What limits the evolutionary emergence of pathogens?

Emergence failure of early epidemics: A mathematical modeling approach

Epidemic or pathogen emergence is the phenomenon by which a poorly transmissible pathogen finds its evolutionary pathway to become a mutant that can cause an epidemic. Many mathematical models of pathogen emergence rely on branching processes. Here, we discuss pathogen emergence using Markov chains, for a more tractable analysis, generalizing previous work by Kendall and Bartlett about disease invasion. We discuss the probability of emergence failure for early epidemics, when the number of infected individuals is small and the number of the susceptible individuals is virtually unlimited. Our formalism addresses both directly transmitted and vector-borne diseases, in the cases where the original pathogen is 1) one step-mutation away from the epidemic strain, and 2) undergoing a long chain of neutral mutations that do not change the epidemiology. We obtain analytic results for the probabilities of emergence failure and two features transcending the transmission mechanism. First, the reproduction number of the original pathogen is determinant for the probability of pathogen emergence, more important than the mutation rate or the transmissibility of the emerged pathogen. Second, the probability of mutation within infected individuals must be sufficiently high for the pathogen undergoing neutral mutations to start an epidemic, the mutation threshold depending again on the basic reproduction number of the original pathogen. Finally, we discuss the parameterization of models of pathogen emergence, using SARS-CoV1 as an example of zoonotic emergence and HIV as an example for the emergence of drug resistance. We also discuss assumptions of our models and implications for epidemiology.

Building pyramids against the evolutionary emergence of pathogens

Mutations allowing pathogens to escape host immunity promote the spread of infectious diseases in heterogeneous host populations and can lead to major epidemics. Understanding the conditions that slow down this evolution is key for the development of durable control strategies against pathogens. Here, we use theory and experiments to compare the efficacy of three strategies for the deployment of resistance: (i) a mixing strategy where the host population contains two single-resistant genotypes, (ii) a pyramiding strategy where the host carries a double-resistant genotype, (iii) a combining strategy where the host population is a mix of a single-resistant genotype and a double-resistant genotype. First, we use evolutionary epidemiology theory to clarify the interplay between demographic stochasticity and evolutionary dynamics to show that the pyramiding strategy always yields lower probability of evolutionary emergence. Second, we test experimentally these predictions with the introduction of bacteriophages into bacterial populations where we manipulated the diversity and the depth of immunity using a Clustered Regularly Interspaced Short Palindromic Repeats-CRISPR associated (CRISPR-Cas) system. These biological assays confirm that pyramiding multiple defences into the same host genotype and avoiding combination with single-defence genotypes is a robust way to reduce pathogen evolutionary emergence. The experimental validation of these theoretical recommendations has practical implications in various areas, including for the optimal deployment of resistance varieties in agriculture and for the design of durable vaccination strategies.

Demographic feedbacks during evolutionary rescue can slow or speed adaptive evolution

Populations declining toward extinction can persist via genetic adaptation in a process called evolutionary rescue. Predicting evolutionary rescue has applications ranging from conservation biology to medicine, but requires understanding and integrating the multiple effects of a stressful environmental change on population processes. Here we derive a simple expression for how generation time, a key determinant of the rate of evolution, varies with population size during evolutionary rescue. Change in generation time is quantitatively predicted by comparing how intraspecific competition and the source of maladaptation each affect the rates of births and deaths in the population. Depending on the difference between two parameters quantifying these effects, the model predicts that populations may experience substantial changes in their rate of adaptation in both positive and negative directions, or adapt consistently despite severe stress. These predictions were then tested by comparison to the results of individual-based simulations of evolutionary rescue, which validated that the tolerable rate of environmental change varied considerably as described by analytical results. We discuss how these results inform efforts to understand wildlife disease and adaptation to climate change, evolution in managed populations and treatment resistance in pathogens.

Molecular epidemiology of the citrus bacterial pathogen Xanthomonas citri pv. citri from the Arabian Peninsula reveals a complex structure of specialist and generalist strains

Molecular epidemiology studies are essential to refine our understanding of migrations of phytopathogenic bacteria, the major determining factor in their emergence, and to understand the factors that shape their population structure. Microsatellite and minisatellite typing are useful techniques for deciphering the population structure of Xanthomonas citri pv. citri, the causal agent of Asiatic citrus canker. This paper presents a molecular epidemiology study, which has improved our understanding of the history of the pathogen's introductions into the Arabian Peninsula, since it was first reported in the 1980s. An unexpectedly high genetic diversity of the pathogen was revealed. The four distinct genetic lineages within X. citri pv. citri, which have been reported throughout the world, were identified in the Arabian Peninsula, most likely as the result of multiple introductions. No copper‐resistant X. citri pv. citri strains were identified. The pathogen's population structure on Mexican lime (their shared host species) was closely examined in two countries, Saudi Arabia and Yemen. We highlighted the marked prevalence of specialist pathotype A* strains in both countries, which suggests that specialist strains of X. citri pv. citri may perform better than generalist strains when they occur concomitantly in this environment. Subclade 4.2 was the prevailing lineage identified. Several analyses (genetic structure deciphered by discriminant analysis of principal components, R_ST‐based genetic differentiation, geographic structure) congruently suggested the role of human activities in the pathogen's spread. We discuss the implications of these results on the management of Asiatic citrus canker in the region.

The source of individual heterogeneity shapes infectious disease outbreaks

There is known heterogeneity between individuals in infectious disease transmission patterns. The source of this heterogeneity is thought to affect epidemiological dynamics but studies tend not to control for the overall heterogeneity in the number of secondary cases caused by an infection. To explore the role of individual variation in infection duration and transmission rate in parasite emergence and spread, while controlling for this potential bias, we simulate stochastic outbreaks with and without parasite evolution. As expected, heterogeneity in the number of secondary cases decreases the probability of outbreak emergence. Furthermore, for epidemics that do emerge, assuming more realistic infection duration distributions leads to faster outbreaks and higher epidemic peaks. When parasites require adaptive mutations to cause large epidemics, the impact of heterogeneity depends on the underlying evolutionary model. If emergence relies on within-host evolution, decreasing the infection duration variance decreases the probability of emergence. These results underline the importance of accounting for realistic distributions of transmission rates to anticipate the effect of individual heterogeneity on epidemiological dynamics.

Point of care diagnostics and non-invasive sampling strategy: a review on major advances in veterinary diagnostics

The use of point of care diagnostics (POCD) in animal diseases has steadily increased over the years since its introduction. Its potential application to diagnose infectious diseases in remote and resource limited settings have made it an ideal diagnostic in animal disease diagnosis and surveillance. The rapid increase in incidence of emerging infectious diseases requires urgent attention where POCD could be indispensable tools for immediate detection and early warning of a potential pathogen. The advantages of being rapid, easily affordable and the ability to diagnose an infectious disease on spot has driven an intense effort to refine and build on the existing technologies to generate advanced POCD with incremental improvements in analytical performance to diagnose a broad spectrum of animal diseases. The rural communities in developing countries are invariably affected by the burden of infectious animal diseases due to limited access to diagnostics and animal health personnel. Besides, the alarming trend of emerging and transboundary diseases with pathogen spill-overs at livestock-wildlife interfaces has been identified as a threat to the domestic population and wildlife conservation. Under such circumstances, POCD coupled with non-invasive sampling techniques could be successfully deployed at field level without the use of sophisticated laboratory infrastructures. This review illustrates the current and prospective POCD for existing and emerging animal diseases, the status of non-invasive sampling strategies for animal diseases, and the tremendous potential of POCD to uplift the status of global animal health care.

