Community interactions drive the evolution of antibiotic tolerance in bacteria.

The emergence of antibiotic tolerance (prolonged survival against exposure) in natural bacterial populations is a major concern. Since it has been studied primarily in isogenic populations, we do not yet understand how ecological interactions in a diverse community impact the evolution of tolerance. To address this, we studied the evolutionary dynamics of a synthetic bacterial community composed of two interacting strains. In this community, an antibiotic-resistant strain protected the other, susceptible strain by degrading the antibiotic ampicillin in the medium. Surprisingly, we found that in the presence of antibiotics, the susceptible strain evolved tolerance. Tolerance was typified by an increase in survival as well as an accompanying decrease in the growth rate, highlighting a trade-off between the two. A simple mathematical model explained that the observed decrease in the death rate, even when coupled with a decreased growth rate, is beneficial in a community with weak protective interactions. In the presence of strong interactions, the model predicted that the trade-off would instead be detrimental, and tolerance would not emerge, which we experimentally verified. By whole genome sequencing the evolved tolerant isolates, we identified two genetic hot spots which accumulated mutations in parallel lines, suggesting their association with tolerance. Our work highlights that ecological interactions can promote antibiotic tolerance in bacterial communities, which has remained understudied.

The impact of Hfq-mediated sRNA-mRNA interactome on the virulence of enteropathogenic Escherichia coli.

Small RNAs (sRNAs) exert their regulation posttranscriptionally by base pairing with their target mRNAs, often in association with the RNA chaperone protein Hfq. Here, integrating RNA-seq­based technologies and bioinformatics, we deciphered the Hfq-mediated sRNA-target interactome of enteropathogenic Escherichia coli (EPEC). The emerging network comprises hundreds of sRNA-mRNA pairs, including mRNAs of virulence-associated genes interacting with known sRNAs encoded within the core genome, as well as with newly found sRNAs encoded within pathogenicity islands. Some of the sRNAs affect multiple virulence genes, suggesting they function as hubs of virulence control. We further analyzed one such sRNA hub, MgrR, and one of its targets identified here, the major virulence-associated chaperon, cesT. We show that MgrR adjusts the level of EPEC cytotoxicity via regulation of CesT expression. Our results reveal an elaborate sRNA-mRNA interactome controlling the pathogenicity of EPEC and reinforce a role for sRNAs in the control of pathogen-host interaction.

Persistence to anti-cancer treatments in the stationary to proliferating transition.

The heterogeneous responses of clonal cancer cells to treatment is understood to be caused by several factors, including stochasticity, cell-cycle dynamics, and different micro-environments. In a tumor, cancer cells may encounter fluctuating conditions and transit from a stationary culture to a proliferating state, for example this may occur following treatment. Here, we undertake a quantitative evaluation of the response of single cancerous lymphoblasts (L1210 cells) to various treatments administered during this transition. Additionally, we developed an experimental system, a "Mammalian Mother Machine," that tracks the fate of thousands of mammalian cells over several generations under transient exposure to chemotherapeutic drugs. Using our developed system, we were able to follow the same cell under repeated treatments and continuously track many generations. We found that the dynamics of the transition between stationary and proliferative states are highly variable and affect the response to drug treatment. Using cell-cycle markers, we were able to isolate a subpopulation of persister cells with distinctly higher than average survival probability. The higher survival rate encountered with cell-cycle phase specific drugs was associated with a significantly longer time-till-division, and was reduced by a non cell-cycle specific drug. Our results suggest that the variability of transition times from the stationary to the proliferating state may be an obstacle hampering the effectiveness of drugs and should be taken into account when designing treatment regimens.

Distinguishing between stochasticity and determinism: Examples from cell cycle duration variability.

We describe a recent approach for distinguishing between stochastic and deterministic sources of variability, focusing on the mammalian cell cycle. Variability between cells is often attributed to stochastic noise, although it may be generated by deterministic components. Interestingly, lineage information can be used to distinguish between variability and determinism. Analysis of correlations within a lineage of the mammalian cell cycle duration revealed its deterministic nature. Here, we discuss the sources of such variability and the possibility that the underlying deterministic process is due to the circadian clock. Finally, we discuss the "kicked cell cycle" model and its implication on the study of the cell cycle in healthy and cancerous tissues.