Modeling spatial evolution of multi-drug resistance under drug environmental gradients.

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria based on a drug-concentration rescaling approach. We show how the resistance to drugs in space, and the consequent adaptation of growth rate, is governed by a Price equation with diffusion, integrating features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Although in many evolution models, per capita growth rate is a natural surrogate for fitness, in spatially-extended, potentially heterogeneous habitats, fitness is an emergent property that potentially reflects additional complexities, from boundary conditions to the specific spatial variation of growth rates. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical metric for characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem, λ1. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits to the relative advantage of each mutant across the environment. Our approach allows one to predict the precise outcomes of selection among mutants over space, ultimately from comparing their λ1 values, which encode a critical interplay between growth functions, movement traits, habitat size and boundary conditions. Such mathematical understanding opens new avenues for multi-drug therapeutic optimization.

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 BRAFV600E mutant melanoma models at the population and single-clone levels. Melanocytic differentiated models exhibited a much greater tendency to give rise to drug-dependent progeny than their dedifferentiated counterparts. Mechanistically, acquired loss of microphthalmia-associated transcription factor in differentiated melanoma models drives ERK-JunB-p21 signalling to enforce drug dependence. We identified the optimal scheduling of 'drug holidays' using simple mathematical models that we validated across short and long timescales. Without detailed knowledge of tumour characteristics, we found that a simple adaptive therapy protocol can produce near-optimal outcomes using only measurements of total population size. Finally, a spatial agent-based model showed that optimal schedules derived from exponentially growing cells in culture remain nearly optimal in the context of tumour cell turnover and limited environmental carrying capacity. These findings may guide the implementation of improved evolution-inspired treatment strategies for drug-dependent cancers.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients.

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria, based on a rescaling approach (Gjini and Wood, 2021). We show how the resistance to drugs in space, and the consequent adaptation of growth rate is governed by a Price equation with diffusion. The covariance terms in this equation integrate features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits, to the relative advantage of each mutant across the environment. Such a mathematical understanding allows to predict the precise outcomes of selection over space, ultimately from the fundamental balance between growth and movement traits, and their diversity in a population.

Blue carbon benefits from global saltmarsh restoration.

Coastal saltmarshes are found globally, yet are 25%-50% reduced compared with their historical cover. Restoration is incentivised by the promise that marshes are efficient storers of 'blue' carbon, although the claim lacks substantiation across global contexts. We synthesised data from 431 studies to quantify the benefits of saltmarsh restoration to carbon accumulation and greenhouse gas uptake. The results showed global marshes store approximately 1.41-2.44 Pg carbon. Restored marshes had very low greenhouse gas (GHG) fluxes and rapid carbon accumulation, resulting in a mean net accumulation rate of 64.70 t CO2 e ha-1 year-1 . Using this estimate and potential restoration rates, we find saltmarsh regeneration could result in 12.93-207.03 Mt CO2 e accumulation per year, offsetting the equivalent of up to 0.51% global energy-related CO2 emissions-a substantial amount, considering marshes represent <1% of Earth's surface. Carbon accumulation rates and GHG fluxes varied contextually with temperature, rainfall and dominant vegetation, with the eastern coasts of the USA and Australia particular hotspots for carbon storage. While the study reveals paucity of data for some variables and continents, suggesting need for further research, the potential for saltmarsh restoration to offset carbon emissions is clear. The ability to facilitate natural carbon accumulation by saltmarshes now rests principally on the action of the management-policy community and on financial opportunities for supporting restoration.

Conservation implications of a mismatch between data availability and demographic impact.