Challenges in modeling the emergence of novel pathogens

The emergence of infectious agents with pandemic potential present scientific challenges from detection to data interpretation to understanding determinants of risk and forecasts. Mathematical models could play an essential role in how we prepare for future emergent pathogens. Here, we describe core directions for expansion of the existing tools and knowledge base, including: using mathematical models to identify critical directions and paths for strengthening data collection to detect and respond to outbreaks of novel pathogens; expanding basic theory to identify infectious agents and contexts that present the greatest risks, over both the short and longer term; by strengthening estimation tools that make the most use of the likely range and uncertainties in existing data; and by ensuring modelling applications are carefully communicated and developed within diverse and equitable collaborations for increased public health benefit.

Digest: Evolutionary rescue following the introduction of a pathogen. Evolution 2021;75:2998-2999. [PMID: 34558060 DOI: 10.1111/evo.14361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

To what extent can adaptive evolution rescue a population from extinction following the introduction of a pathogen? Searle and Christie (2021) show how evolutionary rescues in host-pathogen systems may differ from those that occur in response to abiotic changes. In particular, they pinpoint how epidemiological feedback and pathogen evolution, inherent to host-pathogen systems, can greatly affect the likelihood of rescue.

Host genotype and genetic diversity shape the evolution of a novel bacterial infection

Pathogens continue to emerge from increased contact with novel host species. Whilst these hosts can represent distinct environments for pathogens, the impacts of host genetic background on how a pathogen evolves post-emergence are unclear. In a novel interaction, we experimentally evolved a pathogen (Staphylococcus aureus) in populations of wild nematodes (Caenorhabditis elegans) to test whether host genotype and genetic diversity affect pathogen evolution. After ten rounds of selection, we found that pathogen virulence evolved to vary across host genotypes, with differences in host metal ion acquisition detected as a possible driver of increased host exploitation. Diverse host populations selected for the highest levels of pathogen virulence, but infectivity was constrained, unlike in host monocultures. We hypothesise that population heterogeneity might pool together individuals that contribute disproportionately to the spread of infection or to enhanced virulence. The genomes of evolved populations were sequenced, and it was revealed that pathogens selected in distantly-related host genotypes diverged more than those in closely-related host genotypes. S. aureus nevertheless maintained a broad host range. Our study provides unique empirical insight into the evolutionary dynamics that could occur in other novel infections of wildlife and humans.

Inferring genome-wide correlations of mutation fitness effects between populations

The effect of a mutation on fitness may differ between populations depending on environmental and genetic context, but little is known about the factors that underlie such differences. To quantify genome-wide correlations in mutation fitness effects, we developed a novel concept called a joint distribution of fitness effects (DFE) between populations. We then proposed a new statistic w to measure the DFE correlation between populations. Using simulation, we showed that inferring the DFE correlation from the joint allele frequency spectrum is statistically precise and robust. Using population genomic data, we inferred DFE correlations of populations in humans, Drosophila melanogaster, and wild tomatoes. In these species, we found that the overall correlation of the joint DFE was inversely related to genetic differentiation. In humans and D. melanogaster, deleterious mutations had a lower DFE correlation than tolerated mutations, indicating a complex joint DFE. Altogether, the DFE correlation can be reliably inferred, and it offers extensive insight into the genetics of population divergence.

Experimental Evolution in Plant-Microbe Systems: A Tool for Deciphering the Functioning and Evolution of Plant-Associated Microbial Communities

In natural environments, microbial communities must constantly adapt to stressful environmental conditions. The genetic and phenotypic mechanisms underlying the adaptive response of microbial communities to new (and often complex) environments can be tackled with a combination of experimental evolution and next generation sequencing. This combination allows to analyse the real-time evolution of microbial populations in response to imposed environmental factors or during the interaction with a host, by screening for phenotypic and genotypic changes over a multitude of identical experimental cycles. Experimental evolution (EE) coupled with comparative genomics has indeed facilitated the monitoring of bacterial genetic evolution and the understanding of adaptive evolution processes. Basically, EE studies had long been done on single strains, allowing to reveal the dynamics and genetic targets of natural selection and to uncover the correlation between genetic and phenotypic adaptive changes. However, species are always evolving in relation with other species and have to adapt not only to the environment itself but also to the biotic environment dynamically shaped by the other species. Nowadays, there is a growing interest to apply EE on microbial communities evolving under natural environments. In this paper, we provide a non-exhaustive review of microbial EE studies done with systems of increasing complexity (from single species, to synthetic communities and natural communities) and with a particular focus on studies between plants and plant-associated microorganisms. We highlight some of the mechanisms controlling the functioning of microbial species and their adaptive responses to environment changes and emphasize the importance of considering bacterial communities and complex environments in EE studies.

Establishment versus population growth in spatio-temporally varying environments

We consider situations where repeated invasion attempts occur from a source population into a receptor population over extended periods of time. The receptor population contains two locations that provide different expected offspring numbers to invaders. There is demographic stochasticity in offspring numbers. In addition, temporal variation causes local invader fitnesses to vary. We show that effects of environmental autocorrelation on establishment success depend on spatial covariance of the receptor subpopulations. In situations with a low spatial covariance this effect is positive, whereas high spatial covariance and/or high migration probabilities between the subpopulations causes the effect to be negative. This result reconciles seemingly contradictory results from the literature concerning effects of temporal variation on population dynamics with demographic stochasticity. We study an example in the context of genetic introgression, where invasions of cultivar plant genes occur through pollen flow from a source population into wild-type receptor populations, but our results have implications in a wider range of contexts, such as the spread of exotic species, metapopulation dynamics and epidemics.

Genetic analysis reveals long-standing population differentiation and high diversity in the rust pathogen Melampsora lini

A priority for research on infectious disease is to understand how epidemiological and evolutionary processes interact to influence pathogen population dynamics and disease outcomes. However, little is understood about how population adaptation changes across time, how sexual vs. asexual reproduction contribute to the spread of pathogens in wild populations and how diversity measured with neutral and selectively important markers correlates across years. Here, we report results from a long-term study of epidemiological and genetic dynamics within several natural populations of the Linum marginale-Melampsora lini plant-pathogen interaction. Using pathogen isolates collected from three populations of wild flax (L. marginale) spanning 16 annual epidemics, we probe links between pathogen population dynamics, phenotypic variation for infectivity and genomic polymorphism. Pathogen genotyping was performed using 1567 genome-wide SNP loci and sequence data from two infectivity loci (AvrP123, AvrP4). Pathogen isolates were phenotyped for infectivity using a differential set. Patterns of epidemic development were assessed by conducting surveys of infection prevalence in one population (Kiandra) annually. Bayesian clustering analyses revealed host population and ecotype as key predictors of pathogen genetic structure. Despite strong fluctuations in pathogen population size and severe annual bottlenecks, analysis of molecular variance revealed that pathogen population differentiation was relatively stable over time. Annually, varying levels of clonal spread (0–44.8%) contributed to epidemics. However, within populations, temporal genetic composition was dynamic with rapid turnover of pathogen genotypes, despite the dominance of only four infectivity phenotypes across the entire study period. Furthermore, in the presence of strong fluctuations in population size and migration, spatial selection may maintain pathogen populations that, despite being phenotypically stable, are genetically highly dynamic.

