Exploiting Genetic Information to Trace Plant Virus Dispersal in Landscapes

Picard, Coralie; Dallot, Sylvie; Brunker, Kirstyn; Berthier, Karine; Filloux, Denis; Soubeyrand, Samuel; Jacquot, Emmanuel; Thébaud, Gaël

doi:10.1146/annurev-phyto-080516-035616

Cited by 20 publications

(17 citation statements)

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“…The top-down exploratory approach that we applied here can be implemented relatively easily, using a single molecular marker for a clonal organism sampled from wild and cultivated compartments and a robust multiscale statistical approach involving correspondence and join-count analyses. Further insights may be gained on the complex interaction network among host plants and insect vectors by the use of landscape genetic approaches 23,47,48 to analyse the haplotypes of ‘ Ca . P. prunorum’ from insects collected in, and at various distances from, shelter sites.…”

Section: Discussionmentioning

confidence: 99%

Multi-scale spatial genetic structure of a vector-borne plant pathogen in orchards and wild habitat

Marie-Jeanne

Bonnot

Thébaud

et al. 2019

Preprint

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12Inferring the dispersal processes of vector-borne plant pathogens is a great challenge because the 13 plausible epidemiological scenarios often involve complex spread patterns at multiple scales. 14 European stone fruit yellows (ESFY), a disease caused by 'Candidatus Phytoplasma prunorum' 15 and disseminated via planting material and vectors belonging to the species Cacopsylla pruni, is a 16 major threat for stone fruit production throughout Europe. The spatial genetic structure of the 17 pathogen was investigated at multiple scales by the application of a combination of statistical 18 approaches to a large dataset obtained through the intensive sampling of the three ecological 19 compartments hosting the pathogen (psyllids, wild and cultivated Prunus) in three Prunus-growing 20 regions in France. This work revealed new haplotypes of 'Ca. P. prunorum', and showed that the 21 prevalence of the different haplotypes of this pathogen is highly uneven between all regions, and 22 within two of them. In addition, we identified a significant clustering of similar haplotypes within 23 a radius of at most 50 km, but not between nearby wild and cultivated Prunus. We also provide 24 evidence that the two species of the C. pruni complex are unevenly distributed but can spread the 25 pathogen, and that infected plants are transferred between production areas. Altogether, this work 26 supports a main epidemiological scenario where 'Ca. P. prunorum' is endemic in, and mostly 27 acquired from, wild Prunus by immature C. pruni (of both species) who then migrate to "shelter 28 plants" that epidemiologically connect sites less than 50 km apart by later providing infectious mature C. pruni to their "migration basins", which differ in their haplotypic composition. We argue 30 that such multiscale studies would be very useful for other pathosystems. 3132 Keywords: C. pruni, epidemiology, reservoir, join count. 33 34 processes has been a central question for decades in ecology 11-14 and population genetics 15,16 , the 48 development of large-scale pattern-oriented approaches to understand the processes shaping the 49 genetic structure of a population is recent 17-21 . The basic idea of these approaches is to estimate the 50 distance at which two samples become genetically independent by relating genetic data and spatial 51 information obtained for a set of samples, through combining methods of geostatistics 22 and 52 population or landscape genetics 23 . In particular, approaches used for qualitative data comprise 53 join-counts 24 and permutation tests to identify distances at which genetic dissimilarity between 54 pairs of individuals is significantly lower than expected 25-28 . 55

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Section: Discussionmentioning

confidence: 99%

Multi-scale spatial genetic structure of a vector-borne plant pathogen in orchards and wild habitat

Marie-Jeanne

Bonnot

Thébaud

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

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“…The top-down exploratory approach that we applied here can be relatively easily implemented using a single molecular marker for a clonal organism sampled from wild and cultivated compartments and a robust multi-scale statistical approach involving correspondence and join-count analyses. Further insights may be gained on the complex interaction network among host plants and insect vectors by the use of landscape genetic approaches 23,45,46 to analyse the genotypes of 'Ca. P. prunorum' from insects collected in and at various distances from shelter sites.…”

Section: Scientific Reports |mentioning

confidence: 99%

Multi-scale spatial genetic structure of the vector-borne pathogen ‘Candidatus Phytoplasma prunorum’ in orchards and in wild habitats

