Genomics of antibiotic-resistance prediction in Pseudomonas aeruginosa

Microbial cell-free DNA-sequencing as an addition to conventional diagnostics in neonatal sepsis

BACKGROUND

Bloodstream infections remain a challenge for neonatologists, as traditional culture-based methods are time-consuming and rely on adequate blood volume. Next-generation sequencing (NGS) offers an alternative, as it can identify microbial cell-free DNA (mcfDNA) in a small blood sample, providing rapid pathogen detection. This study aimed to assess the diagnostic performance of DISQVER®-NGS compared to blood cultures in neonatal patients with suspected sepsis.

METHODS

In neonates with suspected sepsis, blood cultures and samples for NGS were prospectively collected. Patients were divided into four categories: 1) sepsis, blood culture positive, 2) clinical sepsis, culture negative, 3) suspected sepsis, 4) validation cohort.

RESULTS

NGS detected bacterial, viral or fungal mcfDNA in 24 of 82 samples. Blood cultures were collected in 46 of 84 patients (15/46 positive). DISQVER® correctly identified pathogens in 9/15 patients with a positive blood culture, two with intrinsic resistance to their antibiotic regimen. In seven samples NGS reported the mcfDNA of bacteria that could have theoretically grown in culture but did not.

CONCLUSIONS

NGS may enhance sensitivity in sepsis diagnostics by detecting mcfDNA in neonates with suspected sepsis. Interpreting NGS results requires correlation with clinical data, laboratory values, and routine microbiological tests for a comprehensive understanding of the patient's condition.

IMPACT

Conventional blood culture methods have limitations in accuracy and turnaround time. The study aimed to investigate the diagnostic performance of the Next-Generation Sequencing method DISQVER® compared to traditional blood cultures in neonatal patients with suspected sepsis. Our findings suggest that NGS has the potential to augment the precision of conventional diagnostic techniques, can lead to improved detection of pathogens and targeted treatment approaches in neonatal sepsis. It is emphasized that further validation and integration with clinical and microbiological data are required to ensure optimal clinical utility.

Tracking of Bacteriophage Predation on Pseudomonas aeruginosa Using a New Radiofrequency Biofilm Sensor

Confronting the challenge of biofilm resistance and widespread antimicrobial resistance (AMR), this study emphasizes the need for innovative monitoring methods and explores the potential of bacteriophages against bacterial biofilms. Traditional methods, like optical density (OD) measurements and confocal microscopy, crucial in studying biofilm-virus interactions, often lack real-time monitoring and early detection capabilities, especially for biofilm formation and low bacterial concentrations. Addressing these gaps, we developed a new real-time, label-free radiofrequency sensor for monitoring bacteria and biofilm growth. The sensor, an open-ended coaxial probe, offers enhanced monitoring of bacterial development stages. Tested on a biological model of bacteria and bacteriophages, our results indicate the limitations of traditional OD measurements, influenced by factors like sedimented cell fragments and biofilm formation on well walls. While confocal microscopy provides detailed 3D biofilm architecture, its real-time monitoring application is limited. Our novel approach using radio frequency measurements (300 MHz) overcomes these shortcomings. It facilitates a finer analysis of the dynamic interaction between bacterial populations and phages, detecting real-time subtle changes. This method reveals distinct phases and breakpoints in biofilm formation and virion interaction not captured by conventional techniques. This study underscores the sensor's potential in detecting irregular viral activity and assessing the efficacy of anti-biofilm treatments, contributing significantly to the understanding of biofilm dynamics. This research is vital in developing effective monitoring tools, guiding therapeutic strategies, and combating AMR.

Responses of antibiotic resistance genes in the enhanced biological phosphorus removal system under various antibiotics: Mechanisms and implications

The effects of antibiotics on the proliferation of antibiotic resistant genes (ARGs) in WWTPs have drawn great attention in recent years. The effects of antibiotics on ARGs in the enhanced biological phosphorus removal (EBPR) system and its mechanisms, however, are still not well understood. In this study, EBPR systems were constructed using activated sludge to investigate the effects of ten commonly detected antibiotics in the environment on the proliferation of ARGs and the mechanisms involved. The results showed that the total abundance of ARGs increased to varying degrees with the addition of different antibiotics (0.05 mmol/L), and the top 30 ARGs increased by 271.1 % to 370.0 %. Mobile genetic elements (MGEs), functional modules, and the bacteria community were consistently related to the changes in ARGs. Refractory antibiotics, in particular, have a stronger promoting effect on transduction in the EBPR system. The insertion sequence common region (ISCR) and transposon (Tnp) were identified as crucial factors in the proliferation of ARGs. Moreover, the risk of polyphosphate accumulating organisms (PAOs) carrying ARGs in the presence of antibiotics should not be ignored. Our findings emphasize the potential efficacy of employing strategies that target the reduction of MGEs, regulation of cellular communication, and management of microbial communities to effectively mitigate the risks associated with ARGs.

Genome-Scale Characterization of Mycobacterium abscessus Complex Isolates from Portugal

The Mycobacterium abscessus complex (MABC) is an emerging, difficult to treat, multidrug-resistant nontuberculous mycobacteria responsible for a wide spectrum of infections and associated with an increasing number of cases worldwide. Dominant circulating clones (DCCs) of MABC have been genetically identified as groups of strains associated with higher prevalence, higher levels of antimicrobial resistance, and worse clinical outcomes. To date, little is known about the genomic characteristics of MABC species circulating in Portugal. Here, we examined the genetic diversity and antimicrobial resistance profiles of 30 MABC strains isolated between 2014 and 2022 in Portugal. The genetic diversity of circulating MABC strains was assessed through a gene-by-gene approach (wgMLST), allowing their subspecies differentiation and the classification of isolates into DCCs. Antimicrobial resistance profiles were defined using phenotypic, molecular, and genomic approaches. The majority of isolates were resistant to at least two antimicrobials, although a poor correlation between phenotype and genotype data was observed. Portuguese genomes were highly diverse, and data suggest the existence of MABC lineages with potential international circulation or cross-border transmission. This study highlights the genetic diversity and antimicrobial resistance profile of circulating MABC isolates in Portugal while representing the first step towards the implementation of a genomic-based surveillance system for MABC at the Portuguese NIH.

