20 research outputs found
The physiology and genetics of bacterial responses to antibiotic combinations
No full textCombining several antibiotics, either in mixtures or sequential order, is proposed to increase treatment efficacy and reduce resistance evolution. In this Review, Andersson and colleagues discuss the effects of antibiotic combinations, the directional effects of previous antibiotic treatments and the role of stress-response systems as well as the interactions between drugs and resistance mutations. Several promising strategies based on combining or cycling different antibiotics have been proposed to increase efficacy and counteract resistance evolution, but we still lack a deep understanding of the physiological responses and genetic mechanisms that underlie antibiotic interactions and the clinical applicability of these strategies. In antibiotic-exposed bacteria, the combined effects of physiological stress responses and emerging resistance mutations (occurring at different time scales) generate complex and often unpredictable dynamics. In this Review, we present our current understanding of bacterial cell physiology and genetics of responses to antibiotics. We emphasize recently discovered mechanisms of synergistic and antagonistic drug interactions, hysteresis in temporal interactions between antibiotics that arise from microbial physiology and interactions between antibiotics and resistance mutations that can cause collateral sensitivity or cross-resistance. We discuss possible connections between the different phenomena and indicate relevant research directions. A better and more unified understanding of drug and genetic interactions is likely to advance antibiotic therapy.</p
Beauty’s Price: Femininity as an Aesthetic Commodity in Elizabeth’s von Arnim’s Novels
No full textWas Elizabeth von Arnim an aesthetic writer? Scholars and critics have claimed her as an Edwardian, a middlebrow author, a popular bestselling writer and a satirist of acerbic wit. By including her in her seminal work The Female Forgotten Aesthetes (2000) Talia Schaffer has encouraged readers to look at von Arnim’s early works from yet another and, as it turns out, very fruitful perspective. Schaffer suggests that Elizabeth, the diarist of Elizabeth and Her German Garden (1898), who was von A..
Regulators of CS.
No full textChange of susceptibility to NIT relative to wild-type strains for constructed single and double mutants. MIC was measured using Etest (bioMérieux, Marcy-l’Étoile, France) with a high inoculum of 108 cells (mean ± SD, n ≥ 2 biological replicates). Coloured horizontal lines connect genotypes with the same CS mutation. The spoT mutant produced a high frequency of revertants, which are indicated in brighter colour and showed suppression of CS. “Acs1” and “Atox1” are resistance cassettes for genetic engineering. “dup” indicates a forced duplication. Numerical data are available in S1 Data. Acs1, amilCP-cat-sacB cassette 1; Atox1, amilCP-toxin cassette 1; CS, collateral sensitivity; MIC, minimum inhibitory concentration; NIT, nitrofurantoin.</p
NIT CS.
No full textThree single-step mutants with resistance against 3 diverse antibiotics (upward bars) show CS to NIT (downward bars). Fold change of the MIC compared to susceptible wild-type strains. Light shading denotes an inoculum of 106 cells; the overlaid dark shading corresponds to a larger inoculum of 108 cells. Numerical data are available in S1 Data. CS, collateral sensitivity; MEC, mecillinam; MIC, minimum inhibitory concentration; NIT, nitrofurantoin; PRO, protamine sulfate; TGC, tigecycline.</p
CS due to interference with the cellular drug response.
No full text(A) Model for elevated susceptibility to NIT in a lon mutant due to induction of the SOS response. (B) Relative accumulation of SulA protein in the E. coli lon mutant in drug-free medium (mean ± SD, n = 2 biological replicates). (C) Transcription of sulA is unchanged in the lon mutant in drug-free medium (mean ± SD, n = 3 biological replicates). (D) Partial suppression of CS at high temperature (mean ± SD, n = 5 biological replicates). (E) Relative change of MIC compared to wild-type E. coli in mutants of the SOS response. CS is completely suppressed by deletion of sulA and partially suppressed by a noninducible allele of lexA (mean ± SD, n = 3–5 biological replicates). Numerical data are available in S1 Data. CS, collateral sensitivity; MIC, minimum inhibitory concentration; NIT, nitrofurantoin; SulA, Suppressor of Lon.</p
Nitroreductase expression increases susceptibility to NIT.
