ABSTRACT
The RNA-binding pentatricopeptide repeat (PPR) family comprises hundreds to thousands of genes in most plants, but only a few dozen in algae, indicating massive gene expansions during land plant evolution. The nature and timing of these expansions has not been well defined due to the sparse sequence data available from early-diverging land plant lineages. In this study, we exploit the comprehensive OneKP datasets of over 1000 transcriptomes from diverse plants and algae toward establishing a clear picture of the evolution of this massive gene family, focusing on the proteins typically associated with RNA editing, which show the most spectacular variation in numbers and domain composition across the plant kingdom. We characterize over 2 250 000 PPR motifs in over 400 000 proteins. In lycophytes, polypod ferns, and hornworts, nearly 10% of expressed protein-coding genes encode putative PPR editing factors, whereas they are absent from algae and complex-thalloid liverworts. We show that rather than a single expansion, most land plant lineages with high numbers of editing factors have continued to generate novel sequence diversity. We identify sequence variations that imply functional differences between PPR proteins in seed plants versus non-seed plants and variations we propose to be linked to seed-plant-specific editing co-factors. Finally, using the sequence variations across the datasets, we develop a structural model of the catalytic DYW domain associated with C-to-U editing and identify a clade of unique DYW variants that are strong candidates as U-to-C RNA-editing factors, given their phylogenetic distribution and sequence characteristics.
Subject(s)
Embryophyta/genetics , Plant Proteins/genetics , RNA Editing/genetics , RNA-Binding Proteins/genetics , Amino Acid Motifs , Databases, Genetic , Embryophyta/classification , Evolution, Molecular , Gene Duplication , Genetic Variation , Models, Molecular , Phylogeny , Plant Proteins/chemistry , Plant Proteins/metabolism , Plants/classification , Plants/genetics , Protein Domains , RNA, Plant/metabolism , RNA-Binding Proteins/chemistry , RNA-Binding Proteins/metabolism , Repetitive Sequences, Amino AcidABSTRACT
MOTIVATION: Knowing the subcellular location of proteins is critical for understanding their function and developing accurate networks representing eukaryotic biological processes. Many computational tools have been developed to predict proteome-wide subcellular location, and abundant experimental data from green fluorescent protein (GFP) tagging or mass spectrometry (MS) are available in the model plant, Arabidopsis. None of these approaches is error-free, and thus, results are often contradictory. RESULTS: To help unify these multiple data sources, we have developed the SUBcellular Arabidopsis consensus (SUBAcon) algorithm, a naive Bayes classifier that integrates 22 computational prediction algorithms, experimental GFP and MS localizations, protein-protein interaction and co-expression data to derive a consensus call and probability. SUBAcon classifies protein location in Arabidopsis more accurately than single predictors. AVAILABILITY: SUBAcon is a useful tool for recovering proteome-wide subcellular locations of Arabidopsis proteins and is displayed in the SUBA3 database (http://suba.plantenergy.uwa.edu.au). The source code and input data is available through the SUBA3 server (http://suba.plantenergy.uwa.edu.au//SUBAcon.html) and the Arabidopsis SUbproteome REference (ASURE) training set can be accessed using the ASURE web portal (http://suba.plantenergy.uwa.edu.au/ASURE).