Prioritization of Critical Factors for Surveillance of the Dissemination of Antibiotic Resistance in Pseudomonas aeruginosa: A Systematic Review

Pseudomonas aeruginosa is the primary opportunistic human pathogen responsible for a range of acute and chronic infections; it poses a significant threat to immunocompromised patients and is the leading cause of morbidity and mortality for nosocomial infections. Its high resistance to a diverse array of antimicrobial agents presents an urgent health concern. Among the mechanisms contributing to resistance in P. aeruginosa, the horizontal acquisition of antibiotic resistance genes (ARGs) via mobile genetic elements (MGEs) has gained recognition as a substantial concern in clinical settings, thus indicating that a comprehensive understanding of ARG dissemination within the species is strongly required for surveillance. Here, two approaches, including a systematic literature analysis and a genome database survey, were employed to gain insights into ARG dissemination. The genome database enabled scrutinizing of all the available sequence information and various attributes of P. aeruginosa isolates, thus providing an extensive understanding of ARG dissemination within the species. By integrating both approaches, with a primary focus on the genome database survey, mobile ARGs that were linked or correlated with MGEs, important sequence types (STs) carrying diverse ARGs, and MGEs responsible for ARG dissemination were identified as critical factors requiring strict surveillance. Although human isolates play a primary role in dissemination, the importance of animal and environmental isolates has also been suggested. In this study, 25 critical mobile ARGs, 45 critical STs, and associated MGEs involved in ARG dissemination within the species, are suggested as critical factors. Surveillance and management of these prioritized factors across the One Health sectors are essential to mitigate the emergence of multidrug-resistant (MDR) and extensively resistant (XDR) P. aeruginosa in clinical settings.

Mutation Analysis in Regulator DNA-Binding Regions for Antimicrobial Efflux Pumps in 17,000 Pseudomonas aeruginosa Genomes

Mutations leading to upregulation of efflux pumps can produce multiple drug resistance in the pathogen Pseudomonas aeruginosa. Changes in their DNA binding regions, i.e., palindromic operators, can compromise pump depression and subsequently enhance resistance against several antibacterials and biocides. Here, we have identified (pseudo)palindromic repeats close to promoters of genes encoding 13 core drug-efflux pumps of P. aeruginosa. This framework was applied to detect mutations in these repeats in 17,292 genomes. Eighty-nine percent of isolates carried at least one mutation. Eight binary genetic properties potentially related to expression were calculated for mutations. These included palindromicity reduction, mutation type, positioning within the repeat and DNA-bending shift. High-risk ST298, ST308 and ST357 clones commonly carried four conserved mutations while ST175 and the cystic fibrosis-linked ST649 clones showed none. Remarkably, a T-to-C transition in the fourth position of the upstream repeat for mexEF-oprN was nearly exclusive of the high-risk ST111 clone. Other mutations were associated with high-risk sublineages using sample geotemporal metadata. Moreover, 1.5% of isolates carried five or more mutations suggesting they undergo an alternative program for regulation of their effluxome. Overall, P. aeruginosa shows a wide range of operator mutations with a potential effect on efflux pump expression and antibiotic resistance.

Potassium Chloride, Sodium Lactate and Sodium Citrate Impaired the Antimicrobial Resistance and Virulence of Pseudomonas aeruginosa NT06 Isolated from Fish

Sodium chloride (NaCl) is a commonly used additive in minimally processed fish-based products. The addition of NaCl to fish products and packaging in a modified atmosphere is usually efficient with regard to limiting the occurrence of the aquatic environmental pathogen Pseudomonas aeruginosa. Given the negative effects of excess NaCl in the diet, there is a growing demand to reduce NaCl in food products with safer substituents, but the knowledge of their impact on antibiotic resistant P. aeruginosa is limited. This study aimed to evaluate the physiological and transcriptome characteristics of P. aeruginosa NT06 isolated from fish and to determine the effect of selected concentrations of alternative NaCl compounds (KCl/NaL/NaC) on the P. aeruginosa NT06 virulence phenotype and genotype. In the study, among the isolated microorganisms, P. aeruginosa NT06 showed the highest antibiotic resistance (to ampicillin, ceftriaxone, nalidixic acid, and norfloxacin) and the ability to grow at 4 °C. The Comprehensive Antibiotic Resistance Database (CARD) and the Virulence Factor Database (VFDB) revealed the presence of 24 and 134 gene products assigned to AMR and VF in the P. aeruginosa NT06 transcriptome, respectively. KCl, KCl/NaL and KCl/NaL/NaC inhibited pyocyanin biosynthesis, elastase activity, and protease activity from 40 to 77%. The above virulence phenotypic observations were confirmed via RT-qPCR analyses, which showed that all tested AMR and VF genes were the most downregulated due to KCl/NaL/NaC treatment. In conclusion, this study provides insight into the potential AMR and VF among foodborne P. aeruginosa and the possible impairment of those features by KCl, NaL, and NaC, which exert synergistic effects and can be used in minimally processed fish-based products.

Mechanisms of Antibiotic and Biocide Resistance That Contribute to Pseudomonas aeruginosa Persistence in the Hospital Environment

Pseudomonas aeruginosa is an opportunistic bacterial pathogen responsible for multiple hospital- and community-acquired infections, both in human and veterinary medicine. P. aeruginosa persistence in clinical settings is worrisome and is a result of its remarkable flexibility and adaptability. This species exhibits several characteristics that allow it to thrive under different environmental conditions, including the ability to colonize inert materials such as medical equipment and hospital surfaces. P. aeruginosa presents several intrinsic mechanisms of defense that allow it to survive external aggressions, but it is also able to develop strategies and evolve into multiple phenotypes to persevere, which include antimicrobial-tolerant strains, persister cells, and biofilms. Currently, these emergent pathogenic strains are a worldwide problem and a major concern. Biocides are frequently used as a complementary/combination strategy to control the dissemination of P. aeruginosa-resistant strains; however, tolerance to commonly used biocides has also already been reported, representing an impediment to the effective elimination of this important pathogen from clinical settings. This review focuses on the characteristics of P. aeruginosa responsible for its persistence in hospital environments, including those associated with its antibiotic and biocide resistance ability.

