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SummaryThe constant arms race between bacteria and their phages has resulted in a large diversity of bacterial defence systems1,2, with many bacteria carrying several systems3,4. In response, phages often carry counter-defence genes5–9.... more
SummaryThe constant arms race between bacteria and their phages has resulted in a large diversity of bacterial defence systems1,2, with many bacteria carrying several systems3,4. In response, phages often carry counter-defence genes5–9. If and how bacterial defence mechanisms interact to protect against phages with counter-defence genes remains unclear. Here, we report the existence of a novel defence system, coined MADS (Methylation Associated Defence System), which is located in a strongly conserved genomic defence hotspot inPseudomonas aeruginosaand distributed across Gram-positive and Gram-negative bacteria. We find that the natural co-existence of MADS and a Type IE CRISPR-Cas adaptive immune system in the genome ofP. aeruginosaSMC4386 provides synergistic levels of protection against phage DMS3, which carries an anti-CRISPR (acr) gene. Previous work has demonstrated that Acr-phages need to cooperate to overcome CRISPR immunity, with a first sacrificial phage causing host immun...
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Following the initiation of the unprecedented global vaccination campaign against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), attention has now turned to the potential impact of this large-scale intervention on the... more
Following the initiation of the unprecedented global vaccination campaign against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), attention has now turned to the potential impact of this large-scale intervention on the evolution of the virus. In this Essay, we summarize what is currently known about pathogen evolution in the context of immune priming (including vaccination) from research on other pathogen species, with an eye towards the future evolution of SARS-CoV-2.
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Since its emergence in late 2019, the SARS-CoV-2 virus has spread globally, causing the ongoing COVID-19 pandemic. In the fall of 2020, the Alpha variant (lineage B.1.1.7) was detected in England and spread rapidly, outcompeting the... more
Since its emergence in late 2019, the SARS-CoV-2 virus has spread globally, causing the ongoing COVID-19 pandemic. In the fall of 2020, the Alpha variant (lineage B.1.1.7) was detected in England and spread rapidly, outcompeting the previous lineage. Yet, very little is known about the underlying modifications of the infection process that can explain this selective advantage. Here, we try to quantify how the Alpha variant differed from its predecessor on two phenotypic traits: the transmission rate and the duration of infectiousness. To this end, we analysed the joint epidemiological and evolutionary dynamics as a function of the Stringency Index, a measure of the amount of Non-Pharmaceutical Interventions. Assuming that these control measures reduce contact rates and transmission, we developed a two-step approach based onSEIRmodels and the analysis of a combination of epidemiological and evolutionary information. First, we quantify the link between Stringency Index and the reducti...
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CRISPR-Cas immune systems are widespread in bacteria and archaea, but not ubiquitous. Previous work has demonstrated that CRISPR immunity is associated with an infection-induced fitness cost, which may help explain the patchy distribution... more
CRISPR-Cas immune systems are widespread in bacteria and archaea, but not ubiquitous. Previous work has demonstrated that CRISPR immunity is associated with an infection-induced fitness cost, which may help explain the patchy distribution observed. However, the mechanistic basis of this cost has remained unclear. Using Pseudomonas aeruginosa PA14 and its phage DMS3vir as a model, we perform a 30-day evolution experiment under phage mediated selection. We demonstrate that although CRISPR is initially selected for, bacteria carrying mutations in the phage receptor rapidly invade the population following subsequent reinfections. We then test three potential mechanisms for the observed cost of CRISPR: (1) autoimmunity from the acquisition of self-targeting spacers, (2) immunopathology or energetic costs from increased cas gene expression and (3) toxicity caused by phage gene expression prior to CRISPR-mediated cleavage. We find that phages can express genes before the immune system clea...
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Most spatial models of host–parasite interactions either neglect the possibility of pathogen evolution or consider that this process is slow enough for epidemiological dynamics to reach an equilibrium on a fast timescale. Here, we propose... more
Most spatial models of host–parasite interactions either neglect the possibility of pathogen evolution or consider that this process is slow enough for epidemiological dynamics to reach an equilibrium on a fast timescale. Here, we propose a novel approach to jointly model the epidemiological and evolutionary dynamics of spatially structured host and pathogen populations. Starting from a multi-strain epidemiological model, we use a combination of spatial moment equations and quantitative genetics to analyse the dynamics of mean transmission and virulence in the population. A key insight of our approach is that, even in the absence of long-term evolutionary consequences, spatial structure can affect the short-term evolution of pathogens because of the build-up of spatial differentiation in mean virulence. We show that spatial differentiation is driven by a balance between epidemiological and genetic effects, and this quantity is related to the effect of kin competition discussed in pr...
