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

Refined methodology for quantifying Pseudomonas aeruginosa virulence using Galleria mellonella

Larvae of Galleria mellonella (the greater wax moth) are being increasingly used as a model to study microbial pathogenesis. In this model, bacterial virulence is typically measured by determining the 50% lethal dose (LD50) of a bacterial strain or mutant. The use of G. mellonella to study Pseudomonas aeruginosa pathogenesis, however, is challenging because of the extreme sensitivity of larvae to this bacterium. For some P. aeruginosa strains, as few as 1-5 colony-forming units are sufficient to kill G. mellonella, which poses challenges for determining LD50 values. For this reason, some groups have used time-to-death as a measure of P. aeruginosa virulence, but methodologies have not been standardized. We provide a detailed protocol for using the time at which 50% of larvae have died (LT50) at a particular inoculum as a measure of P. aeruginosa virulence. We also describe a quality control metric for enhancing the reproducibility of LT50 values. This approach provides an accurate and reproducible methodology for using G. mellonella larvae to measure and compare the virulence of P. aeruginosa strains.IMPORTANCEPseudomonas aeruginosa is a significant cause of morbidity and mortality. The invertebrate Galleria mellonella is used as a model to determine the virulence of P. aeruginosa strains. We provide a protocol and analytical approach for using a time-to-death metric to accurately quantify the virulence of P. aeruginosa strains in G. mellonella larvae. This methodology, which has several advantages over 50% lethal dose approaches, is a useful resource for the study of P. aeruginosa pathogenicity.

Genomic virulence markers are associated with severe outcomes in patients with Pseudomonas aeruginosa bloodstream infection

BACKGROUND

Pseudomonas aeruginosa (PA) bloodstream infection (BSI) is a common healthcare-associated complication linked to antimicrobial resistance and high mortality. Ongoing clinical trials are exploring novel anti-virulence agents, yet studies on how bacterial virulence affects PA infection outcomes is conflicting and data from real-world clinical populations is limited.

METHODS

We studied a multicentre cohort of 773 adult patients with PA BSI consecutively collected during 7-years from sites in Europe and Australia. Comprehensive clinical data and whole-genome sequencing of all bacterial strains were obtained.

RESULTS

Based on the virulence genotype, we identify several virulence clusters, each showing varying proportions of multidrug-resistant phenotypes. Genes tied to biofilm synthesis and epidemic clones ST175 and ST235 are associated with mortality, while the type III secretion system is associated with septic shock. Adding genomic biomarkers to machine learning models based on clinical data indicates improved prediction of severe outcomes in PA BSI patients.

CONCLUSIONS

These findings suggest that virulence markers provide prognostic information with potential applications in guiding adjuvant sepsis treatments.

Impact of multidrug resistance on the virulence and fitness of Pseudomonas aeruginosa: a microbiological and clinical perspective

Pseudomonas aeruginosa is one of the most common nosocomial pathogens and part of the top emergent species associated with antimicrobial resistance that has become one of the greatest threat to public health in the twenty-first century. This bacterium is provided with a wide set of virulence factors that contribute to pathogenesis in acute and chronic infections. This review aims to summarize the impact of multidrug resistance on the virulence and fitness of P. aeruginosa. Although it is generally assumed that acquisition of resistant determinants is associated with a fitness cost, several studies support that resistance mutations may not be associated with a decrease in virulence and/or that certain compensatory mutations may allow multidrug resistance strains to recover their initial fitness. We discuss the interplay between resistance profiles and virulence from a microbiological perspective but also the clinical consequences in outcomes and the economic impact.

Phylogenetic evaluation and genotypic identification of burn-related Pseudomonas aeruginosa strains isolated from post-burn human infections during hospitalization

