Bacterial persisters in long-term infection: Emergence and fitness in a complex host environment.

Despite intensive antibiotic treatment, Pseudomonas aeruginosa often persists in the airways of cystic fibrosis (CF) patients for decades, and can do so without antibiotic resistance development. Using high-throughput screening assays of bacterial survival after treatment with high concentrations of ciprofloxacin, we have determined the prevalence of persisters in a large patient cohort using 460 longitudinal isolates of P. aeruginosa from 39 CF patients. Isolates were classed as high persister variants (Hip) if they regrew following antibiotic treatment in at least 75% of the experimental replicates. Strain genomic data, isolate phenotyping, and patient treatment records were integrated in a lineage-based analysis of persister formation and clinical impact. In total, 19% of the isolates were classified as Hip and Hip emergence increased over lineage colonization time within 22 Hip+ patients. Most Hip+ lineages produced multiple Hip isolates, but few Hip+ lineages were dominated by Hip. While we observed no strong signal of adaptive genetic convergence within Hip isolates, they generally emerged in parallel or following the development of ciprofloxacin resistance and slowed growth. Transient lineages were majority Hip-, while strains that persisted over a clinically diagnosed 'eradication' period were majority Hip+. Patients received indistinguishable treatment regimens before Hip emergence, but Hip+ patients overall were treated significantly more than Hip- patients, signaling repeated treatment failure. When subjected to in vivo-similar antibiotic dosing, a Hip isolate survived better than a non-Hip in a structured biofilm environment. In sum, the Hip phenotype appears to substantially contribute to long-term establishment of a lineage in the CF lung environment. Our results argue against the existence of a single dominant molecular mechanism underlying bacterial antibiotic persistence. We instead show that many routes, both phenotypic and genetic, are available for persister formation and consequent increases in strain fitness and treatment failure in CF airways.

Omics-based tracking of Pseudomonas aeruginosa persistence in "eradicated" cystic fibrosis patients.

Whenever Pseudomonas aeruginosa is cultured from cystic fibrosis (CF) patient airways, the primary goal is eradication by antibiotic therapy. Success is defined by ≥6 months of negative bacterial airway cultures. However, we suspect that P. aeruginosa persists in airways without clinical detection for long periods.Out of 298 P. aeruginosa-infected Copenhagen CF patients, we identified 80 with complete P. aeruginosa monitoring records and measured their maximum P. aeruginosa-free eradication periods (MEP). Isolates from 72 patients were whole-genome sequenced (n=567) and clone typed. Select isolate relatedness was examined through phylogenetic analysis and phenotypic multivariate modelling.69 (86%) patients exhibited eradication in the monitoring period (2002-2018). Sequenced isolates bridged the MEP of 42 patients, and the same clone type persisted over the MEP in 18 (43%) patients. Patients with failed eradication were on average treated more intensively with antibiotics, but this may be linked to their more severe pre-MEP infection trajectories. Of the 42 patients, 26 also had sinus surgery; the majority (n=15) showed MEPs adjacent to surgery, and only five had persisting clone types. Importantly, combined phylogenetic-phenomic evaluation suggests that persisting clone types are a result of re-emergence of the same strain rather than re-infection from the environment, and similar relatedness is exhibited by paired lower and upper airway samples and in transmission cases.In conclusion, nearly half of CF patients with supposed eradication may not truly be cleared of their original bacteria according to omics-based monitoring. This distinct cohort that is persistently infected would probably benefit from tailored antibiotic therapy.

Transmission and Antibiotic Resistance of Achromobacter in Cystic Fibrosis.

