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PATH2EVOL SIGNED

Unravelling pathogen evolution breaking down crop resistance in agricultural ecosystems

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 PATH2EVOL project word cloud

Explore the words cloud of the PATH2EVOL project. It provides you a very rough idea of what is the project "PATH2EVOL" about.

flow    reverse    tritici    genomes    blotch    fungal    prevent    association    emergence    predictions    filamentous    evolutionary    strategies    analyze    epidemics    holistic    agricultural    statistical    associate    avenue    losses    previously    functional    collections    ecosystems    genomic    responding    loci    virulence    repeatedly    isolated    locations    food    conducive    thought    causing    varieties    replicated    combination    me    traits    favor    pathogen    plots    hosts    zymoseptoria    mechanisms    guide    elusive    basis    septoria    phenotypic    adaptive    pandemic    overcome    speed    rapid    crop    unbiased    resistance    data    architecture    deploying    virulent    frameworks    genetic    severe    evolution    model    disease    host    substantially    wheat    levels    ecology    threaten    largely    genome    deployment    fungi    breakdown    genes    diverse    yield    mapping    full    sustainable    gene    lost    security    environment    link    causal    ecosystem    pathogens    settings    resistant    populations    pathogenic    stb    gained   

Project "PATH2EVOL" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITE DE NEUCHATEL 

Organization address
address: FAUBOURG DE L'HOPITAL 41
city: NEUCHATEL
postcode: 2000
website: www.unine.ch

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Total cost 175˙419 €
 EC max contribution 175˙419 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2017
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2018
 Duration (year-month-day) from 2018-07-01   to  2020-06-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITE DE NEUCHATEL CH (NEUCHATEL) coordinator 175˙419.00

Map

 Project objective

'Fungal crop pathogens cause severe yield losses and threaten food security. To prevent epidemics, deploying resistant varieties is currently the major avenue. However, agricultural ecosystems are highly conducive to the emergence of virulent pathogens and host resistance is rapidly overcome. The evolutionary mechanisms how virulence is gained on previously resistant hosts remains largely elusive. Identifying the genetic basis of adaptive evolution of pathogenic fungi in agricultural fields will be crucial to design future sustainable disease control strategies. The proposed project will analyze the process of pathogen adaptation to overcome crop resistance in agricultural ecosystem. The genomic architecture (i.e. 'two-speed genome') of filamentous pathogens is thought to favor the rapid evolution of virulence genes and the rapid breakdown of host resistance. However, the causal link between pathogen adaptation in the field and rapidly evolving loci has not been established. I propose to use “reverse ecology”, an unbiased and holistic approach to associate genomic loci with adaptation to the host and environment using the fungal pathogen Zymoseptoria tritici as a model. Z. tritici is a pandemic pathogen causing the severe Septoria Tritici Blotch (STB) on wheat. Populations are highly diverse with high levels of gene flow and wheat resistance was repeatedly lost in field settings. To identify loci responding to selection driven by host resistance, I will analyze full genomes of large pathogen collections isolated from replicated field plots using a robust statistical frameworks. This will allow me to test for an association of selection responses and genomic locations. I will also identify the phenotypic traits under selection with a combination of association mapping data and functional predictions. My research will substantially increase our understanding of pathogen adaptation and guide future resistance deployment strategies in agricultural ecosystem.'

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The information about "PATH2EVOL" are provided by the European Opendata Portal: CORDIS opendata.

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