No abstract

The antifouling potential of electric polarization combined and not combined with biocides was studied in nonsaline warm water with high organic content. Deinococcus geothermalis is a bacterium known for forming colored biofilms in paper machines and for its persistence against cleaning and chemical treatments. When D. geothermalis biofilms grown for 24 h in simulated paper machine water were exposed to cathodic or cathodically weighted pulsed polarization at least 60% (P < 0.05) of the biofilms were removed from stainless steel (AISI 316L). Biofilm removal by 25 ppm (effective substances 5-25 ppm) of oxidizing biocides (bromochloro-5,5-dimethylhydantoin, 2,2-dibromo-2-cyanoacetamide, peracetic acid) increased to 70% when combined with cathodically weighted pulsed polarization. Using a novel instrument that allows real-time detection of reactive oxygen species (ROS) we showed that the polarization program effective in antifouling generated ROS in a pulsed manner on the steel surface. We thus suggest that the observed added value of oxidative biocides combined with polarization depended on ROS. This suggestion was supported by the finding that a reductive biocide, methylene bisthiocyanate, counteracted the antifouling effect of polarization.

Population turnover, a key trait shaped by the organism's life history strategy, plays an important role in eco-evolutionary dynamics by fixing the timescale for individual birth and death events as well as in determining the level of demographic stochasticity related to growth. Yet, the standard theory of population genetics, and the models heavily used in the related data analysis, have largely ignored the role of turnover. Here we propose a reformulation of population genetics starting from the first principles of birth and death and show that the role of turnover is evolutionarily important. We derive a general stochastic differential equation for the frequency dynamics of competing birth-death processes and determine the appropriate turnover corrections for the essential results regarding fixation, establishment, and substitution of mutants. Our results reveal how both the absolute and relative turnover rates influence evolution. We further describe a deterministic turnover selection, the turnover flux, which operates in small populations. Finally, we analyse the evolution of mean turnover and show how it explains the key eco-evolutionary mechanisms underlying demographic transitions. In conclusion, our results explicitly show how competing life-history strategies, demographic stochasticity, ecological feedback, and evolution are inseparably intertwined, thus calling for a unified theory development starting from the underlying mechanisms of birth and death.

Increasing body of experimental evidence suggests that anticancer and antimicrobial therapies may themselves promote the acquisition of drug resistance by increasing mutability. The successful control of evolving populations requires that such biological costs of control are identified, quantified and included to the evolutionarily informed treatment protocol. Here we identify, characterise and exploit a trade-off between decreasing the target population size and generating a surplus of treatment-induced rescue mutations. We show that the probability of cure is maximized at an intermediate dosage, below the drug concentration yielding maximal population decay, suggesting that treatment outcomes may in some cases be substantially improved by less aggressive treatment strategies. We also provide a general analytical relationship that implicitly links growth rate, pharmacodynamics and dose-dependent mutation rate to an optimal control law. Our results highlight the important, but often neglected, role of fundamental eco-evolutionary costs of control. These costs can often lead to situations, where decreasing the cumulative drug dosage may be preferable even when the objective of the treatment is elimination, and not containment. Taken together, our results thus add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and motivate further experimental and clinical investigation of the mutagenicity and other hidden collateral costs of therapies.

Evolution of drug resistance to anticancer, antimicrobial and antiviral therapies is widespread among cancer and pathogen cell populations. Classical theory posits strictly that genetic and phenotypic variation is generated in evolving populations independently of the selection pressure. However, recent experimental findings among antimicrobial agents, traditional cytotoxic chemotherapies and targeted cancer therapies suggest that treatment not only imposes selection but also affects the rate of adaptation via altered mutational processes. Here we analyze a model with drug-induced increase in mutation rate and explore its consequences for treatment optimization. We argue that the true biological cost of treatment is not limited to the harmful side-effects, but instead realizes even more profoundly by fundamentally changing the underlying eco-evolutionary dynamics within the microenvironment. Factoring in such costs (or collateral damage) of control is at the core of successful therapy design and can unify different evolution-based approaches to therapy optimization. Using the concept of evolutionary rescue, we formulate the treatment as an optimal control problem and solve the optimal elimination strategy, which minimizes the probability of evolutionary rescue. Our solution exploits a trade-off, where increasing the drug concentration has two opposing effects. On the one hand, it reduces de novo mutations by decreasing the size of the target cell population faster; on the other hand, a higher dosage generates a surplus of treatment-induced mutations. We show that aggressive elimination strategies, which aim at eradication as fast as possible and which represent the current standard of care, can be detrimental even with modest drug-induced increases (fold change ≤10) to the baseline mutation rate. Our findings highlight the importance of dose dependencies in resistance evolution and motivate further investigation of the mutagenicity and other hidden collateral costs of therapies that promote resistance.Author summaryThe evolution of drug resistance is a particularly problematic and frequent outcome of cancer and antimicrobial therapies. Recent research suggests that these treatments may enhance the evolvability of the target population not only via inducing intense selection pressures but also via altering the underlying mutational processes. Here we investigate the consequences of such drug-induced evolution by considering a mathematical model with explicitly dose-dependent mutation rate. We identify, characterize and exploit a trade-off between decreasing the target population size as fast as possible and generating a surplus of treatment-induced de novo mutations. By formulating the treatment as an optimal control problem over the evolution of the target population, we find the optimal treatment strategy, which minimizes the probability of evolutionary rescue. We show that this probability changes non-monotonically with the cumulative drug concentration and is minimized at an intermediate dosage. Our results are immediately amenable to experimental investigation and motivate further study of the various mutagenic and other hidden collateral costs of treatment. Taken together, our results add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and stimulate further clinical trials on alternative treatment strategies.