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<p>(A) Probability of pathogen emergence with (full curve, <i>u</i><sub>0,1</sub> = 0.01) or without mutations (dashed curve, <i>u</i><sub>0,1</sub> = 0). The shaded area indicates the... more
<p>(A) Probability of pathogen emergence with (full curve, <i>u</i><sub>0,1</sub> = 0.01) or without mutations (dashed curve, <i>u</i><sub>0,1</sub> = 0). The shaded area indicates the effect of pathogen adaptation on emergence. The threshold value <i>f</i><sub><i>R</i></sub> = <i>f</i><sub><i>T</i></sub> that prevents pathogen emergence in the absence of pathogen adaptation is indicated with a vertical dashed line. (B) Evolutionary emergence of pathogens (the shaded area in A) is maximized for an intermediate value of the fraction of resistant hosts. The dashed red curve shows the theoretical prediction when we account for the change in the escape mutation frequency after the pathogen emergence took place (see section S1.3 of <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2006738#pbio.2006738.s001" target="_blank">S1 Text</a>). Other parameter values: <i>b</i> = 2.5, <i>d</i> = 1, <i>ϕ</i> = 0, <i>c</i> = 0.2, <i>T</i> = 24.</p
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<p>Boxplot of the number of oocysts per midgut among 15 haphazardly chosen blood fed individuals on each bird (only includes mosquitoes harbouring ≥1 oocysts) for the 3 exposure sessions (see <a... more
<p>Boxplot of the number of oocysts per midgut among 15 haphazardly chosen blood fed individuals on each bird (only includes mosquitoes harbouring ≥1 oocysts) for the 3 exposure sessions (see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1004308#ppat-1004308-g004" target="_blank">Fig. 4</a>): (A) session 1 (34–40 dpi), (B) session 2 (122–128 dpi) and (C) session 3 (291–297 dpi).</p
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<p>Proportion of replicate populations with evolutionary emergence (i.e., in which the amplification of an escape phage is detected) for increasing values of the proportion of resistant bacteria... more
<p>Proportion of replicate populations with evolutionary emergence (i.e., in which the amplification of an escape phage is detected) for increasing values of the proportion of resistant bacteria (<i>f</i><sub><i>R</i></sub>). The different colors correspond to replicate experiments performed using eight different BIMs (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2006738#pbio.2006738.s015" target="_blank">S2 Table</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2006738#pbio.2006738.s010" target="_blank">S9 Fig</a>). For each treatment, each of the 96 replicate host populations was inoculated with an initial quantity approximately equal to <i>V</i><sub>0</sub> = 300 unevolved phages. Black lines indicate the mean across the eight BIMs; gray shaded areas represent 95% confidence intervals of the mean. Data are available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2006738#pbio.2006738.s016" target="_blank">S1 Data</a>. BIM, bacteriophage-insensitive mutant.</p
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<p>We consider here that the influx of mosquitoes, <i>θ</i>(<i>t</i>), is a periodic square wave (see <a... more
<p>We consider here that the influx of mosquitoes, <i>θ</i>(<i>t</i>), is a periodic square wave (see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1004308#ppat.1004308.e017" target="_blank">equation (5)</a> in the main text). The parameter <i>T</i> measures the duration of the period and the parameter <i>τ</i> measures seasonality: the fraction of time where the environment is not suitable for vector reproduction.The epidemiologic dynamics converges to a periodic equilibrium characterised by fluctuations of the uninfected and infected vector the densities: <i>V</i>(<i>t</i>) and <i>V<sub>I</sub></i>(<i>t</i>) (black and red dashed lines, respectively). We also plot the dynamics of the density of infected hosts: <i>I</i>(<i>t</i>)/10 (red line). Parameter values: , , , . See default values in the <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1004308#ppat.1004308.s004" target="_blank">Text S1</a> for other parameters.</p
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<p>The color shading indicates the value of the pathogen strategies and the warmer color indicates higher values. A higher investment in <i>ε<sub>P</sub></i> indicates that the pathogen invests more into the... more
