Bacterial resistance response and resource availability mediate viral coexistence

Viruses that infect bacteria, known as bacteriophages or phages, are the most prevalent entities on Earth. Their genetic diversity in nature is well documented, and members of divergent lineages can be found sharing the same ecological niche. This viral diversity can be influenced by a number of factors, including productivity, spatial structuring of the environment, and host-range trade-offs. Rapid evolution is also known to promote diversity by buffering ecological systems from extinction. There is, however, little known about the impact of coevolution on the maintenance of viral diversity within a microbial community. To address this, we developed a 4 species experimental system where two bacterial hosts, a generalist and a specialist phage, coevolved in a spatially homogenous environment over time. We observed the persistence of both viruses if the resource availability was sufficiently high. This coexistence occurred in the absence of any detectable host-range trade-offs that are costly for generalists and thus known to promote viral diversity. However, the coexistence was lost if two bacteria were not permitted to evolve alongside the phages or if two phages coevolved with a single bacterial host. Our findings indicate that a host's resistance response in mixed-species communities plays a significant role in maintaining viral diversity in the environment.

Ribosome-binding antibiotics increase bacterial longevity and growth efficiency

Antibiotics, by definition, reduce bacterial growth rates in optimal culture conditions; however, the real-world environments bacteria inhabit see rapid growth punctuated by periods of low nutrient availability. How antibiotics mediate population decline during these periods is poorly understood. Bacteria cannot optimize for all environmental conditions because a growth-longevity tradeoff predicts faster growth results in faster population decline, and since bacteriostatic antibiotics slow growth, they should also mediate longevity. We quantify how antibiotics, their targets, and resistance mechanisms influence longevity using populations of Escherichia coli and, as the tradeoff predicts, populations are maintained for longer if they encounter ribosome-binding antibiotics doxycycline and erythromycin, a finding that is not observed using antibiotics with alternative cellular targets. This tradeoff also predicts resistance mechanisms that increase growth rates during antibiotic treatment could be detrimental during nutrient stresses, and indeed, we find resistance by ribosomal protection removes benefits to longevity provided by doxycycline. We therefore liken ribosomal protection to a "Trojan horse" because it provides protection from an antibiotic but, during nutrient stresses, it promotes the demise of the bacteria. Seeking mechanisms to support these observations, we show doxycycline promotes efficient metabolism and reduces the concentration of reactive oxygen species. Seeking generality, we sought another mechanism that affects longevity and we found the number of doxycycline targets, namely, the ribosomal RNA operons, mediates growth and longevity even without antibiotics. We conclude that slow growth, as observed during antibiotic treatment, can help bacteria overcome later periods of nutrient stress.

Metabolic efficiency reshapes the seminal relationship between pathogen growth rate and virulence

A cornerstone of classical virulence evolution theories is the assumption that pathogen growth rate is positively correlated with virulence, the amount of damage pathogens inflict on their hosts. Such theories are key for incorporating evolutionary principles into sustainable disease management strategies. Yet, empirical evidence raises doubts over this central assumption underpinning classical theories, thus undermining their generality and predictive power. In this paper, we identify a key component missing from current theories which redefines the growth-virulence relationship in a way that is consistent with data. By modifying the activity of a single metabolic gene, we engineered strains of Magnaporthe oryzae with different nutrient acquisition and growth rates. We conducted in planta infection studies and uncovered an unexpected non-monotonic relationship between growth rate and virulence that is jointly shaped by how growth rate and metabolic efficiency interact. This novel mechanistic framework paves the way for a much-needed new suite of virulence evolution theories.

Canonical host-pathogen tradeoffs subverted by mutations with dual benefits

AbstractHost-parasite coevolution is expected to drive the evolution of genetic diversity because the traits used in arms races-namely, host range and parasite resistance-are hypothesized to trade off with traits used in resource competition. We therefore tested data for several trade-offs among 93 isolates of bacteriophage λ and 51 Escherichia coli genotypes that coevolved during a laboratory experiment. Surprisingly, we found multiple trade-ups (positive trait correlations) but little evidence of several canonical trade-offs. For example, some bacterial genotypes evaded a trade-off between phage resistance and absolute fitness, instead evolving simultaneous improvements in both traits. This was surprising because our experimental design was predicted to expose resistance-fitness trade-offs by culturing E. coli in a medium where the phage receptor, LamB, is also used for nutrient acquisition. On reflection, LamB mediates not one but many trade-offs, allowing for more complex trait interactions than just pairwise trade-offs. Here, we report that mathematical reasoning and laboratory data highlight how trade-ups should exist whenever an evolutionary system exhibits multiple interacting trade-offs. Does this mean that coevolution should not promote genetic diversity? No, quite the contrary. We deduce that whenever positive trait correlations are observed in multidimensional traits, other traits may trade off and so provide the right circumstances for diversity maintenance. Overall, this study reveals that there are predictive limits when data account only for pairwise trait correlations, and it argues that a wider range of circumstances than previously anticipated can promote genetic and species diversity.

