Genetic Structure Of Pathogen Populations Research Articles

Genetic diversity and population structure of Epichloë fungal pathogens of plants in natural ecosystems

Understanding the population genetic processes driving the evolution of plant pathogens is of central interest to plant pathologists and evolutionary biologists alike. However, most studies focus on host-pathogen associations in agricultural systems of high genetic and environmental homogeneity and less is known about the genetic structure of pathogen populations infecting wild plants in natural ecosystems. We performed parallel population sampling of two pathogenic Epichloë species occurring sympatrically on different host grasses in natural and seminatural grasslands in Europe: E. typhina infecting Dactylis glomerata and E. clarkii infecting Holcus lanatus. We sequenced 422 haploid isolates and generated genome-wide SNP datasets to investigate genetic diversity and population structure. In both species geographically separated populations formed genetically distinct groups, however, population separation was less distinct in E. typhina compared to E. clarkii. The patterns of among population admixture also differed between species across the same geographic range: we found higher levels of population genetic differentiation and a stronger effect of isolation by distance in E. clarkii compared to E. typhina, consistent with lower levels of gene flow in the former. This pattern may be explained by the different dispersal abilities of the two pathogens and is expected to be influenced by the genetic structure of host populations. In addition, genetic diversity was higher in E. typhina populations compared to E. clarkii, indicative of higher effective population size in E. typhina. These results suggest that the effect of genetic drift and the efficacy of selection may differ in the two species. Our study provides evidence of how ecologically similar species occupying the same geographical space can experience different evolutionary contexts, which could influence local adaptation and co-evolutionary dynamics of these fungal pathogens.

Monitoring Plasmodium falciparum and Plasmodium vivax using microsatellite markers indicates limited changes in population structure after substantial transmission decline in Papua New Guinea.

Monitoring the genetic structure of pathogen populations may be an economical and sensitive approach to quantify the impact of control on transmission dynamics, highlighting the need for a better understanding of changes in population genetic parameters as transmission declines. Here we describe the first population genetic analysis of two major human malaria parasites, Plasmodium falciparum (Pf) and Plasmodium vivax (Pv), following nationwide distribution of long-lasting insecticide-treated nets (LLINs) in Papua New Guinea (PNG). Parasite isolates from pre- (2005-2006) and post-LLIN (2010-2014) were genotyped using microsatellite markers. Despite parasite prevalence declining substantially (East Sepik Province: Pf=54.9%-8.5%, Pv=35.7%-5.6%, Madang Province: Pf=38.0%-9.0%, Pv: 31.8%-19.7%), genetically diverse and intermixing parasite populations remained. Pf diversity declined modestly post-LLIN relative to pre-LLIN (East Sepik: Rs =7.1-6.4, HE =0.77-0.71; Madang: Rs =8.2-6.1, HE =0.79-0.71). Unexpectedly, population structure present in pre-LLIN populations was lost post-LLIN, suggesting that more frequent human movement between provinces may have contributed to higher gene flow. Pv prevalence initially declined but increased again in one province, yet diversity remained high throughout the study period (East Sepik: Rs =11.4-9.3, HE =0.83-0.80; Madang: Rs =12.2-14.5, HE =0.85-0.88). Although genetic differentiation values increased between provinces over time, no significant population structure was observed at any time point. For both species, a decline in multiple infections and increasing clonal transmission and significant multilocus linkage disequilibrium post-LLIN were positive indicators of impact on the parasite population using microsatellite markers. These parameters may be useful adjuncts to traditional epidemiological tools in the early stages of transmission reduction.

The ecological genetics of Pseudomonas syringae from kiwifruit leaves.

Interactions between commensal microbes and invading pathogens are understudied, despite their likely effects on pathogen population structure and infection processes. We describe the population structure and genetic diversity of a broad range of co-occurring Pseudomonas syringae isolated from infected and uninfected kiwifruit during an outbreak of bleeding canker disease caused by P. syringae pv. actinidiae (Psa) in New Zealand. Overall population structure was clonal and affected by ecological factors including infection status and cultivar. Most isolates are members of a new clade in phylogroup 3 (PG3a), also present on kiwifruit leaves in China and Japan. Stability of the polymorphism between pathogenic Psa and commensal P. syringae PG3a isolated from the same leaf was tested using reciprocal invasion from rare assays in vitro and in planta. P.syringae G33C (PG3a) inhibited Psa NZ54, while the presence of Psa NZ54 enhanced the growth of P. syringae G33C. This effect could not be attributed to virulence activity encoded by the Type 3 secretion system of Psa. Together our data contribute toward the development of an ecological perspective on the genetic structure of pathogen populations.