Achromobacter species are increasingly being detected in patients with cystic fibrosis (CF), and this emerging pathogen is associated with antibiotic resistance and more-severe disease outcomes. Nonetheless, little is known about the extent of transmission and antibiotic resistance development in Achromobacter infections. We sequenced the genomes of 101 Achromobacter clinical isolates (identified as Achromobacter xylosoxidans based on matrix-assister laser desorption ionization-time of flight [MALDI-TOF] or API N20 typing) collected from 51 patients with CF-the largest longitudinal data set to date. We performed phylogenetic analysis on the genomes and combined this with epidemiological and antibiotic resistance data to identify patient-to-patient transmission and the development of antibiotic resistance. We confirmed that the MALDI-TOF or API N20 method was not sufficient for Achromobacter species-level typing and that the population of Achromobacter isolates was composed of five different species, among which A. xylosoxidans accounted for 52% of infections. Most patients were infected by unique Achromobacter clone types; nonetheless, suspected patient-to-patient transmission cases identified by shared clone types were observed in 35% (n = 18) of patients. In 15 of 16 cases, the suspected transmissions were further supported by genome- or clinic visit-based epidemiological analysis. Finally, we found that resistance developed over time. We show that whole-genome sequencing (WGS) is essential for Achromobacter species typing and identification of patient-to-patient transmission, which was revealed for Achromobacter ruhlandii, A. xylosoxidans, and, for the first time, Achromobacter insuavis Furthermore, we show that the development of antibiotic resistance is associated with chronic Achromobacter infections. Our findings emphasize that transmission and antibiotic resistance should be considered in future treatment strategies.

Genotypic and phenotypic analyses of a Pseudomonas aeruginosa chronic bronchiectasis isolate reveal differences from cystic fibrosis and laboratory strains.

BACKGROUND: Pseudomonas aeruginosa is an environmentally ubiquitous Gram-negative bacterium and important opportunistic human pathogen, causing severe chronic respiratory infections in patients with underlying conditions such as cystic fibrosis (CF) or bronchiectasis. In order to identify mechanisms responsible for adaptation during bronchiectasis infections, a bronchiectasis isolate, PAHM4, was phenotypically and genotypically characterized. RESULTS: This strain displays phenotypes that have been associated with chronic respiratory infections in CF including alginate over-production, rough lipopolysaccharide, quorum-sensing deficiency, loss of motility, decreased protease secretion, and hypermutation. Hypermutation is a key adaptation of this bacterium during the course of chronic respiratory infections and analysis indicates that PAHM4 encodes a mutated mutS gene responsible for a ~1,000-fold increase in mutation rate compared to wild-type laboratory strain P. aeruginosa PAO1. Antibiotic resistance profiles and sequence data indicate that this strain acquired numerous mutations associated with increased resistance levels to ß-lactams, aminoglycosides, and fluoroquinolones when compared to PAO1. Sequencing of PAHM4 revealed a 6.38 Mbp genome, 5.9 % of which were unrecognized in previously reported P. aeruginosa genome sequences. Transcriptome analysis suggests a general down-regulation of virulence factors, while metabolism of amino acids and lipids is up-regulated when compared to PAO1 and metabolic modeling identified further potential differences between PAO1 and PAHM4. CONCLUSIONS: This work provides insights into the potential differential adaptation of this bacterium to the lung of patients with bronchiectasis compared to other clinical settings such as cystic fibrosis, findings that should aid the development of disease-appropriate treatment strategies for P. aeruginosa infections.

Comparative metabolic systems analysis of pathogenic Burkholderia.

Burkholderia cenocepacia and Burkholderia multivorans are opportunistic drug-resistant pathogens that account for the majority of Burkholderia cepacia complex infections in cystic fibrosis patients and also infect other immunocompromised individuals. While they share similar genetic compositions, B. cenocepacia and B. multivorans exhibit important differences in pathogenesis. We have developed reconciled genome-scale metabolic network reconstructions of B. cenocepacia J2315 and B. multivorans ATCC 17616 in parallel (designated iPY1537 and iJB1411, respectively) to compare metabolic abilities and contextualize genetic differences between species. The reconstructions capture the metabolic functions of the two species and give insight into similarities and differences in their virulence and growth capabilities. The two reconstructions have 1,437 reactions in common, and iPY1537 and iJB1411 have 67 and 36 metabolic reactions unique to each, respectively. After curating the extensive reservoir of metabolic genes in Burkholderia, we identified 6 genes essential to growth that are unique to iPY1513 and 13 genes uniquely essential to iJB1411. The reconstructions were refined and validated by comparing in silico growth predictions to in vitro growth capabilities of B. cenocepacia J2315, B. cenocepacia K56-2, and B. multivorans ATCC 17616 on 104 carbon sources. Overall, we identified functional pathways that indicate B. cenocepacia can produce a wider array of virulence factors compared to B. multivorans, which supports the clinical observation that B. cenocepacia is more virulent than B. multivorans. The reconciled reconstructions provide a framework for generating and testing hypotheses on the metabolic and virulence capabilities of these two related emerging pathogens.