Seeking patterns of antibiotic resistance in ATLAS, an open, raw MIC database with patient metadata

Antibiotic resistance represents a growing medical concern where raw, clinical datasets are under-exploited as a means to track the scale of the problem. We therefore sought patterns of antibiotic resistance in the Antimicrobial Testing Leadership and Surveillance (ATLAS) database. ATLAS holds 6.5M minimal inhibitory concentrations (MICs) for 3,919 pathogen-antibiotic pairs isolated from 633k patients in 70 countries between 2004 and 2017. We show most pairs form coherent, although not stationary, timeseries whose frequencies of resistance are higher than other databases, although we identified no systematic bias towards including more resistant strains in ATLAS. We sought data anomalies whereby MICs could shift for methodological and not clinical or microbiological reasons and found artefacts in over 100 pathogen-antibiotic pairs. Using an information-optimal clustering methodology to classify pathogens into low and high antibiotic susceptibilities, we used ATLAS to predict changes in resistance. Dynamics of the latter exhibit complex patterns with MIC increases, and some decreases, whereby subpopulations' MICs can diverge. We also identify pathogens at risk of developing clinical resistance in the near future.

Would that it were so simple: Interactions between multiple traits undermine classical single-trait-based predictions of microbial community function and evolution

Understanding how microbial traits affect the evolution and functioning of microbial communities is fundamental for improving the management of harmful microorganisms, while promoting those that are beneficial. Decades of evolutionary ecology research has focused on examining microbial cooperation, diversity, productivity and virulence but with one crucial limitation. The traits under consideration, such as public good production and resistance to antibiotics or predation, are often assumed to act in isolation. Yet, in reality, multiple traits frequently interact, which can lead to unexpected and undesired outcomes for the health of macroorganisms and ecosystem functioning. This is because many predictions generated in a single-trait context aimed at promoting diversity, reducing virulence or controlling antibiotic resistance can fail for systems where multiple traits interact. Here, we provide a much needed discussion and synthesis of the most recent research to reveal the widespread and diverse nature of multi-trait interactions and their consequences for predicting and controlling microbial community dynamics. Importantly, we argue that synthetic microbial communities and multi-trait mathematical models are powerful tools for managing the beneficial and detrimental impacts of microbial communities, such that past mistakes, like those made regarding the stewardship of antimicrobials, are not repeated.

The Antibiotic Dosage of Fastest Resistance Evolution: gene amplifications underpinning the inverted-U

To determine the dosage at which antibiotic resistance evolution is most rapid, we treated Escherichia coli in vitro, deploying the antibiotic erythromycin at dosages ranging from zero to high. Adaptation was fastest just below erythromycin’s minimal inhibitory concentration (MIC) and genotype-phenotype correlations determined from whole genome sequencing revealed the molecular basis: simultaneous selection for copy number variation in three resistance mechanisms which exhibited an “inverted-U” pattern of dose-dependence, as did several insertion sequences and an integron. Many genes did not conform to this pattern, however, reflecting changes in selection as dose increased: putative media adaptation polymorphisms at zero antibiotic dosage gave way to drug target (ribosomal RNA operon) amplification at mid dosages whereas prophage-mediated drug efflux amplifications dominated at the highest dosages. All treatments exhibited E. coli increases in the copy number of efflux operons acrAB and emrE at rates that correlated with increases in population density. For strains where the inverted-U was no longer observed following the genetic manipulation of acrAB, it could be recovered by prolonging the antibiotic treatment at subMIC dosages.

Predicting microbial growth dynamics in response to nutrient availability

Developing mathematical models to accurately predict microbial growth dynamics remains a key challenge in ecology, evolution, biotechnology, and public health. To reproduce and grow, microbes need to take up essential nutrients from the environment, and mathematical models classically assume that the nutrient uptake rate is a saturating function of the nutrient concentration. In nature, microbes experience different levels of nutrient availability at all environmental scales, yet parameters shaping the nutrient uptake function are commonly estimated for a single initial nutrient concentration. This hampers the models from accurately capturing microbial dynamics when the environmental conditions change. To address this problem, we conduct growth experiments for a range of micro-organisms, including human fungal pathogens, baker's yeast, and common coliform bacteria, and uncover the following patterns. We observed that the maximal nutrient uptake rate and biomass yield were both decreasing functions of initial nutrient concentration. While a functional form for the relationship between biomass yield and initial nutrient concentration has been previously derived from first metabolic principles, here we also derive the form of the relationship between maximal nutrient uptake rate and initial nutrient concentration. Incorporating these two functions into a model of microbial growth allows for variable growth parameters and enables us to substantially improve predictions for microbial dynamics in a range of initial nutrient concentrations, compared to keeping growth parameters fixed.