Ceftazidime resistance in Pseudomonas aeruginosa is multigenic and complex

Pseudomonas aeruginosa causes a wide range of severe infections. Ceftazidime, a cephalosporin, is a key antibiotic for treating infections but a significant proportion of isolates are ceftazidime-resistant. The aim of this research was to identify mutations that contribute to resistance, and to quantify the impacts of individual mutations and mutation combinations. Thirty-five mutants with reduced susceptibility to ceftazidime were evolved from two antibiotic-sensitive P. aeruginosa reference strains PAO1 and PA14. Mutations were identified by whole genome sequencing. The evolved mutants tolerated ceftazidime at concentrations between 4 and 1000 times that of the parental bacteria, with most mutants being ceftazidime resistant (minimum inhibitory concentration [MIC] ≥ 32 mg/L). Many mutants were also resistant to meropenem, a carbapenem antibiotic. Twenty-eight genes were mutated in multiple mutants, with dacB and mpl being the most frequently mutated. Mutations in six key genes were engineered into the genome of strain PAO1 individually and in combinations. A dacB mutation by itself increased the ceftazidime MIC by 16-fold although the mutant bacteria remained ceftazidime sensitive (MIC < 32 mg/L). Mutations in ampC, mexR, nalC or nalD increased the MIC by 2- to 4-fold. The MIC of a dacB mutant was increased when combined with a mutation in ampC, rendering the bacteria resistant, whereas other mutation combinations did not increase the MIC above those of single mutants. To determine the clinical relevance of mutations identified through experimental evolution, 173 ceftazidime-resistant and 166 sensitive clinical isolates were analysed for the presence of sequence variants that likely alter function of resistance-associated genes. dacB and ampC sequence variants occur most frequently in both resistant and sensitive clinical isolates. Our findings quantify the individual and combinatorial effects of mutations in different genes on ceftazidime susceptibility and demonstrate that the genetic basis of ceftazidime resistance is complex and multifactorial.

Glucose-Potentiated Amikacin Killing of Cefoperazone/Sulbactam Resistant Pseudomonas aeruginosa

Multidrug-resistant Pseudomonas aeruginosa has become one of global threat pathogens for human health due to insensitivity to antibiotics. Recently developed reprogramming metabolomics can identify biomarkers, and then, the biomarkers were used to revert the insensitivity and elevate antibiotic-mediated killing. Here, the methodology was used to study cefoperazone/sulbactam (SCF)-resistant P. aeruginosa (PA-R_SCF) and identified reduced glycolysis and pyruvate cycle, a recent clarified cycle providing respiratory energy in bacteria, as the most key enriched pathways and the depressed glucose as one of the most crucial biomarkers. Further experiments showed that the depression of glucose was attributed to reduction of glucose transport. However, exogenous glucose reverted the reduction to elevate intracellular glucose via activating glucose transport. The elevated glucose fluxed to the glycolysis, pyruvate cycle, and electron transport chain to promote downstream proton motive force (PMF). Consistently, exogenous glucose did not promote SCF-mediated elimination but potentiated aminoglycosides-mediated killing since aminoglycosides uptake is PMF-dependent, where amikacin was the best one. The glucose-potentiated amikacin-mediated killing was effective to both lab-evolved PA-R_SCF and clinical multidrug-resistant P. aeruginosa. These results reveal the depressed glucose uptake causes the reduced intracellular glucose and expand the application of metabolome-reprogramming on selecting conventional antibiotics to achieve the best killing efficacy.

β-lactam Resistance in Pseudomonas aeruginosa: Current Status, Future Prospects

Pseudomonas aeruginosa is a major opportunistic pathogen, causing a wide range of acute and chronic infections. β-lactam antibiotics including penicillins, carbapenems, monobactams, and cephalosporins play a key role in the treatment of P. aeruginosa infections. However, a significant number of isolates of these bacteria are resistant to β-lactams, complicating treatment of infections and leading to worse outcomes for patients. In this review, we summarize studies demonstrating the health and economic impacts associated with β-lactam-resistant P. aeruginosa. We then describe how β-lactams bind to and inhibit P. aeruginosa penicillin-binding proteins that are required for synthesis and remodelling of peptidoglycan. Resistance to β-lactams is multifactorial and can involve changes to a key target protein, penicillin-binding protein 3, that is essential for cell division; reduced uptake or increased efflux of β-lactams; degradation of β-lactam antibiotics by increased expression or altered substrate specificity of an AmpC β-lactamase, or by the acquisition of β-lactamases through horizontal gene transfer; and changes to biofilm formation and metabolism. The current understanding of these mechanisms is discussed. Lastly, important knowledge gaps are identified, and possible strategies for enhancing the effectiveness of β-lactam antibiotics in treating P. aeruginosa infections are considered.

Profile of Bacteria with ARGs Among Real-World Samples from ICU Admission Patients with Pulmonary Infection Revealed by Metagenomic NGS

Background

Treatment of pulmonary infections in the intensive care unit (ICU) represents a great challenge, especially infections caused by antibiotic resistance pathogens. A thorough and up-to-date knowledge of the local spectrum of antibiotic resistant bacteria can improve the antibiotic treatment efficiency. In this study, we aimed to reveal the profile of bacteria with antibiotic resistance genes (ARGs) in real-world samples from ICU admission patients with pulmonary infection in Mainland, China, by metagenomic next-generation sequencing (mNGS).

Methods

A total of 504 different types of clinical samples from 452 ICU admission patients with pulmonary infection were detected by mNGS analysis.

Results

A total of 485 samples from 434 patients got successful mNGS results. Among 434 patients, one or more bacteria with ARGs were detected in 192 patients (44.24%, 192/434), and ≥2 bacteria with ARGs were detected in 85 (19.59%, 85/434) patients. The predominant detected bacteria were Corynebacterium striatum (C. striatum) (11.76%, 51/434), Acinetobacter baumannii (A. baumannii) (11.52%, 50/434) and Enterococcus faecium (E. faecium) (8.99%, 39/434). ermX conferred resistance to MSL_B and cmx to phenicol were the only two ARGs detected in C. striatum; in A. baumannii, most of ARGs were resistance-nodulation-division (RND)-type efflux pumps genes, which conferred resistance to multi-drug; ermB conferred resistance to MSL_B and efmA to multi-drug were the predominant ARGs in E. faecium. Bacteria with ARGs were detected in 50% (140/280) bronchoalveolar lavage fluid (BALF) and 50.5% (48/95) sputum samples, which were significantly higher than in blood and cerebrospinal fluid (CSF) samples.

Conclusion

High level of bacteria with ARGs was observed in clinical samples, especially BALF and sputum samples from ICU admission patients with pulmonary infection in Mainland, China. And C. striatum resistant to MSL_B and/or phenicol, multi-drug resistance A. baumannii and E. faecium were the lead bacteria.

The Pseudomonas aeruginosa whole genome sequence: A 20th anniversary celebration

Toward the end of August 2000, the 6.3 Mbp whole genome sequence of Pseudomonas aeruginosa strain PAO1 was published. With 5570 open reading frames (ORFs), PAO1 had the largest microbial genome sequenced up to that point in time-including a large proportion of metabolic, transport and antimicrobial resistance genes supporting its ability to colonize diverse environments. A remarkable 9% of its ORFs were predicted to encode proteins with regulatory functions, providing new insight into bacterial network complexity as a function of network size. In this celebratory article, we fast forward 20 years, and examine how access to this resource has transformed our understanding of P. aeruginosa. What follows is more than a simple review or commentary; we have specifically asked some of the leaders in the field to provide personal reflections on how the PAO1 genome sequence, along with the Pseudomonas Community Annotation Project (PseudoCAP) and Pseudomonas Genome Database (pseudomonas.com), have contributed to the many exciting discoveries in this field. In addition to bringing us all up to date with the latest developments, we also ask our contributors to speculate on how the next 20 years of Pseudomonas research might pan out.