Cutaneous burn trauma, compromise of dermal layers and immune defense system is a physical and fiscal burden on healthcare systems. Burn-wound infections are a serious complication of thermal injury and contribute significantly to care burden. After burn-induced trauma, sepsis by Pseudomonas aeruginosa impairs patient recovery and contributes to mortality and morbidity. Past studies show positive correlation between detection of Pseudomonas species and healing-impaired traumatic wounds. Pseudomonas aeruginosa is a resilient opportunistic human pathogen and a nosocomial agent involved in pathology of healing-impaired wounds, especially in burn patients. Expansive array of virulence determinants has resulted in gentamicin- and silver-resistant P. aeruginosa outbreaks. Knowledge of molecular dynamics and phylogeny of P. aeruginosa associated with burn wounds is limited. Therefore, we conducted whole-genome sequencing for genotyping and phylogenetic analysis of P. aeruginosa burn-associated strains (n = 19) isolated from 7 burn cases during hospitalization. Comparison of genetic features in P. aeruginosa strains in the core genome and mobilome detected genetic variations within some clonal infections over time. Genetic variations were observed among different burn cases, with some features identified in severe lung infections. Polyclonal infections were also observed, with differing genotypes and virulence potentials, highlighting the importance of reasoned sampling of isolates for clinical testing.

Predicting Listeria monocytogenes virulence potential using whole genome sequencing and machine learning

Contamination with food-borne pathogens, such as Listeria monocytogenes, remains a big concern for food safety. Hence, rigorous and continuous microbial surveillance is a standard procedure. At this point, however, the food industry and authorities only focus on detection of Listeria monocytogenes without characterization of individual strains into groups of more or less concern. As whole genome sequencing (WGS) gains increasing interest in the industry, this methodology presents an opportunity to obtain finer resolution of microbial traits such as virulence. Within this study, we therefore aimed to explore the use of WGS in combination with Machine Learning (ML) to predict L. monocytogenes virulence potential on a sub-species level. The WGS datasets used in this study for ML model training consisted of i) national surveillance isolates (n = 169, covering 38 MLST types) and ii) publicly available isolates acquired through the GenomeTrakr network (n = 2880, spanning 80 MLST types). We used the clinical frequency, i.e., ratio of the number of clinical isolates to total amount of isolates, as estimate for virulence potential. The predictive performance of input features from three different genomic levels (i.e., virulence genes, pan-genome genes, and single nucleotide polymorphisms (SNPs)) and six machine learning algorithms (i.e., Support Vector Machine with a linear kernel, Support Vector Machine with a radial kernel, Random Forrest, Neural Networks, LogitBoost, and Majority Voting) were compared. Our machine learning models predicted sub-species virulence potential with nested cross-validation F1-scores up to 0.88 for the majority voting classifier trained on national surveillance data and using pan-genome genes as input features. The validation of the pre-trained ML models based on 101 previously in vivo studied isolates resulted in F1-scores up to 0.76. Furthermore, we found that the more rapid and less computationally intensive raw read alignment yields comparably accurate models as de novo assembly. The results of our study suggest that a majority voting classifier trained on pan-genome genes is the best and most robust choice for the prediction of clinical frequency. Our study contributes to more rapid and precise characterization of L. monocytogenes virulence and its variation on a sub-species level. We further demonstrated a possible application of WGS data in the context of microbial hazard characterization for food safety. In the future, predictive models may assist case-specific microbial risk management in the food industry. The python code, pre-trained models, and prediction pipeline are deposited at (https://github.com/agmei/LmonoVirulenceML).

Genomics of Aminoglycoside Resistance in Pseudomonas aeruginosa Bloodstream Infections at a United States Academic Hospital