Evolution of drug-resistant and virulent small colonies in phenotypically diverse populations of the human fungal pathogen Candida glabrata

Antimicrobial resistance frequently carries a fitness cost to a pathogen, measured as a reduction in growth rate compared to the sensitive wild-type, in the absence of antibiotics. Existing empirical evidence points to the following relationship between cost of resistance and virulence. If a resistant pathogen suffers a fitness cost in terms of reduced growth rate it commonly has lower virulence compared to the sensitive wild-type. If this cost is absent so is the reduction in virulence. Here we show, using experimental evolution of drug resistance in the fungal human pathogen Candida glabrata, that reduced growth rate of resistant strains need not result in reduced virulence. Phenotypically heterogeneous populations were evolved in parallel containing highly resistant sub-population small colony variants (SCVs) alongside sensitive sub-populations. Despite their low growth rate in the absence of an antifungal drug, the SCVs did not suffer a marked alteration in virulence compared with the wild-type ancestral strain, or their co-isolated sensitive strains. This contrasts with classical theory that assumes growth rate to positively correlate with virulence. Our work thus highlights the complexity of the relationship between resistance, basic life-history traits and virulence.

Predicting community dynamics of antibiotic-sensitive and -resistant species in fluctuating environments

Microbes occupy almost every niche within and on their human hosts. Whether colonizing the gut, mouth or bloodstream, microorganisms face temporal fluctuations in resources and stressors within their niche but we still know little of how environmental fluctuations mediate certain microbial phenotypes, notably antimicrobial-resistant ones. For instance, do rapid or slow fluctuations in nutrient and antimicrobial concentrations select for, or against, resistance? We tackle this question using an ecological approach by studying the dynamics of a synthetic and pathogenic microbial community containing two species, one sensitive and the other resistant to an antibiotic drug where the community is exposed to different rates of environmental fluctuation. We provide mathematical models, supported by experimental data, to demonstrate that simple community outcomes, such as competitive exclusion, can shift to coexistence and ecosystem bistability as fluctuation rates vary. Theory gives mechanistic insight into how these dynamical regimes are related. Importantly, our approach highlights a fundamental difference between resistance in single-species populations, the context in which it is usually assayed, and that in communities. While fast environmental changes are known to select against resistance in single-species populations, here we show that they can promote the resistant species in mixed-species communities. Our theoretical observations are verified empirically using a two-species Candida community.

Author Correction: Drug-mediated metabolic tipping between antibiotic resistant states in a mixed-species community

In the version of this Article originally published, the following sentence was missing from the Acknowledgements: "R.E.B. is an EPSRC Healthcare Technologies Impact Fellow EP/N033671/1; I.G. is funded by ERC Consolidator grant 647292 MathModExp; A.J.P.B., N.A.R.G. and A.T. were funded by BBSRC grant BB/F00513X/1; K.H., I.G., S.N. and E.C. were funded by BBSRC grant BB/F005210/2." This text has now been added.

Drug-mediated metabolic tipping between antibiotic resistant states in a mixed-species community

Microbes rarely exist in isolation, rather, they form intricate multi-species communities that colonize our bodies and inserted medical devices. However, the efficacy of antimicrobials is measured in clinical laboratories exclusively using microbial monocultures. Here, to determine how multi-species interactions mediate selection for resistance during antibiotic treatment, particularly following drug withdrawal, we study a laboratory community consisting of two microbial pathogens. Single-species dose responses are a poor predictor of community dynamics during treatment so, to better understand those dynamics, we introduce the concept of a dose-response mosaic, a multi-dimensional map that indicates how species' abundance is affected by changes in abiotic conditions. We study the dose-response mosaic of a two-species community with a 'Gene × Gene × Environment × Environment' ecological interaction whereby Candida glabrata, which is resistant to the antifungal drug fluconazole, competes for survival with Candida albicans, which is susceptible to fluconazole. The mosaic comprises several zones that delineate abiotic conditions where each species dominates. Zones are separated by loci of bifurcations and tipping points that identify what environmental changes can trigger the loss of either species. Observations of the laboratory communities corroborated theory, showing that changes in both antibiotic concentration and nutrient availability can push populations beyond tipping points, thus creating irreversible shifts in community composition from drug-sensitive to drug-resistant species. This has an important consequence: resistant species can increase in frequency even if an antibiotic is withdrawn because, unwittingly, a tipping point was passed during treatment.

