Pathogen Phylogenies Research Articles

Estimating pathogen spread using structured coalescent and birth–death models: A quantitative comparison

Elucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged as key principles to estimate such spread from pathogen phylogenies derived from molecular data. Two well-established structured phylodynamic methodologies – based on the coalescent and the birth–death model – are frequently employed to estimate viral spread between populations. Nonetheless, these methodologies operate under distinct assumptions whose impact on the accuracy of migration rate inference is yet to be thoroughly investigated.In this manuscript, we present a simulation study, contrasting the inferential outcomes of the structured coalescent model with constant population size and the multitype birth–death model with a constant rate. We explore this comparison across a range of migration rates in endemic diseases and epidemic outbreaks. The results of the epidemic outbreak analysis revealed that the birth–death model exhibits a superior ability to retrieve accurate migration rates compared to the coalescent model, regardless of the actual migration rate. Thus, to estimate accurate migration rates, the population dynamics have to be accounted for. On the other hand, for the endemic disease scenario, our investigation demonstrates that both models produce comparable coverage and accuracy of the migration rates, with the coalescent model generating more precise estimates. Regardless of the specific scenario, both models similarly estimated the source location of the disease.This research offers tangible modelling advice for infectious disease analysts, suggesting the use of either model for endemic diseases. For epidemic outbreaks, or scenarios with varying population size, structured phylodynamic models relying on the Kingman coalescent with constant population size should be avoided as they can lead to inaccurate estimates of the migration rate. Instead, coalescent models accounting for varying population size or birth–death models should be favoured. Importantly, our study emphasises the value of directly capturing exponential growth dynamics which could be a useful enhancement for structured coalescent models.

Identifying Impacts of Contact Tracing on Epidemiological Inference from Phylogenetic Data.

Robust sampling methods are foundational to inferences using phylogenies. Yet the impact of using contact tracing, a type of non-uniform sampling used in public health applications such as infectious disease outbreak investigations, has not been investigated in the molecular epidemiology field. To understand how contact tracing influences a recovered phylogeny, we developed a new simulation tool called SEEPS (Sequence Evolution and Epidemiological Process Simulator) that allows for the simulation of contact tracing and the resulting transmission tree, pathogen phylogeny, and corresponding virus genetic sequences. Importantly, SEEPS takes within-host evolution into account when generating pathogen phylogenies and sequences from transmission histories. Using SEEPS, we demonstrate that contact tracing can significantly impact the structure of the resulting tree, as described by popular tree statistics. Contact tracing generates phylogenies that are less balanced than the underlying transmission process, less representative of the larger epidemiological process, and affects the internal/external branch length ratios that characterize specific epidemiological scenarios. We also examined real data from a 2007-2008 Swedish HIV-1 outbreak and the broader 1998-2010 European HIV-1 epidemic to highlight the differences in contact tracing and expected phylogenies. Aided by SEEPS, we show that the data collection of the Swedish outbreak was strongly influenced by contact tracing even after downsampling, while the broader European Union epidemic showed little evidence of universal contact tracing, agreeing with the known epidemiological information about sampling and spread. Overall, our results highlight the importance of including possible non-uniform sampling schemes when examining phylogenetic trees. For that, SEEPS serves as a useful tool to evaluate such impacts, thereby facilitating better phylogenetic inferences of the characteristics of a disease outbreak. SEEPS is available at github.com/MolEvolEpid/SEEPS.

A Phylogeny-aware GWAS Framework to Correct for Heritable Pathogen Effects on Infectious Disease Traits.

Infectious diseases are particularly challenging for genome-wide association studies (GWAS) because genetic effects from two organisms (pathogen and host) can influence a trait. Traditional GWAS assume individual samples are independent observations. However, pathogen effects on a trait can be heritable from donor to recipient in transmission chains. Thus, residuals in GWAS association tests for host genetic effects may not be independent due to shared pathogen ancestry. We propose a new method to estimate and remove heritable pathogen effects on a trait based on the pathogen phylogeny prior to host GWAS, thus restoring independence of samples. In simulations, we show this additional step can increase GWAS power to detect truly associated host variants when pathogen effects are highly heritable, with strong phylogenetic correlations. We applied our framework to data from two different host–pathogen systems, HIV in humans and X. arboricola in A. thaliana. In both systems, the heritability and thus phylogenetic correlations turn out to be low enough such that qualitative results of GWAS do not change when accounting for the pathogen shared ancestry through a correction step. This means that previous GWAS results applied to these two systems should not be biased due to shared pathogen ancestry. In summary, our framework provides additional information on the evolutionary dynamics of traits in pathogen populations and may improve GWAS if pathogen effects are highly phylogenetically correlated amongst individuals in a cohort.