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.

Label-free quantitative proteomics identifies unique proteomes of clinical isolates of the Liverpool Epidemic Strain of Pseudomonas aeruginosa and laboratory strain PAO1

PURPOSE

Comparative genomics and phenotypic assays have shown that antibiotic resistance profiles differ among clinical isolates of Pseudomonas aeruginosa and that genotype-phenotype associations are difficult to establish for resistance phenotypes based on these comparisons alone.

EXPERIMENTAL DESIGN

Here, we used label-free quantitative proteomics to compare two isolates of the Liverpool Epidemic Strain (LES) of P. aeruginosa, LESlike1 and LESB58, and the common laboratory strain P. aeruginosa PAO1 to more accurately predict functional differences between strains.

RESULTS

Our results show that the proteomes of the LES isolates are more similar to each other than to PAO1; however, a number of differences were observed in the abundance of proteins involved in quorum sensing, virulence, and antibiotic resistance, including in the comparison of LESlike1 and LESB58. Additionally, the proteomic data revealed a higher abundance of proteins involved in polymyxin and aminoglycoside resistance in LESlike1. Minimum inhibitory concentration assays showed that LESlike1 had up to 128-fold higher resistance to antibiotics from these classes.

CONCLUSIONS

These findings provide an example of the ability of proteomic data to complement genotypic and phenotypic studies to understand resistance in clinical isolates.

CLINICAL RELEVANCE

P. aeruginosa is a predominant pathogen in chronic lung infections in individuals with cystic fibrosis (CF). LES isolates are capable of transferring between CF patients and have been associated with increased hospital visits and antibiotic treatments.

Predictive Antibiotic Susceptibility Testing by Next-Generation Sequencing for Periprosthetic Joint Infections: Potential and Limitations

Joint replacement surgeries are one of the most frequent medical interventions globally. Infections of prosthetic joints are a major health challenge and typically require prolonged or even indefinite antibiotic treatment. As multidrug-resistant pathogens continue to rise globally, novel diagnostics are critical to ensure appropriate treatment and help with prosthetic joint infections (PJI) management. To this end, recent studies have shown the potential of molecular methods such as next-generation sequencing to complement established phenotypic, culture-based methods. Together with advanced bioinformatics approaches, next-generation sequencing can provide comprehensive information on pathogen identity as well as antimicrobial susceptibility, potentially enabling rapid diagnosis and targeted therapy of PJIs. In this review, we summarize current developments in next generation sequencing based predictive antibiotic susceptibility testing and discuss potential and limitations for common PJI pathogens.

Gene-Gene Interactions Dictate Ciprofloxacin Resistance in Pseudomonas aeruginosa and Facilitate Prediction of Resistance Phenotype from Genome Sequence Data

Ciprofloxacin is one of the most widely used antibiotics for treating Pseudomonas aeruginosa infections. However, P. aeruginosa acquires mutations that confer ciprofloxacin resistance, making treatment more difficult. Resistance is multifactorial, with mutations in multiple genes influencing the resistance phenotype. However, the contributions of individual mutations and mutation combinations to the amounts of ciprofloxacin that P. aeruginosa can tolerate are not well understood. Engineering P. aeruginosa strain PAO1 to contain mutations in any one of the resistance-associated genes gyrA, nfxB, rnfC, parC, and parE showed that only gyrA mutations increased the MIC for ciprofloxacin. Mutations in parC and parE increased the MIC of a gyrA mutant, making the bacteria ciprofloxacin resistant. Mutations in nfxB and rnfC increased the MIC, conferring resistance, only if both were mutated in a gyrA background. Mutations in all of gyrA, nfxB, rnfC, and parC/E further increased the MIC. These findings reveal an epistatic network of gene-gene interactions in ciprofloxacin resistance. We used this information to predict ciprofloxacin resistance/susceptibility for 274 isolates of P. aeruginosa from their genome sequences. Antibiotic susceptibility profiles were predicted correctly for 84% of the isolates. The majority of isolates for which prediction was unsuccessful were ciprofloxacin resistant, demonstrating the involvement of additional as yet unidentified genes and mutations in resistance. Our data show that gene-gene interactions can play an important role in antibiotic resistance and can be successfully incorporated into models predicting resistance phenotype.

Cloning and Characterization of phzR Gene from Pseudomonas aeruginosa

A gene encoding phzR was isolated from a phenazine-producing bacterium Pseudomonas aeruginosa 2016NX1. This paper provided the full-length cDNA encoding phzR (GenBank Accession no., MW143078). The cDNA of the phzR contained an open reading frame (ORF) of 714 bp. The potential regulatory elements were predicted in the phzR promoter region. The deduced amino acid sequence of P. aeruginosa phzR showed significant homology to the known phzRs from different organisms. Gene overexpression analysis showed that the phenazine content was improved (44.39%) in comparison to wild-type 2016NX1.

Antibiotic Resistance and Phylogeny of Pseudomonas spp. Isolated over Three Decades from Chicken Meat in the Norwegian Food Chain

Pseudomonas is ubiquitous in nature and a predominant genus in many foods and food processing environments, where it primarily represents major food spoilage organisms. The food chain has also been reported to be a potential reservoir of antibiotic-resistant Pseudomonas. The purpose of the current study was to determine the occurrence of antibiotic resistance in psychrotrophic Pseudomonas spp. collected over a time span of 26 years from retail chicken in Norway and characterize their genetic diversity, phylogenetic distribution and resistance genes through whole-genome sequence analyses. Among the 325 confirmed Pseudomonas spp. isolates by 16S rRNA gene sequencing, antibiotic susceptibility profiles of 175 isolates to 12 antibiotics were determined. A subset of 31 isolates being resistant to ≥3 antibiotics were whole-genome sequenced. The isolates were dominated by species of the P. fluorescens lineage. Isolates susceptible to all antibiotics or resistant to ≥3 antibiotics comprised 20.6% and 24.1%, respectively. The most common resistance was to aztreonam (72.6%), colistin (30.2%), imipenem (25.6%) and meropenem (12.6%). Resistance properties appeared relatively stable over the 26-year study period but with taxa-specific differences. Whole-genome sequencing showed high genome variability, where isolates resistant to ≥3 antibiotics belonged to seven species. A single metallo-betalactmase gene (cphA) was detected, though intrinsic resistance determinants dominated, including resistance–nodulation (RND), ATP-binding cassette (ABC) and small multidrug resistance (Smr) efflux pumps. This study provides further knowledge on the distribution of psychrotrophic Pseudomonas spp. in chicken meat and their antibiotic resistance properties. Further monitoring should be encouraged to determine food as a source of antibiotic resistance and maintain the overall favorable situation with regard to antibiotic resistance in the Norwegian food chain.