Melampsora lini is a rust fungus that infects native flax, Linum marginale in south-eastern Australia where its epidemiology and evolution have been intensively studied since 1987. Over that time, substantial diversity in the pathotypic structure of M. lini has been demonstrated but an understanding of how genetic diversity in pathogen populations is maintained through space and time is lacking. Here we integrated phenotypic, genotypic and epidemiological datasets spanning 16 annual epidemics across three host populations to examine long-term pathogen genetic dynamics. The results show that host ecotype is the dominant selective force in the face of strong bottlenecks and annual patterns of genetic turnover. Results from previous studies indicate that in this geographic region, M. lini lacks the capacity to reproduce sexually–we thus expected to find limited genetic diversity and evidence for strong clonality influencing genetic dynamics within growing seasons. However, the breadth of genomic coverage provided by the SNP markers revealed high levels of genotypic variation within M. lini populations. This discovery contrasts with observed phenotypic dynamics as the epidemics of this pathogen were largely dominated by four pathotypes across the study period. Based on a detailed assessment and comparison of pathotypic and genotypic patterns, our study increases the understanding of how genetic diversity is generated and maintained through space and time within wild pathogen populations. The implications for the management of resistance to pathogens in agricultural or conservation contexts are significant: the appearance of clonality may be hiding high levels of pathogen diversity and recombination. Understanding how this diversity is generated could provide new and unique ways to mitigate or suppress the emergence of infectious strains, allowing to efficiently combat harmful diseases.

Evolutionary Timeline and Genomic Plasticity Underlying the Lifestyle Diversity in Rhizobiales

Bacteria form diverse interactions with eukaryotic hosts. This is well represented by the Rhizobiales, a clade of Alphaproteobacteria strategically important for their large diversity of lifestyles with implications for agricultural and medical research. To investigate their lifestyle evolution, we compiled a comprehensive data set of genomes and lifestyle information for over 1,000 Rhizobiales genomes. We show that the origins of major host-associated lineages in Rhizobiales broadly coincided with the emergences of their host plants/animals, suggesting bacterium-host interactions as a driving force in the evolution of Rhizobiales. We further found that, in addition to gene gains, preexisting traits and recurrent losses of specific genomic traits may have played underrecognized roles in the origin of host-associated lineages, providing clues to genetic engineering of microbial agricultural inoculants and prevention of the emergence of potential plant/animal pathogens.

Members of the order Rhizobiales include those capable of nitrogen fixation in nodules as well as pathogens of animals and plants. This lifestyle diversity has important implications for agricultural and medical research. Leveraging large-scale genomic data, we infer that Rhizobiales originated as a free-living ancestor ∼1,500 million years ago (Mya) and that the later emergence of host-associated lifestyles broadly coincided with the rise of their eukaryotic hosts. In particular, the first nodulating lineage arose from either Azorhizobium or Bradyrhizobium 150 to 80 Mya, a time range in general concurrent with the emergence of legumes. The rates of lifestyle transitions are highly variable; nodule association is more likely to be lost than gained, whereas animal association likely represents an evolutionary dead end. We searched for statistical correlations between gene presence and lifestyle and identified genes likely contributing to the transition and adaptation to the same lifestyle in divergent lineages. Among the genes potentially promoting successful transitions to major nodulation lineages, the nod and nif clusters for nodulation and nitrogen fixation, respectively, were repeatedly acquired during each transition; the fix, dct, and phb clusters involved in energy conservation under micro-oxic conditions were present in the nonnodulating ancestors; and the secretion systems were acquired in lineage-specific patterns. Our study data suggest that increased eukaryote diversity drives lifestyle diversification of bacteria and highlight both acquired and preexisting traits facilitating the origin of host association.

IMPORTANCE Bacteria form diverse interactions with eukaryotic hosts. This is well represented by the Rhizobiales, a clade of Alphaproteobacteria strategically important for their large diversity of lifestyles with implications for agricultural and medical research. To investigate their lifestyle evolution, we compiled a comprehensive data set of genomes and lifestyle information for over 1,000 Rhizobiales genomes. We show that the origins of major host-associated lineages in Rhizobiales broadly coincided with the emergences of their host plants/animals, suggesting bacterium-host interactions as a driving force in the evolution of Rhizobiales. We further found that, in addition to gene gains, preexisting traits and recurrent losses of specific genomic traits may have played underrecognized roles in the origin of host-associated lineages, providing clues to genetic engineering of microbial agricultural inoculants and prevention of the emergence of potential plant/animal pathogens.

Winter is coming: Pathogen emergence in seasonal environments

Many infectious diseases exhibit seasonal dynamics driven by periodic fluctuations of the environment. Predicting the risk of pathogen emergence at different points in time is key for the development of effective public health strategies. Here we study the impact of seasonality on the probability of emergence of directly transmitted pathogens under different epidemiological scenarios. We show that when the period of the fluctuation is large relative to the duration of the infection, the probability of emergence varies dramatically with the time at which the pathogen is introduced in the host population. In particular, we identify a new effect of seasonality (the winter is coming effect) where the probability of emergence is vanishingly small even though pathogen transmission is high. We use this theoretical framework to compare the impact of different preventive control strategies on the average probability of emergence. We show that, when pathogen eradication is not attainable, the optimal strategy is to act intensively in a narrow time interval. Interestingly, the optimal control strategy is not always the strategy minimizing R₀, the basic reproduction ratio of the pathogen. This theoretical framework is extended to study the probability of emergence of vector borne diseases in seasonal environments and we show how it can be used to improve risk maps of Zika virus emergence.

Seasonality drives fluctuations in the probability of pathogen emergence, with dramatic consequences for public health and agriculture. We show that this probability of pathogen emergence can be vanishingly small before the low transmission season. We derive the conditions for the existence of this winter is coming effect and identify optimal control strategies that minimize the risk of pathogen emergence. We generalize this framework to account for different forms of environmental variations, different modes of control and complex pathogen life cycles. We illustrate how this framework can be used to improve predictions of Zika emergence at different points in space and time.

Understanding the emergence of bacterial pathogens in novel hosts

Our understanding of the ecological and evolutionary context of novel infections is largely based on viral diseases, even though bacterial pathogens may display key differences in the processes underlying their emergence. For instance, host-shift speciation, in which the jump of a pathogen into a novel host species is followed by the specialization on that host and the loss of infectivity of previous host(s), is commonly observed in viruses, but less often in bacteria. Here, we suggest that the extent to which pathogens evolve host generalism or specialism following a jump into a novel host will depend on their level of adaptation to dealing with different environments, their rates of molecular evolution and their ability to recombine. We then explore these hypotheses using a formal model and show that the high levels of phenotypic plasticity, low rates of evolution and the ability to recombine typical of bacterial pathogens should reduce their propensity to specialize on novel hosts. Novel bacterial infections may therefore be more likely to result in transient spillovers or increased host ranges than in host shifts. Finally, consistent with our predictions, we show that, in two unusual cases of contemporary bacterial host shifts, the bacterial pathogens both have small genomes and rapid rates of substitution. Further tests are required across a greater number of emerging pathogens to assess the validity of our hypotheses. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Industrial bees: The impact of apicultural intensification on local disease prevalence

It is generally thought that the intensification of farming will result in higher disease prevalences, although there is little specific modelling testing this idea. Focussing on honeybees, we build multi-colony models to inform how "apicultural intensification" is predicted to impact honeybee pathogen epidemiology at the apiary scale.We used both agent-based and analytical models to show that three linked aspects of apicultural intensification (increased population sizes, changes in population network structure and increased between-colony transmission) are unlikely to greatly increase disease prevalence in apiaries. Principally this is because even low-intensity apiculture exhibits high disease prevalence.The greatest impacts of apicultural intensification are found for diseases with relatively low R0 (basic reproduction number), however, such diseases cause little overall disease prevalence and, therefore, the impacts of intensification are minor. Furthermore, the smallest impacts of intensification are for diseases with high R0 values, which we argue are typical of important honeybee diseases. Policy Implications: Our findings contradict the idea that apicultural intensification by crowding honeybee colonies in large, dense apiaries leads to notably higher disease prevalences for established honeybee pathogens. More broadly, our work demonstrates the need for informative models of all agricultural systems and management practices in order to understand the implications of management changes on diseases.

