1,721,066 research outputs found
Rapid accumulation and low degradation: key parameters of Tomato yellow leaf curl virus persistence in its insect vector Bemisia tabaci
No full textOf worldwide economic importance, Tomato yellow leaf curl virus (TYLCV, Begomovirus) is responsible for one of the most devastating plant diseases in warm and temperate regions. The DNA begomoviruses (Geminiviridae) are transmitted by the whitefly species complex Bemisia tabaci. Although geminiviruses have long been described as circulative non-propagative viruses, observations such as long persistence of TYLCV in B. tabaci raised the question of their possible replication in the vector. We monitored two major TYLCV strains, Mild (Mld) and Israel (IL), in the invasive B. tabaci Middle East-Asia Minor 1 cryptic species, during and after the viral acquisition, within two timeframes (0-144 hours or 0-20 days). TYLCV DNA was quantified using real-time PCR, and the complementary DNA strand of TYLCV involved in viral replication was specifically quantified using anchored real-time PCR. The DNA of both TYLCV strains accumulated exponentially during acquisition but remained stable after viral acquisition had stopped. Neither replication nor vertical transmission were observed. In conclusion, our quantification of the viral loads and complementary strands of both Mld and IL strains of TYLCV in B. tabaci point to an efficient accumulation and preservation mechanism, rather than to a dynamic equilibrium between replication and degradation
Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens
No full textGenetically-controlled plant resistance can reduce the damage caused by pathogens. However, pathogens have the ability to evolve and overcome such resistance. This often occurs quickly after resistance is deployed, resulting in significant crop losses and a continuing need to develop new resistant cultivars. To tackle this issue, several strategies have been proposed to constrain the evolution of pathogen populations and thus increase genetic resistance durability. These strategies mainly rely on varying different combinations of resistance sources across time (crop rotations) and space. The spatial scale of deployment can vary from multiple resistance sources occurring in a single cultivar (pyramiding), in different cultivars within the same field (cultivar mixtures) or in different fields (mosaics). However, experimental comparison of the efficiency (i.e. ability to reduce disease impact) and durability (i.e. ability to limit pathogen evolution and delay resistance breakdown) of landscape-scale deployment strategies presents major logistical challenges. Therefore, we developed a spatially explicit stochastic model able to assess the epidemiological and evolutionary outcomes of the four major deployment options described above, including both qualitative resistance (i.e. major genes) and quantitative resistance traits against several components of pathogen aggressiveness: infection rate, latent period duration, propagule production rate, and infectious period duration. This model, implemented in the R package landsepi, provides a new and useful tool to assess the performance of a wide range of deployment options, and helps investigate the effect of landscape, epidemiological and evolutionary parameters. This article describes the model and its parameterisation for rust diseases of cereal crops, caused by fungi of the genus Puccinia. To illustrate the model, we use it to assess the epidemiological and evolutionary potential of the combination of a major gene and different traits of quantitative resistance. The comparison of the four major deployment strategies described above will be the objective of future studies
Differential impact of landscape-scale strategies for crop cultivar deployment on disease dynamics, resistance durability and long-term evolutionary control
No full textA multitude of resistance deployment strategies have been proposed to tackle the evolutionary potential of pathogens to overcome plant resistance. In particular, many landscape-based strategies rely on the deployment of resistant and susceptible cultivars in an agricultural landscape as a mosaic. However, the design of such strategies is not easy as strategies targeting epidemiological or evolutionary outcomes may not be the same. Using a stochastic spatially explicit model, we studied the impact of landscape organization (as defined by the proportion of fields cultivated with a resistant cultivar and their spatial aggregation) and key pathogen life-history traits on three measures of disease control. Our results show that short-term epidemiological dynamics are optimized when landscapes are planted with a high proportion of the resistant cultivar in low aggregation. Importantly, the exact opposite situation is optimal for resistance durability. Finally, well-mixed landscapes (balanced proportions with low aggregation) are optimal for long-term evolutionary equilibrium (defined here as the level of long-term pathogen adaptation). This work offers a perspective on the potential for contrasting effects of landscape organization on different goals of disease management and highlights the role of pathogen life history
Assessing the performance of landscape-based strategies to deploy major-gene resistance
No full textGenetically-controlled plant resistance can reduce the damage caused by pathogens. However, pathogens have the ability to evolve and overcome such resistance. This often occurs very quickly after resistance is deployed, resulting in significant crop losses and a continuing need to breed new resistant cultivars. To tackle this issue, several strategies have been proposed to constrain the evolutionary potential of pathogen populations and thus increase the durability of resistance deployment. These strategies mainly rely on using different combinations of resistance sources in time, space, or both. In time, such combination consists of crop rotations. In space, resistance sources can be deployed in the same cultivar (pyramiding), in different cultivars within the same field (cultivar mixtures) or in different fields (mosaics). However, experimental assessment of the efficiency (i.e. ability to reduce disease impact) and the durability (i.e. ability to limit pathogen evolution and delay resistance breakdown) of different deployment strategies presents a major challenge.
Therefore, we developed a spatially-explicit stochastic model to assess the epidemiological and evolutionary outcomes of the major deployment options described above when one or two major genes for resistance are present. In addition, we analysed the impact of landscape organisation (as defined by the proportion of fields cultivated with a resistant cultivar, and their spatial aggregation) and epidemiological or evolutionary parameters (e.g. dispersal abilities, mutation rate, cost of infectivity) through sensitivity analyses and polynomial regression.