The Role of Human Transportation Networks in Mediating the Genetic Structure of Seasonal Influenza in the United States.

Recent studies have demonstrated the importance of accounting for human mobility networks when modeling epidemics in order to accurately predict spatial dynamics. However, little is known about the impact these movement networks have on the genetic structure of pathogen populations and whether these effects are scale-dependent. We investigated how human movement along the aviation and commuter networks contributed to intra-seasonal genetic structure of influenza A epidemics in the continental United States using spatially-referenced hemagglutinin nucleotide sequences collected from 2003–2013 for both the H3N2 and H1N1 subtypes. Comparative analysis of these transportation networks revealed that the commuter network is highly spatially-organized and more heavily traveled than the aviation network, which instead is characterized by high connectivity between all state pairs. We found that genetic distance between sequences often correlated with distance based on interstate commuter network connectivity for the H1N1 subtype, and that this correlation was not as prevalent when geographic distance or aviation network connectivity distance was assessed against genetic distance. However, these patterns were not as apparent for the H3N2 subtype at the scale of the continental United States. Finally, although sequences were spatially referenced at the level of the US state of collection, a community analysis based on county to county commuter connections revealed that commuting communities did not consistently align with state geographic boundaries, emphasizing the need for the greater availability of more specific sequence location data. Our results highlight the importance of utilizing host movement data in characterizing the underlying genetic structure of pathogen populations and demonstrate a need for a greater understanding of the differential effects of host movement networks on pathogen transmission at various spatial scales.

Estimation of genetic structure of a Mycosphaerella musicola population using inter-simple sequence repeat markers.

Among the diseases affecting banana (Musa sp), yellow Sigatoka, caused by the fungal pathogen Mycosphaerella musicola Leach, is considered one of the most important in Brazil, causing losses throughout the year. Understanding the genetic structure of pathogen populations will provide insight into the life history of pathogens, including the evolutionary processes occurring in agrosystems. Tools for estimating the possible emergence of pathogen variants with altered pathogenicity, virulence, or aggressiveness, as well as resistance to systemic fungicides, can also be developed from such data. The objective of this study was to analyze the genetic diversity and population genetics of M. musicola in the main banana-producing regions in Brazil. A total of 83 isolates collected from different banana cultivars in the Brazilian states of Bahia, Rio Grande do Norte, and Minas Gerais were evaluated using inter-simple sequence repeat markers. High variability was detected between the isolates, and 85.5% of the haplotypes were singletons in the populations. The highest source of genetic diversity (97.22%) was attributed to variations within populations. Bayesian cluster analysis revealed the presence of 2 probable ancestral groups, however, showed no relationship to population structure in terms of collection site, state of origin, or cultivar. Similarly, we detected noevidence of genetic recombination between individuals within different states, indicating that asexual cycles play a major role in M. musicola reproduction and that long-distance dispersal of the pathogen is the main factor contributing to the lack of population structure in the fungus.

Temporal and Spatial Scaling of the Genetic Structure of a Vector-Borne Plant Pathogen

The ecology of plant pathogens of perennial crops is affected by the long-lived nature of their immobile hosts. In addition, changes to the genetic structure of pathogen populations may affect disease epidemiology and management practices; examples include local adaptation of more fit genotypes or introduction of novel genotypes from geographically distant areas via human movement of infected plant material or insect vectors. We studied the genetic structure of Xylella fastidiosa populations causing disease in sweet orange plants in Brazil at multiple scales using fast-evolving molecular markers (simple-sequence DNA repeats). Results show that populations of X. fastidiosa were regionally isolated, and that isolation was maintained for populations analyzed a decade apart from each other. However, despite such geographic isolation, local populations present in year 2000 were largely replaced by novel genotypes in 2009 but not as a result of migration. At a smaller spatial scale (individual trees), results suggest that isolates within plants originated from a shared common ancestor. In summary, new insights on the ecology of this economically important plant pathogen were obtained by sampling populations at different spatial scales and two different time points.