When increasing population density can promote the evolution of metabolic cooperation

Microbial cooperation drives ecological and epidemiological processes and is affected by the ecology and demography of populations. Population density influences the selection for cooperation, with spatial structure and the type of social dilemma, namely public-goods production or self-restraint, shaping the outcome. While existing theories predict that in spatially structured environments increasing population density can select either for or against cooperation, experimental studies with both public-goods production and self-restraint systems have only ever shown that increasing population density favours cheats. We suggest that the disparity between theory and empirical studies results from experimental procedures not capturing environmental conditions predicted by existing theories to influence the outcome. Our study resolves this issue and provides the first experimental evidence that high population density can favour cooperation in spatially structured environments for both self-restraint and public-goods production systems. Moreover, using a multi-trait mathematical model supported by laboratory experiments we extend this result to systems where the self-restraint and public-goods social dilemmas interact. We thus provide a systematic understanding of how the strength of interaction between the two social dilemmas and the degree of spatial structure within an environment affect selection for cooperation. These findings help to close the current gap between theory and experiments.

Stability of Cross-Feeding Polymorphisms in Microbial Communities

Cross-feeding, a relationship wherein one organism consumes metabolites excreted by another, is a ubiquitous feature of natural and clinically-relevant microbial communities and could be a key factor promoting diversity in extreme and/or nutrient-poor environments. However, it remains unclear how readily cross-feeding interactions form, and therefore our ability to predict their emergence is limited. In this paper we developed a mathematical model parameterized using data from the biochemistry and ecology of an E. coli cross-feeding laboratory system. The model accurately captures short-term dynamics of the two competitors that have been observed empirically and we use it to systematically explore the stability of cross-feeding interactions for a range of environmental conditions. We find that our simple system can display complex dynamics including multi-stable behavior separated by a critical point. Therefore whether cross-feeding interactions form depends on the complex interplay between density and frequency of the competitors as well as on the concentration of resources in the environment. Moreover, we find that subtly different environmental conditions can lead to dramatically different results regarding the establishment of cross-feeding, which could explain the apparently unpredictable between-population differences in experimental outcomes. We argue that mathematical models are essential tools for disentangling the complexities of cross-feeding interactions.

Kinase Inhibition Leads to Hormesis in a Dual Phosphorylation-Dephosphorylation Cycle

Many antimicrobial and anti-tumour drugs elicit hormetic responses characterised by low-dose stimulation and high-dose inhibition. While this can have profound consequences for human health, with low drug concentrations actually stimulating pathogen or tumour growth, the mechanistic understanding behind such responses is still lacking. We propose a novel, simple but general mechanism that could give rise to hormesis in systems where an inhibitor acts on an enzyme. At its core is one of the basic building blocks in intracellular signalling, the dual phosphorylation-dephosphorylation motif, found in diverse regulatory processes including control of cell proliferation and programmed cell death. Our analytically-derived conditions for observing hormesis provide clues as to why this mechanism has not been previously identified. Current mathematical models regularly make simplifying assumptions that lack empirical support but inadvertently preclude the observation of hormesis. In addition, due to the inherent population heterogeneities, the presence of hormesis is likely to be masked in empirical population-level studies. Therefore, examining hormetic responses at single-cell level coupled with improved mathematical models could substantially enhance detection and mechanistic understanding of hormesis.

Hormesis is a highly controversial and poorly understood phenomenon. It describes the idea that an inhibitor molecule, like an anti-cancer or anti-microbial drug, can inadvertently stimulate cell growth instead of suppressing it. This can have a profound effect on human health leading to failures in clinical treatments. Therefore, getting at the mechanistic basis of hormesis is critical for drug development and clinical practice, however molecular mechanisms underpinning hormesis remain poorly understood. In this paper we use a mathematical model to propose a simple and yet general mechanism that could explain why we find hormesis so widely in living systems. In particular, we discover that hormesis is present within a fundamental structure that forms a basic building block of many intracellular signalling pathways found in diverse processes including control of cell reproduction and programmed cell death. The benefits of our study are two-fold. Having simple molecular understanding of the causes of hormetic responses can greatly improve the design of new drug compounds that avoid such responses. Moreover, due to the fundamental nature of the newly proposed mechanism, our findings have a potential broad applicability to both anti-cancer and anti-microbial drugs.

Harbouring public good mutants within a pathogen population can increase both fitness and virulence

Existing theory, empirical, clinical and field research all predict that reducing the virulence of individuals within a pathogen population will reduce the overall virulence, rendering disease less severe. Here, we show that this seemingly successful disease management strategy can fail with devastating consequences for infected hosts. We deploy cooperation theory and a novel synthetic system involving the rice blast fungus Magnaporthe oryzae. In vivo infections of rice demonstrate that M. oryzae virulence is enhanced, quite paradoxically, when a public good mutant is present in a population of high-virulence pathogens. We reason that during infection, the fungus engages in multiple cooperative acts to exploit host resources. We establish a multi-trait cooperation model which suggests that the observed failure of the virulence reduction strategy is caused by the interference between different social traits. Multi-trait cooperative interactions are widespread, so we caution against the indiscriminant application of anti-virulence therapy as a disease-management strategy.