Genomic pattern analysis of Burkholderia mallei field isolates by pulsed-field gel electrophoresis (PFGE) discriminatory typing.

Background and Objectives:Glanders is a serious zoonotic disease caused by Burkholderia mallei. Prevention, control, and treatment strategies of glanders are prerequisites for microbial source tracking. The present study was aimed to analyze the genomic pattern of B. mallei Iranian field isolates by pulsed-field gel electrophoresis (PFGE) typing.Materials and Methods:B. mallei isolates were aerobically cultured in nutrient broth/agar supplemented with glycerol 4% for 48 h at 37°C. API 20NE identification system was used for the biochemical characterization. Genomic DNA of bacterial isolates was extracted using OIE-recommended protocol. Molecular identification of bacterial isolates was done based on amplification of BimA and IS407-flip genes. PFGE was applied to prepare the genomic pattern of B. mallei isolates. The guinea pig was used as a suitable model for studying the histopathological characterization of B. mallei.Results:In both enzymatic digestion patterns by using Af1II and VspI, we found three different clonal types; І) PFGE type of B. mallei Razi 325 strain, ІІ) PFGE type of Tiger, Kordan, and Oshnavieh strains, and ІІІ) PFGE type of Semirom strain. B. mallei Razi 325 was categorized as unrelated strain which was belonged to the different cluster differing more than four bands.Conclusion:PFGE showed more discriminatory power and considerable reproducibility for molecular typing of B. mallei strains in our study. It is standardized the approaches for outbreak detection, pathogen phylogeny, molecular epidemiology, and population studies.

Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo.

Comparing the pathogen genomes from several cases of an infectious disease has the potential to help us understand and control outbreaks. Many methods exist to reconstruct a phylogeny from such genomes, which represents how the genomes are related to one another. However, such a phylogeny is not directly informative about transmission events between individuals. TransPhylo is a software tool implemented as an R package designed to bridge the gap between pathogen phylogenies and transmission trees. TransPhylo is based on a combined model of transmission between hosts and pathogen evolution within each host. It can simulate both phylogenies and transmission trees jointly under this combined model. TransPhylo can also reconstruct a transmission tree based on a dated phylogeny, by exploring the space of transmission trees compatible with the phylogeny. A transmission tree can be represented as a coloring of a phylogeny where each color represents a different host of the pathogen, and TransPhylo provides convenient ways to plot these colorings and explore the results. This article presents the basic protocols that can be used to make the most of TransPhylo. © 2021 The Authors. Basic Protocol 1: First steps with TransPhylo Basic Protocol 2: Simulation of outbreak data Basic Protocol 3: Inference of transmission Basic Protocol 4: Exploring the results of inference

Evolutionary trait-based approaches for predicting future global impacts of plant pathogens in the genus Phytophthora.

