Pseudomonas aeruginosa Pneumonia: Evolution of Antimicrobial Resistance and Implications for Therapy

Genetic Characteristics of Novel IncpSE5381-aadB Plasmids, Integrative and Mobilizable Elements, and Integrative and Conjugative Elements in Pseudomonas aeruginosa

Purpose

Pseudomonas aeruginosa is a common causative bacteria in nosocomial infections. This study aims to describe the structure and evolutionary characteristics of mobile genetic elements (MGEs) carrying antibiotic resistance genes (ARGs) from P. aeruginosa and to conduct bioinformatics and comparative genomic analysis to provide a deeper understanding of the genetic characteristics and diversity of MGEs in P. aeruginosa.

Methods

Fifteen clinical isolates of P. aeruginosa from China were collected and sequenced in this study, and 15 novel MGEs were identified. Together with four MGEs from GenBank, a total of 19 MGEs were used to perform detailed modular structure dissection and sequence comparison. Then, the biological experiments were carried out to verify the biological characteristics of these isolates and MEGs.

Results

The novel MGEs identified in this study displayed diversification in modular structures, which showed complex mosaic natures. The seven types of 19 MGEs included in this study were divided into three groups: i) novel MGEs (firstly identified in this study): four IncpSE5381-aadB plasmids and three Tn7495-related integrative and mobilizable elements (IMEs); ii) newly defined MGEs (firstly designated in this study, but with previously determined sequences): four Tn7665-related IMEs; iii) novel transposons with reference prototypes identified in this study: two Tn6417-related integrative and conjugative elements (ICEs), two IS-based transposition units, two Tn501-related unit transposons, two Tn1403-related unit transposons. At least 36 ARGs involved in resistance to 11 different classes of antimicrobials and heavy metals were identified. Additionally, three novel blaOXA variants were identified. Antimicrobial susceptibility testing showed that these variants were resistant to some β-lactamase antibiotics and blaOXA-1204 was additionally resistant to cephalosporins.

Conclusion

The continuous evolution of ARG-carrying MGEs during transmission, leading to the emergence of novel MGEs or ARGs, which facilitates the spread of antibiotic resistance in P. aeruginosa and enhances the diversity of transmission modes of bacterial resistance.

Molecular pharmacodynamics of meropenem for nosocomial pneumonia caused by Pseudomonas aeruginosa

Hospital-acquired pneumonia (HAP) is a leading cause of morbidity and mortality, commonly caused by Pseudomonas aeruginosa. Meropenem is a commonly used therapeutic agent, although emergent resistance occurs during treatment. We used a rabbit HAP infection model to assess the bacterial kill and resistance pharmacodynamics of meropenem. Meropenem 5 mg/kg administered subcutaneously (s.c.) q8h (±amikacin 3.33-5 mg/kg q8h administered intravenously[i.v.]) or meropenem 30 mg/kg s.c. q8h regimens were assessed in a rabbit lung infection model infected with P. aeruginosa, with bacterial quantification and phenotypic/genotypic characterization of emergent resistant isolates. The pharmacokinetic/pharmacodynamic output was fitted to a mathematical model, and human-like regimens were simulated to predict outcomes in a clinical context. Increasing meropenem monotherapy demonstrated a dose-response effect to bacterial kill and an inverted U relationship with emergent resistance. The addition of amikacin to meropenem suppressed the emergence of resistance. A network of porin loss, efflux upregulation, and increased expression of AmpC was identified as the mechanism of this emergent resistance. A bridging simulation using human pharmacokinetics identified meropenem 2 g i.v. q8h as the licensed clinical regimen most likely to suppress resistance. We demonstrate an innovative experimental platform to phenotypically and genotypically characterize bacterial emergent resistance pharmacodynamics in HAP. For meropenem, we have demonstrated the risk of resistance emergence during therapy and identified two mitigating strategies: (i) regimen intensification and (ii) use of combination therapy. This platform will allow pre-clinical assessment of emergent resistance risk during treatment of HAP for other antimicrobials, to allow construction of clinical regimens that mitigate this risk.IMPORTANCEThe emergence of antimicrobial resistance (AMR) during antimicrobial treatment for hospital-acquired pneumonia (HAP) is a well-documented problem (particularly in pneumonia caused by Pseudomonas aeruginosa) that contributes to the wider global antimicrobial resistance crisis. During drug development, regimens are typically determined by their sufficiency to achieve bactericidal effect. Prevention of the emergence of resistance pharmacodynamics is usually not characterized or used to determine the regimen. The innovative experimental platform described here allows characterization of the emergence of AMR during the treatment of HAP and the development of strategies to mitigate this. We have demonstrated this specifically for meropenem-a broad-spectrum antibiotic commonly used to treat HAP. We have characterized the antimicrobial resistance pharmacodynamics of meropenem when used to treat HAP, caused by initially meropenem-susceptible P. aeruginosa, phenotypically and genotypically. We have also shown that intensifying the regimen and using combination therapy are both strategies that can both treat HAP and suppress the emergence of resistance.

Evolving trends among Pseudomonas aeruginosa: a 12-year retrospective study from the United Arab Emirates

Introduction

Pseudomonas is a group of ubiquitous non-fermenting Gram-negative bacteria (NFGNB). Of the several species associated with humans, Pseudomonas aeruginosa (PA) can acclimate to diverse environments. The global frequency of PA infections is rising and is complicated by this organism's high intrinsic and acquired resistance to several clinically relevant antibiotics. Data on the epidemiology, levels, and trends of antimicrobial resistance of PA in clinical settings in the MENA/GCC region is scarce.