Testing the optimality properties of a dual antibiotic treatment in a two-locus, two-allele model

Mathematically speaking, it is self-evident that the optimal control of complex, dynamical systems with many interacting components cannot be achieved with 'non-responsive' control strategies that are constant through time. Although there are notable exceptions, this is usually how we design treatments with antimicrobial drugs when we give the same dose and the same antibiotic combination each day. Here, we use a frequency- and density-dependent pharmacogenetics mathematical model based on a standard, two-locus, two-allele representation of how bacteria resist antibiotics to probe the question of whether optimal antibiotic treatments might, in fact, be constant through time. The model describes the ecological and evolutionary dynamics of different sub-populations of the bacterium Escherichia coli that compete for a single limiting resource in a two-drug environment. We use in vitro evolutionary experiments to calibrate and test the model and show that antibiotic environments can support dynamically changing and heterogeneous population structures. We then demonstrate, theoretically and empirically, that the best treatment strategies should adapt through time and constant strategies are not optimal.

Do-or-die life cycles and diverse post-infection resistance mechanisms limit the evolution of parasite host ranges

In light of the dynamic nature of parasite host ranges and documented potential for rapid host shifts, the observed high host specificity of most parasites remains an ecological paradox. Different variants of host-use trade-offs have become a mainstay of theoretical explanations of the prevalence of host specialism, but empirical evidence for such trade-offs is rare. We propose an alternative theory based on basic features of the parasite life cycle: host selection and subsequent intrahost replication. We introduce a new concept of effective burst size that accounts for the fact that successful host selection does not guarantee intrahost replication. Our theory makes a general prediction that a parasite will expand its host range if its effective burst size is positive. An in silico model of bacteria-phage coevolution verifies our predictions and demonstrates that the tendency for relatively narrow host ranges in parasites can be explained even in the absence of trade-offs.

Dispersal network structure and infection mechanism shape diversity in a coevolutionary bacteria-phage system

Resource availability, dispersal and infection genetics all have the potential to fundamentally alter the coevolutionary dynamics of bacteria-bacteriophage interactions. However, it remains unclear how these factors synergise to shape diversity within bacterial populations. We used a combination of laboratory experiments and mathematical modeling to test how the structure of a dispersal network affects host phenotypic diversity in a coevolving bacteria-phage system in communities of differential resource input. Unidirectional dispersal of bacteria and phage from high to low resources consistently increased host diversity compared with a no dispersal regime. Bidirectional dispersal, on the other hand, led to a marked decrease in host diversity. Our mathematical model predicted these opposing outcomes when we incorporated modified gene-for-gene infection genetics. To further test how host diversity depended on the genetic underpinnings of the bacteria-phage interaction, we expanded our mathematical model to include different infection mechanisms. We found that the direction of dispersal had very little impact on bacterial diversity when the bacteria-phage interaction was mediated by matching alleles, gene-for-gene or related infection mechanisms. Our experimental and theoretical results demonstrate that the effects of dispersal on diversity in coevolving host-parasite systems depend on an intricate interplay of the structure of the underlying dispersal network and the specifics of the host-parasite interaction.

The form of a trade-off determines the response to competition

Understanding how populations and communities respond to competition is a central concern of ecology. A seminal theoretical solution first formalised by Levins (and re-derived in multiple fields) showed that, in theory, the form of a trade-off should determine the outcome of competition. While this has become a central postulate in ecology it has evaded experimental verification, not least because of substantial technical obstacles. We here solve the experimental problems by employing synthetic ecology. We engineer strains of Escherichia coli with fixed resource allocations enabling accurate measurement of trade-off shapes between bacterial survival and multiplication in multiple environments. A mathematical chemostat model predicts different, and experimentally verified, trajectories of gene frequency changes as a function of condition-specific trade-offs. The results support Levins' postulate and demonstrates that otherwise paradoxical alternative outcomes witnessed in subtly different conditions are predictable.

Sarcoidosis and tuberculosis in South Croatia: are there epidemiological similarities or not?

OBJECTIVE

Tuberculosis and sarcoidosis are chronic granulomatous diseases. Clinical, pathologic and immunologic aspects are similar although different. The authors were interested to highlight possible epidemiological similarities of these two granulomatous diseases. The objective of this study was to evaluate incidence rate as well as age, sex and geographic distribution of sarcoidosis in South Croatia and to compare it with these epidemiological characteristics of tuberculosis.

