Transcriptome and proteome analysis of walnut ( <i>Juglans regia</i> L.) fruit in response to infection by <i>Colletotrichum gloeosporioides</i>

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Abstract

Background

Walnut anthracnose induced by Colletotrichum gloeosporioides is a disastrous disease affecting walnut production. The resistance of walnut fruit to C. gloeosporioides is a highly complicated and genetically programmed process. However, the underlying mechanisms have not yet been elucidated.

Results

To understand the molecular mechanism underlying the defense of walnut to C. gloeosporioides, we used RNA sequencing and label-free quantitation technologies to generate transcriptomic and proteomic profiles of tissues at various lifestyle transitions of C. gloeosporioides, including 0 hpi, pathological tissues at 24 hpi, 48 hpi, and 72 hpi, and distal uninoculated tissues at 120 hpi, in anthracnose-resistant F26 fruit bracts and anthracnose-susceptible F423 fruit bracts, which were defined through scanning electron microscopy. A total of 21,798 differentially expressed genes (DEGs) and 1929 differentially expressed proteins (DEPs) were identified in F26 vs. F423 at five time points, and the numbers of DEGs and DEPs were significantly higher in the early infection stage. Using pairwise comparisons and weighted gene co-expression network analysis of the transcriptome, we identified two modules significantly related to disease resistance and nine hub genes in the transcription expression gene networks. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of the DEGs and DEPs revealed that many genes were mainly related to immune response, plant hormone signal transduction, and secondary metabolites, and many DEPs were involved in carbon metabolism and photosynthesis. Correlation analysis between the transcriptome data and proteome data also showed that the consistency of the differential expression of the mRNA and corresponding proteins was relatively higher in the early stage of infection.

Conclusions

Collectively, these results help elucidate the molecular response of walnut fruit to C. gloeosporioides and provide a basis for the genetic improvement of walnut disease resistance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-021-03042-1.

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Most cited references 58

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WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

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The Top 10 fungal pathogens in molecular plant pathology.

RALPH DEAN, JAN A. L. VAN KAN, ZACHARIAS A. PRETORIUS … (2012)

The aim of this review was to survey all fungal pathologists with an association with the journal Molecular Plant Pathology and ask them to nominate which fungal pathogens they would place in a 'Top 10' based on scientific/economic importance. The survey generated 495 votes from the international community, and resulted in the generation of a Top 10 fungal plant pathogen list for Molecular Plant Pathology. The Top 10 list includes, in rank order, (1) Magnaporthe oryzae; (2) Botrytis cinerea; (3) Puccinia spp.; (4) Fusarium graminearum; (5) Fusarium oxysporum; (6) Blumeria graminis; (7) Mycosphaerella graminicola; (8) Colletotrichum spp.; (9) Ustilago maydis; (10) Melampsora lini, with honourable mentions for fungi just missing out on the Top 10, including Phakopsora pachyrhizi and Rhizoctonia solani. This article presents a short resumé of each fungus in the Top 10 list and its importance, with the intent of initiating discussion and debate amongst the plant mycology community, as well as laying down a bench-mark. It will be interesting to see in future years how perceptions change and what fungi will comprise any future Top 10. © 2012 THE AUTHORS. MOLECULAR PLANT PATHOLOGY © 2012 BSPP AND BLACKWELL PUBLISHING LTD.

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Perception of the bacterial PAMP EF-Tu by the receptor EFR restricts Agrobacterium-mediated transformation.

Cyril Zipfel, Gernot Kunze, Delphine Chinchilla … (2006)

Higher eukaryotes sense microbes through the perception of pathogen-associated molecular patterns (PAMPs). Arabidopsis plants detect a variety of PAMPs including conserved domains of bacterial flagellin and of bacterial EF-Tu. Here, we show that flagellin and EF-Tu activate a common set of signaling events and defense responses but without clear synergistic effects. Treatment with either PAMP results in increased binding sites for both PAMPs. We used this finding in a targeted reverse-genetic approach to identify a receptor kinase essential for EF-Tu perception, which we called EFR. Nicotiana benthamiana, a plant unable to perceive EF-Tu, acquires EF-Tu binding sites and responsiveness upon transient expression of EFR. Arabidopsis efr mutants show enhanced susceptibility to the bacterium Agrobacterium tumefaciens, as revealed by a higher efficiency of T-DNA transformation. These results demonstrate that EFR is the EF-Tu receptor and that plant defense responses induced by PAMPs such as EF-Tu reduce transformation by Agrobacterium.

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Author and article information

Contributors

Ke Qiang Yang: yangwere@126.com

Journal

Journal ID (nlm-ta): BMC Plant Biol

Journal ID (iso-abbrev): BMC Plant Biol

Title: BMC Plant Biology

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2229

Publication date (Electronic): 31 May 2021

Publication date PMC-release: 31 May 2021

Publication date Collection: 2021

Volume: 21

Electronic Location Identifier: 249

Affiliations

[1 ]GRID grid.440622.6, ISNI 0000 0000 9482 4676, College of Forestry, , Shandong Agricultural University, ; Tai’an, Shandong Province China

[2 ]GRID grid.440622.6, ISNI 0000 0000 9482 4676, State Forestry and Grassland Administr, ation Key Laboratory of Silviculture inthe Downstream Areas of the Yellow River, , Shandong Agricultural University, ; Tai’an, Shandong Province China

[3 ]GRID grid.440622.6, ISNI 0000 0000 9482 4676, Shandong Taishan Forest Ecosystem Research Station, , Shandong Agricultural University, ; Tai’an, Shandong Province China

[4 ]GRID grid.412608.9, ISNI 0000 0000 9526 6338, Department of Science and Technology, , Qingdao Agricultural University, ; Qingdao, Shandong Province China

Article

Publisher ID: 3042

DOI: 10.1186/s12870-021-03042-1

PMC ID: 8166054

PubMed ID: 34059002

SO-VID: 90d5cd3c-eff7-4427-83e3-f333c3557827

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 23 March 2021

Date accepted : 13 May 2021

Custom metadata

ScienceOpen disciplines: Plant science & Botany

Keywords: walnut,colletotrichum gloeosporioides,transcriptomic,proteomic,differentially expressed genes (degs)

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ScienceOpen disciplines: Plant science & Botany

Keywords: walnut, colletotrichum gloeosporioides, transcriptomic, proteomic, differentially expressed genes (degs)

Transcriptome and proteome analysis of walnut ( Juglans regia L.) fruit in response to infection by Colletotrichum gloeosporioides

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Abstract

Background

Results

Conclusions

Supplementary Information

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Plant MYBs

Most cited references 58

WGCNA: an R package for weighted correlation network analysis

The Top 10 fungal pathogens in molecular plant pathology.

Perception of the bacterial PAMP EF-Tu by the receptor EFR restricts Agrobacterium-mediated transformation.

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Contributors

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