<p>The color shading indicates the value of the pathogen strategies and the warmer color indicates higher values. A higher investment in <i>ε<sub>P</sub></i> indicates that the pathogen invests more into the mechanisms that allow it to react to the presence of mosquitoes. A higher investment in <i>ε<sub>F</sub></i> indicates that the pathogen invests more into transmission (and virulence). For both strategies the lower value (blue) is 0. The maximal value (red) of <i>ε<sub>P</sub></i> is 4 and the maximal value (red) of <i>ε<sub>F</sub></i> is 1.1. See default values in the <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1004308#ppat.1004308.s004" target="_blank">Text S1</a> for the other parameters.</p
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Data were obtained in 14 different experiments. The data file was created with Microsoft Excel Open XML. Data were collected in laboratory. In the one hand, data concern the impact of plasmodium infection on its host and its vector. There... more
Data were obtained in 14 different experiments. The data file was created with Microsoft Excel Open XML. Data were collected in laboratory. In the one hand, data concern the impact of plasmodium infection on its host and its vector. There are data on the variance in infection rate in bird, on the impact of plasmodium on bird anaemia. Regarding the impact of Plasmodium on mosquitoes, there are data on prevalence, parasite burden and the costs induced by Plasmodium on mosquitoes' fecundity and longevity. In the other hand, there are data on a time series regarding the evolution of birds' parasitaemia and gametocytemia maintained in laboratory since 5 years
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Avian malaria has historically played an important role as a model in the study of human malaria, being a stimulus for the development of medical parasitology. Avian malaria has recently come back to the research scene as a unique animal... more
Avian malaria has historically played an important role as a model in the study of human malaria, being a stimulus for the development of medical parasitology. Avian malaria has recently come back to the research scene as a unique animal model to understand the ecology and evolution of the disease, both in the field and in the laboratory. Avian malaria is highly prevalent in birds and mosquitoes around the world and is amenable to laboratory experimentation at each stage of the parasite's life cycle. Here, we take stock of 5 years of experimental laboratory research carried out using Plasmodium relictum SGS1, the most prevalent avian malaria lineage in Europe, and its natural vector, the mosquito Culex pipiens. For this purpose, we compile and analyse data obtained in our laboratory in 14 different experiments. We provide statistical relationships between different infection-related parameters, including parasitaemia, gametocytaemia, host morbidity (anaemia) and transmission rates to mosquitoes. This analysis provides a wide-ranging picture of the within-host and between-host parameters that may bear on malaria transmission and epidemiology
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The malaria parasite Plasmodium relictum is one of the most widespread species of avian malaria. As is the case in its human counterparts, bird Plasmodium undergoes a complex life cycle infecting two hosts: the arthropod vector and the... more
The malaria parasite Plasmodium relictum is one of the most widespread species of avian malaria. As is the case in its human counterparts, bird Plasmodium undergoes a complex life cycle infecting two hosts: the arthropod vector and the vertebrate host. In this study, we examine the transcriptome of P. relictum (SGS1) during crucial timepoints within its natural vector, Culex pipiens quinquefasciatus. Differential gene-expression analyses identified genes linked to the parasites life-stages at: i) a few minutes after the blood meal is ingested, ii) during peak oocyst production phase, iii) during peak sporozoite phase and iv) during the late-stages of the infection. A large amount of genes coding for functions linked to host-immune invasion and multifunctional genes was active throughout the infection cycle. One gene associated with a conserved Plasmodium membrane protein with unknown function was upregulated throughout the parasite development in the vector, suggesting an important ...
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The Supplementary Information has a “supplementary methods” section which details the mathematical aspects of the model and a “supplementary figures and tables” section which contains Fig. S1 to S5 and Table S1
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Additional file 2: Optical microscope (x400) image of Cx pipiens haemocytes showing the two different morphotypes described in this study: granulocytes and oenocytoids (see main text for details).