Individual and temporal variation in pathogen load predicts long-term impacts of an emerging infectious disease

Emerging infectious diseases increasingly threaten wildlife populations. Most studies focus on managing short-term epidemic properties, such as controlling early outbreaks. Predicting long-term endemic characteristics with limited retrospective data is more challenging. We used individual-based modeling informed by individual variation in pathogen load and transmissibility to predict long-term impacts of a lethal, transmissible cancer on Tasmanian devil (Sarcophilus harrisii) populations. For this, we employed approximate Bayesian computation to identify model scenarios that best matched known epidemiological and demographic system properties derived from 10 yr of data after disease emergence, enabling us to forecast future system dynamics. We show that the dramatic devil population declines observed thus far are likely attributable to transient dynamics (initial dynamics after disease emergence). Only 21% of matching scenarios led to devil extinction within 100 yr following devil facial tumor disease (DFTD) introduction, whereas DFTD faded out in 57% of simulations. In the remaining 22% of simulations, disease and host coexisted for at least 100 yr, usually with long-period oscillations. Our findings show that pathogen extirpation or host-pathogen coexistence are much more likely than the DFTD-induced devil extinction, with crucial management ramifications. Accounting for individual-level disease progression and the long-term outcome of devil-DFTD interactions at the population-level, our findings suggest that immediate management interventions are unlikely to be necessary to ensure the persistence of Tasmanian devil populations. This is because strong population declines of devils after disease emergence do not necessarily translate into long-term population declines at equilibria. Our modeling approach is widely applicable to other host-pathogen systems to predict disease impact beyond transient dynamics.

Resource-driven changes to host population stability alter the evolution of virulence and transmission

What drives the evolution of parasite life-history traits? Recent studies suggest that linking within- and between-host processes can provide key insight into both disease dynamics and parasite evolution. Still, it remains difficult to understand how to pinpoint the critical factors connecting these cross-scale feedbacks, particularly under non-equilibrium conditions; many natural host populations inherently fluctuate and parasites themselves can strongly alter the stability of host populations. Here, we develop a general model framework that mechanistically links resources to parasite evolution across a gradient of stable and unstable conditions. First, we dynamically link resources and between-host processes (host density, stability, transmission) to virulence evolution, using a 'non-nested' model. Then, we consider a 'nested' model where population-level processes (transmission and virulence) depend on resource-driven changes to individual-level (within-host) processes (energetics, immune function, parasite production). Contrary to 'non-nested' model predictions, the 'nested' model reveals complex effects of host population dynamics on parasite evolution, including regions of evolutionary bistability; evolution can push parasites towards strongly or weakly stabilizing strategies. This bistability results from dynamic feedbacks between resource-driven changes to host density, host immune function and parasite production. Together, these results highlight how cross-scale feedbacks can provide key insights into the structuring role of parasites and parasite evolution.This article is part of the theme issue 'Anthropogenic resource subsidies and host-parasite dynamics in wildlife'.

Mapping the global potential distributions of two arboviral vectors Aedes aegypti and Ae. albopictus under changing climate

BACKGROUND

Aedes aegypti and Ae. albopictus are the primary vectors that transmit several arboviral diseases, including dengue, chikungunya, and Zika. The world is presently experiencing a series of outbreaks of these diseases, so, we still require to better understand the current distributions and possible future shifts of their vectors for successful surveillance and control programs. Few studies assessed the influences of climate change on the spatial distributional patterns and abundance of these important vectors, particularly using the most recent climatic scenarios. Here, we updated the current potential distributions of both vectors and assessed their distributional changes under future climate conditions.

METHODS

We used ecological niche modeling approach to estimate the potential distributions of Ae. aegypti and Ae. albopictus under present-day and future climate conditions. This approach fits ecological niche model from occurrence records of each species and environmental variables. For each species, future projections were based on climatic data from 9 general circulation models (GCMs) for each representative concentration pathway (RCP) in each time period, with a total of 72 combinations in four RCPs in 2050 and 2070. All ENMs were tested using the partial receiver operating characteristic (pROC) and a set of 2,048 and 2,003 additional independent records for Ae. aegypti and Ae. albopictus, respectively. Finally, we used background similarity test to assess the similarity between the ENMs of Ae. aegypti and Ae. albopictus.

RESULTS

The predicted potential distribution of Ae. aegypti and Ae. albopictus coincided with the current and historical known distributions of both species. Aedes aegypti showed a markedly broader distributional potential across tropical and subtropical regions than Ae. albopictus. Interestingly, Ae. albopictus was markedly broader in distributional potential across temperate Europe and the United States. All ecological niche models (ENMs) were statistically robust (P < 0.001). ENMs successfully anticipated 98% (1,999/2,048) and 99% (1,985/2,003) of additional independent records for both Ae. aegypti and Ae. albopictus, respectively (P < 0.001). ENMs based on future conditions showed similarity between the overall distributional patterns of future-day and present-day conditions; however, there was a northern range expansion in the continental USA to include parts of Southern Canada in case of Ae. albopictus in both 2050 and 2070. Future models also anticipated further expansion of Ae. albopictus to the East to include most of Europe in both time periods. Aedes aegypti was anticipated to expand to the South in East Australia in 2050 and 2070. The predictions showed differences in distributional potential of both species between diverse RCPs in 2050 and 2070. Finally, the background similarity test comparing the ENMs of Ae. aegypti and Ae. albopictus was unable to reject the null hypothesis of niche similarity between both species (P > 0.05).

CONCLUSION

These updated maps provided details to better guide surveillance and control programs of Ae. aegypti and Ae. albopictus. They have also significant public health importance as a baseline for predicting the emergence of arboviral diseases transmitted by both vectors in new areas across the world.

Analysis of Fitness Trade-Offs in the Host Range Expansion of an RNA Virus, Tobacco Mild Green Mosaic Virus

The acquisition of new hosts provides a virus with more opportunities for transmission and survival but may be limited by across-host fitness trade-offs. Major causes of across-host trade-offs are antagonistic pleiotropy, that is, host differential phenotypic effects of mutations, a Genotype x Environment interaction, and epistasis, a Genotype x Genotype interaction. Here, we analyze if there are trade-offs, and what are the causes, associated with the acquisition by tobacco mild green mosaic virus (TMGMV) of a new host. For this, the multiplication of sympatric field isolates of TMGMV from its wild reservoir host Nicotiana glauca and from pepper crops was quantified in the original and the heterologous hosts. TMGMV isolates from N. glauca were adapted to their host, but pepper isolates were not adapted to pepper, and the acquisition of this new host was associated with a fitness penalty in the original host. Analyses of the collection of field isolates and of mutant genotypes derived from biologically active cDNA clones showed a role of mutations in the coat protein and the 3' untranslated region in determining within-host virus fitness. Fitness depended on host-specific effects of these mutations, on the genetic background in which they occurred, and on higher-order interactions of the type Genotype x Genotype x Environment. These types of effects had been reported to generate across-host fitness trade-offs under experimental evolution. Our results show they may also operate in heterogeneous natural environments and could explain why pepper isolates were not adapted to pepper and their lower fitness in N. glaucaIMPORTANCE The acquisition of new hosts conditions virus epidemiology and emergence; hence it is important to understand the mechanisms behind host range expansion. Experimental evolution studies have identified antagonistic pleiotropy and epistasis as genetic mechanisms that limit host range expansion, but studies from virus field populations are few. Here, we compare the performance of isolates of tobacco mild green mosaic virus from its reservoir host, Nicotiana glauca, and its new host, pepper, showing that acquisition of a new host was not followed by adaptation to it but was associated with a fitness loss in the original host. Analysis of mutations determining host-specific virus multiplication identified antagonistic pleiotropy, epistasis, and host-specific epistasis as mechanisms generating across-host fitness trade-offs that may prevent adaptation to pepper and cause a loss of fitness in N. glauca Thus, mechanisms determining trade-offs, identified under experimental evolution, could also operate in the heterogeneous environment in which natural plant virus populations occur.

