Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Colletotrichum fructicola, which is part of the C. gloeosporioides species complex, can cause anthracnose diseases in strawberries worldwide. However, the molecular interactions between C. fructicola and strawberry are largely unknown. A deep RNA-sequencing approach was applied to gain insights into the pathogenicity mechanisms of C. fructicola and the defense response of strawberry plants at different stages of infection. The transcriptome data showed stage-specific transcription accompanied by a step-by-step strawberry defense response and the evasion of this defense system by fungus. Fungal genes involved in plant cell wall degradation, secondary metabolism, and detoxification were up-regulated at different stage of infection. Most importantly, C. fructicola infection was accompanied by a large number of highly expressed effectors. Four new identified effectors function in the suppression of Bax-mediated programmed cell death. Strawberry utilizes pathogen-associated molecular patterns (PAMP)-triggered immunity and effector-triggered immunity to prevent C. fructicola invasion, followed by the initiation of downstream innate immunity. The up-regulation of genes related to salicylic acid provided evidence that salicylic acid signaling may serve as the core defense signaling mechanism, while jasmonic acid and ethylene pathways were largely inhibited by C. fructicola. The necrotrophic stage displayed a significant up-regulation of genes involved in reactive oxygen species activation. Collectively, the transcriptomic data of both C. fructicola and strawberry shows that even though plants build a multilayered defense against infection, C. fructicola employs a series of escape or antagonizing mechanisms to successfully infect host cells.
Pubmed ID: 29922301
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Server that predicts N-Glycosylation sites in human proteins using artificial neural networks that examine the sequence context of Asn-Xaa-Ser/Thr sequons. NetNGlyc 1.0 is also available as a stand-alone software package, with the same functionality as the service above. Ready-to-ship packages exist for the most common UNIX platforms.
View all literature mentionsComputable knowledge regarding functions of genes and gene products. GO resources include biomedical ontologies that cover molecular domains of all life forms as well as extensive compilations of gene product annotations to these ontologies that provide largely species-neutral, comprehensive statements about what gene products do. Used to standardize representation of gene and gene product attributes across species and databases.
View all literature mentionsServer that produces predictions of mucin-type GalNAc O-glycosylation sites in mammalian proteins.
View all literature mentionsSoftware that estimates expression at transcript-level resolution and controls for variability evident across replicate libraries.
View all literature mentionsA database of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Users can analyze protein sequences for Pfam matches, view Pfam family annotation and alignments, see groups of related families, look at the domain organization of a protein sequence, find the domains on a PDB structure, and query Pfam by keywords. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families that may automatically generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans (collections of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM).
View all literature mentionsR package to identify differentially expressed genes from RNA-Seq data.
View all literature mentions