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Data on growth rate, lag time, yield and competitive fitness of several strains of Streptococcus thermophilus. Data is provided for individual replicates, and organised separately for each set of figures.See methods section for... more
Data on growth rate, lag time, yield and competitive fitness of several strains of Streptococcus thermophilus. Data is provided for individual replicates, and organised separately for each set of figures.See methods section for experimental details
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The theory of life-history evolution provides a powerful framework to understand the evolutionary dynamics of pathogens. It assumes, however, that host populations are large and that one can neglect the effects of demographic... more
The theory of life-history evolution provides a powerful framework to understand the evolutionary dynamics of pathogens. It assumes, however, that host populations are large and that one can neglect the effects of demographic stochasticity. Here, we expand the theory to account for the effects of finite population size on the evolution of pathogen virulence. We show that demographic stochasticity introduces additional evolutionary forces that can qualitatively affect the dynamics and the evolutionary outcome. We discuss the importance of the shape of the pathogen fitness landscape and host heterogeneity on the balance between mutation, selection and genetic drift. This analysis reconciles adaptive dynamics with population genetics in finite populations and provides a new theoretical toolbox to study life-history evolution in realistic ecological scenarios.
The theory of life-history evolution provides a powerful framework to understand the evolutionary dynamics of pathogens. It assumes, however, that host populations are large and that one can neglect the effects of demographic... more
The theory of life-history evolution provides a powerful framework to understand the evolutionary dynamics of pathogens. It assumes, however, that host populations are large and that one can neglect the effects of demographic stochasticity. Here, we expand the theory to account for the effects of finite population size on the evolution of pathogen virulence. We show that demographic stochasticity introduces additional evolutionary forces that can qualitatively affect the dynamics and the evolutionary outcome. We discuss the importance of the shape of the pathogen fitness landscape and host heterogeneity on the balance between mutation, selection and genetic drift. This analysis reconciles adaptive dynamics with population genetics in finite populations and provides a new theoretical toolbox to study life-history evolution in realistic ecological scenarios.
What is the influence of periodic environmental fluctuations on life-history evolution? We present a general theoretical framework to understand and predict the long-term evolution of lifehistory traits under a broad range of ecological... more
What is the influence of periodic environmental fluctuations on life-history evolution? We present a general theoretical framework to understand and predict the long-term evolution of lifehistory traits under a broad range of ecological scenarios. Specifically, we investigate how periodic fluctuations affect selection when the population is also structured in distinct classes. This analysis yields time-varying selection gradients that clarify the influence of the fluctuations of the environment on the competitive ability of a specific life-history mutation. We use this framework to analyse the evolution of key life-history traits of pathogens. We examine three different epidemiological scenarios and we show how periodic fluctuations of the environment can affect the evolution of virulence and transmission as well as the preference for different hosts. These examples yield new and testable predictions on pathogen evolution, and illustrate how our approach can provide a better underst...
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Over a decade ago, the discovery of transgenerational immunity in invertebrates shifted existing paradigms on the lack of sophistication of their immune system. Nonetheless, the prevalence of this trait and the ecological factors driving... more
Over a decade ago, the discovery of transgenerational immunity in invertebrates shifted existing paradigms on the lack of sophistication of their immune system. Nonetheless, the prevalence of this trait and the ecological factors driving its evolution in invertebrates remain poorly understood. Here, we develop a theoretical host–parasite model and predict that long lifespan and low dispersal should promote the evolution of transgenerational immunity. We also predict that in species that produce both philopatric and dispersing individuals, it may pay to have a plastic allocation strategy with a higher transgenerational immunity investment in philopatric offspring because they are more likely to encounter locally adapted pathogens. We review all experimental studies published to date, comprising 21 invertebrate species in nine different orders, and we show that, as expected, longevity and dispersal correlate with the transfer immunity to offspring. The validity of our prediction regarding the plasticity of investment in transgenerational immunity remains to be tested in invertebrates, but also in vertebrate species. We discuss the implications of our work for the study of the evolution of immunity, and we suggest further avenues of research to expand our knowledge of the impact of transgenerational immune protection in host–parasite interactions.
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The diversity of resistance challenges the ability of pathogens to spread and to exploit host populations [1–3]. Yet, how this host diversity evolves over time remains unclear because it depends on the interplay between intraspecific... more
The diversity of resistance challenges the ability of pathogens to spread and to exploit host populations [1–3]. Yet, how this host diversity evolves over time remains unclear because it depends on the interplay between intraspecific competition among host genotypes and coevolution with pathogens. Here we study experimentally the effect of coevolving phage populations on the diversification of bacterial CRISPR immunity across space and time. We demonstrate that the negative-frequency-dependent selection generated by coevolution is a powerful force that maintains host resistance diversity and selects for new resistance mutations in the host. We also find that host evolution is driven by asymmetries in competitive abilities among different host genotypes. Even if the fittest host genotypes are targeted preferentially by the evolving phages they often escape extinctions through the acquisition of new CRISPR immunity. Together, these fluctuating selective pressures maintain diversity, b...
