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.

Evaluating the effect of low dose rate of gamma rays in germ cells of Drosophila melanogaster.

Purpose: Evaluation of genetic risk in germ cells is still matter of research, mainly due to their role in the transmission of genetic information from one generation to another. Although numerous experiments have been carried out in Drosophila in order to study the effect of radiation on germ cells, the role of dose rate (DR) has not been fully explored. The purpose of this study was to evaluate the action of DR on the radioprotection induction on male germ cell of D. melanogaster.Material and method: The productivity and the sex-linked recessive lethal (SLRL) tests were used to evaluate the radio-sensitivity of different states of the germ line of males. Two-day-old males of Canton-S wild type strain were pretreated with 0.2 Gy at 5.4 or 34.3 Gy/h of gamma rays from a 60Co source, three hours later, they were irradiated with 20 Gy at 907.7 Gy/h. Thereafter, each single male was crossed with 3 five-day old Basc virgin females, that were replaced every other day by new females. This procedure was conducted three times, to test the whole germ cell stages.Results: Females crossed with males irradiated with 0.2 Gy at both DR tested, laid a higher number of eggs than control, but egg-viability was reduced. On the other hand, in the group of 0.2 Gy + 20 Gy -combined treatments- the total number of eggs laid decreased only when 0.2 Gy were delivered at 34.3 Gy/h however, the egg-viability increased. The dose of 0.2 Gy at both DR did not modify the baseline frequency of SLRL. A tendency to decrease in the frequency of lethals in brood III was found in combined treatments at both DR.Conclusion: The fact that 0.2 Gy at 5.4 or 34.3 Gy/h induced an increase in the egg-viability and a tendency to decrease the genetic damage in pre-meiotic cells provoked by 20 Gy, might indicate the induction of any mechanism that could be interpreted as radioprotection in male germ cells of D. melanogaster. Results emphasize the need to carry out more studies on the effect of the DR on the induction of genetic damage in germ cells.

Statistical Package for Growth Rates Made Easy.

Growth rates are an important tool in microbiology because they provide high throughput fitness measurements. The release of GrowthRates, a program that uses the output of plate reader files to automatically calculate growth rates, has facilitated experimental procedures in many areas. However, many sources of variation within replicate growth rate data exist and can decrease data reliability. We have developed a new statistical package, CompareGrowthRates (CGR), to enhance the program GrowthRates and accurately measure variation in growth rate data sets. We define a metric, Variability-score (V-score), that can help determine if variation within a data set might result in false interpretations. CGR also uses the bootstrap method to determine the fraction of bootstrap replicates in which a strain will grow the fastest. We illustrate the usage of CGR with growth rate data sets similar to those in Mira, Meza, et al. (Adaptive landscapes of resistance genes change as antibiotic concentrations change. Mol Biol Evol. 32(10): 2707-2715). These statistical methods are compatible with the analytic methods described in Growth Rates Made Easy and can be used with any set of growth rate output from GrowthRates.

Adaptive Landscapes of Resistance Genes Change as Antibiotic Concentrations Change.

Most studies on the evolution of antibiotic resistance are focused on selection for resistance at lethal antibiotic concentrations, which has allowed the detection of mutant strains that show strong phenotypic traits. However, solely focusing on lethal concentrations of antibiotics narrowly limits our perspective of antibiotic resistance evolution. New high-resolution competition assays have shown that resistant bacteria are selected at relatively low concentrations of antibiotics. This finding is important because sublethal concentrations of antibiotics are found widely in patients undergoing antibiotic therapies, and in nonmedical conditions such as wastewater treatment plants, and food and water used in agriculture and farming. To understand the impacts of sublethal concentrations on selection, we measured 30 adaptive landscapes for a set of TEM ß-lactamases containing all combinations of the four amino acid substitutions that exist in TEM-50 for 15 ß-lactam antibiotics at multiple concentrations. We found that there are many evolutionary pathways within this collection of landscapes that lead to nearly every TEM-genotype that we studied. While it is known that the pathways change depending on the type of ß-lactam, this study demonstrates that the landscapes including fitness optima also change dramatically as the concentrations of antibiotics change. Based on these results we conclude that the presence of multiple concentrations of ß-lactams in an environment result in many different adaptive landscapes through which pathways to nearly every genotype are available. Ultimately this may increase the diversity of genotypes in microbial populations.

Rational design of antibiotic treatment plans: a treatment strategy for managing evolution and reversing resistance.

The development of reliable methods for restoring susceptibility after antibiotic resistance arises has proven elusive. A greater understanding of the relationship between antibiotic administration and the evolution of resistance is key to overcoming this challenge. Here we present a data-driven mathematical approach for developing antibiotic treatment plans that can reverse the evolution of antibiotic resistance determinants. We have generated adaptive landscapes for 16 genotypes of the TEM ß-lactamase that vary from the wild type genotype "TEM-1" through all combinations of four amino acid substitutions. We determined the growth rate of each genotype when treated with each of 15 ß-lactam antibiotics. By using growth rates as a measure of fitness, we computed the probability of each amino acid substitution in each ß-lactam treatment using two different models named the Correlated Probability Model (CPM) and the Equal Probability Model (EPM). We then performed an exhaustive search through the 15 treatments for substitution paths leading from each of the 16 genotypes back to the wild type TEM-1. We identified optimized treatment paths that returned the highest probabilities of selecting for reversions of amino acid substitutions and returning TEM to the wild type state. For the CPM model, the optimized probabilities ranged between 0.6 and 1.0. For the EPM model, the optimized probabilities ranged between 0.38 and 1.0. For cyclical CPM treatment plans in which the starting and ending genotype was the wild type, the probabilities were between 0.62 and 0.7. Overall this study shows that there is promise for reversing the evolution of resistance through antibiotic treatment plans.

Inhibition of cholinesterase activity by soil extracts and predicted environmental concentrations (PEC) to select relevant pesticides in polluted soils.

The correlation of predicted environmental concentrations (PEC) with cholinesterase activity inhibition detected in soil extracts was determined. PEC was derived from organophosphate (OP) and carbamate (CA) compounds applied to a flower crop area. Samples of surface soil (0 - 30 cm in depth) and subsurface soil (30 to 60 cm in depth) were taken from a flower crop area in which OP pesticides such as acephate ((RS)-N-[methoxy(methylthio)phosphinoyl]acetamide), dimethoate (2-dimethoxyphosphinothioylthio-N-methylacetamide) and methyl parathion (O,O-dimethyl O-4-nitrophenyl phosphorothioate), and CA pesticides such as carbendazim (methyl benzimidazol-2-ylcarbamate), carbofuran (2,3-dihydro-2,2-dimethylbenzofuran-7-yl methylcarbamate) and methomyl (S-methyl (EZ)-N-(methylcarbamoyloxy) thioacetimidate) were applied for two years. Weekly loads of these pesticides were registered to estimate the annual load of each compound. Physicochemical analysis and relative inhibition of cholinesterasic activity were measured for each soil sample. PEC values were estimated with Pesticide Analytical Model (PESTAN), a leach model, for each pesticide using soil sample data obtained from physicochemical analysis. From all pesticides tested, only acephate and methomyl showed a significant correlation (p < 0.01) between PEC values and inhibition cholinesterase activity of soil extracts. These results suggest that inhibition of cholinesterase activity observed in soil extracts is produced mainly by these two pesticides. Further studies could be developed to measure acephate and methomyl concentrations to reduce their environmental impact.