STUDY DESIGN

Retrospective.

METHODS

The study was including ten years follow up period (1997-2006), and was performed in Split-Dalmatia County, Croatia. All data were collected retrospectively and analyzed using Statistica 7 programme.

RESULTS

The mean annual incidence of sarcoidosis was 3.3/100,000 inhabitants with a mean of 15,6 cases per year. Woman accounted for 61% of all sarcoidosis cases. The mean sarcoidosis patient age was 44.94 ± 11.85 years. The peak age group was 40-49 years (31%). Significant difference according to incidence rate on the islands comparing to the rates on the coast and the mainland was observed (P = 0.003). The mean sarcoidosis mortality rate was 1.2/100,000. Statistically significant differences between sarcoidosis and tuberculosis were observed according the higher number of tuberculosis patients (P < 0.000), among males (P < 0.000), and females, too (P < 0.000) as well as in mortality rates (P = 0.401). Significantly more patients had tuberculosis on the mainland (P < 0.000) and on the coast (P < 0.000), but not in the islands (P = 0.260).

CONCLUSIONS

The results from this study showed dissimilarities in classic epidemiological patterns between sarcoidosis and tuberculosis, incidence rates, as well as sex and geographic distribution. Our findings resulted from this study might be starting point for the future epidemiological, genetic, and immunological studies.

Metabolic trade-offs and the maintenance of the fittest and the flattest

How is diversity maintained? Environmental heterogeneity is considered to be important, yet diversity in seemingly homogeneous environments is nonetheless observed. This, it is assumed, must either be owing to weak selection, mutational input or a fitness advantage to genotypes when rare. Here we demonstrate the possibility of a new general mechanism of stable diversity maintenance, one that stems from metabolic and physiological trade-offs. The model requires that such trade-offs translate into a fitness landscape in which the most fit has unfit near-mutational neighbours, and a lower fitness peak also exists that is more mutationally robust. The 'survival of the fittest' applies at low mutation rates, giving way to 'survival of the flattest' at high mutation rates. However, as a consequence of quasispecies-level negative frequency-dependent selection and differences in mutational robustness we observe a transition zone in which both fittest and flattest coexist. Although diversity maintenance is possible for simple organisms in simple environments, the more trade-offs there are, the wider the maintenance zone becomes. The principle may be applied to lineages within a species or species within a community, potentially explaining why competitive exclusion need not be observed in homogeneous environments. This principle predicts the enigmatic richness of metabolic strategies in clonal bacteria and questions the safety of lethal mutagenesis as an antimicrobial treatment.

Evolution of cooperative cross-feeding could be less challenging than originally thought

The act of cross-feeding whereby unrelated species exchange nutrients is a common feature of microbial interactions and could be considered a form of reciprocal altruism or reciprocal cooperation. Past theoretical work suggests that the evolution of cooperative cross-feeding in nature may be more challenging than for other types of cooperation. Here we re-evaluate a mathematical model used previously to study persistence of cross-feeding and conclude that the maintenance of cross-feeding interactions could be favoured for a larger parameter ranges than formerly observed. Strikingly, we also find that large populations of cross-feeders are not necessarily vulnerable to extinction from an initially small number of cheats who receive the benefit of cross-feeding but do not reciprocate in this cooperative interaction. This could explain the widespread cooperative cross-feeding observed in natural populations.

Molecular and evolutionary bases of within-patient genotypic and phenotypic diversity in Escherichia coli extraintestinal infections

Although polymicrobial infections, caused by combinations of viruses, bacteria, fungi and parasites, are being recognised with increasing frequency, little is known about the occurrence of within-species diversity in bacterial infections and the molecular and evolutionary bases of this diversity. We used multiple approaches to study the genomic and phenotypic diversity among 226 Escherichia coli isolates from deep and closed visceral infections occurring in 19 patients. We observed genomic variability among isolates from the same site within 11 patients. This diversity was of two types, as patients were infected either by several distinct E. coli clones (4 patients) or by members of a single clone that exhibit micro-heterogeneity (11 patients); both types of diversity were present in 4 patients. A surprisingly wide continuum of antibiotic resistance, outer membrane permeability, growth rate, stress resistance, red dry and rough morphotype characteristics and virulence properties were present within the isolates of single clones in 8 of the 11 patients showing genomic micro-heterogeneity. Many of the observed phenotypic differences within clones affected the trade-off between self-preservation and nutritional competence (SPANC). We showed in 3 patients that this phenotypic variability was associated with distinct levels of RpoS in co-existing isolates. Genome mutational analysis and global proteomic comparisons in isolates from a patient revealed a star-like relationship of changes amongst clonally diverging isolates. A mathematical model demonstrated that multiple genotypes with distinct RpoS levels can co-exist as a result of the SPANC trade-off. In the cases involving infection by a single clone, we present several lines of evidence to suggest diversification during the infectious process rather than an infection by multiple isolates exhibiting a micro-heterogeneity. Our results suggest that bacteria are subject to trade-offs during an infectious process and that the observed diversity resembled results obtained in experimental evolution studies. Whatever the mechanisms leading to diversity, our results have strong medical implications in terms of the need for more extensive isolate testing before deciding on antibiotic therapies.