Pseudomonas aeruginosa frequently becomes resistant to aminoglycosides by the acquisition of aminoglycoside modifying enzyme (AME) genes and the occurrence of mutations in the mexZ, fusA1, parRS, and armZ genes. We examined resistance to aminoglycosides in a collection of 227 P. aeruginosa bloodstream isolates collected over 2 decades from a single United States academic medical institution. Resistance rates of tobramycin and amikacin were relatively stable over this time, while the resistance rates of gentamicin were somewhat more variable. For comparison, we examined resistance rates to piperacillin-tazobactam, cefepime, meropenem, ciprofloxacin, and colistin. Resistance rates to the first four antibiotics were also stable, although uniformly higher for ciprofloxacin. Colistin resistance rates were initially quite low, rose substantially, and then began to decrease at the end of the study. Clinically relevant AME genes were identified in 14% of isolates, and mutations predicted to cause resistance were relatively common in the mexZ and armZ genes. In a regression analysis, resistance to gentamicin was associated with the presence of at least one gentamicin-active AME gene and significant mutations in mexZ, parS, and fusA1. Resistance to tobramycin was associated with the presence of at least one tobramycin-active AME gene. An extensively drug-resistant strain, PS1871, was examined further and found to contain five AME genes, most of which were within clusters of antibiotic resistance genes embedded in transposable elements. These findings demonstrate the relative contributions of aminoglycoside resistance determinants to P. aeruginosa susceptibilities at a United States medical center. IMPORTANCE Pseudomonas aeruginosa is frequently resistant to multiple antibiotics, including aminoglycosides. The rates of resistance to aminoglycosides in bloodstream isolates collected over 2 decades at a United States hospital remained constant, suggesting that antibiotic stewardship programs may be effective in countering an increase in resistance. Mutations in the mexZ, fusA1, parR, pasS, and armZ genes were more common than acquisition of genes encoding aminoglycoside modifying enzymes. The whole-genome sequence of an extensively drug resistant isolate indicates that resistance mechanisms can accumulate in a single strain. Together, these results suggest that aminoglycoside resistance in P. aeruginosa remains problematic and confirm known resistance mechanisms that can be targeted for the development of novel therapeutics.

Single nucleotide variants in Pseudomonas aeruginosa populations from sputum correlate with baseline lung function and predict disease progression in individuals with cystic fibrosis

The severity and progression of lung disease are highly variable across individuals with cystic fibrosis (CF) and are imperfectly predicted by mutations in the human gene CFTR, lung microbiome variation or other clinical factors. The opportunistic pathogen Pseudomonas aeruginosa (Pa) dominates airway infections in most CF adults. Here we hypothesized that within-host genetic variation of Pa populations would be associated with lung disease severity. To quantify Pa genetic variation within CF sputum samples, we used deep amplicon sequencing (AmpliSeq) of 209 Pa genes previously associated with pathogenesis or adaptation to the CF lung. We trained machine learning models using Pa single nucleotide variants (SNVs), microbiome diversity data and clinical factors to classify lung disease severity at the time of sputum sampling, and to predict lung function decline after 5 years in a cohort of 54 adult CF patients with chronic Pa infection. Models using Pa SNVs alone classified lung disease severity with good sensitivity and specificity (area under the receiver operating characteristic curve: AUROC=0.87). Models were less predictive of lung function decline after 5 years (AUROC=0.74) but still significantly better than random. The addition of clinical data, but not sputum microbiome diversity data, yielded only modest improvements in classifying baseline lung function (AUROC=0.92) and predicting lung function decline (AUROC=0.79), suggesting that Pa AmpliSeq data account for most of the predictive value. Our work provides a proof of principle that Pa genetic variation in sputum tracks lung disease severity, moderately predicts lung function decline and could serve as a disease biomarker among CF patients with chronic Pa infections.

Pseudomonas aeruginosa clinical blood isolates display significant phenotypic variability

Pseudomonas aeruginosa is a significant threat in healthcare settings where it deploys a wide host of virulence factors to cause disease. Many virulence-related phenotypes such as pyocyanin production, biofilm formation, and twitching motility have been implicated in causing disease in a number of hosts. In this study, we investigate these three virulence factors in a collection of 22 clinical strains isolated from blood stream infections. Despite the fact that all 22 strains caused disease and came from the same body site of different patients, they show significant variability in assays for each of the three specific phenotypes examined. There was no significant correlation between the strength of the three phenotypes across our collection, suggesting that they can be independently modulated. Furthermore, strains deficient in each of the virulence-associated phenotypes examined could be identified. To understand the genetic basis of this variability we sequenced the genomes of the 22 strains. We found that the majority of genes responsible for pyocyanin production, biofilm formation, and twitching motility were highly conserved among the strains despite their phenotypic variability, suggesting that the phenotypic variability is likely due to regulatory changes. Our findings thus demonstrate that no one lab-assayed phenotype of pyocyanin production, biofilm production, and twitching motility is necessary for a P. aeruginosa strain to cause blood stream infection and that additional factors may be needed to fully predict what strains will lead to specific human diseases.