Marie-Jeanne

Bonnot

Thébaud

et al. 2020

Sci Rep

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inferring the dispersal processes of vector-borne plant pathogens is a great challenge because the plausible epidemiological scenarios often involve complex spread patterns at multiple scales. the spatial genetic structure of 'Candidatus Phytoplasma prunorum', responsible for European stone fruit yellows disease, was investigated by the application of a combination of statistical approaches to genotype data of the pathogen sampled from cultivated and wild compartments in three french Prunus-growing regions. This work revealed that the prevalence of the different genotypes is highly uneven both between regions and compartments. In addition, we identified a significant clustering of similar genotypes within a radius of 50 km or less, but not between nearby wild and cultivated Prunus. We also provide evidence that infected plants are transferred between production areas, and that both species of the Cacopsylla pruni complex can spread the pathogen. Altogether, this work supports a main epidemiological scenario where 'Ca. p. prunorum' is endemic in -and generally acquired from -wild Prunus by its immature psyllid vectors. the latter then migrate to shelter plants that epidemiologically connect sites less than 50 km apart by later providing infectious mature psyllids to their "migration basins". Such multi-scale studies could be useful for other pathosystems.Vector-borne pathogens have caused some of the most devastating plant diseases in perennial and annual crops 1,2 . Managing these threats requires an understanding of their epidemiological cycle, particularly the dispersal processes of pathogens in the landscape 3 . However, deciphering the complexity of plausible epidemiological scenarios poses a great challenge, especially since insects are involved in disease spread. Indeed, most of the vector-borne plant pathogens are transmitted by small piercing-sucking insects of the Hemiptera order, whose dispersal distances are generally only vaguely known, while searching host plants for food resources and/or reproduction 4 . Added to this question of dispersal distance is the issue of different host plant species that insects can occupy during their life cycle and how they move among them. In this context, a key question that often arises is the role of wild plants as a reservoir of pathogen and/or insect vectors 5-10 .One of the first difficulties in identifying patterns of disease spread in agricultural landscapes is to determine the optimal scale of the study design. Although the spatial scale of the processes has been a central question for decades in ecology [11][12][13][14] and population genetics 15,16 , the development of large-scale pattern-oriented approaches to understand the processes that shape the genetic structure of a population is recent 17-21 . The basic idea of these approaches is to estimate the distance at which two samples become genetically independent by linking genetic data and spatial information obtained for a set of samples using a combination of geostatistics 22 and population or landscape g...

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“…This enables the joint inference of epidemiological parameters (infection date, latent and infectious periods, dispersal function) and pathogen transmission chains. Despite its potential, this approach still has to be applied to plant pathogens (Picard et al, 2017).…”

Section: Estimating the Epidemic Dynamicsmentioning

confidence: 99%

PESO: a modelling framework to help improve management strategies for epidemics – application to sharka

Picard

Rimbaud

Hendrikx³

et al. 2017

EPPO Bulletin

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BGPI: équipe 6Paper based on work presented at the Joint EFSA-EPPO Workshop: Modelling in Plant Health – how can models support risk assessment of plant pests and decision-making? 12th–14th December 2016, Parma, ItalyThe optimization of management strategies for plant diseases is a difficult task because of the complexity and variability of epidemic dynamics. Thanks to their ability to numerically simulate many scenarios, models can be used to estimate epidemiological parameters, assess the effectiveness of different management strategies and optimize them. This article presents the PESO (parameter estimation–simulation–optimization) modelling framework to help improve plant disease management strategies. This framework is based on (i) the characterization of the epidemic dynamics to estimate key epidemiological parameters, (ii) the use of spatially explicit models to simulate epidemic dynamics and disease management, and (iii) the use of numerical optimization methods to identify better management strategies. This approach is generic and can be applied to many diseases. The work presented here focuses on sharka (caused by Plum pox virus), which has a worldwide impact on the Prunus industry, and is associated with huge disease management costs in many countries, especially in France

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Exploiting Genetic Information to Trace Plant Virus Dispersal in Landscapes

Cited by 20 publications

References 139 publications

Multi-scale spatial genetic structure of a vector-borne plant pathogen in orchards and wild habitat

Multi-scale spatial genetic structure of a vector-borne plant pathogen in orchards and wild habitat

Multi-scale spatial genetic structure of the vector-borne pathogen ‘Candidatus Phytoplasma prunorum’ in orchards and in wild habitats

PESO: a modelling framework to help improve management strategies for epidemics – application to sharka

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