We investigated whether an infection is a site of pathogen within-species diversity. Our results indicate that there is indeed extensive diversity during human extraintestinal infections by Escherichia coli. This diversity was of two types, not mutually exclusive, as we found that patients were infected either by several distinct E. coli clones or by members of a single clone that exhibit micro-heterogeneity. The high degree of phenotypic diversity, including antibiotic resistance, suggests that there is no uniform selection pressure leading to a single fitter clone during an infection. We discuss a possible mechanism and a mathematical model that explains these unexpected results. Our data suggest that the evolution of diversity in the course of an infection and in in vitro experimental evolution in the absence of host immune selective pressure may have many parallels. Whatever the mechanisms leading to diversity, our results have strong medical implications in terms of the need for more extensive isolate testing before deciding on antibiotic therapies.

An integrative approach to understanding microbial diversity: from intracellular mechanisms to community structure

Trade-offs have been put forward as essential to the generation and maintenance of diversity. However, variation in trade-offs is often determined at the molecular level, outside the scope of conventional ecological inquiry. In this study, we propose that understanding the intracellular basis for trade-offs in microbial systems can aid in predicting and interpreting patterns of diversity. First, we show how laboratory experiments and mathematical models have unveiled the hidden intracellular mechanisms underlying trade-offs key to microbial diversity: (i) metabolic and regulatory trade-offs in bacteria and yeast; (ii) life-history trade-offs in bacterial viruses. Next, we examine recent studies of marine microbes that have taken steps toward reconciling the molecular and the ecological views of trade-offs, despite the challenges in doing so in natural settings. Finally, we suggest avenues for research where mathematical modelling, experiments and studies of natural microbial communities provide a unique opportunity to integrate studies of diversity across multiple scales.

Understanding the limits to generalizability of experimental evolutionary models

Given the difficulty of testing evolutionary and ecological theory in situ, in vitro model systems are attractive alternatives; however, can we appraise whether an experimental result is particular to the in vitro model, and, if so, characterize the systems likely to behave differently and understand why? Here we examine these issues using the relationship between phenotypic diversity and resource input in the T7-Escherichia coli co-evolving system as a case history. We establish a mathematical model of this interaction, framed as one instance of a super-class of host-parasite co-evolutionary models, and show that it captures experimental results. By tuning this model, we then ask how diversity as a function of resource input could behave for alternative co-evolving partners (for example, E. coli with lambda bacteriophages). In contrast to populations lacking bacteriophages, variation in diversity with differences in resources is always found for co-evolving populations, supporting the geographic mosaic theory of co-evolution. The form of this variation is not, however, universal. Details of infectivity are pivotal: in T7-E. coli with a modified gene-for-gene interaction, diversity is low at high resource input, whereas, for matching-allele interactions, maximal diversity is found at high resource input. A combination of in vitro systems and appropriately configured mathematical models is an effective means to isolate results particular to the in vitro system, to characterize systems likely to behave differently and to understand the biology underpinning those alternatives.

Constraints on microbial metabolism drive evolutionary diversification in homogeneous environments

Understanding the evolution of microbial diversity is an important and current problem in evolutionary ecology. In this paper, we investigated the role of two established biochemical trade-offs in microbial diversification using a model that connects ecological and evolutionary processes with fundamental aspects of biochemistry. The trade-offs that we investigated are as follows:(1) a trade-off between the rate and affinity of substrate transport; and (2) a trade-off between the rate and yield of ATP production. Our model shows that these biochemical trade-offs can drive evolutionary diversification under the simplest possible ecological conditions: a homogeneous environment containing a single limiting resource. We argue that the results of a number of microbial selection experiments are consistent with the predictions of our model.

Resource competition and social conflict in experimental populations of yeast

Understanding the conditions that promote the maintenance of cooperation is a classic problem in evolutionary biology. The essence of this dilemma is captured by the 'tragedy of the commons': how can a group of individuals that exploit resources in a cooperative manner resist invasion by 'cheaters' who selfishly use common resources to maximize their individual reproduction at the expense of the group? Here, we investigate this conflict through experimental competitions between isogenic cheater and cooperator strains of yeast with alternative pathways of glucose metabolism, and by using mathematical models of microbial biochemistry. We show that both coexistence and competitive exclusion are possible outcomes of this conflict, depending on the spatial and temporal structure of the environment. Both of these outcomes are driven by trade-offs between the rate and efficiency of conversion of resources into offspring that are mediated by metabolic intermediates.

