Joël Chadœuf

Different mutations in the genome-linked protein VPg of Potato virus Y confer virulence on the pvr23 resistance in pepper

Five different amino acid substitutions in the VPg of Potato virus Y were shown to be independently responsible for virulence toward pvr2(3) resistance gene of pepper. A consequence of these multiple mutations toward virulence involving single nucleotide substitutions is a particularly high frequency of resistance breaking (37% of inoculated plants from the first inoculation) and suggests a potentially low durability of pvr2(3) resistance. These five mutants were observed with significantly different frequencies, one of them being overrepresented. Genetic drift alone could not explain the observed distribution of virulent mutants. More plausible scenarios were obtained by taking into account either the relative substitution rates, the relative fitness of the mutants in pvr2(3) pepper plants, or both.

A Bayesian Inference Framework to Reconstruct Transmission Trees Using Epidemiological and Genetic Data

The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.

The relationship between mutation frequency and replication strategy in positive-sense single-stranded RNA viruses

For positive-sense single-stranded RNA virus genomes, there is a trade-off between the mutually exclusive tasks of transcription, translation and encapsidation. The replication strategy that maximizes the intracellular growth rate of the virus requires iterative genome transcription from positive to negative, and back to positive sense. However, RNA viruses experience high mutation rates, and the proportion of genomes with lethal mutations increases with the number of replication cycles. Thus, intracellular mutant frequency will depend on the replication strategy. Introducing apparently realistic mutation rates into a model of viral replication demonstrates that strategies that maximize viral growth rate could result in an average of 26 mutations per genome by the time plausible numbers of positive strands have been generated, and that virus viability could be as low as 0.1 per cent. At high mutation rates or when a high proportion of mutations are deleterious, the optimal strategy shifts towards synthesizing more negative strands per positive strand, and in extremis towards a ‘stamping-machine’ replication mode where all the encapsidated genomes come from only two transcriptional steps. We conclude that if viral mutation rates are as high as current estimates suggest, either mutation frequency must be considerably higher than generally anticipated and the proportion of viable viruses produced extremely small, or replication strategies cannot be optimized to maximize viral growth rate. Mechanistic models linking mutation frequency to replication mechanisms coupled with data generated through new deep-sequencing technologies could play an important role in improving the estimates of viral mutation rate.

Autoinfection in wheat leaf rust epidemics

Autoinfection (within-host inoculum transmission) allows plant pathogens locally to increase their density on an infected host. Estimating autoinfection is of particular importance in understanding epidemic development in host mixtures. More generally, autoinfection influences the rate of host colonization by the pathogen, as well as pathogen evolution. Despite its importance in epidemiological models, autoinfection has not yet been directly quantified. It was measured here on wheat (Triticum aestivum) leaves infected by a pathogenic fungus (Puccinia triticina). Autoinfection was measured either on inoculated leaves or by assessing the local progeny of spontaneous infections, and was described by a model of the form y = microx(alpha), where alpha accounts for host saturation and micro represents the pathogen multiplication rate resulting from autoinfection. It was shown that autoinfection resulted in typical patterns of disease aggregation at the leaf level and influenced lesion distribution in the crop during the first epidemic stages. The parameter micro was calculated by taking overdispersion of the data and density dependence into account. It was found that a single lesion produced between 50 and 200 offspring by autoinfection, within a pathogen generation. By taking into account environmental variability, it was possible to estimate autoinfection under optimal conditions for epidemic development.

Herbicides do not ensure for higher wheat yield, but eliminate rare plant species

Weed control is generally considered to be essential for crop production and herbicides have become the main method used for weed control in developed countries. However, concerns about harmful environmental consequences have led to strong pressure on farmers to reduce the use of herbicides. As food demand is forecast to increase by 50% over the next century, an in-depth quantitative analysis of crop yields, weeds and herbicides is required to balance economic and environmental issues. This study analysed the relationship between weeds, herbicides and winter wheat yields using data from 150 winter wheat fields in western France. A Bayesian hierarchical model was built to take account of farmers’ behaviour, including implicitly their perception of weeds and weed control practices, on the effectiveness of treatment. No relationship was detected between crop yields and herbicide use. Herbicides were found to be more effective at controlling rare plant species than abundant weed species. These results suggest that reducing the use of herbicides by up to 50% could maintain crop production, a result confirmed by previous studies, while encouraging weed biodiversity. Food security and biodiversity conservation may, therefore, be achieved simultaneously in intensive agriculture simply by reducing the use of herbicides.