Evolutionary emergence of infectious diseases in heterogeneous host populations

The emergence and re-emergence of pathogens remains a major public health concern. Unfortunately, when and where pathogens will (re-)emerge is notoriously difficult to predict, as the erratic nature of those events is reinforced by the stochastic nature of pathogen evolution during the early phase of an epidemic. For instance, mutations allowing pathogens to escape host resistance may boost pathogen spread and promote emergence. Yet, the ecological factors that govern such evolutionary emergence remain elusive because of the lack of ecological realism of current theoretical frameworks and the difficulty of experimentally testing their predictions. Here, we develop a theoretical model to explore the effects of the heterogeneity of the host population on the probability of pathogen emergence, with or without pathogen evolution. We show that evolutionary emergence and the spread of escape mutations in the pathogen population is more likely to occur when the host population contains an intermediate proportion of resistant hosts. We also show that the probability of pathogen emergence rapidly declines with the diversity of resistance in the host population. Experimental tests using lytic bacteriophages infecting their bacterial hosts containing Clustered Regularly Interspaced Short Palindromic Repeat and CRISPR-associated (CRISPR-Cas) immune defenses confirm these theoretical predictions. These results suggest effective strategies for cross-species spillover and for the management of emerging infectious diseases.

The probability that an epidemic will break out is highly dependent on the ability of the pathogen to acquire new adaptive mutations and to induce evolutionary emergence. Forecasting pathogen emergence thus requires a good understanding of the interplay between the epidemiology and evolution taking place at the onset of an outbreak. Here, we provide a comprehensive theoretical framework to analyze the impact of host population heterogeneity on the probability of pathogen evolutionary emergence. We use this model to predict the impact of the fraction of susceptible hosts, the inoculum size of the pathogen, and the diversity of host resistance on pathogen emergence. Our experiments using lytic bacteriophages and CRISPR-resistant bacteria support our theoretical predictions and demonstrate that manipulating the diversity of resistance alleles in a host population may be an effective way to limit the emergence of new pathogens.

Evolutionary Rescue in a Linearly Changing Environment: Limits on Predictability

Populations subject to substantial environmental change that decreases absolute fitness (expected number of offspring per individual) to less than one must adapt to persist. The probability of adaptive evolutionary rescue may be influenced by factors intrinsic to the organism itself, or by features specific to the individual population and its environment. An important question (given the increasing prevalence of environmental change) is the predictability of evolutionary rescue. We used an individual-based simulation model and a related analytic model to examine population persistence, given a continuously changing environment that leads to a linear change in the optimum for a phenotypic trait under selection. Population persistence was not well predicted by the population genetics at the start of environmental change, which contrasts strongly with the results shown in prior work for persistence after a sudden environmental change. Larger populations, which had a greater scope for the generation and maintenance of beneficial genetic variation, showed a clear advantage, but increasing the rate of environmental change always decreased the probability of persistence. Extinctions occurred throughout the period of continuous change, and populations that went extinct showed little sign of their eventual fate until shortly before extinction. Partially clonal populations showed less predictability and greater vulnerability to extinction when impacted by continuous change than did fully sexual populations-any advantage gained by the initial transmission of well-adapted phenotypes via clonal reproduction is lost as the phenotypic optimum continues to shift and the generation of novel variation is required for continuous adaptation.

Rapid Antagonistic Coevolution in an Emerging Pathogen and Its Vertebrate Host

Host-pathogen coevolution is assumed to play a key role in eco-evolutionary processes, including epidemiological dynamics and the evolution of sexual reproduction [1-4]. Despite this, direct evidence for host-pathogen coevolution is exceptional [5-7], particularly in vertebrate hosts. Indeed, although vertebrate hosts have been shown to evolve in response to pathogens or vice versa [8-12], there is little evidence for the necessary reciprocal changes in the success of both antagonists over time [13]. Here, we generate a time-shift experiment to demonstrate adaptive, reciprocal changes in North American house finches (Haemorhous mexicanus) and their emerging bacterial pathogen, Mycoplasma gallisepticum [14-16]. Our experimental design is made possible by the existence of disease-exposed and unexposed finch populations, which were known to exhibit equivalent responses to experimental inoculation until the recent spread of genetic resistance in the former [14, 17]. Whereas inoculations with pathogen isolates from epidemic outbreak caused comparable sub-lethal eye swelling in hosts from exposed (hereafter adapted) and unexposed (hereafter ancestral) populations, inoculations with isolates sampled after the spread of resistance were threefold more likely to cause lethal symptoms in hosts from ancestral populations. Similarly, the probability that pathogens successfully established an infection in the primary host and, before inducing death, transmitted to an uninfected sentinel was highest when recent isolates were inoculated in hosts from ancestral populations and lowest when early isolates were inoculated in hosts from adapted populations. Our results demonstrate antagonistic host-pathogen coevolution, with hosts and pathogens displaying increased resistance and virulence in response to each other over time.

Effects of Transmission Bottlenecks on the Diversity of Influenza A Virus

We investigate the fate of de novo mutations that occur during the in-host replication of a pathogenic virus, predicting the probability that such mutations are passed on during disease transmission to a new host. Using influenza A virus as a model organism, we develop a life-history model of the within-host dynamics of the infection, deriving a multitype branching process with a coupled deterministic model to capture the population of available target cells. We quantify the fate of neutral mutations and mutations affecting five life-history traits: clearance, attachment, budding, cell death, and eclipse phase timing. Despite the severity of disease transmission bottlenecks, our results suggest that in a single transmission event, several mutations that appeared de novo in the donor are likely to be transmitted to the recipient. Even in the absence of a selective advantage for these mutations, the sustained growth phase inherent in each disease transmission cycle generates genetic diversity that is not eliminated during the transmission bottleneck.

Could human coronavirus OC43 have co-evolved with early humans?

This paper reports on an investigation of the role of codon usage evolution on the suggested bovine-to-human spillover of Bovine coronavirus (BCoV), an enteric/respiratory virus of cattle, resulting in the emergence of the exclusively respiratory Human coronavirus OC43 (HCoV-OC43). Analyses based on full genomes of BCoV and HCoV-OC43 and on both human and bovine mRNAs sequences of cholecystokinin (CCK) and surfactant protein 1 A (SFTP1-A), representing the enteric and respiratory tract codon usage, respectively, have shown natural selection leading to optimization or deoptimization of viral codon usage to the human enteric and respiratory tracts depending on the virus genes under consideration. A higher correlation was found for the nucleotide distance at the 3rd nucleotide position of codons and codon usage optimization to the human respiratory tract when BCoV and HCoV-OC43 were compared. An MCC tree based on relative synonymous codon usage (RSCU) data integrating data from both viruses and hosts into a same analysis indicated three putative host/virus contact dates ranging from 1.54E8 to 2.44E5 years ago, suggesting that an ancestor coronavirus might have followed human evolution.