Novel Molecular Markers Linked to Pseudomonas aeruginosa Epidemic High-Risk Clones

The population structure of Pseudomonas aeruginosa is panmictic-epidemic in nature, with the prevalence of some high-risk clones. These clones are often linked to virulence, antibiotic resistance, and more morbidity. The clonal success of these lineages has been linked to acquisition and spread of mobile genetic elements. The main aim of the study was to explore other molecular markers that explain their global success. A comprehensive set of 528 completely sequenced P. aeruginosa genomes was analyzed. The population structure was examined using Multilocus Sequence Typing (MLST). Strain relationships analysis and diversity analysis were performed using the geoBURST Full Minimum Spanning Tree (MST) algorithm and hierarchical clustering. A phylogenetic tree was constructed using the Unweighted Pair Group Method with Arithmetic mean (UPGMA) algorithm. A panel of previously investigated resistance markers were examined for their link to high-risk clones. A novel panel of molecular markers has been identified in relation to risky clones including armR, ampR, nalC, nalD, mexZ, mexS, gyrAT83I, gyrAD87N, nalCE153Q, nalCS46A, parCS87W, parCS87L, ampRG283E, ampRM288R, pmrALeu71Arg, pmrBGly423Cys, nuoGA890T, pstBE89Q, phoQY85F, arnAA170T, arnDG206C, and gidBE186A. In addition to mobile genetic elements, chromosomal variants in membrane proteins and efflux pump regulators can play an important role in the success of high-risk clones. Finding risk-associated markers during molecular surveillance necessitates applying more infection-control precautions.

Predicting antimicrobial resistance using conserved genes

A growing number of studies are using machine learning models to accurately predict antimicrobial resistance (AMR) phenotypes from bacterial sequence data. Although these studies are showing promise, the models are typically trained using features derived from comprehensive sets of AMR genes or whole genome sequences and may not be suitable for use when genomes are incomplete. In this study, we explore the possibility of predicting AMR phenotypes using incomplete genome sequence data. Models were built from small sets of randomly-selected core genes after removing the AMR genes. For Klebsiella pneumoniae, Mycobacterium tuberculosis, Salmonella enterica, and Staphylococcus aureus, we report that it is possible to classify susceptible and resistant phenotypes with average F1 scores ranging from 0.80-0.89 with as few as 100 conserved non-AMR genes, with very major error rates ranging from 0.11-0.23 and major error rates ranging from 0.10-0.20. Models built from core genes have predictive power in cases where the primary AMR mechanisms result from SNPs or horizontal gene transfer. By randomly sampling non-overlapping sets of core genes, we show that F1 scores and error rates are stable and have little variance between replicates. Although these small core gene models have lower accuracies and higher error rates than models built from the corresponding assembled genomes, the results suggest that sufficient variation exists in the core non-AMR genes of a species for predicting AMR phenotypes.

Antimicrobial Resistance, an Update from the Ward: Increased Incidence of New Potential Pathogens and Site of Infection-Specific Antibacterial Resistances

In order to monitor the spread of antimicrobial resistance, the European Union requires hospitals to be equipped with infection control centers. With this aim, we analyzed 1583 bacterial strains isolated from samples of different origin from patients with community-onset and nosocomial infections in a public tertiary University Hospital on the outskirts of Turin, Italy. Statistical analyses of the isolates (source, type) and their antimicrobial resistance (AMR) were performed. The survey revealed infections associated with bacterial species considered as not-commensal and not-pathogenic, hence potentially emerging as new threats for human health. Conversely to the general observation of nosocomial strains being more resistant to antibiotics compared to community-acquired strains, nosocomial strains isolated in this study were more resistant only to 1/42 tested antibiotics (tetracycline). By adopting an ecological approach, we observed that blood infections are associated with the broadest range of species compared to infections affecting other areas and we obtained clear indications on the antibiotics that should be preferred in the treatment of infections at specific body sites. Future investigations carried out on a larger geographical scale will clarify whether these indications are limited to the geographical region investigated over this study, or whether the same trends are visible at national or international level.

Novel therapeutic strategies for treating Pseudomonas aeruginosa infection

INTRODUCTION

Persistent infections caused by the superbug Pseudomonas aeruginosa and its resistance to multiple antimicrobial agents are huge threats to patients with cystic fibrosis as well as those with compromised immune systems. Multidrug-resistant P. aeruginosa has posed a major challenge to conventional antibiotics and therapeutic approaches, which show limited efficacy and cause serious side effects. The public demand for new antibiotics is enormous; yet, drug development pipelines have started to run dry with limited targets available for inventing new antibacterial drugs. Consequently, it is important to uncover potential therapeutic targets.

AREAS COVERED

The authors review the current state of drug development strategies that are promising in terms of the development of novel and potent drugs to treat P. aeruginosa infection.

EXPERT OPINION

The prevention of P. aeruginosa infection is increasingly challenging. Furthermore, targeting key virulence regulators has great potential for developing novel anti-P. aeruginosa drugs. Additional promising strategies include bacteriophage therapy, immunotherapies, and antimicrobial peptides. Additionally, the authors believe that in the coming years, the overall network of molecular regulatory mechanism of P. aeruginosa virulence will be fully elucidated, which will provide more novel and promising drug targets for treating P. aeruginosa infections.

Cherry picking by pseudomonads: After a century of research on canker, genomics provides insights into the evolution of pathogenicity towards stone fruits

Bacterial canker disease is a major limiting factor in the growing of cherry and other Prunus species worldwide. At least five distinct clades within the bacterial species complex Pseudomonas syringae are known to be causal agents of the disease. The different pathogens commonly coexist in the field. Reducing canker is a challenging prospect as the efficacy of chemical controls and host resistance may vary against each of the diverse clades involved. Genomic analysis has revealed that the pathogens use a variable repertoire of virulence factors to cause the disease. Significantly, strains of P. syringae pv. syringae possess more genes for toxin biosynthesis and fewer encoding type III effector proteins. There is also a shared pool of key effector genes present on mobile elements such as plasmids and prophages that may have roles in virulence. By contrast, there is evidence that absence or truncation of certain effector genes, such as hopAB, is characteristic of cherry pathogens. Here we highlight how recent research, underpinned by the earlier epidemiological studies, is allowing significant progress in our understanding of the canker pathogens. This fundamental knowledge, combined with emerging insights into host genetics, provides the groundwork for development of precise control measures and informed approaches to breed for disease resistance.