Estimation of the dispersal distances of an aphid-borne virus in a patchy landscape

Characterising the spatio-temporal dynamics of pathogens in natura is key to ensuring their efficient prevention and control. However, it is notoriously difficult to estimate dispersal parameters at scales that are relevant to real epidemics. Epidemiological surveys can provide informative data, but parameter estimation can be hampered when the timing of the epidemiological events is uncertain, and in the presence of interactions between disease spread, surveillance, and control. Further complications arise from imperfect detection of disease and from the huge number of data on individual hosts arising from landscape-level surveys. Here, we present a Bayesian framework that overcomes these barriers by integrating over associated uncertainties in a model explicitly combining the processes of disease dispersal, surveillance and control. Using a novel computationally efficient approach to account for patch geometry, we demonstrate that disease dispersal distances can be estimated accurately in a patchy (i.e. fragmented) landscape when disease control is ongoing. Applying this model to data for an aphid-borne virus (Plum pox virus) surveyed for 15 years in 605 orchards, we obtain the first estimate of the distribution of flight distances of infectious aphids at the landscape scale. About 50% of aphid flights terminate beyond 90 m, which implies that most infectious aphids leaving a tree land outside the bounds of a 1-ha orchard. Moreover, long-distance flights are not rare–10% of flights exceed 1 km. By their impact on our quantitative understanding of winged aphid dispersal, these results can inform the design of management strategies for plant viruses, which are mainly aphid-borne.

Genetic diversity of pepper (Capsicum spp.) germplasm resources in China reflects selection for cultivar types and spatial distribution

Abstract Pepper (Capsicum spp.) is an important vegetable crop in the world. Now the pepper in China contributes one-third of the worlds peppers production. Genetic diversity of the pepper germplasm of China is expected interesting to know. To explore the structure of genetic diversity in Chinese pepper germplasm resources and possible relationship with cultivar types or geographic origin, we sampled and compared 372 GenBank pepper accessions (local cultivars and landraces) from 31 provinces, autonomous regions and municipalities of China and 31 additional accessions from other countries. These accessions were genotyped using 28 simple sequence repeat (SSR) markers spanning the entire pepper genome. We then investigated the genetic structure of the sampled collection using model-based analysis in STRUCTURE v2.3.4 and examined genetic relationships by the unweighted pair-group method of mathematical averages (UPGMA) in MEGA. In addition to geographic origin, we evaluated eight plant and fruit traits. In total, 363 alleles were amplified using the 28 SSR primers. Gene diversity, polymorphism information content and heterozygosity of the 28 SSR loci were estimated as 0.09–0.92, 0.08–0.92 and 0.01–0.34, respectively. The UPGMA cluster analysis clearly distinguished Capsicum annuum L. from other cultivated pepper species. Population structure analysis of the 368 C. annuum accessions uncovered three genetic groups which also corresponded to distinct cultivar types with respect to the plant and fruit descriptors. The genetic structure was also related to the geographic origin of the landraces. Overall results indicate that genetic diversity of Chinese pepper landraces were structured by migration of genotypes followed by human selection for cultivar types in agreement with consumption modes and adaptation to the highly diversified agro-climatic conditions.

Estimating second order characteristics of point processes with known independent noise

The analysis of point patterns often begins with a test of complete spatial randomness using summaries such as the emptyspace function F or the nearest neighbour distance distribution function G. These functions constitute basic summaries upon which many studies are based, depending on their shape. As the map of points is usually considered accurate, Monte Carlo tests are performed on the observed pattern without taking into account position errors. However, position errors usually occur during the mapping process. The aim of this article is to quantify the impact of measurement error on descriptive distance statistics and to integrate these errors in the non-parametric analysis. An application to tropical forest species is presented.