The hidden factors of resistance: how pathogen diversity drives antibiotic response

In a recent study published in Nature Communications, researchers employed evolutionary approaches to understand the role of within-host microbial diversity on antimicrobial resistance (AMR) evolution in patients admitted to intensive care units (ICU) and receiving antibiotic treatment.

Study: Mixed strain pathogen populations accelerate the evolution of antibiotic resistance in patients. Image Credit: nobeastsofierce/Shutterstock.com

Background

Mixed strain populations of pathogens, e.g., Pseudomonas aeruginosa, contain both novel and pre-existing genetic variations, where the former are acquired in situ by mutation or horizontal gene transfer.

Since they have high genetic diversity compared to other co-colonizing strains, which the selection process has to act on, it accelerates their evolutionary response to antibiotic treatment. It gives rise to the notion that resistance evolves rapidly in hosts colonized by more diverse pathogenic populations, which is a fundamental threat to human health. 

AMR to opportunistic pathogens, such as Pseudomonas aeruginosa, are an important cause of nosocomial infections, particularly in immunocompromised and severely ill ICU patients.

Studies have shown that its different strains can co-occur in the lungs of patients suffering from cystic fibrosis and bronchiectasis. Thus, the rapid evolution of AMR in Pseudomonas species, compared to other ESKAPE pathogens, poses an important challenge for treating Pseudomonas infections during treatment.

The Advanced understanding of Staphylococcus aureus and Pseudomonas aeruginosa Infections in Europe–Intensive Care Units (ASPIRE-ICU), an observational trial of Pseudomonas infection in European hospitals sampled Pseudomonas isolates unbiasedly.

Despite the short ICU stay of most ICU patients enrolled in the ASPIRE-ICU trial, they also collected longitudinal samples from some patients, which facilitated the direct investigation of within-host drivers of AMR. 

About the study

In the present study, researchers used lower respiratory samples collected within the ASPIRE-ICU study to test the impact of within-patient Pseudomonas diversity in ICU patients using a combination of phenotypic assays (resistance phenotyping, growth assays) and genomic sequencing and variant calling for its quantitative estimates.

Nearly 50%, i.e., 17 of 35 patients with a high Pseudomonas colonization rate, were admitted to an ICU in a single hospital. They also selected 12 randomly chosen isolates from all patient samples containing Pseudomonas.

Specifically, they used the minimum inhibitory concentration (MIC) assays to measure the AMR of Pseudomonas isolates against a panel of antibiotics representing six major families, viz., ciprofloxacin, gentamicin, meropenem, aztreonam, piperacillin/tazobactam, and ceftazidime. However, the test panel was biased towards β-lactam antibiotics, the most widely used for treating Pseudomonas infections.

Measuring resistance to a panel of antibiotics helped the researchers distinguish, for all 35 patients, between direct responses to antibiotic treatment and collateral effects of resistance to antibiotics not used for treatment. 

AMR measurements of 441 Pseudomonas isolates against six antibiotics generated a large quantitative dataset of the antibiotic-resistant phenotypes. However, the team analyzed each isolate's resistance to each antibiotic as either sensitive or resistant.

They also team computed the variations in the proportion of Pseudomonas isolates resistant to each antibiotic over time for all patient-antibiotic combinations. 

For deeper insights into the evolutionary drivers of AMR, the team measured changes in resistance using Pseudomonas isolates from samples collected from patients before and after antibiotic treatment that were active against P. aeruginosa. 

This subset of 13 longitudinal samples included seven and six patients colonized with single-strain and mixed-strain Pseudomonas populations, respectively. Importantly, the time gap between the first and final sampling did not vary for these patient subpopulations. 

Results

Conventional methods used in clinical microbiology fail to determine the significance of pre-existing diversity in AMR across bacterial pathogens. However, this assessment is important because the rate of AMR evolution in patients varies widely across pathogens. 

Consistent with the speculations, researchers found that AMR evolved rapidly in patients colonized by diverse P. aeruginosa populations due to selection for pre-existing resistant strains. Conversely, AMR evolved sporadically in patients colonized by single strains due to selection for novel resistance mutations.

Given the tradeoff between resistance and growth rate in mixed strain populations, within-host diversity could also drive the loss of resistance in the absence of antibiotic treatment. These observations confirmed that within-host diversity of pathogenic populations shapes the AMR emergence of resistance in response to antibiotic treatment.

Conclusions

Taken together, the study results suggest that measuring the diversity of pathogen populations could help predict the likelihood of antibiotic treatment failure at a patient level more accurately, in the same way as diversity measurements in cancer cell populations help predict the success of chemotherapy.

Yet, it will remain challenging to determine if mixed strain pathogen populations arise by single colonization events or superinfection.

Likewise, patients with particular health conditions are likely more likely to be colonized by mixed strains of pathogenic microbes. For instance, some P. aeruginosa strains superinfect some cystic fibrosis patients, giving rise to high within-patient strain diversity. 

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.