Genetic structure and demographic history of Colletotrichum gloeosporioides sensu lato and C. truncatum isolates from Trinidad and Mexico

BackgroundC. gloeosporioides sensu lato is one of the most economically important post-harvest diseases affecting papaya production worldwide. There is currently no information concerning the genetic structure or demographic history of this pathogen in any of the affected countries. Knowledge of molecular demographic parameters for different populations will improve our understanding of the biogeographic history as well as the evolutionary and adaptive potential of these pathogens. In this study, sequence data for ACT, GPDH, β-TUB and ITS gene regions were analyzed for C. gloeosporioides sensu lato and C. truncatum isolates infecting papaya in Trinidad and Mexico in order to determine the genetic structure and demographic history of these populations.ResultsThe data indicated that Mexico is the ancestral C. gloeosporioides sensu lato population with asymmetrical migration to Trinidad. Mexico also had the larger effective population size but, both Mexico and Trinidad populations exhibited population expansion. Mexico also had greater nucleotide diversity and high levels of diversity for each gene. There was significant sub-division of the Trinidad and Mexico populations and low levels of genetic divergence among populations for three of the four gene regions; β-TUB was shown to be under positive selection. There were also dissimilar haplotype characteristics for both populations. Mutation may play a role in shaping the population structure of C. gloeosporioides sensu lato isolates from Trinidad and from Mexico, especially with respect to the ACT and GPDH gene regions. There was no evidence of gene flow between the C. truncatum populations and it is possible that the Mexico and Trinidad populations emerged independently of each other.ConclusionsThe study revealed relevant information based on the genetic structure as well as the demographic history of two fungal pathogens infecting papaya, C. gloeosporioides sensu lato and C. truncatum, in Trinidad and Mexico. Understanding the genetic structure of pathogen populations will assist in determining the evolutionary potential of the pathogen and in identifying which evolutionary forces may have the greatest impact on durability of resistance. Intervention strategies that target these evolutionary forces would prove to be the most practical.

SIPPOM-WOSR: A Simulator for Integrated Pathogen POpulation Management of phoma stem canker on Winter OilSeed Rape: I. Description of the model

To durably control phoma stem canker ( Leptosphaeria maculans/ Leptosphaeria biglobosa) in oilseed rape by preserving the efficacy of specific resistances, the design of cropping systems is required, combining different aspects of crop management over time and at the regional scale. Modelling is a practical means to do so. To this end, the structure of SIPPOM, a Simulator for Integrated Pathogen POpulation Management, is presented. By linking epidemiological, population and crop model approaches, it simulates both the quantitative evolution (size) and qualitative evolution (genetic structure) of L. maculans population under the influence of various cropping systems. It involves five sub-models: (i) primary inoculum production; (ii) ascospore dispersal; (iii) crop development; (iv) plant infection, disease severity, and yield loss; and (v) changes in the genetic structure of pathogen populations over time. The input variables are weather data, soil characteristics, the description of cropping systems (crop sequence and winter oilseed rape crop management, including cultivars, sowing, fungicide, and tillage) and their spatial distribution (the model is spatially explicit), plus the initial size and genetic structure of pathogen populations. The genetic evolutionary forces taken into account are migration, selection, and recombination. The model simulates disease severity and the genetic structure of the pathogen population, in addition to the attainable yield less yield loss caused by the disease. The economic and environmental performances of the simulated strategies are computed using technical operation costs, crop prices, and yields. The underlying hypotheses are discussed in light of advantages and possible improvements of the model. Possible uses of SIPPOM are proposed with respect to the design of collective strategies for the durable control of phoma stem canker on winter oilseed rape at a regional scale.

SIPPOM-WOSR: A Simulator for Integrated Pathogen POpulation Management of phoma stem canker on Winter OilSeed Rape: II. Sensitivity analysis