Evolutionary rescue in a host-pathogen system results in coexistence not clearance

The evolutionary rescue of host populations may prevent extinction from novel pathogens. However, the conditions that facilitate rapid evolution of hosts, in particular the population variation in host susceptibility, and the effects of host evolution in response to pathogens on population outcomes remain largely unknown. We constructed an individual-based model to determine the relationships between genetic variation in host susceptibility and population persistence in an amphibian-fungal pathogen (Batrachochytrium dendrobatidis) system. We found that host populations can rapidly evolve reduced susceptibility to a novel pathogen and that this rapid evolution led to a 71-fold increase in the likelihood of host-pathogen coexistence. However, the increased rates of coexistence came at a cost to host populations; fewer populations cleared infection, population sizes were depressed, and neutral genetic diversity was lost. Larger adult host population sizes and greater adaptive genetic variation prior to the onset of pathogen introduction led to substantially reduced rates of extinction, suggesting that populations with these characteristics should be prioritized for conservation when species are threatened by novel infectious diseases.

Can Ebola virus evolve to be less virulent in humans?

Understanding Ebola virus (EBOV) virulence evolution not only is timely but also raises specific questions because it causes one of the most virulent human infections and it is capable of transmission after the death of its host. Using a compartmental epidemiological model that captures three transmission routes (by regular contact, via dead bodies and by sexual contact), we infer the evolutionary dynamics of case fatality ratio on the scale of an outbreak and in the long term. Our major finding is that the virus's specific life cycle imposes selection for high levels of virulence and that this pattern is robust to parameter variations in biological ranges. In addition to shedding a new light on the ultimate causes of EBOV's high virulence, these results generate testable predictions and contribute to informing public health policies. In particular, burial management stands out as the most appropriate intervention since it decreases the R0 of the epidemics, while imposing selection for less virulent strains.

Effects of Clonal Reproduction on Evolutionary Lag and Evolutionary Rescue

Evolutionary lag-the difference between mean and optimal phenotype in the current environment-is of keen interest in light of rapid environmental change. Many ecologically important organisms have life histories that include stage structure and both sexual and clonal reproduction, yet how stage structure and clonality interplay to govern a population's rate of evolution and evolutionary lag is unknown. Effects of clonal reproduction on mean phenotype partition into two portions: one that is phenotype dependent, and another that is genotype dependent. This partitioning is governed by the association between the nonadditive genetic plus random environmental component of phenotype of clonal offspring and their parents. While clonality slows phenotypic evolution toward an optimum, it can dramatically increase population survival after a sudden step change in optimal phenotype. Increased adult survival slows phenotypic evolution but facilitates population survival after a step change; this positive effect can, however, be lost given survival-fecundity trade-offs. Simulations indicate that the benefits of increased clonality under environmental change greatly depend on the nature of that change: increasing population persistence under a step change while decreasing population persistence under a continuous linear change requiring de novo variation. The impact of clonality on the probability of persistence for species in a changing world is thus inexorably linked to the temporal texture of the change they experience.

Asymptotic profile in selection-mutation equations: Gauss versus Cauchy distributions

In this paper, we study the asymptotic (large time) behaviour of a selection-mutation-competition model for a population structured with respect to a phenotypic trait when the rate of mutation is very small. We assume that the reproduction is asexual, and that the mutations can be described by a linear integral operator. We are interested in the interplay between the time variable t and the rate ε of mutations. We show that depending on α > 0 , the limit ε → 0 with t = ε - α can lead to population number densities which are either Gaussian-like (when α is small) or Cauchy-like (when α is large).

Enhanced in planta Fitness through Adaptive Mutations in EfpR, a Dual Regulator of Virulence and Metabolic Functions in the Plant Pathogen Ralstonia solanacearum

Experimental evolution of the plant pathogen Ralstonia solanacearum, where bacteria were maintained on plant lineages for more than 300 generations, revealed that several independent single mutations in the efpR gene from populations propagated on beans were associated with fitness gain on bean. In the present work, novel allelic efpR variants were isolated from populations propagated on other plant species, thus suggesting that mutations in efpR were not solely associated to a fitness gain on bean, but also on additional hosts. A transcriptomic profiling and phenotypic characterization of the efpR deleted mutant showed that EfpR acts as a global catabolic repressor, directly or indirectly down-regulating the expression of multiple metabolic pathways. EfpR also controls virulence traits such as exopolysaccharide production, swimming and twitching motilities and deletion of efpR leads to reduced virulence on tomato plants after soil drenching inoculation. We studied the impact of the single mutations that occurred in efpR during experimental evolution and found that these allelic mutants displayed phenotypic characteristics similar to the deletion mutant, although not behaving as complete loss-of-function mutants. These adaptive mutations therefore strongly affected the function of efpR, leading to an expanded metabolic versatility that should benefit to the evolved clones. Altogether, these results indicated that EfpR is a novel central player of the R. solanacearum virulence regulatory network. Independent mutations therefore appeared during experimental evolution in the evolved clones, on a crucial node of this network, to favor adaptation to host vascular tissues through regulatory and metabolic rewiring.

Among plant pathogens of major economic and food crops, Ralstonia solanacearum, the causal agent of bacterial wilt, is recognized as one of the most destructive plant bacterial diseases. In addition, the emergence of new pathotypes, more aggressive and adapted to new hosts, has been reported. During an evolution experiment of R. solanacearum, where bacteria were maintained on plant lineages for more than 300 generations, we demonstrated that several single mutations in the regulatory gene efpR were associated with fitness gain on plants. However, the function of the EfpR regulator was totally unknown. In this work, we provided evidence that EfpR controls several metabolic pathways and important virulence traits of R. solanacearum. We then demonstrated that the single mutations selected in the efpR gene during the evolution experiment strongly alter the efpR expression, and thus enlarge the metabolic capacities of the bacterial cell. Altogether, our study reveals that EfpR is a novel key component of the complex regulatory network of the R. solanacearum cell, tightly linking the bacterial metabolism to virulence in response to multiple environmental signals.

Mutations That Determine Resistance Breaking in a Plant RNA Virus Have Pleiotropic Effects on Its Fitness That Depend on the Host Environment and on the Type, Single or Mixed, of Infection

Overcoming host resistance in gene-for-gene host-virus interactions is an important instance of host range expansion, which can be hindered by across-host fitness trade-offs. Trade-offs are generated by negative effects of host range mutations on the virus fitness in the original host, i.e., by antagonistic pleiotropy. It has been reported that different mutations in Pepper mild mottle virus (PMMoV) coat protein result in overcoming L-gene resistance in pepper. To analyze if resistance-breaking mutations in PMMoV result in antagonistic pleiotropy, all reported mutations determining the overcoming of L(3) and L(4) alleles were introduced in biologically active cDNA clones. Then, the parental and mutant virus genotypes were assayed in susceptible pepper genotypes with an L(+), L(1), or L(2) allele, in single and in mixed infections. Resistance-breaking mutations had pleiotropic effects on the virus fitness that, according to the specific mutation, the host genotype, and the type of infection, single or mixed with other virus genotypes, were antagonistic or positive. Thus, resistance-breaking mutations can generate fitness trade-offs both across hosts and across types of infection, and the frequency of host range mutants will depend on the genetic structure of the host population and on the frequency of mixed infections by different virus genotypes. Also, resistance-breaking mutations variously affected virulence, which may further influence the evolution of host range expansion.

