PRAS: Predicting functional targets of RNA binding proteins based on CLIP-seq peaks

RNA binding proteins (RBPs) on genetic stability and diseases

RNA-binding proteins (RBPs) are integral components of cellular machinery, playing crucial roles in the regulation of gene expression and maintaining genetic stability. Their interactions with RNA molecules govern critical processes such as mRNA splicing, stability, localization, and translation, which are essential for proper cellular function. These proteins interact with RNA molecules and other proteins to form ribonucleoprotein complexes (RNPs), hence controlling the fate of target RNAs. The interaction occurs via RNA recognition motif, the zinc finger domain, the KH domain and the double stranded RNA binding motif (all known as RNA-binding domains (RBDs). These domains are found within the coding sequences (intron and exon domains), 5' untranslated regions (5'UTR) and 3' untranslated regions (3'UTR). Dysregulation of RBPs can lead to genomic instability, contributing to various pathologies, including cancer neurodegenerative diseases, and metabolic disorders. This study comprehensively explores the multifaceted roles of RBPs in genetic stability, highlighting their involvement in maintaining genomic integrity through modulation of RNA processing and their implications in cellular signalling pathways. Furthermore, it discusses how aberrant RBP function can precipitate genetic instability and disease progression, emphasizing the therapeutic potential of targeting RBPs in restoring cellular homeostasis. Through an analysis of current literature, this study aims to delineate the critical role of RBPs in ensuring genetic stability and their promise as targets for innovative therapeutic strategies.

PolyAtailor: measuring poly(A) tail length from short-read and long-read sequencing data

The poly(A) tail is a dynamic addition to the eukaryotic mRNA and the change in its length plays an essential role in regulating gene expression through affecting nuclear export, mRNA stability and translation. Only recently high-throughput sequencing strategies began to emerge for transcriptome-wide profiling of poly(A) tail length in diverse developmental stages and organisms. However, there is currently no easy-to-use and universal tool for measuring poly(A) tails in sequencing data from different sequencing protocols. Here we established PolyAtailor, a unified and efficient framework, for identifying and analyzing poly(A) tails from PacBio-based long reads or next generation short reads. PolyAtailor provides two core functions for measuring poly(A) tails, namely Tail_map and Tail_scan, which can be used for profiling tails with or without using a reference genome. Particularly, PolyAtailor can identify all potential tails in a read, providing users with detailed information such as tail position, tail length, tail sequence and tail type. Moreover, PolyAtailor integrates rich functions for poly(A) tail and poly(A) site analyses, such as differential poly(A) length analysis, poly(A) site identification and annotation, and statistics and visualization of base composition in tails. We compared PolyAtailor with three latest methods, FLAMAnalysis, FLEPSeq and PAIsoSeqAnalysis, using data from three sequencing protocols in HeLa samples and Arabidopsis. Results show that PolyAtailor is effective in measuring poly(A) tail length and detecting significance of differential poly(A) length, which achieves much higher sensitivity and accuracy than competing methods. PolyAtailor is available at https://github.com/BMILAB/PolyAtailor.

Integrative genome-wide analysis reveals EIF3A as a key downstream regulator of translational repressor protein Musashi 2 (MSI2)

Musashi 2 (MSI2) is an RNA binding protein (RBP) that regulates asymmetric cell division and cell fate decisions in normal and cancer stem cells. MSI2 appears to repress translation by binding to 3′ untranslated regions (3′UTRs) of mRNA, but the identity of functional targets remains unknown. Here, we used individual nucleotide resolution cross-linking and immunoprecipitation (iCLIP) to identify direct RNA binding partners of MSI2 and integrated these data with polysome profiling to obtain insights into MSI2 function. iCLIP revealed specific MSI2 binding to thousands of mRNAs largely in 3′UTRs, but translational differences were restricted to a small fraction of these transcripts, indicating that MSI2 regulation is not triggered by simple binding. Instead, the functional targets identified here were bound at higher density and contain more ‘UAG’ motifs compared to targets bound nonproductively. To further distinguish direct and indirect targets, MSI2 was acutely depleted. Surprisingly, only 50 transcripts were found to undergo translational induction on acute loss. Using complementary approaches, we determined eukaryotic translation initiation factor 3A (EIF3A) to be an immediate, direct target. We propose that MSI2 downregulation of EIF3A amplifies these effects on translation. Our results also underscore the challenges in defining functional targets of RBPs since mere binding does not imply a discernible functional interaction.