Cost-effective use of limited conservation resources requires understanding which data most contribute to alleviating biodiversity declines. Interventions might reasonably prioritise life-cycle transitions with the greatest influence on population dynamics, yet some contributing vital rates are particularly challenging to document. This risks managers making decisions without sufficient empirical coverage of the spatiotemporal variation experienced by the species. Here, we aimed to explore whether the number of studies contributing estimates for a given life-stage transition aligns with that transition's demographic impact on population growth rate, λ. We parameterised a matrix population model using meta-analysis of vital rates for the common eider (Somateria mollissima), an increasingly threatened yet comparatively data-rich species of seaduck, for which some life stages are particularly problematic to study. Female common eiders exhibit intermittent breeding, with some established breeders skipping one or more years between breeding attempts. Our meta-analysis yielded a breeding propensity of 0.72, which we incorporated into our model with a discrete and reversible 'nonbreeder' stage (to which surviving adults transition with a probability of 0.28). The transitions between breeding and nonbreeding states had twice the influence on λ than fertility (summed matrix-element elasticities of 24% and 11%, respectively), whereas almost 15 times as many studies document components of fertility than breeding propensity (n = 103 and n = 7, respectively). The implications of such mismatches are complex because the motivations for feasible on-the-ground conservation actions may be different from what is needed to reduce uncertainty in population projections. Our workflow could form an early part of the toolkit informing future investment of finite resources, to avoid repeated disconnects between data needs and availability thwarting evidence-led conservation.

Scale-free correlations and potential criticality in weakly ordered populations of brain cancer cells.

Collective behavior spans several orders of magnitude of biological organization, from cell colonies to flocks of birds. We used time-resolved tracking of individual glioblastoma cells to investigate collective motion in an ex vivo model of glioblastoma. At the population level, glioblastoma cells display weakly polarized motion in the (directional) velocities of single cells. Unexpectedly, fluctuations in velocities are correlated over distances many times the size of a cell. Correlation lengths scale linearly with the maximum end-to-end length of the population, indicating that they are scale-free and lack a characteristic decay scale other than the size of the system. Last, a data-driven maximum entropy model captures statistical features of the experimental data with only two free parameters: the effective length scale (nc) and strength (J) of local pairwise interactions between tumor cells. These results show that glioblastoma assemblies exhibit scale-free correlations in the absence of polarization, suggesting that they may be poised near a critical point.

Antibiotic resistance in bacterial communities.

Bacteria are single-celled organisms, but the survival of microbial communities relies on complex dynamics at the molecular, cellular, and ecosystem scales. Antibiotic resistance, in particular, is not just a property of individual bacteria or even single-strain populations, but depends heavily on the community context. Collective community dynamics can lead to counterintuitive eco-evolutionary effects like survival of less resistant bacterial populations, slowing of resistance evolution, or population collapse, yet these surprising behaviors are often captured by simple mathematical models. In this review, we highlight recent progress - in many cases, advances driven by elegant combinations of quantitative experiments and theoretical models - in understanding how interactions between bacteria and with the environment affect antibiotic resistance, from single-species populations to multispecies communities embedded in an ecosystem.

Assessing trade-offs in avian behaviour using remotely collected data from a webcam.

Individual animals engage in many behaviours which are mutually exclusive, and so where individuals increase the duration of time spent on one type of behavioural activity, this must be offset by a corresponding decrease in at least one other type of behaviour. To understand the variation observed in animal behaviour, researchers need to know how individuals trade-off these mutually-exclusive behaviours within their time-activity budget. In this study, we used remotely collected behavioural observations made from a live-streaming webcam to investigate trade-offs in the behaviour of two bird species, the mute swan (Cygnus olor) and whooper swan (Cygnus cygnus). For both species, we tested for correlations in the duration of time spent on key mutually exclusive behaviours: aggression, foraging, maintenance, and resting. We detected a negative association between aggression and resting behaviours in both species, indicating that increased aggression is achieved at the expense of resting behaviour. In contrast, there was no apparent trade-off between aggression and foraging, aggression and maintenance, or maintenance and resting. Foraging and resting behaviours were negatively correlated in both species, highlighting a trade-off between these distinct modes of behaviour. A trade-off between foraging and maintenance behaviours was detected for the sedentary mute swans, but not the migratory whooper swans. Our findings show how birds can trade-off their time investments in mutually exclusive behaviours within their time-activity budgets. Moreover, our study demonstrates how remotely-collected data can be used to investigate fundamental questions in behavioural research.

Finding the right sequence of drugs.

Rapidly switching between similar antibiotics may help to slow down the evolution of resistance.

A Qualitative Analysis of UK Wetland Visitor Centres as a Health Resource.