Plant pathogens are introduced to new geographical regions ever more frequently as global connectivity increases. Predicting the threat they pose to plant health can be difficult without in-depth knowledge of behaviour, distribution and spread. Here, we evaluate the potential for using biological traits and phylogeny to predict global threats from emerging pathogens.We use a species-level trait database and phylogeny for 179 Phytophthora species: oomycete pathogens impacting natural, agricultural, horticultural and forestry settings. We compile host and distribution reports for Phytophthora species across 178 countries and evaluate the power of traits, phylogeny and time since description (reflecting species-level knowledge) to explain and predict their international transport, maximum latitude and host breadth using Bayesian phylogenetic generalised linear mixed models.In the best-performing models, traits, phylogeny and time since description together explained up to 90%, 97% and 87% of variance in number of countries reached, latitudinal limits and host range, respectively. Traits and phylogeny together explained up to 26%, 41% and 34% of variance in the number of countries reached, maximum latitude and host plant families affected, respectively, but time since description had the strongest effect.Root-attacking species were reported in more countries, and on more host plant families than foliar-attacking species. Host generalist pathogens had thicker-walled resting structures (stress-tolerant oospores) and faster growth rates at their optima. Cold-tolerant species are reported in more countries and at higher latitudes, though more accurate interspecific empirical data are needed to confirm this finding. Policy implications. We evaluate the potential of an evolutionary trait-based framework to support horizon-scanning approaches for identifying pathogens with greater potential for global-scale impacts. Potential future threats from Phytophthora include Phytophthora x heterohybrida, P. lactucae, P. glovera, P. x incrassata, P. amnicola and P. aquimorbida, which are recently described, possibly under-reported species, with similar traits and/or phylogenetic proximity to other high-impact species. Priority traits to measure for emerging species may be thermal minima, oospore wall index and growth rate at optimum temperature. Trait-based horizon-scanning approaches would benefit from the development of international and cross-sectoral collaborations to deliver centralised databases incorporating pathogen distributions, traits and phylogeny.

Inferring putative transmission clusters with Phydelity.

Current phylogenetic clustering approaches for identifying pathogen transmission clusters are limited by their dependency on arbitrarily defined genetic distance thresholds for within-cluster divergence. Incomplete knowledge of a pathogen’s underlying dynamics often reduces the choice of distance threshold to an exploratory, ad hoc exercise that is difficult to standardise across studies. Phydelity is a new tool for the identification of transmission clusters in pathogen phylogenies. It identifies groups of sequences that are more closely related than the ensemble distribution of the phylogeny under a statistically principled and phylogeny-informed framework, without the introduction of arbitrary distance thresholds. Relative to other distance threshold- and model-based methods, Phydelity outputs clusters with higher purity and lower probability of misclassification in simulated phylogenies. Applying Phydelity to empirical datasets of hepatitis B and C virus infections showed that Phydelity identified clusters with better correspondence to individuals that are more likely to be linked by transmission events relative to other widely used non-parametric phylogenetic clustering methods without the need for parameter calibration. Phydelity is generalisable to any pathogen and can be used to identify putative direct transmission events. Phydelity is freely available at https://github.com/alvinxhan/Phydelity.

Tree shape-based approaches for the comparative study of cophylogeny.

Cophylogeny is the congruence of phylogenetic relationships between two different groups of organisms due to their long‐term interaction. We investigated the use of tree shape distance measures to quantify the degree of cophylogeny. We implemented a reverse‐time simulation model of pathogen phylogenies within a fixed host tree, given cospeciation probability, host switching, and pathogen speciation rates. We used this model to evaluate 18 distance measures between host and pathogen trees including two kernel distances that we developed for labeled and unlabeled trees, which use branch lengths and accommodate different size trees. Finally, we used these measures to revisit published cophylogenetic studies, where authors described the observed associations as representing a high or low degree of cophylogeny. Our simulations demonstrated that some measures are more informative than others with respect to specific coevolution parameters especially when these did not assume extreme values. For real datasets, trees’ associations projection revealed clustering of high concordance studies suggesting that investigators are describing it in a consistent way. Our results support the hypothesis that measures can be useful for quantifying cophylogeny. This motivates their usage in the field of coevolution and supports the development of simulation‐based methods, i.e., approximate Bayesian computation, to estimate the underlying coevolutionary parameters.

The rise of the rosette agent in Europe: An epidemiological enigma.

International biodiversity assessments often overlook the role of emerging infectious pathogens in the decline of freshwater fish populations despite the many examples of emerging diseases in other more visible taxa on a global scale. Whilst the introduction of the rosette agent Sphaerothecum destruens in Europe remained an epidemiological enigma, recent findings have shown that this parasite arrived in Europe with the introduction of the healthy carrier Pseudorasbora parva from China nearly 60years ago and its emergence went unnoticed for over 45years despite its severe impact on European fish biodiversity. Recent reports on the host and pathogen phylogeny point towards an ancient host-pathogen co-evolution with direct implications on disease risk. Here, we postulate that the observed rapid population decline of native fish species following their infection with virulent strains of S.destruens has underpinned the rapid establishment of P.parva populations during the invasion process. We reviewed the existing evidence supporting the claim of an S.destruens' emergence worldwide and also suggest that the origin of the US strains is to be found among contaminated Asian Oncorhynchus tshawytscha living in sympatry with native Asian P.parva population. Finally, several important preventative steps are suggested as a way to manage the impact of S.destruens on local fish communities.