Pseudomonas aeruginosa adaptation and evolution in patients with cystic fibrosis.

Intense genome sequencing of Pseudomonas aeruginosa isolates from cystic fibrosis (CF) airways has shown inefficient eradication of the infecting bacteria, as well as previously undocumented patient-to-patient transmission of adapted clones. However, genome sequencing has limited potential as a predictor of chronic infection and of the adaptive state during infection, and thus there is increasing interest in linking phenotypic traits to the genome sequences. Phenotypic information ranges from genome-wide transcriptomic analysis of patient samples to determination of more specific traits associated with metabolic changes, stress responses, antibiotic resistance and tolerance, biofilm formation and slow growth. Environmental conditions in the CF lung shape both genetic and phenotypic changes of P. aeruginosa during infection. In this Review, we discuss the adaptive and evolutionary trajectories that lead to early diversification and late convergence, which enable P. aeruginosa to succeed in this niche, and we point out how knowledge of these biological features may be used to guide diagnosis and therapy.

Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Evolutionary highways to persistent bacterial infection.

Persistent infections require bacteria to evolve from their naïve colonization state by optimizing fitness in the host via simultaneous adaptation of multiple traits, which can obscure evolutionary trends and complicate infection management. Accordingly, here we screen 8 infection-relevant phenotypes of 443 longitudinal Pseudomonas aeruginosa isolates from 39 young cystic fibrosis patients over 10 years. Using statistical modeling, we map evolutionary trajectories and identify trait correlations accounting for patient-specific influences. By integrating previous genetic analyses of 474 isolates, we provide a window into early adaptation to the host, finding: (1) a 2-3 year timeline of rapid adaptation after colonization, (2) variant "naïve" and "adapted" states reflecting discordance between phenotypic and genetic adaptation, (3) adaptive trajectories leading to persistent infection via three distinct evolutionary modes, and (4) new associations between phenotypes and pathoadaptive mutations. Ultimately, we effectively deconvolute complex trait adaptation, offering a framework for evolutionary studies and precision medicine in clinical microbiology.

Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis.

Virulence-linked pathways in opportunistic pathogens are putative therapeutic targets that may be associated with less potential for resistance than targets in growth-essential pathways. However, efficacy of virulence-linked targets may be affected by the contribution of virulence-related genes to metabolism. We evaluate the complex interrelationships between growth and virulence-linked pathways using a genome-scale metabolic network reconstruction of Pseudomonas aeruginosa strain PA14 and an updated, expanded reconstruction of P. aeruginosa strain PAO1. The PA14 reconstruction accounts for the activity of 112 virulence-linked genes and virulence factor synthesis pathways that produce 17 unique compounds. We integrate eight published genome-scale mutant screens to validate gene essentiality predictions in rich media, contextualize intra-screen discrepancies and evaluate virulence-linked gene distribution across essentiality datasets. Computational screening further elucidates interconnectivity between inhibition of virulence factor synthesis and growth. Successful validation of selected gene perturbations using PA14 transposon mutants demonstrates the utility of model-driven screening of therapeutic targets.

Whole-genome metabolic network reconstruction and constraint-based modeling.

With the advent of modern high-throughput genomics, there is a significant need for genome-scale analysis techniques that can assist in complex systems analysis. Metabolic genome-scale network reconstructions (GENREs) paired with constraint-based modeling are an efficient method to integrate genomics, transcriptomics, and proteomics to conduct organism-specific analysis. This text explains key steps in the GENRE construction process and several methods of constraint-based modeling that can help elucidate basic life processes and development of disease treatment, bioenergy solutions, and industrial bioproduction applications.