Methods

A retrospective 12-year analysis of 56,618 non-duplicate diagnostic Pseudomonas spp. from the United Arab Emirates (UAE) was conducted. Data was generated at 317 surveillance sites by routine patient care during 2010-2021, collected by trained personnel and reported by participating surveillance sites to the UAE National antimicrobial resistance (AMR) Surveillance program. Data analysis was conducted with WHONET (https://whonet.org/).

Results

Among the total isolates (N = 56,618), the majority were PA (95.6%). Data on nationality revealed 44.1% were UAE nationals. Most isolates were from soft tissue (55.7%), followed by respiratory tract (26.7%). PA was more commonly found among inpatients than among outpatients, followed by ICUs. PA showed a horizontal trend for resistance to fluoroquinolones, 3rd- and 4th-generation cephalosporins, and decreasing trends of resistance for aminoglycosides and meropenem. The highest percentage of multidrug resistant (MDR) isolates was reported in 2011 at 35.6%. As an overall trend, the percentage of MDR, extensively drug-resistant (XDR), and possible pandrug-resistant (PDR) isolates generally declined over the study period. Carbapenem-resistant PA (CRPA) were associated with a higher mortality (RR: 2.7), increased admission to ICU (RR: 2.3), and increased length of stay (LOS) (12 excess inpatient days per case), as compared to carbapenem-susceptible PA (CSPA).

Conclusion

The resistance trends in Pseudomonas species in the UAE indicated a decline in AMR and in percentages of Pseudomonas isolates with MDR and XDR profiles. The sustained Pseudomonas spp. circulation particularly in the hospital settings highlights the importance of surveillance techniques, infection control strategies, and stewardship to limit the continued dissemination. This data also shows that CRPA are associated with higher mortality, increased ICU admission rates, and a longer hospitalization, thus higher costs due to increased number of in-hospital and ICU days.

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.

Machine Learning Techniques for Antimicrobial Resistance Prediction of Pseudomonas Aeruginosa from Whole Genome Sequence Data

AIM

Due to the growing availability of genomic datasets, machine learning models have shown impressive diagnostic potential in identifying emerging and reemerging pathogens. This study aims to use machine learning techniques to develop and compare a model for predicting bacterial resistance to a panel of 12 classes of antibiotics using whole genome sequence (WGS) data of Pseudomonas aeruginosa.

METHOD

A machine learning technique called Random Forest (RF) and BioWeka was used for classification accuracy assessment and logistic regression (LR) for statistical analysis.

RESULTS

Our results show 44.66% of isolates were resistant to twelve antimicrobial agents and 55.33% were sensitive. The mean classification accuracy was obtained ≥98% for BioWeka and ≥96 for RF on these families of antimicrobials. Where ampicillin was 99.31% and 94.00%, amoxicillin was 99.02% and 95.21%, meropenem was 98.27% and 96.63%, cefepime was 99.73% and 98.34%, fosfomycin was 96.44% and 99.23%, ceftazidime was 98.63% and 94.31%, chloramphenicol was 98.71% and 96.00%, erythromycin was 95.76% and 97.63%, tetracycline was 99.27% and 98.25%, gentamycin was 98.00% and 97.30%, butirosin was 99.57% and 98.03%, and ciprofloxacin was 96.17% and 98.97% with 10-fold-cross validation. In addition, out of twelve, eight drugs have found no false-positive and false-negative bacterial strains.

CONCLUSION

The ability to accurately detect antibiotic resistance could help clinicians make educated decisions about empiric therapy based on the local antibiotic resistance pattern. Moreover, infection prevention may have major consequences if such prescribing practices become widespread for human health.

Clinical characteristics and risk factors predictive of pulmonary embolism complicated in bronchiectasis patients: a retrospective study

Objective

Pulmonary embolism (PE) is a rare complication in bronchiectasis (BE) patients associated with a high rate of mortality and morbidity. However, data regarding bronchiectasis patients complicated with PE are limited. Early diagnosis of PE in bronchiectasis patients can improve the prognosis, this study aimed to investigate the clinical features and potential risk factors for early diagnosis of PE in bronchiectasis patients.

Methods

Data of Patients were collected from Tongji Hospital of Tongji University of China. Bronchiectasis patients complicated with pulmonary embolism were named as BE/PE group (n = 63), as well as contemporaneous aged- and sex-matched bronchiectasis patients without pulmonary embolism named as BE group (n = 189), at a ratio of 1:3(cases to controls). Clinical parameters and risk factors were analyzed.

Results

Univariate analysis shows that long-term bed rest, chronic lung disease, autoimmune disease, peripheral artery disease (PAD), tuberculosis history, dyspnea, blood homocysteine, CD4/CD8 ratio, or SIQIIITIII syndrome were closely correlated with the incidence of PE in the bronchiectasis patients (p < 0.05). Multivariate logistic regression analysis of significant variables showed that CD4/CD8 ratio (OR 1.409, 95% CI 1.045–1.901) and autoimmune disease (OR 0.264, 95% CI 0.133–0.524) are independent risk factors for BE/PE patients, compared with the BE patients. 53 out of 189 (28.0%) BE patients had hemoptysis, and 15 out of 63 (23.8%) BE/PE patients had hemoptysis (p > 0.05).

Conclusions

The coexistence of pulmonary embolism and bronchiectasis are rarely encountered and easily to be ignored. Early identification of the clinical characteristic and potential risk factors of pulmonary embolism in bronchiectasis patients may help optimize the treatment strategies.