Detection of HIV transmission clusters from phylogenetic trees using a multi-state birth–death model

HIV patients form clusters in HIV transmission networks. Accurate identification of these transmission clusters is essential to effectively target public health interventions. One reason for clustering is that the underlying contact network contains many local communities. We present a new maximum-likelihood method for identifying transmission clusters caused by community structure, based on phylogenetic trees. The method employs a multi-state birth–death (MSBD) model which detects changes in transmission rate, which are interpreted as the introduction of the epidemic into a new susceptible community, i.e. the formation of a new cluster. We show that the MSBD method is able to reliably infer the clusters and the transmission parameters from a pathogen phylogeny based on our simulations. In contrast to existing cutpoint-based methods for cluster identification, our method does not require that clusters be monophyletic nor is it dependent on the selection of a difficult-to-interpret cutpoint parameter. We present an application of our method to data from the Swiss HIV Cohort Study. The method is available as an easy-to-use R package.

Quantifying Transmission Heterogeneity Using Both Pathogen Phylogenies and Incidence Time Series.

Heterogeneity in individual-level transmissibility can be quantified by the dispersion parameter of the offspring distribution. Quantifying heterogeneity is important as it affects other parameter estimates, it modulates the degree of unpredictability of an epidemic, and it needs to be accounted for in models of infection control. Aggregated data such as incidence time series are often not sufficiently informative to estimate . Incorporating phylogenetic analysis can help to estimate concurrently with other epidemiological parameters. We have developed an inference framework that uses particle Markov Chain Monte Carlo to estimate and other epidemiological parameters using both incidence time series and the pathogen phylogeny. Using the framework to fit a modified compartmental transmission model that includes the parameter to simulated data, we found that more accurate and less biased estimates of the reproductive number were obtained by combining epidemiological and phylogenetic analyses. However, was most accurately estimated using pathogen phylogeny alone. Accurately estimating was necessary for unbiased estimates of the reproductive number, but it did not affect the accuracy of reporting probability and epidemic start date estimates. We further demonstrated that inference was possible in the presence of phylogenetic uncertainty by sampling from the posterior distribution of phylogenies. Finally, we used the inference framework to estimate transmission parameters from epidemiological and genetic data collected during a poliovirus outbreak. Despite the large degree of phylogenetic uncertainty, we demonstrated that incorporating phylogenetic data in parameter inference improved the accuracy and precision of estimates.

Phylodynamics on local sexual contact networks.

Phylodynamic models are widely used in infectious disease epidemiology to infer the dynamics and structure of pathogen populations. However, these models generally assume that individual hosts contact one another at random, ignoring the fact that many pathogens spread through highly structured contact networks. We present a new framework for phylodynamics on local contact networks based on pairwise epidemiological models that track the status of pairs of nodes in the network rather than just individuals. Shifting our focus from individuals to pairs leads naturally to coalescent models that describe how lineages move through networks and the rate at which lineages coalesce. These pairwise coalescent models not only consider how network structure directly shapes pathogen phylogenies, but also how the relationship between phylogenies and contact networks changes depending on epidemic dynamics and the fraction of infected hosts sampled. By considering pathogen phylogenies in a probabilistic framework, these coalescent models can also be used to estimate the statistical properties of contact networks directly from phylogenies using likelihood-based inference. We use this framework to explore how much information phylogenies retain about the underlying structure of contact networks and to infer the structure of a sexual contact network underlying a large HIV-1 sub-epidemic in Switzerland.

Phylodynamic Inference across Epidemic Scales.