Multidrug Resistance (MDR) and Collateral Sensitivity in Bacteria, with Special Attention to Genetic and Evolutionary Aspects and to the Perspectives of Antimicrobial Peptides-A Review

Antibiotic poly-resistance (multidrug-, extreme-, and pan-drug resistance) is controlled by adaptive evolution. Darwinian and Lamarckian interpretations of resistance evolution are discussed. Arguments for, and against, pessimistic forecasts on a fatal “post-antibiotic era” are evaluated. In commensal niches, the appearance of a new antibiotic resistance often reduces fitness, but compensatory mutations may counteract this tendency. The appearance of new antibiotic resistance is frequently accompanied by a collateral sensitivity to other resistances. Organisms with an expanding open pan-genome, such as Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae, can withstand an increased number of resistances by exploiting their evolutionary plasticity and disseminating clonally or poly-clonally. Multidrug-resistant pathogen clones can become predominant under antibiotic stress conditions but, under the influence of negative frequency-dependent selection, are prevented from rising to dominance in a population in a commensal niche. Antimicrobial peptides have a great potential to combat multidrug resistance, since antibiotic-resistant bacteria have shown a high frequency of collateral sensitivity to antimicrobial peptides. In addition, the mobility patterns of antibiotic resistance, and antimicrobial peptide resistance, genes are completely different. The integron trade in commensal niches is fortunately limited by the species-specificity of resistance genes. Hence, we theorize that the suggested post-antibiotic era has not yet come, and indeed might never come.

Antibiotic Resistance Patterns of Pseudomonas spp. Isolated From Raw Milk Revealed by Whole Genome Sequencing

Psychrotrophic bacteria in raw milk are most well known for their spoilage potential and the economic losses they cause to the dairy industry. Food-related psychrotrophic bacteria are increasingly reported to have antibiotic resistance features. The aim of this study was to evaluate the resistance patterns of Pseudomonas spp. isolated from bulk-tank milk. In total, we investigated the antibiotic susceptibility profiles of 86 Pseudomonas spp. isolates from raw milk. All strains were tested against 15 antimicrobial agents. Pseudomonas isolates were most highly resistant to imipenem (95.3%), followed by trimethoprim-sulfamethoxazole (69.8%), aztreonam (60.5%), chloramphenicol (45.3%), and meropenem (27.9%). Their multiple antibiotic resistance (MAR) index values ranged from 0.0 to 0.8. Whole-genome sequencing revealed the presence of intrinsic resistance determinants, such as BcI, ampC-09, blaCTX-M, oprD, sul1, dfrE, catA1, catB3, catI, floR, and cmlV. Moreover, resistance-nodulation-cell division (RND) and ATP-binding cassette (ABC) antibiotic efflux pumps were also found. This study provides further knowledge of the antibiotic resistance patterns of Pseudomonas spp. in milk, which may advance our understanding of resistance in Pseudomonas and suggests that antibiotic resistance of Pseudomonas spp. in raw milk should be a concern.

Molecular epidemiology of clinically high-risk Pseudomonas aeruginosa strains: Practical overview

In recent years, numerous outbreaks of multidrug-resistant Pseudomonas aeruginosa have been reported across the world. Once an outbreak occurs, besides routinely testing isolates for susceptibility to antimicrobials, it is required to check their virulence genotypes and clonality profiles. Replacing pulsed-field gel electrophoresis DNA fingerprinting are faster, easier-to-use, and less expensive polymerase chain reaction (PCR)-based methods for characterizing hospital isolates. P. aeruginosa possesses a mosaic genome structure and a highly conserved core genome displaying low sequence diversity and a highly variable accessory genome that communicates with other Pseudomonas species via horizontal gene transfer. Multiple-locus variable-number tandem-repeat analysis and multilocus sequence typing methods allow for phylogenetic analysis of isolates by PCR amplification of target genes with the support of Internet-based services. The target genes located in the core genome regions usually contain low-frequency mutations, allowing the resulting phylogenetic trees to infer evolutionary processes. The multiplex PCR-based open reading frame typing (POT) method, integron PCR, and exoenzyme genotyping can determine a genotype by PCR amplifying a specific insertion gene in the accessory genome region using a single or a multiple primer set. Thus, analyzing P. aeruginosa isolates for their clonality, virulence factors, and resistance characteristics is achievable by combining the clonality evaluation of the core genome based on multiple-locus targeting methods with other methods that can identify specific virulence and antimicrobial genes. Software packages such as eBURST, R, and Dendroscope, which are powerful tools for phylogenetic analyses, enable researchers and clinicians to visualize clonality associations in clinical isolates.

Metabolomics Study of the Synergistic Killing of Polymyxin B in Combination with Amikacin against Polymyxin-Susceptible and -Resistant Pseudomonas aeruginosa

In the present study, we employed untargeted metabolomics to investigate the synergistic killing mechanism of polymyxin B in combination with an aminoglycoside, amikacin, against a polymyxin-susceptible isolate of Pseudomonas aeruginosa, FADDI-PA111 (MIC = 2 mg/liter for both polymyxin B and amikacin), and a polymyxin-resistant Liverpool epidemic strain (LES), LESB58 (the corresponding MIC for both polymyxin B and amikacin is 16 mg/liter). The metabolites were extracted 15 min, 1 h, and 4 h following treatment with polymyxin B alone (2 mg/liter for FADDI-PA111; 4 mg/liter for LESB58), amikacin alone (2 mg/liter), and both in combination and analyzed using liquid chromatography-mass spectrometry (LC-MS). At 15 min and 1 h, polymyxin B alone induced significant perturbations in glycerophospholipid and fatty acid metabolism pathways in FADDI-PA111 and, to a lesser extent, in LESB58. Amikacin alone at 1 and 4 h induced significant perturbations in peptide and amino acid metabolism, which is in line with the mode of action of aminoglycosides. Pathway analysis of FADDI-PA111 revealed that the synergistic effect of the combination was largely due to the inhibition of cell envelope biogenesis, which was driven initially by polymyxin B via suppression of key metabolites involved in lipopolysaccharide, peptidoglycan, and membrane lipids (15 min and 1 h) and later by amikacin (4 h). Overall, these novel findings demonstrate that the disruption of cell envelope biogenesis and central carbohydrate metabolism, decreased levels of amino sugars, and a downregulated nucleotide pool are the metabolic pathways associated with the synergistic killing of the polymyxin-amikacin combination against P. aeruginosa This mechanistic study might help optimize synergistic polymyxin B combinations in the clinical setting.

