RNA-binding proteins (RBPs) play a crucial role in regulating RNA function and fate. However, the full complement of RBPs has only recently begun to be uncovered through proteome-wide approaches such as RNA interactome capture (RIC). RIC has been applied to various cell lines and organisms, including plants, greatly expanding the repertoire of RBPs. However, several technical challenges have limited the efficacy of RIC when applied to plant tissues. Here, we report an improved version of RIC that overcomes the difficulties imposed by leaf tissue. Using this improved RIC method in Arabidopsis leaves, we identified 717 RBPs, generating a deep RNA-binding proteome for leaf tissues. While 75% of these RBPs can be linked to RNA biology, the remaining 25% were previously not known to interact with RNA. Interestingly, we observed that a large number of proteins related to photosynthesis associate with RNA in vivo, including proteins from the four major photosynthetic supercomplexes. As has previously been reported for mammals, a large proportion of leaf RBPs lack known RNA-binding domains, suggesting unconventional modes of RNA binding. We anticipate that this improved RIC method will provide critical insights into RNA metabolism in plants, including how cellular RBPs respond to environmental, physiological and pathological cues.
BackgroundNicotiana benthamiana is an important model organism of the Solanaceae (Nightshade) family. Several draft assemblies of the N. benthamiana genome have been generated, but many of the gene-models in these draft assemblies appear incorrect.ResultsHere we present an improved proteome based on the Niben1.0.1 draft genome assembly guided by gene models from other Nicotiana species. Due to the fragmented nature of the Niben1.0.1 draft genome, many protein-encoding genes are missing or partial. We complement these missing proteins by similarly annotating other draft genome assemblies. This approach overcomes problems caused by mis-annotated exon-intron boundaries and mis-assigned short read transcripts to homeologs in polyploid genomes. With an estimated 98.1% completeness; only 53,411 protein-encoding genes; and improved protein lengths and functional annotations, this new predicted proteome is better in assigning spectra than the preceding proteome annotations. This dataset is more sensitive and accurate in proteomics applications, clarifying the detection by activity-based proteomics of proteins that were previously predicted to be inactive. Phylogenetic analysis of the subtilase family of hydrolases reveal inactivation of likely homeologs, associated with a contraction of the functional genome in this alloploid plant species. Finally, we use this new proteome annotation to characterize the extracellular proteome as compared to a total leaf proteome, which highlights the enrichment of hydrolases in the apoplast.ConclusionsThis proteome annotation provides the community working with Nicotiana benthamiana with an important new resource for functional proteomics.Electronic supplementary materialThe online version of this article (10.1186/s12864-019-6058-6) contains supplementary material, which is available to authorized users.
Nicotiana benthamiana is an important model organism of the Solanaceae (Nightshade) family. Several draft assemblies of the N. benthamiana genome have been generated, but many of the gene-models in these draft assemblies appear incorrect. Here we present an improved re-annotation of the Niben1.0.1 draft genome assembly guided by gene models from other Nicotiana species. This approach overcomes problems caused by mis-annotated exon-intron boundaries and mis-assigned short read transcripts to homeologs in polyploid genomes. With an estimated 98.1% completeness; only 53,411 protein-encoding genes; and improved protein lengths and functional annotations, this new predicted proteome is better than the preceding proteome annotations. This dataset is more sensitive and accurate in proteomics applications, clarifying the detection by activity-based proteomics of proteins that were previously mis-annotated to be inactive. Phylogenetic analysis of the subtilase family of hydrolases reveal a pseudogenisation of likely homeologs, associated with a contraction of the functional genome in this alloploid plant species. We use this gene annotation to assign extracellular proteins in comparison to a total leaf proteome, to display the enrichment of hydrolases in the apoplast.
Adapted plant pathogens from various microbial kingdoms produce hundreds of unrelated small secreted proteins (SSPs) with elusive roles. Some of these SSPs might be inhibitors targeting the most harmful hydrolases secreted by the host. Here, we used Alphafold-Multimer (AFM) to screen 1,879 SSPs of seven tomato pathogens for interacting with six defence-related hydrolases of tomato that accumulate to high levels in the apoplast during infection. This screen of 11,274 protein pairs identified 15 SSPs that are predicted to obstruct the active site of chitinases and proteases with an intrinsic fold. Four SSPs were experimentally verified to be inhibitors of pathogenesis-related subtilase P69B, including extracellular protein-36 (Ecp36) and secreted-into-xylem-15 (Six15) of the fungal tomato pathogens Cladosporium fulvum and Fusarium oxysporum, respectively. Together with a novel P69B inhibitor from the bacterial pathogen Xanthomonas perforans and the previously reported Kazal-like inhibitors of the oomycete pathogen Phytophthora infestans, P69B emerges as an important effector hub targeted by different microbial kingdoms, consistent with the presence of a hyper-variant residue in P69B orthologs and gene duplication and diversification of P69B paralogs that could avoid inhibitor binding. This study demonstrates the power of artificial intelligence to accurately predict novel cross-kingdom interactions at the plant-pathogen interface.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.