IMPORTANCE

A major cause of virus emergence is host range expansion, which may be hindered by across-host fitness trade-offs caused by negative pleiotropy of host range mutations. An important instance of host range expansion is overcoming host resistance in gene-for-gene plant-virus interactions. We analyze here if mutations in the coat protein of Pepper mild mottle virus determining L-gene resistance-breaking in pepper have associated fitness penalties in susceptible host genotypes. Results show that pleiotropic effects of resistance-breaking mutations on virus fitness depend on the specific mutation, the susceptible host genotype, and the type of infection, single or mixed, with other virus genotypes. Accordingly, resistance-breaking mutations can have negative, positive, or no pleiotropic effects on virus fitness. These results underscore the complexity of host range expansion evolution and, specifically, the difficulty of predicting the overcoming of resistance factors in crops.

Virulence evolution at the front line of spreading epidemics

Understanding and predicting the spatial spread of emerging pathogens is a major challenge for the public health management of infectious diseases. Theoretical epidemiology shows that the speed of an epidemic is governed by the life-history characteristics of the pathogen and its ability to disperse. Rapid evolution of these traits during the invasion may thus affect the speed of epidemics. Here we study the influence of virulence evolution on the spatial spread of an epidemic. At the edge of the invasion front, we show that more virulent and transmissible genotypes are expected to win the competition with other pathogens. Behind the front line, however, more prudent exploitation strategies outcompete virulent pathogens. Crucially, even when the presence of the virulent mutant is limited to the edge of the front, the invasion speed can be dramatically altered by pathogen evolution. We support our analysis with individual-based simulations and we discuss the additional effects of demographic stochasticity taking place at the front line on virulence evolution. We confirm that an increase of virulence can occur at the front, but only if the carrying capacity of the invading pathogen is large enough. These results are discussed in the light of recent empirical studies examining virulence evolution at the edge of spreading epidemics.

Models for managing wildlife disease

Modelling wildlife disease poses some unique challenges. Wildlife disease systems are data poor in comparison with human or livestock disease systems, and the impact of disease on population size is often the key question of interest. This review concentrates specifically on the application of dynamic models to evaluate and guide management strategies. Models have proved useful particularly in two areas. They have been widely used to evaluate vaccination strategies, both for protecting endangered species and for preventing spillover from wildlife to humans or livestock. They have also been extensively used to evaluate culling strategies, again both for diseases in species of conservation interest and to prevent spillover. In addition, models are important to evaluate the potential of parasites and pathogens as biological control agents. The review concludes by identifying some key research gaps, which are further development of models of macroparasites, deciding on appropriate levels of complexity, modelling genetic management and connecting models to data.

Could the human papillomavirus vaccines drive virulence evolution?

The human papillomavirus (HPV) vaccines hold great promise for preventing several cancers caused by HPV infections. Yet little attention has been given to whether HPV could respond evolutionarily to the new selection pressures imposed on it by the novel immunity response created by the vaccine. Here, we present and theoretically validate a mechanism by which the vaccine alters the transmission-recovery trade-off that constrains HPV's virulence such that higher oncogene expression is favoured. With a high oncogene expression strategy, the virus is able to increase its viral load and infected cell population before clearance by the vaccine, thus improving its chances of transmission. This new rapid cell-proliferation strategy is able to circulate between hosts with medium to high turnover rates of sexual partners. We also discuss the importance of better quantifying the duration of challenge infections and the degree to which a vaccinated host can shed virus. The generality of the models presented here suggests a wider applicability of this mechanism, and thus highlights the need to investigate viral oncogenicity from an evolutionary perspective.

Rapid evolution of virulence leading to host extinction under host-parasite coevolution

BACKGROUND

Host-parasite coevolution is predicted to result in changes in the virulence of the parasite in order to maximise its reproductive success and transmission potential, either via direct host-to-host transfer or through the environment. The majority of coevolution experiments, however, do not allow for environmental transmission or persistence of long lived parasite stages, in spite of the fact that these may be critical for the evolutionary success of spore forming parasites under natural conditions. We carried out a coevolution experiment using the red flour beetle, Tribolium castaneum, and its natural microsporidian parasite, Paranosema whitei. Beetles and their environment, inclusive of spores released into it, were transferred from generation to generation. We additionally took a modelling approach to further assess the importance of transmissive parasite stages on virulence evolution.

RESULTS

In all parasite treatments of the experiment, coevolution resulted in extinction of the host population, with a pronounced increase in virulence being seen. Our modelling approach highlighted the presence of environmental transmissive parasite stages as being critical to the trajectory of virulence evolution in this system.

CONCLUSIONS

The extinction of host populations was unexpected, particularly as parasite virulence is often seen to decrease in host-parasite coevolution. This, in combination with the increase in virulence and results obtained from the model, suggest that the inclusion of transmissive parasite stages is important to improving our understanding of virulence evolution.

Host Genotype and Coinfection Modify the Relationship of within and between Host Transmission

Variation in individual-level disease transmission is well documented, but the underlying causes of this variation are challenging to disentangle in natural epidemics. In general, within-host replication is critical in determining the extent to which infected hosts shed transmission propagules, but which factors cause variation in this relationship are poorly understood. Here, using a plant host, Plantago lanceolata, and the powdery mildew fungus Podosphaera plantaginis, we quantify how the distinct stages of within-host spread (autoinfection), spore release, and successful transmission to new hosts (alloinfection) are influenced by host genotype, pathogen genotype, and the coinfection status of the host. We find that within-host spread alone fails to predict transmission rates, as this relationship is modified by genetic variation in hosts and pathogens. Their contributions change throughout the course of the epidemic. Host genotype and coinfection had particularly pronounced effects on the dynamics of spore release from infected hosts. Confidently predicting disease spread from local levels of individual transmission, therefore, requires a more nuanced understanding of genotype-specific infection outcomes. This knowledge is key to better understanding the drivers of epidemiological dynamics and the resulting evolutionary trajectories of infectious disease.

Friends or foes: can we make a distinction between beneficial and harmful strains of the Stenotrophomonas maltophilia complex?

Stenotrophomonas maltophilia is an emerging multi-drug-resistant global opportunistic pathogen of environmental, mainly plant-associated origin. It is also used as a biocontrol or stress protecting agent for crops in sustainable agricultural as well as in bioremediation strategies. In order to establish effective protocols to distinguish harmless from harmful strains, our discussion must take into consideration the current data available surrounding the ecology, evolution and pathogenicity of the species complex. The mutation rate was identified as one of several possible criteria for strain plasticity, but it is currently impossible to distinguish beneficial from harmful S. maltophilia strains. This may compromise the possibility of the release and application for environmental biotechnology of this bacterial species. The close relative S. rhizophila, which can be clearly differentiated from S. maltophilia, provides a harmless alternative for biotechnological applications without human health risks. This is mainly because it is unable to growth at the human body temperature, 37^∘C due to the absence of heat shock genes and a potentially temperature-regulated suicide mechanism.