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Lay Summary: Competition often occurs among diverse parasites within a single host, but control efforts could change its strength. We examined how the interplay between competition and control could shape the evolution of parasite traits... more
Lay Summary: Competition often occurs among diverse parasites within a single host, but control efforts could change its strength. We examined how the interplay between competition and control could shape the evolution of parasite traits like drug resistance and disease severity.
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The ability of a pathogen to cause an epidemic when introduced in a new host population often relies on its ability to adapt to this new environment. Here, we give a brief overview of recent theoretical and empirical studies of such... more
The ability of a pathogen to cause an epidemic when introduced in a new host population often relies on its ability to adapt to this new environment. Here, we give a brief overview of recent theoretical and empirical studies of such evolutionary emergence of pathogens. We discuss the effects of several ecological and genetic factors that may affect the likelihood of emergence: migration, life history of the infectious agent, host heterogeneity, and the rate and effects of mutations. We contrast different modelling approaches and indicate how details in the way we model each step of a life cycle can have important consequences on the predicted probability of evolutionary emergence. These different theoretical perspectives yield important insights into optimal surveillance and intervention strategies, which should aim for a reduction in the emergence (and re-emergence) of infectious diseases.
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Pathogen adaptation to public health interventions, such as vaccination, may take tortuous routes and involve multiple mutations at distinct locations in the pathogen genome, acting on distinct phenotypic traits. Despite its importance... more
Pathogen adaptation to public health interventions, such as vaccination, may take tortuous routes and involve multiple mutations at distinct locations in the pathogen genome, acting on distinct phenotypic traits. Despite its importance for public health, how these multilocus adaptations jointly evolve is poorly understood. Here we consider the joint evolution of two adaptations: the pathogen’s ability to escape the vaccine-induced immune response and adjustments to the pathogen’s virulence and transmissi-bility. We elucidate the role played by epistasis and recombination, with an emphasis on the different protective effects of vaccination. We show that vaccines reducing transmission and/or increasing clearance generate positive epistasis between the vaccine-escape and virulence alleles, favouring strains that carry both mutations, whereas vaccines reducing virulence mortality generate negative epistasis, favouring strains that carry either mutation, but not both. High rates of recom...
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The mean fitness of a population, often equal to its growth rate, measures its level of adaptation to particular environmental conditions. A better understanding of the evolution of mean fitness could thus provide a natural link between... more
The mean fitness of a population, often equal to its growth rate, measures its level of adaptation to particular environmental conditions. A better understanding of the evolution of mean fitness could thus provide a natural link between evolution and
It is useful to think of the temporal dynamics of evolutionary change for novel pathogens like SARS-CoV-2 as passing through two phases. In the first phase the host population is immunologically naïve and selection strongly favours... more
It is useful to think of the temporal dynamics of evolutionary change for novel pathogens like SARS-CoV-2 as passing through two phases. In the first phase the host population is immunologically naïve and selection strongly favours adaptation to these abundant naïve hosts. In the second phase a growing proportion of the population will have an immunological history with the pathogen, either through natural infection or vaccination, and thus selection will shift, increasingly favouring adaptation to these hosts. In this article we will focus primarily on vaccine-driven evolution but return to the issue of evolution driven by immunity acquired from natural infections in our conclusions.
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The evolution of multidrug resistance (MDR) is a pressing public health concern. Yet many aspects, such as the role played by population structure, remain poorly understood. Here, we argue that studying MDR evolution by focusing upon the... more
The evolution of multidrug resistance (MDR) is a pressing public health concern. Yet many aspects, such as the role played by population structure, remain poorly understood. Here, we argue that studying MDR evolution by focusing upon the dynamical equations for linkage disequilibrium (LD) can greatly simplify the calculations, generate more insight, and provide a unified framework for understanding the role of population structure. We demonstrate how a general epidemiological model of MDR evolution can be recast in terms of the LD equations. These equations reveal how the different forces generating and propagating LD operate in a dynamical setting at both the population and metapopulation levels. We then apply these insights to show how the LD perspective: (i) explains equilibrium patterns of MDR, (ii) provides a simple interpretative framework for transient evolutionary dynamics, and (iii) can be used to assess the consequences of different drug prescription strategies for MDR evo...