Understanding the effects of sub-inhibitory antibiotic concentrations on the development of ß-lactamase resistance based on quantile regression analysis.

AIMS: Quantile regression is an alternate type of regression analysis that has been shown to have numerous advantages over standard linear regression. Unlike linear regression, which uses the mean to fit a linear model, quantile regression uses a data set's quantiles (or percentiles), which leads to a more comprehensive analysis of the data. However, while relatively common in other scientific fields such as economic and environmental modeling, it is infrequently used to understand biological and microbiological systems. METHODS AND RESULTS: We analyzed a set of bacterial growth rates using quantile regression analysis to better understand the effects of antibiotics on bacterial fitness. Using a bacterial model system containing 16 variant genotypes of the TEM ß-lactamase enzyme, we compared our quantile regression analysis to a previously published study that uses the Tukey's range test, or Tukey honestly significantly difference (HSD) test. We find that trends in the distribution of bacterial growth rate data, as viewed through the lens of quantile regression, can distinguish between novel genotypes and ones that have been clinically isolated from patients. Quantile regression also identified certain combinations of genotypes and antibiotics that resulted in bacterial populations growing faster as the antibiotic concentration increased-the opposite of what was expected. These analyses can provide new insights into the relationships between enzymatic efficacy and antibiotic concentration. CONCLUSIONS: Quantile regression analysis enhances our understanding of the impacts of sublethal antibiotic concentrations on enzymatic (TEM ß-lactamase) efficacy and bacterial fitness. We illustrate that quantile regression analysis can link patterns in growth rates with clinically relevant mutations and provides an understanding of how increasing sub-lethal antibiotic concentrations, like those found in our modern environment, can affect bacterial growth rates, and provide insight into the genetic basis for varied resistance.

Adaptive Processes Change as Multiple Functions Evolve.

Epistasis influences the gene-environment interactions that shape bacterial fitness through antibiotic exposure, which can ultimately affect the availability of certain resistance phenotypes to bacteria. The substitutions present within blaTEM-50 confer both cephalosporin and ß-lactamase inhibitor resistance. We wanted to compare the evolution of blaTEM-50 with that of another variant, blaTEM-85, which differs in that blaTEM-85 contains only substitutions that contribute to cephalosporin resistance. Differences between the landscapes and epistatic interactions of these TEM variants are important for understanding their separate evolutionary responses to antibiotics. We hypothesized the substitutions within blaTEM-50 would result in more epistatic interactions than for blaTEM-85 As expected, we found more epistatic interactions between the substitutions present in blaTEM-50 than in blaTEM-85 Our results suggest that selection from many cephalosporins is required to achieve the full potential resistance to cephalosporins but that a single ß-lactam and inhibitor combination will drive evolution of the full potential resistance phenotype. Surprisingly, we also found significantly positive increases in growth rates as antibiotic concentration increased for some of the strains expressing blaTEM-85 precursor genotypes but not the blaTEM-50 variants. This result further suggests that additive interactions more effectively optimize phenotypes than epistatic interactions, which means that exposure to numerous cephalosporins actually increases the ability of a TEM enzyme to confer resistance to any single cephalosporin.

An Upper-Division, Remote Microbiology Laboratory That Blends Virtual and Hands-on Components to Promote Student Success during the COVID-19 Pandemic.

The COVID-19 shutdown forced many institutions of higher education to shift in-person teaching to emergency remote teaching. This was particularly challenging for laboratory courses, where students are expected to learn hands-on skills needed for their career goals. Here, we describe the transformation of an upper-division microbiology laboratory to a course that seamlessly integrates online simulations with safe, hands-on experiences that can be done from home. This blended lab course helped students attain learning outcomes similar to those achieved in the in-person class. We illustrate the implementation of Unknown Portfolios to help students gain the data analysis and critical thinking skills needed to identify an unknown microorganism. Our data show that students who took these online courses mastered material as well as students who took the lab in person, demonstrating proficiency in laboratory safety skills, hands-on techniques, and theoretical class content. Last, we explore online adaptations to enhance in-person lab classes, aiming at reducing the accessibility and equity gaps inherited in many courses, as well as discussing challenges that instructors might experience in this process.

Distribution of ß-Lactamase Genes in Clinical Isolates from California Central Valley Hospital Deviates from the United States Nationwide Trends.

The evolution and dissemination of antibiotic resistance genes throughout the world are clearly affected by the selection and migration of resistant bacteria. However, the relative contributions of selection and migration at a local scale have not been fully explored. We sought to identify which of these factors has the strongest effect through comparisons of antibiotic resistance gene abundance between a distinct location and its surroundings over an extended period of six years. In this work, we used two repositories of extended spectrum ß-lactamase (ESBL)-producing isolates collected since 2013 from patients at Dignity Health Mercy Medical Center (DHMMC) in Merced, California, USA, and a nationwide database compiled from clinical isolate genomes reported by the National Center for Biotechnology Information (NCBI) since 2013. We analyzed the stability of average resistance gene frequencies over the years since collection of these clinical isolates began for each repository. We then compared the frequencies of resistance genes in the DHMMC collection with the averages of the nationwide frequencies. We found DHMMC gene frequencies are stable over time and differ significantly from nationwide frequencies throughout the period of time we examined. Our results suggest that local selective pressures are a more important influence on the population structure of resistance genes in bacterial populations than migration. This, in turn, indicates the potential for antibiotic resistance to be controlled at a regional level, making it easier to limit the spread through local stewardship.