No full text(A) Susceptibility to NIT, as measured using Etest (bioMérieux, Marcy-l’Étoile, France) with a high inoculum of 108 cells. The nfsA and nfsB open reading frames were cloned into the pBAD vector and expressed using induction by 0.2% arabinose (mean ± SD, n = 3–5 biological replicates). Single expression constructs are labelled “A” and “B” for nfsA and nfsB, respectively. Dual expression is labelled “AB.” (B) Expression of nitroreductases reduces exponential growth rate at low concentrations of NIT. Change of growth rate relative to EV (mean ± SEM, n = 3–5 biological replicates). (C) Deletion of nitroreductases promotes survival at high concentrations of NIT, here measured for E. coli using a time-kill experiment with 24 mg/l (mean ± SEM, n = 3 biological replicates). (D) Expression of nitroreductases reduces survival to NIT, here shown for the pBAD constructs in wild-type E. coli background and 24 mg/l NIT. Dashed lines indicate 0.2% arabinose, solid lines indicate absence of arabinose (mean ± SEM, n = 3 biological replicates). The grey dashed lines in (C) and (D) indicate the limit of detection, based on Poisson estimates. Numerical data are available in S1 Data. ara, arabinose; EV, empty vector; NIT, nitrofurantoin.</p
Temporal dynamics of CS in the <i>lon</i> mutant.
No full textDelayed establishment of inhibition at low concentrations of NIT inhibition in the lon mutant. (A) Growth kinetics of the E. coli wild type and the lon mutant diverge after several hours of equal growth (mean ± SEM, n = 3 biological replicates). (B) Growth kinetics with log-transformed OD600 data for a subset of concentrations. The shaded grey regions indicate the OD600 windows that were used for the determination of exponential growth rate. (C) Dose-dependent inhibition of exponential growth rate in early (bottom) and late (top) OD600 windows (mean ± SD, n = 3 biological replicates). (D) Time point of exponential growth rate divergence between the 2 strains (mean ± SD, n = 3 biological replicates). Numerical data are available in S1 Data. CS, collateral sensitivity; NIT, nitrofurantoin; OD600, optical density at 600 nm.</p
Mechanisms of CS to NIT.
No full textUptake dynamics of radioactively labelled NIT measured as CPM in washed cell pellets of E. coli (A) and S. enterica (B). Measurements are normalized by the OD600 of the sample and expressed relative to the first measurement (n = 3 biological replicates). (C) mRNA expression of nfsA and nfsB relative to wild-type strains of E. coli and S. enterica measured with qRT-PCR (mean ± SD, n = 3 biological replicates). (D) Relative abundance of the proteins NfsA and NfsB measured with mass spectrometry (mean ± SD, n = 2 biological replicates). Student t test, ***P P P S1 Data. CPM, counts per minute; CS, collateral sensitivity; NIT, nitrofurantoin; OD600, optical density at 600 nm; qRT-PCR, quantitative reverse transcription PCR.</p
Data from: Temporal variation in antibiotic environments slows down resistance evolution in pathogenic Pseudomonas aeruginosa
No full textAntibiotic resistance is a growing concern to public health. New treatment strategies may alleviate the situation by slowing down the evolution of resistance. Here, we evaluated sequential treatment protocols using two fully independent laboratory-controlled evolution experiments with the human pathogen Pseudomonas aeruginosa PA14 and two pairs of clinically relevant antibiotics (doripenem/ciprofloxacin and cefsulodin/gentamicin). Our results consistently show that the sequential application of two antibiotics decelerates resistance evolution relative to monotherapy. Sequential treatment enhanced population extinction although we applied antibiotics at sub-lethal dosage. In both experiments, we identified an order-effect of the antibiotics used in the sequential protocol, leading to significant variation in the long-term efficacy of the tested protocols. These variations appear to be caused by asymmetric evolutionary constraints, whereby adaptation to one drug slowed down adaptation to the other drug, but not vice versa. An understanding of such asymmetric constraints may help future development of evolutionary robust treatments against infectious disease
