Variability in cell division among anatomical sites shapes Escherichia coli antibiotic survival in a urinary tract infection mouse model.

Urinary tract infection (UTI), mainly caused by Escherichia coli, are frequent and have a recurrent nature even after antibiotic treatment. Potential bacterial escape mechanisms include growth defects, but probing bacterial division in vivo and establishing its relation to the antibiotic response remain challenging. Using a synthetic reporter of cell division, we follow the temporal dynamics of cell division for different E. coli clinical strains in a UTI mouse model with and without antibiotics. We show that more bacteria are actively dividing in the kidneys and urine compared with the bladder. Bacteria that survive antibiotic treatment are consistently non-dividing in three sites of infection. Additionally, we demonstrate how both the strain in vitro persistence profile and the microenvironment impact infection and treatment dynamics. Understanding the relative contribution of the host environment, growth heterogeneity, non-dividing bacteria, and antibiotic persistence is crucial to improve therapies for recurrent infections.

Perturbation and resilience of the gut microbiome up to 3 months after ß-lactams exposure in healthy volunteers suggest an important role of microbial ß-lactamases.

BACKGROUND: Antibiotics notoriously perturb the gut microbiota. We treated healthy volunteers either with cefotaxime or ceftriaxone for 3 days, and collected in each subject 12 faecal samples up to day 90. Using untargeted and targeted phenotypic and genotypic approaches, we studied the changes in the bacterial, phage and fungal components of the microbiota as well as the metabolome and the ß-lactamase activity of the stools. This allowed assessing their degrees of perturbation and resilience. RESULTS: While only two subjects had detectable concentrations of antibiotics in their faeces, suggesting important antibiotic degradation in the gut, the intravenous treatment perturbed very significantly the bacterial and phage microbiota, as well as the composition of the metabolome. In contrast, treatment impact was relatively low on the fungal microbiota. At the end of the surveillance period, we found evidence of resilience across the gut system since most components returned to a state like the initial one, even if the structure of the bacterial microbiota changed and the dynamics of the different components over time were rarely correlated. The observed richness of the antibiotic resistance genes repertoire was significantly reduced up to day 30, while a significant increase in the relative abundance of ß-lactamase encoding genes was observed up to day 10, consistent with a concomitant increase in the ß-lactamase activity of the microbiota. The level of ß-lactamase activity at baseline was positively associated with the resilience of the metabolome content of the stools. CONCLUSIONS: In healthy adults, antibiotics perturb many components of the microbiota, which return close to the baseline state within 30 days. These data suggest an important role of endogenous ß-lactamase-producing anaerobes in protecting the functions of the microbiota by de-activating the antibiotics reaching the colon. Video Abstract.

Changing fitness effects of mutations through long-term bacterial evolution.

The distribution of fitness effects of new mutations shapes evolution, but it is challenging to observe how it changes as organisms adapt. Using Escherichia coli lineages spanning 50,000 generations of evolution, we quantify the fitness effects of insertion mutations in every gene. Macroscopically, the fraction of deleterious mutations changed little over time whereas the beneficial tail declined sharply, approaching an exponential distribution. Microscopically, changes in individual gene essentiality and deleterious effects often occurred in parallel; altered essentiality is only partly explained by structural variation. The identity and effect sizes of beneficial mutations changed rapidly over time, but many targets of selection remained predictable because of the importance of loss-of-function mutations. Taken together, these results reveal the dynamic-but statistically predictable-nature of mutational fitness effects.