Prediction of pathogenicity genes involved in adaptation to a lupin host in the fungal pathogens Botrytis cinerea and Sclerotinia sclerotiorum via comparative genomics.

Adaptation, Physiological Ascomycota / genetics Botrytis / genetics Fungal Proteins / genetics Genome, Fungal Host Specificity Lupinus / microbiology Plant Diseases / microbiology Virulence Whole Genome Sequencing
Botrytis cinerea Comparative genomics Lupin Sclerotinia sclerotiorum

Journal

BMC genomics
ISSN: 1471-2164
Titre abrégé: BMC Genomics
Pays: England
ID NLM: 100965258

Informations de publication

Date de publication:
17 May 2019
Historique:
received: 25 06 2018
accepted: 03 05 2019
entrez: 19 5 2019
pubmed: 19 5 2019
medline: 12 9 2019
Statut: epublish

Résumé

Narrow-leafed lupin is an emerging crop of significance in agriculture, livestock feed and human health food. However, its susceptibility to various diseases is a major obstacle towards increased adoption. Sclerotinia sclerotiorum and Botrytis cinerea - both necrotrophs with broad host-ranges - are reported among the top 10 lupin pathogens. Whole-genome sequencing and comparative genomics are useful tools to discover genes responsible for interactions between pathogens and their hosts. Genomes were assembled for one isolate of B. cinerea and two isolates of S. sclerotiorum, which were isolated from either narrow-leafed or pearl lupin species. Comparative genomics analysis between lupin-derived isolates and others isolated from alternate hosts was used to predict between 94 to 98 effector gene candidates from among their respective non-conserved gene contents. Detection of minor differences between relatively recently-diverged isolates, originating from distinct regions and with hosts, may highlight novel or recent gene mutations and losses resulting from host adaptation in broad host-range fungal pathogens.

Sections du résumé

BACKGROUND BACKGROUND
Narrow-leafed lupin is an emerging crop of significance in agriculture, livestock feed and human health food. However, its susceptibility to various diseases is a major obstacle towards increased adoption. Sclerotinia sclerotiorum and Botrytis cinerea - both necrotrophs with broad host-ranges - are reported among the top 10 lupin pathogens. Whole-genome sequencing and comparative genomics are useful tools to discover genes responsible for interactions between pathogens and their hosts.
RESULTS RESULTS
Genomes were assembled for one isolate of B. cinerea and two isolates of S. sclerotiorum, which were isolated from either narrow-leafed or pearl lupin species. Comparative genomics analysis between lupin-derived isolates and others isolated from alternate hosts was used to predict between 94 to 98 effector gene candidates from among their respective non-conserved gene contents.
CONCLUSIONS CONCLUSIONS
Detection of minor differences between relatively recently-diverged isolates, originating from distinct regions and with hosts, may highlight novel or recent gene mutations and losses resulting from host adaptation in broad host-range fungal pathogens.

Identifiants

DOI: 10.1186/s12864-019-5774-2 PMID: 31101009 PMC: PMC6525431
pubmed: 31101009
doi: 10.1186/s12864-019-5774-2
pii: 10.1186/s12864-019-5774-2
pmc: PMC6525431
doi:

Substances chimiques

Fungal Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

385

Subventions

Organisme : Grains Research and Development Corporation
ID : CUR00025

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Auteurs

Mahsa Mousavi-Derazmahalleh (M)

UWA School of Agriculture and Environment, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia.

Steven Chang (S)

Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Bentley, WA, 6102, Australia.

Geoff Thomas (G)

Department of Primary Industries and Regional Development, 3 Baron-Hay Court, South Perth, WA, 6151, Australia.

Mark Derbyshire (M)

Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Bentley, WA, 6102, Australia.

Phillip E Bayer (PE)

School of Biological Sciences, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia.
UWA Institute of Agriculture, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia.

David Edwards (D)

School of Biological Sciences, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia.
UWA Institute of Agriculture, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia.

Matthew N Nelson (MN)

UWA School of Agriculture and Environment, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia.
UWA Institute of Agriculture, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia.
Natural Capital and Plant Health, Royal Botanic Gardens Kew, Wakehurst Place, Ardingly, West Sussex, RH17 6TN, UK.
Current address: Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation, Wembley, WA, 6913, Australia.

William Erskine (W)

UWA School of Agriculture and Environment, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia.
UWA Institute of Agriculture, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia.

Francisco J Lopez-Ruiz (FJ)

Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Bentley, WA, 6102, Australia.

Jon Clements (J)

Department of Primary Industries and Regional Development, 3 Baron-Hay Court, South Perth, WA, 6151, Australia.

James K Hane (JK)

Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Bentley, WA, 6102, Australia. james.hane@curtin.edu.au.
Curtin Institute for Computation, Curtin University, Bentley, WA, 6102, Australia. james.hane@curtin.edu.au.
ORCID: 0000-0002-7651-0977

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Classifications MeSH

questionsmedicales.fr › Phénomènes biologiques › Adaptation biologique › Adaptation physiologique › Prediction of pathogenicity genes involved in...
questionsmedicales.fr › Eucaryotes › Champignons › Ascomycota › Prediction of pathogenicity genes involved in...
questionsmedicales.fr › Eucaryotes › Champignons › Deuteromycota › Botrytis › Prediction of pathogenicity genes involved in...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Protéines fongiques › Prediction of pathogenicity genes involved in...
questionsmedicales.fr › Phénomènes génétiques › Structures génétiques › Génome › Génome microbien › Génome fongique › Prediction of pathogenicity genes involved in...
questionsmedicales.fr › Phénomènes biologiques › Interactions hôte-microbes › Interactions hôte-pathogène › Spécificité d'hôte › Prediction of pathogenicity genes involved in...
questionsmedicales.fr › Eucaryotes › Viridiplantae › Streptophyta › Embryophyta › Tracheobionta › Magnoliopsida › Fabaceae › Lupinus › Prediction of pathogenicity genes involved in...
questionsmedicales.fr › Phénomènes physiologiques des plantes › Maladies des plantes › Prediction of pathogenicity genes involved in...
questionsmedicales.fr › Phénomènes microbiologiques › Virulence › Prediction of pathogenicity genes involved in...
questionsmedicales.fr › Techniques d'investigation › Techniques génétiques › Analyse de séquence › Analyse de séquence d'ADN › Séquençage du génome entier › Prediction of pathogenicity genes involved in...