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Tackling microbial threats in agriculture with integrative imaging and computational approaches

Pathogens and pests are one of the major threats to agricultural productivity worldwide. For decades, targeted resistance breeding was used to create crop cultivars that resist pathogens and environmental stress while retaining yields. The often decade-long process of crossing, selection, and field...

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Autores principales: Singh, Nikhil Kumar, Dutta, Anik, Puccetti, Guido, Croll, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787954/
https://www.ncbi.nlm.nih.gov/pubmed/33489007
http://dx.doi.org/10.1016/j.csbj.2020.12.018
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author Singh, Nikhil Kumar
Dutta, Anik
Puccetti, Guido
Croll, Daniel
author_facet Singh, Nikhil Kumar
Dutta, Anik
Puccetti, Guido
Croll, Daniel
author_sort Singh, Nikhil Kumar
collection PubMed
description Pathogens and pests are one of the major threats to agricultural productivity worldwide. For decades, targeted resistance breeding was used to create crop cultivars that resist pathogens and environmental stress while retaining yields. The often decade-long process of crossing, selection, and field trials to create a new cultivar is challenged by the rapid rise of pathogens overcoming resistance. Similarly, antimicrobial compounds can rapidly lose efficacy due to resistance evolution. Here, we review three major areas where computational, imaging and experimental approaches are revolutionizing the management of pathogen damage on crops. Recognizing and scoring plant diseases have dramatically improved through high-throughput imaging techniques applicable both under well-controlled greenhouse conditions and directly in the field. However, computer vision of complex disease phenotypes will require significant improvements. In parallel, experimental setups similar to high-throughput drug discovery screens make it possible to screen thousands of pathogen strains for variation in resistance and other relevant phenotypic traits. Confocal microscopy and fluorescence can capture rich phenotypic information across pathogen genotypes. Through genome-wide association mapping approaches, phenotypic data helps to unravel the genetic architecture of stress- and virulence-related traits accelerating resistance breeding. Finally, joint, large-scale screenings of trait variation in crops and pathogens can yield fundamental insights into how pathogens face trade-offs in the adaptation to resistant crop varieties. We discuss how future implementations of such innovative approaches in breeding and pathogen screening can lead to more durable disease control.
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spelling pubmed-77879542021-01-22 Tackling microbial threats in agriculture with integrative imaging and computational approaches Singh, Nikhil Kumar Dutta, Anik Puccetti, Guido Croll, Daniel Comput Struct Biotechnol J Review Article Pathogens and pests are one of the major threats to agricultural productivity worldwide. For decades, targeted resistance breeding was used to create crop cultivars that resist pathogens and environmental stress while retaining yields. The often decade-long process of crossing, selection, and field trials to create a new cultivar is challenged by the rapid rise of pathogens overcoming resistance. Similarly, antimicrobial compounds can rapidly lose efficacy due to resistance evolution. Here, we review three major areas where computational, imaging and experimental approaches are revolutionizing the management of pathogen damage on crops. Recognizing and scoring plant diseases have dramatically improved through high-throughput imaging techniques applicable both under well-controlled greenhouse conditions and directly in the field. However, computer vision of complex disease phenotypes will require significant improvements. In parallel, experimental setups similar to high-throughput drug discovery screens make it possible to screen thousands of pathogen strains for variation in resistance and other relevant phenotypic traits. Confocal microscopy and fluorescence can capture rich phenotypic information across pathogen genotypes. Through genome-wide association mapping approaches, phenotypic data helps to unravel the genetic architecture of stress- and virulence-related traits accelerating resistance breeding. Finally, joint, large-scale screenings of trait variation in crops and pathogens can yield fundamental insights into how pathogens face trade-offs in the adaptation to resistant crop varieties. We discuss how future implementations of such innovative approaches in breeding and pathogen screening can lead to more durable disease control. Research Network of Computational and Structural Biotechnology 2020-12-29 /pmc/articles/PMC7787954/ /pubmed/33489007 http://dx.doi.org/10.1016/j.csbj.2020.12.018 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review Article
Singh, Nikhil Kumar
Dutta, Anik
Puccetti, Guido
Croll, Daniel
Tackling microbial threats in agriculture with integrative imaging and computational approaches
title Tackling microbial threats in agriculture with integrative imaging and computational approaches
title_full Tackling microbial threats in agriculture with integrative imaging and computational approaches
title_fullStr Tackling microbial threats in agriculture with integrative imaging and computational approaches
title_full_unstemmed Tackling microbial threats in agriculture with integrative imaging and computational approaches
title_short Tackling microbial threats in agriculture with integrative imaging and computational approaches
title_sort tackling microbial threats in agriculture with integrative imaging and computational approaches
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787954/
https://www.ncbi.nlm.nih.gov/pubmed/33489007
http://dx.doi.org/10.1016/j.csbj.2020.12.018
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