Evolution-proof inhibitors of public good cooperation: a screening strategy inspired by social evolution theory

Interference with public good cooperation provides a promising novel antimicrobial strategy since social evolution theory predicts that resistant mutants will be counter-selected if they share the public benefits of their resistance with sensitive cells in the population. Although this hypothesis is supported by a limited number of pioneering studies, an extensive body of more fundamental work on social evolution describes a multitude of mechanisms and conditions that can stabilize public behaviour, thus potentially allowing resistant mutants to thrive. In this paper we theorize on how these different mechanisms can influence the evolution of resistance against public good inhibitors. Based hereon, we propose an innovative 5-step screening strategy to identify novel evolution-proof public good inhibitors, which involves a systematic evaluation of the exploitability of public goods under the most relevant experimental conditions, as well as a careful assessment of the most optimal way to interfere with their action. Overall, this opinion paper is aimed to contribute to long-term solutions to fight bacterial infections.

Rapid expansion and extinction of antibiotic resistance mutations during treatment of acute bacterial respiratory infections

Acute bacterial infections are often treated empirically, with the choice of antibiotic therapy updated during treatment. The effects of such rapid antibiotic switching on the evolution of antibiotic resistance in individual patients are poorly understood. Here we find that low-frequency antibiotic resistance mutations emerge, contract, and even go to extinction within days of changes in therapy. We analyzed Pseudomonas aeruginosa populations in sputum samples collected serially from 7 mechanically ventilated patients at the onset of respiratory infection. Combining short- and long-read sequencing and resistance phenotyping of 420 isolates revealed that while new infections are near-clonal, reflecting a recent colonization bottleneck, resistance mutations could emerge at low frequencies within days of therapy. We then measured the in vivo frequencies of select resistance mutations in intact sputum samples with resistance-targeted deep amplicon sequencing (RETRA-Seq), which revealed that rare resistance mutations not detected by clinically used culture-based methods can increase by nearly 40-fold over 5–12 days in response to antibiotic changes. Conversely, mutations conferring resistance to antibiotics not administered diminish and even go to extinction. Our results underscore how therapy choice shapes the dynamics of low-frequency resistance mutations at short time scales, and the findings provide a possibility for driving resistance mutations to extinction during early stages of infection by designing patient-specific antibiotic cycling strategies informed by deep genomic surveillance.

It remains unclear how rapid antibiotic switching affects the evolution of antibiotic resistance in individual patients. Here, Chung et al. combine short- and long-read sequencing and resistance phenotyping of 420 serial isolates of Pseudomonas aeruginosa collected from the onset of respiratory infection, and show that rare resistance mutations can increase by nearly 40-fold over 5–12 days in response to antibiotic changes, while mutations conferring resistance to antibiotics not administered diminish and even go to extinction.

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes

Antimicrobial resistance (AMR) is a major global health threat that affects millions of people each year. Funding agencies worldwide and the global research community have expended considerable capital and effort tracking the evolution and spread of AMR by isolating and sequencing bacterial strains and performing antimicrobial susceptibility testing (AST). For the last several years, we have been capturing these efforts by curating data from the literature and data resources and building a set of assembled bacterial genome sequences that are paired with laboratory-derived AST data. This collection currently contains AST data for over 67 000 genomes encompassing approximately 40 genera and over 100 species. In this paper, we describe the characteristics of this collection, highlighting areas where sampling is comparatively deep or shallow, and showing areas where attention is needed from the research community to improve sampling and tracking efforts. In addition to using the data to track the evolution and spread of AMR, it also serves as a useful starting point for building machine learning models for predicting AMR phenotypes. We demonstrate this by describing two machine learning models that are built from the entire dataset to show where the predictive power is comparatively high or low. This AMR metadata collection is freely available and maintained on the Bacterial and Viral Bioinformatics Center (BV-BRC) FTP site ftp://ftp.bvbrc.org/RELEASE_NOTES/PATRIC_genomes_AMR.txt.

