Approximate Bayesian Computation applied to time series of population genetic data disentangles rapid genetic changes and demographic variations in a pathogen population.

Adaptation can occur at remarkably short timescales in natural populations, leading to drastic changes in phenotypes and genotype frequencies over a few generations only. The inference of demographic parameters can allow understanding how evolutionary forces interact and shape the genetic trajectories of populations during rapid adaptation. Here we propose a new Approximate Bayesian Computation (ABC) framework that couples a forward and individual-based model with temporal genetic data to disentangle genetic changes and demographic variations in a case of rapid adaptation. We test the accuracy of our inferential framework and evaluate the benefit of considering a dense versus sparse sampling. Theoretical investigations demonstrate high accuracy in both model and parameter estimations, even if a strong thinning is applied to time series data. Then, we apply our ABC inferential framework to empirical data describing the population genetic changes of the poplar rust pathogen following a major event of resistance overcoming. We successfully estimate key demographic and genetic parameters, including the proportion of resistant hosts deployed in the landscape and the level of standing genetic variation from which selection occurred. Inferred values are in accordance with our empirical knowledge of this biological system. This new inferential framework, which contrasts with coalescent-based ABC analyses, is promising for a better understanding of evolutionary trajectories of populations subjected to rapid adaptation.

A point mutation and large deletion at the candidate avirulence locus AvrMlp7 in the poplar rust fungus correlate with poplar RMlp7 resistance breakdown.

The deployment of plant varieties carrying resistance genes (R) exerts strong selection pressure on pathogen populations. Rapidly evolving avirulence genes (Avr) allow pathogens to escape R-mediated plant immunity through a variety of mechanisms, leading to virulence. The poplar rust fungus Melampsora larici-populina is a damaging pathogen of poplars in Europe. It underwent a major adaptive event in 1994, with the breakdown of the poplar RMlp7 resistance gene. Population genomics studies identified a locus in the genome of M. larici-populina that probably corresponds to the candidate avirulence gene AvrMlp7. Here, to further characterize this effector, we used a population genetics approach on a comprehensive set of 281 individuals recovered throughout a 28-year period encompassing the resistance breakdown event. Using two dedicated molecular tools, genotyping at the candidate locus highlighted two different alterations of a predominant allele found mainly before the resistance breakdown: a nonsynonymous mutation and a complete deletion of this locus. This results in six diploid genotypes: three genotypes related to the avirulent phenotype and three related to the virulent phenotype. The temporal survey of the candidate locus revealed that both alterations were found in association during the resistance breakdown event. They pre-existed before the breakdown in a heterozygous state with the predominant allele cited above. Altogether, these results suggest that the association of both alterations at the candidate locus AvrMlp7 drove the poplar rust adaptation to RMlp7-mediated immunity. This study demonstrates for the first time a case of adaptation from standing genetic variation in rust fungi during a qualitative resistance breakdown.

Improving sustainable crop protection using population genetics concepts.

Growing genetically resistant plants allows pathogen populations to be controlled and reduces the use of pesticides. However, pathogens can quickly overcome such resistance. In this context, how can we achieve sustainable crop protection? This crucial question has remained largely unanswered despite decades of intense debate and research effort. In this study, we used a bibliographic analysis to show that the research field of resistance durability has evolved into three subfields: (1) "plant breeding" (generating new genetic material), (2) "molecular interactions" (exploring the molecular dialogue governing plant-pathogen interactions) and (3) "epidemiology and evolution" (explaining and forecasting of pathogen population dynamics resulting from selection pressure[s] exerted by resistant plants). We argue that this triple split of the field impedes integrated research progress and ultimately compromises the sustainable management of genetic resistance. After identifying a gap among the three subfields, we argue that the theoretical framework of population genetics could bridge this gap. Indeed, population genetics formally explains the evolution of all heritable traits, and allows genetic changes to be tracked along with variation in population dynamics. This provides an integrated view of pathogen adaptation, in particular via evolutionary-epidemiological feedbacks. In this Opinion Note, we detail examples illustrating how such a framework can better inform best practices for developing and managing genetically resistant cultivars.

Investigation of Genetic Relationships Between Hanseniaspora Species Found in Grape Musts Revealed Interspecific Hybrids With Dynamic Genome Structures.

Hanseniaspora, a predominant yeast genus of grape musts, includes sister species recently reported as fast evolving. The aim of this study was to investigate the genetic relationships between the four most closely related species, at the population level. A multi-locus sequence typing strategy based on five markers was applied on 107 strains, confirming the clear delineation of species H. uvarum, H. opuntiae, H. guilliermondii, and H. pseudoguilliermondii. Huge variations were observed in the level of intraspecific nucleotide diversity, and differences in heterozygosity between species indicate different life styles. No clear population structure was detected based on geographical or substrate origins. Instead, H. guilliermondii strains clustered into two distinct groups, which may reflect a recent step toward speciation. Interspecific hybrids were detected between H. opuntiae and H. pseudoguilliermondii. Their characterization using flow cytometry, karyotypes and genome sequencing showed different genome structures in different ploidy contexts: allodiploids, allotriploids, and allotetraploids. Subculturing of an allotriploid strain revealed chromosome loss equivalent to one chromosome set, followed by an auto-diploidization event, whereas another auto-diploidized tetraploid showed a segmental duplication. Altogether, these results suggest that Hanseniaspora genomes are not only fast evolving but also highly dynamic.