The health benefits associated with spending time in natural environments have been highlighted during the COVID-19 pandemic. Lockdowns and restrictions to safeguard public health have exacerbated the pre-existing mental health crisis and rise of non-communicable diseases. Thus, the importance of nature as a health resource has been elevated, hastening calls for a better understanding of how health benefits might differ across user groups and nature provisions. In this regard, urban green spaces have become the greatest research focus; however, blue spaces, especially inland freshwater (e.g., wetlands), remain less studied. First-hand user experiences are also under-represented. This exploratory study examines the motivations and benefits of active wetland centre users in the UK, both during and after visits. Responses to three open-ended questions were collated online from 385 participants, and a qualitative content analysis was conducted based on an existing taxonomy from users of urban green spaces. The results showed strong motivations to visit due to the biodiversity at the site (mainly the birdlife), while less tangible nature (e.g., fresh air) and amenities were also important. In contrast to other studies on natural environments, physical activity was a less influential motivation. Salient derived effects included positive and intensely positive emotions, relaxation and mental restoration. After visits to wetland centres, feelings of vitality and satisfaction were the most prominent effects that emerged. For decision-makers looking to leverage inland blue spaces for public health benefit, our results highlight the broad range and relative prominence of the reasons for use and the associated perceived health benefits derived by users of UK wetland centres. They highlight how biodiversity, abiotic nature and good amenities are important qualities to consider when planning, managing and encouraging people to use natural environments for health benefit, qualities that may also provide important environmental co-benefits.

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance.

Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction [DI]) and how resistance to one drug impacts resistance to the other (collateral effects [CE]). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug-sensitive (ancestral) and drug-resistant (mutant) populations. We show that evolved resistance to the component drugs - and in turn, the adaptation of growth rate - is governed by a Price equation whose covariance terms encode geometric features of both the two-drug-response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. We also demonstrate how constraints on available mutational pathways can be incorporated into the framework, adding a third key driver of evolution. Our results clarify the complex relationship between drug interactions and collateral effects in multidrug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally explicit selective outcomes.

Spatial segregation and cooperation in radially expanding microbial colonies under antibiotic stress.

Antibiotic resistance in microbial communities reflects a combination of processes operating at different scales. In this work, we investigate the spatiotemporal dynamics of bacterial colonies comprised of drug-resistant and drug-sensitive cells undergoing range expansion under antibiotic stress. Using the opportunistic pathogen Enterococcus faecalis with plasmid-encoded ß-lactamase, we track colony expansion dynamics and visualize spatial patterns in fluorescently labeled populations exposed to antibiotics. We find that the radial expansion rate of mixed communities is approximately constant over a wide range of drug concentrations and initial population compositions. Imaging of the final populations shows that resistance to ampicillin is cooperative, with sensitive cells surviving in the presence of resistant cells at otherwise lethal concentrations. The populations exhibit a diverse range of spatial segregation patterns that depend on drug concentration and initial conditions. Mathematical models indicate that the observed dynamics are consistent with global cooperation, despite the fact that ß-lactamase remains cell-associated. Experiments confirm that resistant colonies provide a protective effect to sensitive cells on length scales multiple times the size of a single colony, and populations seeded with (on average) no more than a single resistant cell can produce mixed communities in the presence of the drug. While biophysical models of drug degradation suggest that individual resistant cells offer only short-range protection to neighboring cells, we show that long-range protection may arise from synergistic effects of multiple resistant cells, providing surprisingly large protection zones even at small population fractions.

Potential Utility of Pre-Emptive Germline Pharmacogenetics in Breast Cancer.

Patients with breast cancer often receive many drugs to manage the cancer, side effects associated with cancer treatment, and co-morbidities (i.e., polypharmacy). Drug-drug and drug-gene interactions contribute to the risk of adverse events (AEs), which could lead to non-adherence and reduced efficacy. Here we investigated several well-characterized inherited (germline) pharmacogenetic (PGx) targets in 225 patients with breast cancer. All relevant clinical, pharmaceutical, and PGx diplotype data were aggregated into a single unifying informatics platform to enable an exploratory analysis of the cohort and to evaluate pharmacy ordering patterns. Of the drugs recorded, there were 38 for which high levels of evidence for clinical actionability with PGx was available from the US FDA and/or the Clinical Pharmacogenetics Implementation Consortium (CPIC). These data were associated with 10 pharmacogenes: DPYD, CYP2C9, CYP2C19, CYP2D6, CYP3A5, CYP4F2, G6PD, MT-RNR1, SLCO1B1, and VKORC1. All patients were taking at least one of the 38 drugs and had inherited at least one actionable PGx variant that would have informed prescribing decisions if this information had been available pre-emptively. The non-cancer drugs with PGx implications that were common (prescribed to at least one-third of patients) included anti-depressants, anti-infectives, non-steroidal anti-inflammatory drugs, opioids, and proton pump inhibitors. Based on these results, we conclude that pre-emptive PGx testing may benefit patients with breast cancer by informing drug and dose selection to maximize efficacy and minimize AEs.

