A new method to identify global targets of RNA-binding proteins in plants

Abstract

Background: RNA-binding proteins (RBPs) play crucial roles in various aspects of post-transcriptional gene expression; their functions can vary between tissues, cell types, developmental stages, and environmental conditions. Identifying RBP target RNAs and investigating whether they are differentially bound by RBPs in different cell types, stages, or conditions could shed light on RBP functions. Although several strategies have been designed to identify RBP targets, they involve complicated biochemical steps and require large quantities of material, and only a few studies using these techniques have been performed in plants. The TRIBE (targets of RNA binding proteins identified by editing) method was recently developed to identify RBP targets using a RBP coupled to the catalytic domain of a Drosophila RNA editing enzyme and expressing this fusion protein in vivo. The resulting novel editing events can be identified by sequencing. This technique uses little material and does not require complex biochemical steps, however it is not yet adapted for use in plants. Results: We successfully applied an optimized genome-wide TRIBE method in plants. We selected the splicing regulator polypyrimidine tract-binding protein (PTB) as a model protein for testing the TRIBE system in the moss Physcomitrium patens. We demonstrated that 13.81% of protein-coding gene transcripts in P. patens are targets of PTB. Most potential PTB binding sites are located in coding sequences and 3 prime untranslated regions, suggesting that PTB performs multiple functions besides pre-mRNA splicing in this moss. In addition, TRIBE showed reproducible results compared to other methods. Conclusions: We have developed an alternative method based on the TRIBE system to identify RBP targets in plants globally, and we provide guidance here for its application in plants.

Comparison of TRIBE and STAMP for identifying targets of RNA binding proteins in human andDrosophilacells

“…In fact, as shown here and elsewhere, TRIBE identifies target transcripts with editing sites ranging from 1 to 43 nucleotides and also edits cds (Fig. 2D and Cheng et al 2021).…”

“…Importantly, this allows target identification in very small numbers of cells and neurons without requiring biochemistry. TRIBE has been useful to a number of other labs working in different fields and model organisms ranging from humans to malaria parasites (Liu et al 2019;Alizzi et al 2020;Nguyen et al 2020;Arribas-Hernández et al 2021;Cheng et al 2021;Singh et al 2021;van Leeuwen et al 2022).…”

Ribonomics Approaches to Identify RBPome in Plants and Other Eukaryotes: Current Progress and Future Prospects

“…Interacting RNAs that bind to the protein of interest are firstly reverse transcribed into cDNA, PCR amplified, and sequenced [ 14 ], and further bioinformatics tools are used to identify the RBP binding sites [ 15 ]. A breakthrough in protein-centric methods occurred with the development of cross-linking immunoprecipitation (CLIP) [ 16 ], and some CLIP variants ( Figure 1 ) have been employed in Arabidopsis to identify the mRNA targets [ 14 , 17 ]. Recently, a new protein-centric method, Hyper Targets of RNA-Binding Proteins Identified by Editing (HyperTRIBE) was developed to identify RBPs targets in plants [ 17 ].…”

“…A breakthrough in protein-centric methods occurred with the development of cross-linking immunoprecipitation (CLIP) [ 16 ], and some CLIP variants ( Figure 1 ) have been employed in Arabidopsis to identify the mRNA targets [ 14 , 17 ]. Recently, a new protein-centric method, Hyper Targets of RNA-Binding Proteins Identified by Editing (HyperTRIBE) was developed to identify RBPs targets in plants [ 17 ]. Compared to RIP-seq and CLIP, HyperTRIBE is more efficient for the small number of samples [ 17 ].…”

“…Recently, a new protein-centric method, Hyper Targets of RNA-Binding Proteins Identified by Editing (HyperTRIBE) was developed to identify RBPs targets in plants [ 17 ]. Compared to RIP-seq and CLIP, HyperTRIBE is more efficient for the small number of samples [ 17 ].…”