Within-host genetic diversity and large transmission bottlenecks confound phylodynamic inference of epidemiological dynamics. Conventional phylodynamic approaches assume that nodes in a time-scaled pathogen phylogeny correspond closely to the time of transmission between hosts that are ancestral to the sample. However, when hosts harbor diverse pathogen populations, node times can substantially pre-date infection times. Imperfect bottlenecks can cause lineages sampled in different individuals to coalesce in unexpected patterns. To address realistic violations of standard phylodynamic assumptions we developed a new inference approach based on a multi-scale coalescent model, accounting for nonlinear epidemiological dynamics, heterogeneous sampling through time, non-negligible genetic diversity of pathogens within hosts, and imperfect transmission bottlenecks. We apply this method to HIV-1 and Ebola virus (EBOV) outbreak sequence data, illustrating how and when conventional phylodynamic inference may give misleading results. Within-host diversity of HIV-1 causes substantial upwards bias in the number of infected hosts using conventional coalescent models, but estimates using the multi-scale model have greater consistency with reported number of diagnoses through time. In contrast, we find that within-host diversity of EBOV has little influence on estimated numbers of infected hosts or reproduction numbers, and estimates are highly consistent with the reported number of diagnoses through time. The multi-scale coalescent also enables estimation of within-host effective population size using single sequences from a random sample of patients. We find within-host population genetic diversity of HIV-1 p17 to be (95% CI 0.0066–0.023), which is lower than estimates based on HIV envelope serial sequencing of individual patients.

Strong phylogeographic co-structure between the anther-smut fungus and its white campion host.

Although congruence between host and pathogen phylogenies has been extensively investigated, the congruence between host and pathogen genetic structures at the within-species level has received little attention. Using an unprecedented and comprehensive collection of associated plant-pathogen samples, we investigated the degree of congruence between the genetic structures across Europe of two evolutionary and ecological model organisms, the anther-smut pathogen Microbotryum lychnidis-dioicae and its host plant Silene latifolia. We demonstrated a significant and particularly strong level of host-pathogen co-structure, with three main genetic clusters displaying highly similar spatial ranges in Western Europe, Eastern Europe and Italy, respectively. Correcting for the geographical component of genetic variation, significant correlations were still found between the genetic distances of anther-smut and host populations. Inoculation experiments suggested plant local adaptation, at the cluster level, for resistance to pathogens. These findings indicate that the pathogen remained isolated in the same fragmented southern refugia as its host plant during the last glaciation, and that little long-distance dispersal has occurred since the recolonization of Europe for either the plant or the pathogen, despite their known ability to travel across continents. This, together with the inoculation results, suggests that coevolutionary and competitive processes may be drivers of host-pathogen co-structure.

Preferential host switching and its relation with Hantavirus diversification in South America.

In recent years, the notion of co-speciation between Hantavirus species and their hosts was discarded in favour of a more likely explanation: preferential host switching. However, the relative importance of this last process in shaping the evolutionary history of hantaviruses remains uncertain, given the present limited knowledge not only of virus-host relationships but also of the pathogen and reservoir phylogenies. In South America, more than 25 hantavirus genotypes were detected; several of them act as aetiological agents of hantavirus pulmonary syndrome (HPS). An understanding of the diversity of hantaviruses and of the processes underlying host switching is critical since human cases of HPS are almost exclusively the result of human-host interactions. In this study, we tested if preferential host switching is the main process driving hantavirus diversification in South America, by performing a co-phylogenetic analysis of the viruses and their primary hosts. We also suggest a new level of amino acid divergence to define virus species in the group. Our results indicate that preferential host switching would not be the main process driving virus diversification. The historical geographical proximity among rodent hosts emerges as an alternative hypothesis to be tested.

Eight challenges in phylodynamic inference

The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data.

Timing and order of transmission events is not directly reflected in a pathogen phylogeny.

Pathogen phylogenies are often used to infer spread among hosts. There is, however, not an exact match between the pathogen phylogeny and the host transmission history. Here, we examine in detail the limitations of this relationship. First, all splits in a pathogen phylogeny of more than 1 host occur within hosts, not at the moment of transmission, predating the transmission events as described by the pretransmission interval. Second, the order in which nodes in a phylogeny occur may be reflective of the within-host dynamics rather than epidemiologic relationships. To investigate these phenomena, motivated by within-host diversity patterns, we developed a two-phase coalescent model that includes a transmission bottleneck followed by linear outgrowth to a maximum population size followed by either stabilization or decline of the population. The model predicts that the pretransmission interval shrinks compared with predictions based on constant population size or a simple transmission bottleneck. Because lineages coalesce faster in a small population, the probability of a pathogen phylogeny to resemble the transmission history depends on when after infection a donor transmits to a new host. We also show that the probability of inferring the incorrect order of multiple transmissions from the same host is high. Finally, we compare time of HIV-1 infection informed by genetic distances in phylogenies to independent biomarker data, and show that, indeed, the pretransmission interval biases phylogeny-based estimates of when transmissions occurred. We describe situations where caution is needed not to misinterpret which parts of a phylogeny that may indicate outbreaks and tight transmission clusters.