Improved Predicting of The Sequence Specificities of RNA Binding Proteins by Deep Learning

RNA-binding proteins (RBPs) have a significant role in various regulatory tasks. However, the mechanism by which RBPs identify the subsequence target RNAs is still not clear. In recent years, several machine and deep learning-based computational models have been proposed for understanding the binding preferences of RBPs. These methods required integrating multiple features with raw RNA sequences such as secondary structure and their performances can be further improved. In this paper, we propose an efficient and simple convolution neural network, RBPCNN, that relies on the combination of the raw RNA sequence and evolutionary information. We show that conservation scores (evolutionary information) for the RNA sequences can significantly improve the overall performance of the proposed predictor. In addition, the automatic extraction of the binding sequence motifs can enhance our understanding of the binding specificities of RBPs. The experimental results show that RBPCNN outperforms significantly the current state-of-the-art methods. More specifically, the average area under the receiver operator curve was improved by 2.67 percent and the mean average precision was improved by 8.03 percent. The datasets and results can be downloaded from https://home.jbnu.ac.kr/NSCL/RBPCNN.htm.

The landscape of alternative polyadenylation in single cells of the developing mouse embryo

3′ untranslated regions (3′ UTRs) post-transcriptionally regulate mRNA stability, localization, and translation rate. While 3′-UTR isoforms have been globally quantified in limited cell types using bulk measurements, their differential usage among cell types during mammalian development remains poorly characterized. In this study, we examine a dataset comprising ~2 million nuclei spanning E9.5–E13.5 of mouse embryonic development to quantify transcriptome-wide changes in alternative polyadenylation (APA). We observe a global lengthening of 3′ UTRs across embryonic stages in all cell types, although we detect shorter 3′ UTRs in hematopoietic lineages and longer 3′ UTRs in neuronal cell types within each stage. An analysis of RNA-binding protein (RBP) dynamics identifies ELAV-like family members, which are concomitantly induced in neuronal lineages and developmental stages experiencing 3′-UTR lengthening, as putative regulators of APA. By measuring 3′-UTR isoforms in an expansive single cell dataset, our work provides a transcriptome-wide and organism-wide map of the dynamic landscape of alternative polyadenylation during mammalian organogenesis.

Alternative polyadenylation regulates localization, half-life and translation of mRNA isoforms. Here the authors investigate alternative polyadenylation using single cell RNA sequencing data from mouse embryos and identify 3’-UTR isoforms that are regulated across cell types and developmental time.

QuantifyPoly(A): reshaping alternative polyadenylation landscapes of eukaryotes with weighted density peak clustering

The dynamic choice of different polyadenylation sites in a gene is referred to as alternative polyadenylation, which functions in many important biological processes. Large-scale messenger RNA 3' end sequencing has revealed that cleavage sites for polyadenylation are presented with microheterogeneity. To date, the conventional determination of polyadenylation site clusters is subjective and arbitrary, leading to inaccurate annotations. Here, we present a weighted density peak clustering method, QuantifyPoly(A), to accurately quantify genome-wide polyadenylation choices. Applying QuantifyPoly(A) on published 3' end sequencing datasets from both animals and plants, their polyadenylation profiles are reshaped into myriads of novel polyadenylation site clusters. Most of these novel polyadenylation site clusters show significantly dynamic usage across different biological samples or associate with binding sites of trans-acting factors. Upstream sequences of these clusters are enriched with polyadenylation signals UGUA, UAAA and/or AAUAAA in a species-dependent manner. Polyadenylation site clusters also exhibit species specificity, while plants ones generally show higher microheterogeneity than that of animals. QuantifyPoly(A) is broadly applicable to any types of 3' end sequencing data and species for accurate quantification and construction of the complex and dynamic polyadenylation landscape and enables us to decode alternative polyadenylation events invisible to conventional methods at a much higher resolution.

Large-scale analysis of the position-dependent binding and regulation of human RNA binding proteins

Background

RNA binding proteins (RBPs) play essential roles in the regulation of RNA metabolism. Recent studies have disclosed that RBPs achieve their functions via binding to their targets in a position-dependent pattern on RNAs. However, few studies have systematically addressed the associations between the RBP's functions and their positional binding preferences.

Methods

Here, we present large-scale analyses on the functional targets of human RBPs by integrating the enhanced cross-linking and immunoprecipitation followed by sequencing (eCLIP-seq) datasets and the shRNA knockdown followed by RNA-seq datasets that are deposited in the integrated ENCyclopedia of DNA Elements in the human genome (ENCODE) data portal.

Results

We found that (1) binding to the translation termination site and the 3'untranslated region is important to most human RBPs in the RNA decay regulation; (2) RBPs' binding and regulation follow a cell-type specific pattern.

Conclusions

These analysis results show the strong relationship between the binding position and the functions of RBPs, which provides novel insights into the RBPs' regulation mechanisms.