Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance.

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem.

The evolution of diverse antimicrobial responses in vancomycin-intermediate and its therapeutic implications.

causes endocarditis, osteomyelitis, and bacteremia. Clinicians often prescribe vancomycin as an empiric therapy to account for methicillin-resistant (MRSA) and narrow treatment based on culture susceptibility results. However, these results reflect a single time point before empiric treatment and represent a limited subset of the total bacterial population within the patient.

Drug dependence in cancer is exploitable by optimally constructed treatment holidays.

Cancers with acquired resistance to targeted therapy can become simultaneously dependent on the presence of the targeted therapy drug for survival, suggesting that intermittent therapy may slow resistance. However, relatively little is known about which tumours are likely to become dependent and how to schedule intermittent therapy optimally. Here we characterized drug dependence across a panel of over 75 MAPK-inhibitor-resistant BRAF mutant melanoma models at the population and single-clone levels.

Frequency-dependent ecological interactions increase the prevalence, and shape the distribution, of pre-existing drug resistance.

The evolution of resistance remains one of the primary challenges for modern medicine from infectious diseases to cancers. Many of these resistance-conferring mutations often carry a substantial fitness cost in the absence of treatment. As a result, we would expect these mutants to undergo purifying selection and be rapidly driven to extinction.

The balance between intrinsic and ecological fitness defines new regimes in eco-evolutionary population dynamics.

Selection upon intrinsic fitness differences is one of the most basic mechanisms of evolution, fundamental to all biology. Equally, within macroscopic populations and microscopic environments, ecological interactions influence evolution. Direct experimental evidence of ecological selection between microscopic agents continues to grow.

Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance.

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem.

Measuring competitive exclusion in non-small cell lung cancer.

In this study, we experimentally measure the frequency-dependent interactions between a gefitinib-resistant non-small cell lung cancer population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. Using frequency-dependent growth rate data, we then show that gefitinib treatment results in competitive exclusion of the ancestor, while the absence of treatment results in a likely, but not guaranteed, exclusion of the resistant strain.

Evolution in alternating environments with tunable interlandscape correlations.

Natural populations are often exposed to temporally varying environments. Evolutionary dynamics in varying environments have been extensively studied, although understanding the effects of varying selection pressures remains challenging. Here, we investigate how cycling between a pair of statistically related fitness landscapes affects the evolved fitness of an asexually reproducing population.

Antibiotic interactions shape short-term evolution of resistance in E. faecalis.

Antibiotic combinations are increasingly used to combat bacterial infections. Multidrug therapies are a particularly important treatment option for E. faecalis, an opportunistic pathogen that contributes to high-inoculum infections such as infective endocarditis.

Using Selection by Nonantibiotic Stressors to Sensitize Bacteria to Antibiotics.

Evolutionary adaptation of bacteria to nonantibiotic selective forces, such as osmotic stress, has been previously associated with increased antibiotic resistance, but much less is known about potentially sensitizing effects of nonantibiotic stressors. In this study, we use laboratory evolution to investigate adaptation of Enterococcus faecalis, an opportunistic bacterial pathogen, to a broad collection of environmental agents, ranging from antibiotics and biocides to extreme pH and osmotic stress. We find that nonantibiotic selection frequently leads to increased sensitivity to other conditions, including multiple antibiotics.

Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance.

Evolved resistance to one antibiotic may be associated with "collateral" sensitivity to other drugs. Here, we provide an extensive quantitative characterization of collateral effects in Enterococcus faecalis, a gram-positive opportunistic pathogen. By combining parallel experimental evolution with high-throughput dose-response measurements, we measure phenotypic profiles of collateral sensitivity and resistance for a total of 900 mutant-drug combinations.

A metabolic interpretation for the response of cellular autofluorescence to chemical perturbations assessed using spectral phasor analysis.

Analytical approaches for sensing cellular NADH conformation from autofluorescence signals have significance because NADH is a metabolic indicator and endogenous biomarker. Recently, spectral detection of multiple cellular NADH forms during chemically-induced metabolic response was reported, however because NADH is solvatochromic and the spectral change is small, the possibility of a non-metabolic interpretation needs to be considered. Here we investigate the response of UV-excited autofluorescence to a range of well-known chemicals affecting fermentation, respiration, and oxidative-stress pathways in .

Sensing NADH conformation using phasor analysis on fluorescence spectra.

Phasor analysis on fluorescence signals is a sensitive approach for analyzing multicomponent systems. Initially developed for time-resolved measurements, a spectral version has been used for the rapid identification of regions during the spectral imaging of biological systems. Here we show that quantitative information regarding conformation can be obtained from phasor analysis of fluorescence spectrum shape.

Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections.

The inoculum effect (IE) is an increase in the minimum inhibitory concentration (MIC) of an antibiotic as a function of the initial size of a microbial population. The IE has been observed in a wide range of bacteria, implying that antibiotic efficacy may depend on population density. Such density dependence could have dramatic effects on bacterial population dynamics and potential treatment strategies, but explicit measures of per capita growth as a function of density are generally not available.

Autofluorescence from NADH Conformations Associated with Different Metabolic Pathways Monitored Using Nanosecond-Gated Spectroscopy and Spectral Phasor Analysis.

Cellular NADH conformation is increasingly recognized as an endogenous optical biomarker and metabolic indicator. Recently, we reported a real-time approach for tracking metabolism on the basis of the quantification of UV-excited autofluorescence spectrum shape. Here, we use nanosecond-gated spectral acquisition, combined with spectrum-shape quantification, to monitor the long excited-state lifetime autofluorescence (usually associated with protein-bound NADH conformations) separately from the autofluorescence signal as a whole.

Note: A micro-perfusion system for use during real-time physiological studies under high pressure.

We construct a micro-perfusion system using piston screw pump generators for use during real-time, high-pressure physiological studies. Perfusion is achieved using two generators, with one generator being compressed while the other is retracted, thus maintaining pressurization while producing fluid flow. We demonstrate control over perfusion rates in the 10-μl/s range and the ability to change between fluid reservoirs at up to 50 MPa.

The real-time quantification of autofluorescence spectrum shape for the monitoring of mitochondrial metabolism.

The cellular proportion of free and protein-bound NADH complexes is increasingly recognized as a metabolic indicator and biomarker. Because free and bound forms exhibit different fluorescence spectra, we consider whether autofluorescence shape sufficiently correlates with mitochondrial metabolism to be useful for monitoring in cellular suspensions. Several computational approaches for rapidly quantifying spectrum shape are used to detect Saccharomyces cereviseae response to oxygenation, and to the addition of mitochondrial functional modifiers and metabolic substrates.