Genomic Features Associated with the Degree of Phenotypic Resistance to Carbapenems in Carbapenem-Resistant Klebsiella pneumoniae

Carbapenem-resistant Klebsiella pneumoniae strains cause severe infections that are difficult to treat. The production of carbapenemases such as the K. pneumoniae carbapenemase (KPC) is a common mechanism by which these strains resist killing by the carbapenems. However, the degree of phenotypic carbapenem resistance (MIC) may differ markedly between isolates with similar carbapenemase genes, suggesting that our understanding of the underlying mechanisms of carbapenem resistance remains incomplete. To address this problem, we determined the whole-genome sequences of 166 K. pneumoniae clinical isolates resistant to meropenem, imipenem, or ertapenem. Multiple linear regression analysis of this collection of largely bla_KPC-3-containing sequence type 258 (ST258) isolates indicated that bla_KPC copy number and some outer membrane porin gene mutations were associated with higher MICs to carbapenems. A trend toward higher MICs was also observed with those bla_KPC genes carried by the d isoform of Tn4401. In contrast, ompK37 mutations were associated with lower carbapenem MICs, and extended spectrum β-lactamase genes were not associated with higher or lower MICs in carbapenem-resistant K. pneumoniae. A machine learning approach based on the whole-genome sequences of these isolates did not result in a substantial improvement in prediction of isolates with high or low MICs. These results build upon previous findings suggesting that multiple factors influence the overall carbapenem resistance levels in carbapenem-resistant K. pneumoniae isolates.

IMPORTANCEKlebsiella pneumoniae can cause severe infections in the blood, urinary tract, and lungs. Resistance to carbapenems in K. pneumoniae is an urgent public health threat, since it can make these isolates difficult to treat. While individual contributors to carbapenem resistance in K. pneumoniae have been studied, few reports explore their combined effects in clinical isolates. We sequenced 166 clinical carbapenem-resistant K. pneumoniae isolates to evaluate the contribution of known genes to carbapenem MICs and to try to identify novel genes associated with higher carbapenem MICs. The bla_KPC copy number and some outer membrane porin gene mutations were associated with higher carbapenem MICs. In contrast, mutations in one specific porin, ompK37, were associated with lower carbapenem MICs. Machine learning did not result in a substantial improvement in the prediction of carbapenem resistance nor did it identify novel genes associated with carbapenem resistance. These findings enhance our understanding of the many contributors to carbapenem resistance in K. pneumoniae.

Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning

The advent of inexpensive and rapid sequencing technologies has allowed bacterial whole-genome sequences to be generated at an unprecedented pace. This wealth of information has revealed an unanticipated degree of strain-to-strain genetic diversity within many bacterial species. Awareness of this genetic heterogeneity has corresponded with a greater appreciation of intraspecies variation in virulence. A number of comparative genomic strategies have been developed to link these genotypic and pathogenic differences with the aim of discovering novel virulence factors. Here, we review recent advances in comparative genomic approaches to identify bacterial virulence determinants, with a focus on genome-wide association studies and machine learning.

Phenotypic and Genomic Variability of Serial Peri-Lung Transplantation Pseudomonas aeruginosa Isolates From Cystic Fibrosis Patients

Cystic fibrosis (CF) represents one of the major genetic and chronic lung diseases affecting Caucasians of European descent. Patients with CF suffer from recurring infections that lead to further damage of the lungs. Pulmonary infection due to Pseudomonas aeruginosa is most prevalent, further increasing CF-related mortality. The present study describes the phenotypic and genotypic variations among 36 P. aeruginosa isolates obtained serially from a non-CF and five CF patients before, during and after lung transplantation (LTx). The classical and genomic investigation of these isolates revealed a common mucoid phenotype and only subtle differences in the genomes. Isolates originating from an individual patient shared ≥98.7% average nucleotide identity (ANI). However, when considering isolates from different patients, substantial variations in terms of sequence type (ST), virulence factors and antimicrobial resistance (AMR) genes were observed. Whole genome multi-locus sequence typing (MLST) confirmed the presence of unique STs per patient regardless of the time from LTx. It was supported by the monophyletic clustering found in the genome-wide phylogeny. The antibiogram shows that ≥91.6% of the isolates were susceptible to amikacin, colistin and tobramycin. For other antibiotics from the panel, isolates frequently showed resistance. Alternatively, a comparative analysis of the 36 P. aeruginosa isolates with 672 strains isolated from diverse ecologies demonstrated clustering of the CF isolates according to the LTx patients from whom they were isolated. We observed that despite LTx and associated measures, all patients remained persistently colonized with similar isolates. The present study shows how whole genome sequencing (WGS) along with phenotypic analysis can help us understand the evolution of P. aeruginosa over time especially its antibiotic resistance.