The model has been parameterised for wheat qualitative resistance to rusts, caused by fungi of the genus Puccinia, but can be applied to many other pathosystems. Early results suggest that strategies offering the best epidemiological control of the disease are not necessarily the most durable
Conception et évaluation assistée par la modélisation de stratégies de gestion d’une épidémie dans un paysage hétérogène
No full textManagement strategies of epidemics are often based on expert opinions rather than the formal demonstration of their efficiency. This is due to the difficulty of accounting for both biological processes and human interventions in field trials of various control methods designed to identify the most efficient. Thus, improving these strategies, which have no a priori reason to be optimal, is not an easy task. A promising approach to overcome obstacles linked to experiments consists in modeling both the epidemic process and the control measures. The key drivers of such models can be identified by a sensitivity analysis and, for some of them, better characterized experimentally. Control parameters are other key drivers that can be optimized by taking advantage of the potential of sensitivity analysis. This approach, applicable to many epidemic diseases, has been tested on the management of sharka, a damaging disease of trees of the genus Prunus (especially apricot, peach and plum). It is caused by Plum pox virus (PPV, genus Potyvirus, transmitted by aphids) and associated with severe economic losses. In France, sharka management is mainly based on visual orchard surveillance and removal of individuals that can contribute to disease spread. In controlled conditions, experiments were carried out to test hypotheses on the concurrence between the infectious and symptomatic states, on which visual surveillance strategies are based. Results of these experiments, and particularly the synchrony between latent and incubation periods for young peach trees infected by PPV, do not reject these hypotheses. These results feed a stochastic spatiotemporal model simulating sharka spread. Sensitivity analyses performed on this model revealed that the fate of the epidemics is highly dependent on the connectivity of the patch (with other patches of the landscape) where PPV is first introduced, and on the latent period duration. When a panel of management strategies was included into the model, additional sensitivity analyses enabled the identification of economically optimal surveillance and removal parameters in the simulated epidemic context.Les stratégies de gestion des épidémies sont souvent basées sur des opinions d’experts, plutôt que sur une démonstration formelle de leur efficacité. Ce constat résulte de la difficulté de prendre en compte les processus biologiques et les interventions humaines qui s’y ajoutent pour expérimenter différentes méthodes et identifier la plus performante. Il est ainsi difficile d’améliorer ces stratégies, qui n’ont pas de raison a priori d’être optimales. La modélisation conjointe du processus épidémique et de diverses stratégies de gestion constitue un outil innovant pour contourner les contraintes liées à l’expérimentation. Les paramètres clés d’un tel modèle peuvent être identifiés par une analyse de sensibilité et, pour certains d’entre eux, mieux appréhendés grâce à l’expérimentation biologique. D’autres paramètres clés peuvent constituer des leviers d’action dont il est possible d’approcher les valeurs optimales en exploitant le potentiel des méthodes d’analyse de sensibilité. Cette démarche, applicable à de nombreuses maladies infectieuses, a été testée sur la gestion de la sharka, une maladie affectant les arbres du genre Prunus (dont les abricotiers, pêchers et pruniers). Elle est causée par le Plum pox virus (PPV, du genre Potyvirus, transmis par pucerons) et associée à d’importantes pertes économiques. En France, la stratégie de gestion de cette maladie repose notamment sur l’observation visuelle des vergers et l’arrachage des individus pouvant contribuer à la propagation des épidémies. Dans le cadre de cette thèse, des travaux expérimentaux ont été entrepris en conditions contrôlées pour tester des hypothèses liées à la concordance entre les états infectieux et symptomatiques, sur lesquelles s’appuient les stratégies basées sur une surveillance visuelle. Les résultats de ces expériences, et notamment la synchronie entre les périodes de latence et d’incubation chez le jeune pêcher infecté par le PPV, ne remettent pas en cause ces hypothèses et viennent enrichir un modèle de simulation probabiliste et spatiotemporel de la propagation de la sharka. Les analyses de sensibilité réalisées sur ce modèle ont révélé que le devenir des épidémies dépend fortement de la connectivité de la parcelle (avec les autres parcelles du paysage) où est introduit le PPV la première fois, et de la durée de la latence dans les arbres infectés. D’autres analyses de sensibilité du modèle, incluant cette fois un ensemble de stratégies de gestion, ont permis d’optimiser les modalités de surveillance et d’arrachage vis-à-vis d’un critère économique dans le contexte épidémique simul
Modélisation de la cinétique d’accumulation et de persistance du <em>Tomato yellow leaf curl virus</em> (TYLCV) au sein de son insecte vecteur <em>Bemisia tabaci</em> par PCR quantitative normalisée, et enquête agronomique de l’impact du virus sur la filière tomate à La Réunion
No full textRapport de stage de 3ème année d’AgroParisTechMaste
Loup Rimbaud : des mathématiques à la biologie pour étudier les maladies des plantes : Page web INRA/Département Santé des plantes et environnement/Actualités (21/06/2019)
No full textEstimation expérimentale de paramètres clés pour la modélisation de stratégies de gestion d’une épidémie virale
No full textNational audienc
Appréciation quantitative du risque de réaction allergique à l’arachide lors de la consommation de produits ne mentionnant pas l’arachide dans la liste des ingrédients.
No full textRapport de stage de 2ème année d’AgroParisTec
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