Within-host stochastic emergence dynamics of immune-escape mutants

Predicting the emergence of new pathogenic strains is a key goal of evolutionary epidemiology. However, the majority of existing studies have focussed on emergence at the population level, and not within a host. In particular, the coexistence of pre-existing and mutated strains triggers a heightened immune response due to the larger total pathogen population; this feedback can smother mutated strains before they reach an ample size and establish. Here, we extend previous work for measuring emergence probabilities in non-equilibrium populations, to within-host models of acute infections. We create a mathematical model to investigate the emergence probability of a fitter strain if it mutates from a self-limiting strain that is guaranteed to go extinct in the long-term. We show that ongoing immune cell proliferation during the initial stages of infection causes a drastic reduction in the probability of emergence of mutated strains; we further outline how this effect can be accurately measured. Further analysis of the model shows that, in the short-term, mutant strains that enlarge their replication rate due to evolving an increased growth rate are more favoured than strains that suffer a lower immune-mediated death rate (‘immune tolerance’), as the latter does not completely evade ongoing immune proliferation due to inter-parasitic competition. We end by discussing the model in relation to within-host evolution of human pathogens (including HIV, hepatitis C virus, and cancer), and how ongoing immune growth can affect their evolutionary dynamics.

The ongoing evolution of infectious diseases provides a constant health threat. This evolution can either result in the production of new pathogens, or new strains of existing pathogens that escape prevailing drug treatments or immune responses. The latter process, also known as immune escape, is a predominant reason for the persistence of several viruses, including HIV and hepatitis C virus (HCV), in their human host. As a consequence, the within-host emergence of new strains has been the intense focus of modelling studies. However, existing models have neglected important feedbacks that affects this emergence probability. Specifically, once a mutated pathogen arises that spreads more quickly than the initial (resident) strain, it potentially triggers a heightened immune response that can eliminate the mutated strain before it spreads. Our study outlines novel mathematical modelling techniques that accurately quantify how ongoing immune growth reduces the emergence probability of mutated pathogenic strains over the course of an infection. Analysis of this model suggests that, in order to enlarge its emergence probability, it is evolutionary beneficial for a mutated strain to increase its growth rate rather than tolerate immunity by having a lower immune-mediated death-rate. Our model can be readily applied to existing within-host data, as demonstrated with application to HIV, HCV, and cancer dynamics.

Frequency and fitness consequences of bacteriophage φ6 host range mutations

Viruses readily mutate and gain the ability to infect novel hosts, but few data are available regarding the number of possible host range-expanding mutations allowing infection of any given novel host, and the fitness consequences of these mutations on original and novel hosts. To gain insight into the process of host range expansion, we isolated and sequenced 69 independent mutants of the dsRNA bacteriophage Φ6 able to infect the novel host, Pseudomonas pseudoalcaligenes. In total, we found at least 17 unique suites of mutations among these 69 mutants. We assayed fitness for 13 of 17 mutant genotypes on P. pseudoalcaligenes and the standard laboratory host, P. phaseolicola. Mutants exhibited significantly lower fitnesses on P. pseudoalcaligenes compared to P. phaseolicola. Furthermore, 12 of the 13 assayed mutants showed reduced fitness on P. phaseolicola compared to wildtype Φ6, confirming the prevalence of antagonistic pleiotropy during host range expansion. Further experiments revealed that the mechanistic basis of these fitness differences was likely variation in host attachment ability. In addition, using computational protein modeling, we show that host-range expanding mutations occurred in hotspots on the surface of the phage's host attachment protein opposite a putative hydrophobic anchoring domain.

Emerging viruses: why they are not jacks of all trades?

In order to limit the impact of the recent pandemics ignited by viral host jumps, it is necessary to better understand the ecological and evolutionary factors influencing the early steps of emergence and the interactions between them. Antagonistic pleiotropy, that is, the negative fitness effect in the primary host of mutations allowing the infection of and the multiplication in a new host, has long been thought to be the main limitation to the evolution of generalist viruses and thus to emergence. However, the accumulation of experimental examples contradicting the hypothesis of antagonistic pleiotropy has highlighted the importance of other factors such as the epistasis between mutations increasing the adaptation to a new host. Epistasis is pervasive in viruses, affects the shape of the adaptive landscape and consequently the accessibility of evolutionary pathways. Finally, recent studies have gone steps further in the complexity of viral fitness determinism and stressed the potential importance of the epistatic pleiotropy and of the impact of host living conditions.

Multihost experimental evolution of the pathogen Ralstonia solanacearum unveils genes involved in adaptation to plants

Ralstonia solanacearum, the causal agent of a lethal bacterial wilt plant disease, infects an unusually wide range of hosts. These hosts can further be split into plants where R. solanacearum is known to cause disease (original hosts) and those where this bacterium can grow asymptomatically (distant hosts). Moreover, this pathogen is able to adapt to many plants as supported by field observations reporting emergence of strains with enlarged pathogenic properties. To investigate the genetic bases of host adaptation, we conducted evolution experiments by serial passages of a single clone of the pathogen on three original and two distant hosts over 300 bacterial generations and then analyzed the whole-genome of nine evolved clones. Phenotypic analysis of the evolved clones showed that the pathogen can increase its fitness on both original and distant hosts although the magnitude of fitness increase was greater on distant hosts. Only few genomic modifications were detected in evolved clones compared with the ancestor but parallel evolutionary changes in two genes were observed in independent evolved populations. Independent mutations in the regulatory gene efpR were selected for in three populations evolved on beans, a distant host. Reverse genetic approaches confirmed that these mutations were associated with fitness gain on bean plants. This work provides a first step toward understanding the within-host evolutionary dynamics of R. solanacearum during infection and identifying bacterial genes subjected to in planta selection. The discovery of EfpR as a determinant conditioning host adaptation of the pathogen illustrates how experimental evolution coupled with whole-genome sequencing is a potent tool to identify novel molecular players involved in central life-history traits.

Evolution and emergence of plant viruses

Viruses are common agents of plant infectious diseases. During last decades, worldwide agriculture production has been compromised by a series of epidemics caused by new viruses that spilled over from reservoir species or by new variants of classic viruses that show new pathogenic and epidemiological properties. Virus emergence has been generally associated with ecological change or with intensive agronomical practices. However, the complete picture is much more complex since the viral populations constantly evolve and adapt to their new hosts and vectors. This chapter puts emergence of plant viruses into the framework of evolutionary ecology, genetics, and epidemiology. We will stress that viral emergence begins with the stochastic transmission of preexisting genetic variants from the reservoir to the new host, whose fate depends on their fitness on each hosts, followed by adaptation to new hosts or vectors, and finalizes with an efficient epidemiological spread.

Epidemiological feedbacks affect evolutionary emergence of pathogens

The evolutionary emergence of new pathogens via mutation poses a considerable risk to human and animal populations. Most previous studies have investigated cases where a potentially pandemic strain emerges though mutation from an initial maladapted strain (i.e., its basic reproductive ratio R0 < 1). However, an alternative (and arguably more likely) cause of novel pathogen emergence is where a "weakly adapted" strain (with R0 ≈ 1) mutates into a strongly adapted strain (with R0 ≫ 1). In this case, a proportion of the host susceptible population is removed as the first strain spreads, but the impact this feedback has on emergence of mutated strains has yet to be quantified. We produce a model of pathogen emergence that takes into account changes in the susceptible population over time and find that the ongoing depletion of susceptible individuals by the first strain has a drastic effect on the emergence probability of the mutated strain, above that assumed by just scaling the reproductive ratio. Finally, we apply our model to the documented emergence of Chikungunya virus on La Réunion Island and demonstrate that the emergence probability of the mutated strain was reduced approximately 10-fold, compared to models assuming that susceptible depletion would not affect outbreak probability. These results highlight the importance of taking population feedbacks into account when predicting disease emergence.