SIPPOM, a simulator for integrated pathogen population management, has been developed to assess and rank Integrated crop management (ICM) strategies, at the regional scale. The input variables are weather data, soil characteristics, the description of cropping systems (crop sequence and winter oilseed rape crop management) and their spatial distribution, plus the initial size and genetic structure of pathogen populations. Here, we use SIPPOM to simulate phoma stem canker severity, the genetic structure of the pathogen populations, and the yield loss caused by the disease. Sensitivity analysis is conducted to quantify how strongly state variables (sub-model output variables) respond to variations in parameters. The results indicate which parameters need to be more accurately estimated, and it elucidates the steadiness of the rankings of contrasting control strategies under various weather conditions when parameters were varied. Due to the complexity of SIPPOM, the scope of this work was limited to a sensitivity analysis of each sub-model independently. Three values of each parameter were tested under various environmental conditions and crop management according to their expected or known effects on disease and yield. Qualitatively speaking, variations in input variables and parameters provided sub-model output variables that behaved as expected by experts. Parameters with the greatest effect on state variables and that need to be estimated more accurately are for instance those related to pseudothecia maturation and disease severity index estimates. Improvements are foreseen ( e.g., the calculation of both the number of phoma leaf spots and the severity disease index). Because the ranking of the simulated control strategies remained steady, despite large variations in the simulated variables linked with variations in parameters, the sensitivity analysis shows that the model, as it stands, can be used to compare and rank ICM strategies with respect to their effectiveness. Possibilities of a sensitivity analysis of the overall model are discussed.

Influence of spatial structure on pathogen colonization and extinction: a test using an experimental metapopulation

The Linum marginale–Melampsora lini plant–pathogen interaction has been studied extensively with regard to its epidemiology and population genetic structure (host resistance and pathogen virulence) in a natural metapopulation. In this study, this system was used in an experimental metapopulation approach to investigate explicitly how the distance (degree of isolation) between local population patches influences disease dynamics within a growing season, as well as the genetic structure of pathogen populations through stochastic colonization and extinction processes. The experimental design centred on four replicate sets of populations, within which patches were spaced at increasingly greater distances apart. Each patch consisted of an identical set of host and pathogen genotypes, with each pathogen genotype having the ability to attack only one of four host‐resistance types. Over the 2 years of the experiment, the results showed clear ‘boom‐and‐bust’ epidemic patterns, with the strongest determinant of disease dynamics within a growing season being the identity of particular host–pathogen genotypic combinations. However, there were also significant effects of spatial structure, in that more isolated patches tended to exhibit lower levels of disease during epidemic peaks than patches that were close together. Extinction of pathogen genotypes from individual populations was positively related to the severity of disease during preceding epidemic peaks, but negatively related to the level of disease present at the final census prior to overwintering. The probability of recolonization of pathotypes into populations during the second growing season was most strongly related to the distance to the nearest neighbouring source population in which a given pathotype was present. Overall, these results highlight the importance of spatial scale in influencing the numerical and genetical dynamics of pathogen populations.

Molecular genetic variability of Australian isolates of five cereal rust pathogens

Rust fungi cause economically important diseases of cereals, and their ability to rapidly evolve new virulent races has hindered attempts to control them by genetic resistance. PCR-based molecular tools may assist in understanding the genetic structure of pathogen populations. The high multiplex DNA fingerprinting techniques, amplified fragment length polymorphisms (AFLP), selectively amplified microsatellites (SAM) and sequence-specific amplification polymorphisms (S-SAP) were assessed for their potential in investigations of the genetic relationships among isolates of the wheat rust pathogens, Puccinia graminis f. sp. tritici (Pgt), Puccinia triticina (Pt), and P. striiformis f. sp. tritici (Pst), the oat stem rust pathogen P. graminis f. sp. avenae (Pga), and a putative new P. striiformis special form tentatively designated Barley grass yellow rust (Bgyr). Marker information content, as indicated by the number of species-specific fragments, polymorphic fragments among pathotypes, percentage of polymorphic loci, and the marker index, was highest for the SAM assay, followed by the AFLP and S-SAP assays. UPGMA analysis revealed that all marker types efficiently discriminated the five different taxa and Mantel tests revealed significant correlations between the marker types. Within pathogen groups, the marker types differed in the amount of variation detected among isolates; however, the major differences were consistent and polymorphism was generally low. This was reflected by the AMOVA analysis that significantly partitioned 90% of the genetic variation between taxa. Of the three marker types, SAMS were the most informative, and have the potential for the development of locus-specific microsatellites.

The role of selection on the genetic structure of pathogen populations: Evidence from field experiments with Mycosphaerella graminicola on wheat

The role of selection on the genetic structure of pathogen populations: Evidence from field experiments with Mycosphaerella graminicola on wheat