Molecular basis of bacterial disinfectant resistance

Antibiotic resistance could accelerate humanity towards an already fast-approaching post-antibiotic era, where disinfectants and effective biosecurity measures will be critically important to control microbial diseases. Disinfectant resistance has the potential to change our way of life from compromising food security to threatening our medical health systems. Resistance to antimicrobial agents occurs through either intrinsic or acquired resistance mechanisms. Acquired resistance occurs through the efficient transfer of mobile genetic elements, which can carry single, or multiple resistance determinants. Drug resistance genes may form part of integrons, transposons and insertions sequences which are capable of intracellular transfer onto plasmids or gene cassettes. Thereafter, resistance plasmids and gene cassettes mobilize by self-transmission between bacteria, increasing the prevalence of drug resistance determinants in a bacterial population. An accumulation of drug resistance genes through these mechanisms gives rise to multidrug resistant (MDR) bacteria. The study of this mobility is integral to safeguard current antibiotics, disinfectants and other antimicrobials. Literature evidence, however, indicates that knowledge regarding disinfectant resistance is severly limited. Genome engineering such as the CRISPR-Cas system, has identified disinfectant resistance genes, and reversed resistance altogether in certain prokaryotes. Demonstrating that these techniques could prove invaluable in the combat against disinfectant resistance by uncovering the secrets of MDR bacteria.

Diversification of Pseudomonas aeruginosa within the cystic fibrosis lung and its effects on antibiotic resistance

The evolution and diversification of bacterial pathogens within human hosts represent potential barriers to the diagnosis and treatment of life-threatening infections. Tremendous genetic and phenotypic diversity is characteristic of host adaptation in strains of Pseudomonas aeruginosa that infect the airways of individuals with chronic lung diseases and prove to be extremely difficult to eradicate. In this MiniReview, we examine recent advances in understanding within-host diversity and antimicrobial resistance in P. aeruginosa populations from the lower airways of individuals with the fatal genetic disease cystic fibrosis and the potential impacts that this diversity may have on detecting and interpreting antimicrobial susceptibility within these populations.

The Antibacterial Activity and Mechanism of Chlorogenic Acid Against Foodborne Pathogen Pseudomonas aeruginosa

Chlorogenic acid (CA), an ester of caffeic acid, is a major phenolic compound in herbs. The antimicrobial activity of CA against Pseudomonas aeruginosa P1, a foodborne pathogen, was investigated in this study. To understand how CA injured target cells, the influence of CA on cell morphology was assessed. A sunken cell surface and detachment of outer membrane components in P. aeruginosa P1 were observed after being treated by CA. Following this, the intracellular membrane permeability and the content of lipopolysaccharide (LPS), a main component of outer membrane, were determined. The release of intracellular protein and ATP from P. aeruginosa P1 indicated that CA increased intracellular membrane permeability and resulted in the leakage of intracellular materials. The uptake of propidium iodide, a compromised cell membrane nucleic acid stain, further demonstrated that CA acted on the intracellular membrane. CA resulted in the decrease of LPS contents of P. aeruginosa P1, which supported the detachment of outer membrane. CA also downregulated the expression of major genes in LPS biosynthesis, suggesting that CA may inhibit intracellular metabolism of P. aeruginosa P1 cells. Thus, CA increased the intracellular membrane permeability, induced the exfoliation of outer membrane, and disturbed the intracellular metabolism. Damage of intracellular and outer membranes as well as disruption of cell metabolism resulted in cell death eventually. The finding suggested that CA has the potential to be developed as a preservative to control P. aeruginosa-associated foodborne diseases.

Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data

Prediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform for the development of sequence-based diagnostics. However, there has been no systematic evaluation of factors that may influence performance of such models, how they might apply to and vary across clinical populations, and what the implications might be in the clinical setting. Here, we performed a meta-analysis of seven large Neisseria gonorrhoeae datasets, as well as Klebsiella pneumoniae and Acinetobacter baumannii datasets, with whole genome sequence data and antibiotic susceptibility phenotypes using set covering machine classification, random forest classification, and random forest regression models to predict resistance phenotypes from genotype. We demonstrate how model performance varies by drug, dataset, resistance metric, and species, reflecting the complexities of generating clinically relevant conclusions from machine learning-derived models. Our findings underscore the importance of incorporating relevant biological and epidemiological knowledge into model design and assessment and suggest that doing so can inform tailored modeling for individual drugs, pathogens, and clinical populations. We further suggest that continued comprehensive sampling and incorporation of up-to-date whole genome sequence data, resistance phenotypes, and treatment outcome data into model training will be crucial to the clinical utility and sustainability of machine learning-based molecular diagnostics.

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.

Major Release of 161 Whole-Genome Sequences from the International Pseudomonas Consortium Database

Pseudomonas aeruginosa is an environmental bacterium and opportunistic pathogen. Here, we present draft genome sequences for 161 isolates from diverse clinical and environmental sources. This set of genome sequences complements other major public data releases from the International Pseudomonas Consortium Database.

Antibiotic resistant bacteria and resistance genes in biofilms in clinical wastewater networks

Increasing isolation rates of resistant bacteria in the last years require identification of potential infection reservoirs in healthcare facilities. Especially the clinical wastewater network represents a potential source of antibiotic resistant bacteria. In this work, the siphons of the sanitary installations from 18 hospital rooms of two German hospitals were examined for antibiotic resistant bacteria and antibiotic residues including siphons of showers and washbasins and toilets in sanitary units of psychosomatic, haemato-oncological, and rehabilitation wards. In addition, in seven rooms of the haemato-oncological ward, the effect of 24 h of stagnation on the antibiotic concentrations and MDR (multi-drug-resistant) bacteria in biofilms was evaluated. Whereas no antibiotic residues were found in the psychosomatic ward, potential selective concentrations of piperacillin, meropenem and ciprofloxacin were detected at a rehabilitation ward and ciprofloxacin and trimethoprim were present at a haemato-oncology ward. Antibiotic resistant bacteria were isolated from the siphons of all wards, however in the psychosomatic ward, only one MDR strain with resistance to piperacillin, third generation cephalosporins and quinolones (3MRGN) was detected. In contrast, the other two wards yielded 11 carbapenemase producing MDR isolates and 15 3MRGN strains. The isolates from the haemato-oncological ward belonged mostly to two specific rare sequence types (ST) (P. aeruginosa ST823 and Enterobacter cloacae complex ST167). In conclusion, clinical wastewater systems represent a reservoir for multi-drug-resistant bacteria. Consequently, preventive and intervention measures should not start at the wastewater treatment in the treatment plant, but already in the immediate surroundings of the patient, in order to minimize the infection potential.