Formae speciales of cereal powdery mildew: close or distant relatives?

Powdery mildew is an important disease of cereals, affecting both grain yield and end-use quality. The causal agent of powdery mildew on cereals, Blumeria graminis, has been classified into eight formae speciales (ff.spp.), infecting crops and wild grasses. Advances in research on host specificity and resistance, and on pathogen phylogeny and origins, have brought aspects of the subspecific classification system of B. graminis into ff.spp. into question, because it is based on adaptation to certain hosts rather than strict host specialization. Cereals therefore cannot be considered as typical non-hosts to non-adapted ff.spp. We introduce the term 'non-adapted resistance' of cereals to inappropriate ff.spp. of B. graminis, which involves both pathogen-associated molecular pattern-triggered immunity (PTI) and effector-triggered immunity (ETI). There is no clear distinction between the mechanisms of resistance to adapted and non-adapted ff.spp. Molecular evolutionary data suggest that the taxonomic grouping of B. graminis into different ff.spp. is not consistent with the phylogeny of the fungus. Imprecise estimates of mutation rates and the lack of genetic variation in introduced populations may explain the uncertainty with regard to divergence times, in the Miocene or Holocene epochs, of ff.spp. of B. graminis which infect cereal crop species. We propose that most evidence favours divergence in the Holocene, during the course of early agriculture. We also propose that the forma specialis concept should be retained for B. graminis pathogenic on cultivated cereals to include clades of the fungus which are strongly specialized to these hosts, i.e. ff.spp. hordei, secalis and tritici, as well as avenae from cultivated A. sativa, and that the forma specialis concept should no longer be applied to B. graminis from most wild grasses.

How the Dynamics and Structure of Sexual Contact Networks Shape Pathogen Phylogenies

The characteristics of the host contact network over which a pathogen is transmitted affect both epidemic spread and the projected effectiveness of control strategies. Given the importance of understanding these contact networks, it is unfortunate that they are very difficult to measure directly. This challenge has led to an interest in methods to infer information about host contact networks from pathogen phylogenies, because in shaping a pathogen's opportunities for reproduction, contact networks also shape pathogen evolution. Host networks influence pathogen phylogenies both directly, through governing opportunities for evolution, and indirectly by changing the prevalence and incidence. Here, we aim to separate these two effects by comparing pathogen evolution on different host networks that share similar epidemic trajectories. This approach allows use to examine the direct effects of network structure on pathogen phylogenies, largely controlling for confounding differences arising from population dynamics. We find that networks with more heterogeneous degree distributions yield pathogen phylogenies with more variable cluster numbers, smaller mean cluster sizes, shorter mean branch lengths, and somewhat higher tree imbalance than networks with relatively homogeneous degree distributions. However, in particular for dynamic networks, we find that these direct effects are relatively modest. These findings suggest that the role of the epidemic trajectory, the dynamics of the network and the inherent variability of metrics such as cluster size must each be taken into account when trying to use pathogen phylogenies to understand characteristics about the underlying host contact network.

Phylogenetic determinants of potential host shifts in fungal pathogens

Understanding what determines the host range of pathogens and the potential for host shifts is of critical importance to controlling their introductions into new environments. The phylogeny of the hosts has been shown to be important: pathogens are more likely to be infectious on hosts closely related to their host-of-origin because of the similar host environments that is shared by descent. The importance of pathogen phylogenies for predicting host range has never been investigated, although a pathogen should also be able to exploit a new host that its close relative can infect. We performed cross-inoculations using a plant-fungal association and showed that both host and pathogen phylogenies were significant predictors of host range, with at least partly independent effects. Furthermore, we showed that some pathogens were better at infecting novel hosts. Our results should have implications in the context of biological invasions and emergences of new diseases due to globalization.