Significance Bacterial adaptation to the presence of an antibiotic often involves evolutionary trade-offs, such as increased susceptibility to other drugs (collateral sensitivity). Its exploitation to design improved therapeutic strategies is only feasible if collateral sensitivity is robust, reproducible, and emerges in resistant mutants; these issues are rarely addressed in available publications. We describe a robust collateral sensitivity phenotype that emerges in different antibiotic-resistance mutational backgrounds, due to different genetic events, and propose therapeutic strategies effective for treating infections caused by Pseudomonas aeruginosa antibiotic-resistant mutants. Since conserved collateral sensitivity phenotypes do not confer adaptation to the presence of antibiotics, our results are also relevant for understanding convergent evolution processes in which the force selecting the emerging phenotype remains unclear.

The study of the acquisition of antibiotic resistance (AR) has mainly focused on inherited processes, namely, mutations and acquisition of AR genes. However, inducible, noninheritable AR has received less attention, and most information in this field derives from the study of antibiotics as inducers of their associated resistance mechanisms. Less is known about nonantibiotic compounds or situations that can induce AR during infection. Multidrug resistance efflux pumps are a category of AR determinants characterized by the tight regulation of their expression. Their contribution to acquired AR relies in their overexpression. Here, we analyzed potential inducers of the expression of the chromosomally encoded Pseudomonas aeruginosa clinically relevant efflux pumps, MexCD-OprJ and MexAB-OprM. For this purpose, we developed a set of luxCDABE-based P. aeruginosa biosensor strains, which allows the high-throughput analysis of compounds able to modify the expression of these efflux pumps. Using these strains, we analyzed a set of 240 compounds present in Biolog phenotype microarrays. Several inducers of the expression of the genes that encode these efflux pumps were found. The study focused in dequalinium chloride, procaine, and atropine, compounds that can be found in clinical settings. Using real-time PCR, we confirmed that these compounds indeed induce the expression of the mexCD-oprJ operon. In addition, P. aeruginosa presents lower susceptibility to ciprofloxacin (a MexCD-OprJ substrate) when dequalinium chloride, procaine, or atropine are present. This study emphasizes the need to study compounds that can trigger transient AR during antibiotic treatment, a phenotype difficult to discover using classical susceptibility tests.

The aim of this work was to evaluate the response to 10 generations of selection for ovulation rate. Selection was based on the phenotypic value of ovulation rate, estimated at d 12 of the second gestation by laparoscopy. Selection pressure was approximately 30%. Line size was approximately 20 males and 80 females per generation. Traits recorded were ovulation rate at the second gestation, estimated by laparoscopy as the number of corpora lutea in both ovaries; ovulation rate at the last gestation, estimated postmortem; ovulation rate, analyzed as a single trait including ovulation rate at the second gestation and ovulation rate at the last gestation; right and left ovulation rates; ovulatory difference, estimated as the difference between the right and left ovulation rates; litter size, estimated as the total number of kits born and the number of kits born alive, both recorded at each parity. Totals of 1,477 and 3,031 records from 900 females were used to analyze ovulation rate and litter size, respectively, whereas 1,471 records were used to analyze ovulatory difference, right ovulation rate, and left ovulation rate. Data were analyzed using Bayesian methodology. Heritabilities of ovulation rate, litter size, number of kits born alive, right ovulation rate, left ovulation rate, and ovulatory difference were 0.16, 0.09, 0.08, 0.09, 0.04 and 0.03, respectively. Phenotypic correlations of ovulation rate with litter size, number of kits born alive, and ovulatory difference were 0.09, 0.01, and 0.14, respectively. Genetic correlations of ovulation rate with litter size and with number of kits born alive were estimated with low accuracy, and there was not much evidence for the sign of the correlation. The genetic correlation between ovulation rate and ovulatory difference was positive (P = 0.91). In 10 generations of selection, ovulation rate increased in 1.32 oocytes, with most of the response taking place in the right ovary (1.06 oocytes), but there was no correlated response on litter size (-0.15 kits). In summary, the direct response to selection for ovulation rate was relevant, but it did not modify litter size because of an increase in prenatal mortality.