to piperacillin/tazobactam (1.8%), ciprofloxacin (1.8%), piperacillin (4.4%) and carbapenems (doripenem 11.4%, meropenem 8.8% and imipenem 2.6%). Both clinical and environmental isolates showed high prevalence of virulence factor genes, but none were detected in 10 (9.5%) clinical and 18 (15.8%) environmental isolates. The exoT gene was not detected in any of the clinical isolates. Resistance to carbapenems (meropenem, doripenem and imipenem), β-lactamase inhibitors (ticarcillin/clavulanic acid and piperacillin/tazobactam), piperacillin, ceftazidime and ciprofloxacin was observed in some of the isolates without virulence factor genes. Five virulence-negative isolates were susceptible to all of the antimicrobials. Only one MDR strain harbored none of the virulence factor genes. Conclusion:Over a period of 30 years, a rise in antipseudomonal drug resistance particularly to ceftazidime and ciprofloxacin was observed in two hospitals in Malaysia. The occurrence of resistant environmental isolates from densely populated areas is relevant and gives rise to collective anxiety to the community at large.

Project description:The opportunistic pathogen Pseudomonas aeruginosa is ubiquitous in the environment, and in humans, it is capable of causing acute or chronic infections. In the natural environment, predation by bacterivorous protozoa represents a primary threat to bacteria. Here, we determined the impact of long-term exposure of P. aeruginosa to predation pressure. P. aeruginosa persisted when coincubated with the bacterivorous Acanthamoeba castellanii for extended periods and produced genetic and phenotypic variants. Sequencing of late-stage amoeba-adapted P. aeruginosa isolates demonstrated single nucleotide polymorphisms within genes that encode known virulence factors, and this correlated with a reduction in expression of virulence traits. Virulence for the nematode Caenorhabditis elegans was attenuated in late-stage amoeba-adapted P. aeruginosa compared to early-stage amoeba-adapted and nonadapted counterparts. Further, late-stage amoeba-adapted P. aeruginosa showed increased competitive fitness and enhanced survival in amoebae as well as in macrophage and neutrophils. Interestingly, our findings indicate that the selection imposed by amoebae resulted in P. aeruginosa isolates with reduced virulence and enhanced fitness, similar to those recovered from chronic cystic fibrosis infections. Thus, predation by protozoa and long-term colonization of the human host may represent similar environments that select for similar losses of gene function. IMPORTANCE Pseudomonas aeruginosa is an opportunistic pathogen that causes both acute infections in plants and animals, including humans, and chronic infections in immunocompromised and cystic fibrosis patients. This bacterium is commonly found in soils and water, where bacteria are constantly under threat of being consumed by bacterial predators, e.g., protozoa. To escape being killed, bacteria have evolved a suite of mechanisms that protect them from being consumed or digested. Here, we examined the effect of long-term predation on the genotypes and phenotypes expressed by P. aeruginosa. We show that long-term coincubation with protozoa gave rise to mutations that resulted in P. aeruginosa becoming less pathogenic. This is particularly interesting as similar mutations arise in bacteria associated with chronic infections. Importantly, the genetic and phenotypic traits possessed by late-stage amoeba-adapted P. aeruginosa are similar to those observed in isolates obtained from chronic cystic fibrosis infections. This notable overlap in adaptation to different host types suggests similar selection pressures among host cell types as well as similar adaptation strategies.

Dataset Information

A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets