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Plants regulate responses towards herbivory through fine-tuning of defence-related hormone production, expression of defence genes, and production of secondary metabolites. Jasmonic acid (JA) plays a key role in plant-herbivorous arthropod interactions. To understand how pepper (Capsicum annuum) responds to herbivory, leaf transcriptomes and metabolomes of two genotypes different in their susceptibility to spider mites were studied. Mites induced both JA and salicylic acid (SA) signalling. However, mite infestation and exogenous JA resulted in distinct transcriptome profiles. Compared with JA, mites induced fewer differentially expressed genes involved in metabolic processes (except for genes involved in the phenylpropanoid pathway) and lipid metabolic processes. Furthermore, pathogen-related defence responses including WRKY transcription factors were more strongly induced upon mite infestation, probably as a result of induced SA signalling. Untargeted analysis of secondary metabolites confirmed that JA treatment induced larger changes in metabolism than spider mite infestation, resulting in higher terpenoid and flavonoid production. The more resistant genotype exhibited a larger increase in endogenous JA and volatile and non-volatile secondary metabolites upon infestation, which could explain its stronger defence. Reasoning that in JA-SA antagonizing crosstalk, SA defences are prioritized over JA defences, we hypothesize that lack of SA-mediated repression of JA-induced defences could result in gain of resistance towards spider mites in pepper.
Pubmed ID: 31557301
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 10th,2023. An M software package for Clustering 16S rRNA sequences into operational taxonomic units (OTUs). The download link contain the package and some benchmark data sets.
View all literature mentionsA web-based tool and database for the gene ontology analysis. Its focus is on agricultural species and is user-friendly. The agriGO is designed to provide deep support to agricultural community in the realm of ontology analysis. Compared to other available GO analysis tools, unique advantages and features of agriGO are: # The agriGO especially focuses on agricultural species. It supports 45 species and 292 datatypes currently. And agriGO is designed as an user-friendly web server. # New tools including PAGE (Parametric Analysis of Gene set Enrichment), BLAST4ID (Transfer IDs by BLAST) and SEACOMPARE (Cross comparison of SEA) were developed. The arrival of these tools provides users with possibilities for data mining and systematic result exploration and will allow better data analysis and interpretation. # The exploratory capability and result visualization are enhanced. Results are provided in different formats: HTML tables, tabulated text files, hierarchical tree graphs, and flash bar graphs. # In agriGO, PAGE and SEACOMPARE can be used to carry out cross-comparisons of results derived from different data sets, which is very important when studying multiple groups of experiments, such as in time-course research. Platform: Online tool
View all literature mentionsA commercial organization which provides assay technologies to isolate DNA, RNA, and proteins from any biological sample. Assay technologies are then used to make specific target biomolecules, such as the DNA of a specific virus, visible for subsequent analysis.
View all literature mentionsCompany provides laboratories worldwide with analytical instruments and supplies, clinical and diagnostic testing services, consumables, applications and expertise in life sciences and applied chemical markets.
View all literature mentionsQuality control software that perform checks on raw sequence data coming from high throughput sequencing pipelines. This software also provides a modular set of analyses which can give a quick impression of the quality of the data prior to further analysis.
View all literature mentionsWeb server for statistical, functional and integrative analysis of metabolomics data. Web based tool suite used for metabolomic data processing, normalization, multivariate statistical analysis, and data annotation, biomarker discovery and classification.
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