Classifying the role of trade-offs in the evolutionary diversity of pathogens

In this paper we use a system of non-local reaction–diffusion equations to study the effect of host heterogeneity on the phenotypic evolution of a pathogen population. The evolving phenotype is taken to be the transmission rate of the pathogen on the different hosts, and in our system there are two host populations present.

The central feature of our model is a trade-off relationship between the transmission rates on these hosts, which means that an increase in the pathogen transmission on one host will lead to a decrease in the pathogen transmission on the other. The purpose of the paper is to develop a classification of phenotypic diversity as a function of the shape of the trade-off relationship and this is achieved by determining the maximum number of phenotypes a pathogen population can support in the long term, for a given form of the trade-off. Our findings are then compared with results obtained by applying classical theory from evolutionary ecology and the more recent adaptive dynamics method to the same host–pathogen system. We find our work to be in good agreement with these two approaches.

Transmission rates and adaptive evolution of pathogens in sympatric heterogeneous plant populations

Diversification in agricultural cropping patterns is widely practised to delay the build-up of virulent races that can overcome host resistance in pathogen populations. This can lead to balanced polymorphism, but the long-term consequences of this strategy for the evolution of crop pathogen populations are still unclear. The widespread occurrence of sibling species and reproductively isolated sub-species among fungal and oomycete plant pathogens suggests that evolutionary divergence is common. This paper develops a mathematical model of host-pathogen interactions using a simple framework of two hosts to analyse the influences of sympatric host heterogeneity on the long-term evolutionary behaviour of plant pathogens. Using adaptive dynamics, which assumes that sequential mutations induce small changes in pathogen fitness, we show that evolutionary outcomes strongly depend on the shape of the trade-off curve between pathogen transmission on sympatric hosts. In particular, we determine the conditions under which the evolutionary branching of a monomorphic into a dimorphic population occurs, as well as the conditions that lead to the evolution of specialist (single host range) or generalist (multiple host range) pathogen populations.

Spatial heterogeneity, social structure and disease dynamics of animal populations

Social groupings, population dynamics and population movements of animals all give rise to spatio-temporal variations in population levels. These variations may be of crucial importance when considering the spread of infectious diseases since infection levels do not increase unless there is a sufficient pool of susceptible individuals. This paper explores the impact of social groupings on the potential for an endemic disease to develop in a spatially explicit model system. Analysis of the model demonstrates that the explicit inclusion of space allows asymmetry between groups to arise when this was not possible in the equivalent spatially homogeneous system. Moreover, differences in movement behaviours for susceptible and infected individuals gives rise to different spatial profiles for the populations. These profiles were not observed in previous work on an epidemic system. The results are discussed in an ecological context with reference to furious and dumb strains of infectious diseases.

Evolution of sibling fungal plant pathogens in relation to host specialization

ABSTRACT Sibling plant pathogens can be grouped according to their host rangesthe following groups: group 1, sibling pathogens with nonoverlapping host ranges; group 2, sibling pathogens with both overlapping and nonoverlapping host ranges; and group 3, sibling pathogens with overlapping host ranges. Using the adaptive dynamics methodology, we investigated the evolution of sibling pathogens in relation to host specialization for groups 1 to 3. In particular, we focused on the role of multiple host niches and a trade-off in infectivity of pathogens to these hosts on the evolutionary outcome. We have shown that this ecological mechanism can explain only the evolution of sibling pathogens in group 1 and that other ecological and epidemiological mechanisms must be responsible for the evolution of sibling pathogens in the other two groups.

The effects of spatial movement and group interactions on disease dynamics of social animals

The effects of spatial movements of infected and susceptible individuals on disease dynamics is not well understood. Empirical studies on the spatial spread of disease and behaviour of infected individuals are few and theoretical studies may be useful to explore different scenarios. Hence due to lack of detail in empirical studies, theoretical models have become necessary tools in investigating the disease influence in host-pathogen systems. In this paper we developed and analysed a spatially explicit model of two interacting social groups of animals of the same species. We investigated how the movement scenarios of susceptible and infected individuals together with the between-group contact parameter affect the survival rate of susceptible individuals in each group. This work can easily be applied to various host-pathogen systems. We define bounds on the number of susceptibles which avoid infection once the disease has died out as a function of the initial conditions and other model parameters. For example, once disease has passed through the populations, a larger diffusion coefficient for each group can result in higher population levels when there is no between-group interaction but in lower levels when there is between-group interaction. Numerical simulations are used to demonstrate these bounds and behaviours and to describe the different outcomes in ecological terms.