A warm jet in a cold ocean.

Unprecedented quantities of heat are entering the Pacific sector of the Arctic Ocean through Bering Strait, particularly during summer months. Though some heat is lost to the atmosphere during autumn cooling, a significant fraction of the incoming warm, salty water subducts (dives beneath) below a cooler fresher layer of near-surface water, subsequently extending hundreds of kilometers into the Beaufort Gyre. Upward turbulent mixing of these sub-surface pockets of heat is likely accelerating sea ice melt in the region. This Pacific-origin water brings both heat and unique biogeochemical properties, contributing to a changing Arctic ecosystem. However, our ability to understand or forecast the role of this incoming water mass has been hampered by lack of understanding of the physical processes controlling subduction and evolution of this this warm water. Crucially, the processes seen here occur at small horizontal scales not resolved by regional forecast models or climate simulations; new parameterizations must be developed that accurately represent the physics. Here we present novel high resolution observations showing the detailed process of subduction and initial evolution of warm Pacific-origin water in the southern Beaufort Gyre.

Roadmap on biology in time varying environments.

Biological organisms experience constantly changing environments, from sudden changes in physiology brought about by feeding, to the regular rising and setting of the Sun, to ecological changes over evolutionary timescales. Living organisms have evolved to thrive in this changing world but the general principles by which organisms shape and are shaped by time varying environments remain elusive. Our understanding is particularly poor in the intermediate regime with no separation of timescales, where the environment changes on the same timescale as the physiological or evolutionary response. Experiments to systematically characterize the response to dynamic environments are challenging since such environments are inherently high dimensional. This roadmap deals with the unique role played by time varying environments in biological phenomena across scales, from physiology to evolution, seeking to emphasize the commonalities and the challenges faced in this emerging area of research.

Rapid Oxidation and Reduction of Lithium for Improved Cycling Performance and Increased Homogeneity.

This work enables highly "uniform" and "reversible" deposition of Li metal in carbonate electrolytes through a one-time rapid oxidation and reduction (ROAR) treatment. Over the years, Li metal has been plagued with irreversible dendritic growths that create isolated and unusable structures called "dead Li". Accumulation of dead Li negatively impacts the ion transport, performance, and safety of Li metal batteries. To address this long-standing problem, we have developed an in situ process to uniformly create reversible Li deposits. Our results demonstrate that a combination of high-voltage pulses, which rapidly oxidize and reduce Li in both directions (ROAR treatment), leads to strikingly more homogeneous morphology and eliminates reaction pathway transitions. We validate that ROAR treatments eliminate traditional "mossy dendrites" under extended cycling (<250 cycles) in standard carbonate-based electrolytes. Moreover, ROAR treatments create a 500% reduction in overpotential for electrodissolution/deposition and eliminate "peaking" voltage behavior.

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. We construct pairs of fitness landscapes that share global fitness features but are correlated with one another in a tunable way, resulting in landscape pairs with specific correlations. We find that switching between these landscape pairs, depending on the ruggedness of the landscape and the interlandscape correlation, can either increase or decrease steady-state fitness relative to evolution in single environments. In addition, we show that switching between rugged landscapes often selects for increased fitness in both landscapes, even in situations where the landscapes themselves are anticorrelated. We demonstrate that positively correlated landscapes often possess a shared maximum in both landscapes that allows the population to step through sub-optimal local fitness maxima that often trap single landscape evolution trajectories. Finally, we demonstrate that switching between anticorrelated paired landscapes leads to ergodic-like dynamics where each genotype is populated with nonzero probability, dramatically lowering the steady-state fitness in comparison to single landscape evolution.