Integrated analysis of population genomics, transcriptomics and virulence provides novel insights into Streptococcus pyogenes pathogenesis

Streptococcus pyogenes causes 700 million human infections annually worldwide, yet, despite a century of intensive effort, there is no licensed vaccine against this bacterium. Although a number of large-scale genomic studies of bacterial pathogens have been published, the relationships among the genome, transcriptome, and virulence in large bacterial populations remain poorly understood. We sequenced the genomes of 2,101 emm28 S. pyogenes invasive strains, from which we selected 492 phylogenetically diverse strains for transcriptome analysis and 50 strains for virulence assessment. Data integration provided a novel understanding of the virulence mechanisms of this model organism. Genome-wide association study, expression quantitative trait loci analysis, machine learning, and isogenic mutant strains identified and confirmed a one-nucleotide indel in an intergenic region that significantly alters global transcript profiles and ultimately virulence. The integrative strategy that we used is generally applicable to any microbe and may lead to new therapeutics for many human pathogens.

Predicting Antimicrobial Resistance and Associated Genomic Features from Whole-Genome Sequencing

Thanks to the genomics revolution, thousands of strain-specific whole-genome sequences are now accessible for a wide range of pathogenic bacteria. This availability enables big data informatics approaches to be used to study the spread and acquisition of antimicrobial resistance (AMR). In this issue of the Journal of Clinical Microbiology, Nguyen et al. (M. Nguyen, S. W. Long, P. F. McDermott, R. J. Olsen, R. Olson, R. L. Stevens, G. H. Tyson, S. Zhao, and J. J. Davis, J Clin Microbiol 57:e01260-18, 2019, https://doi.org/10.1128/JCM.01260-18) report the results obtained with their machine learning models based on whole-genome sequencing data to predict the MICs of antibiotics for 5,728 nontyphoidal Salmonella genomes collected over 15 years in the United States. Their major finding demonstrates that MICs can be predicted with an average accuracy of 95% within ±1 2-fold dilution step (confidence interval, 95% to 95%), an average very major error rate of 2.7%, and an average major error rate of 0.1%. Importantly, these models predict MICs with no a priori information about the underlying gene content or resistance phenotypes of the strains, enabling the possibility to identify AMR determinants and rapidly diagnose and prioritize antibiotic use directly from the organism sequence. Employing such tools to diagnose and limit the spread of resistance-conferring mechanisms could help ameliorate the looming antibiotic resistance crisis.

Comparative genomics of a drug-resistant Pseudomonas aeruginosa panel and the challenges of antimicrobial resistance prediction from genomes

Antimicrobial resistance (AMR) is now recognized as a global threat to human health. The accessibility of microbial whole-genome sequencing offers an invaluable opportunity for resistance surveillance via the resistome, i.e. the genes and mutations underlying AMR. Unfortunately, AMR prediction from genomic data remains extremely challenging, especially for species with a large pan-genome. One such organism, for which multidrug-resistant (MDR) isolates are frequently encountered in the clinic, is Pseudomonas aeruginosa. This study focuses on a commercially available panel of seven MDR P. aeruginosa strains. The main goals were to sequence and compare these strains' genomes, attempt to predict AMR from whole genomes using two different methods and determine whether this panel could be an informative complement to the international P. aeruginosa reference panel. As expected, the results highlight the complexity of associating genotype and AMR phenotype in P. aeruginosa, mainly due to the intricate regulation of resistance mechanisms. Our results also urge caution in the interpretation of predicted resistomes regarding the occurrence of gene identity discrepancies between strains. We envision that, in addition to accounting for the genomic diversity of P. aeruginosa, future development of predictive tools will need to incorporate a transcriptomic, proteomic and/or metabolomic component.

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ±1 two-fold dilution factor, is 92%. Individual accuracies are ≥90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.

Genomics and Susceptibility Profiles of Extensively Drug-Resistant Pseudomonas aeruginosa Isolates from Spain

This study assessed the molecular epidemiology, resistance mechanisms, and susceptibility profiles of a collection of 150 extensively drug-resistant (XDR) Pseudomonas aeruginosa clinical isolates obtained from a 2015 Spanish multicenter study, with a particular focus on resistome analysis in relation to ceftolozane-tazobactam susceptibility. Broth microdilution MICs revealed that nearly all (>95%) of the isolates were nonsusceptible to piperacillin-tazobactam, ceftazidime, cefepime, aztreonam, imipenem, meropenem, and ciprofloxacin. Most of them were also resistant to tobramycin (77%), whereas nonsusceptibility rates were lower for ceftolozane-tazobactam (31%), amikacin (7%), and colistin (2%). Pulsed-field gel electrophoresis-multilocus sequence typing (PFGE-MLST) analysis revealed that nearly all of the isolates belonged to previously described high-risk clones. Sequence type 175 (ST175) was detected in all 9 participating hospitals and accounted for 68% (n = 101) of the XDR isolates, distantly followed by ST244 (n = 16), ST253 (n = 12), ST235 (n = 8), and ST111 (n = 2), which were detected only in 1 to 2 hospitals. Through phenotypic and molecular methods, the presence of horizontally acquired carbapenemases was detected in 21% of the isolates, mostly VIM (17%) and GES enzymes (4%). At least two representative isolates from each clone and hospital (n = 44) were fully sequenced on an Illumina MiSeq. Classical mutational mechanisms, such as those leading to the overexpression of the β-lactamase AmpC or efflux pumps, OprD inactivation, and/or quinolone resistance-determining regions (QRDR) mutations, were confirmed in most isolates and correlated well with the resistance phenotypes in the absence of horizontally acquired determinants. Ceftolozane-tazobactam resistance was not detected in carbapenemase-negative isolates, in agreement with sequencing data showing the absence of ampC mutations. The unique set of mutations responsible for the XDR phenotype of ST175 clone documented 7 years earlier were found to be conserved, denoting the long-term persistence of this specific XDR lineage in Spanish hospitals. Finally, other potentially relevant mutations were evidenced, including those in penicillin-binding protein 3 (PBP3), which is involved in β-lactam (including ceftolozane-tazobactam) resistance, and FusA1, which is linked to aminoglycoside resistance.