Highly accessible AU-rich regions in 3’ untranslated regions are hotspots for binding of regulatory factors

Abstract

Post-transcriptional regulation is regarded as one of the major processes involved in the regulation of gene expression. It is mainly performed by RNA binding proteins and microRNAs, which target RNAs and typically affect their stability. Recent efforts from the scientific community have aimed at understanding post-transcriptional regulation at a global scale by using high-throughput sequencing techniques such as cross-linking and immunoprecipitation (CLIP), which facilitates identification of binding sites of these regulatory factors. However, the diversity in the experimental procedures and bioinformatics analyses has hindered the integration of multiple datasets and thus limited the development of an integrated view of post-transcriptional regulation. In this work, we have performed a comprehensive analysis of 107 CLIP datasets from 49 different RBPs in HEK293 cells to shed light on the complex interactions that govern post-transcriptional regulation. By developing a more stringent CLIP analysis pipeline we have discovered the existence of conserved regulatory AU-rich regions in the 3’UTRs where miRNAs and RBPs that regulate several processes such as polyadenylation or mRNA stability bind. Analogous to promoters, many factors have binding sites overlapping or in close proximity in these hotspots and hence the regulation of the mRNA may depend on their relative concentrations. This hypothesis is supported by RBP knockdown experiments that alter the relative concentration of RBPs in the cell. Upon AGO2 knockdown (KD), transcripts containing “free” target sites show increased expression levels compared to those containing target sites in hotspots, which suggests that target sites within hotspots are less available for miRNAs to bind. Interestingly, these hotspots appear enriched in genes with regulatory functions such as DNA binding and RNA binding. Taken together, our results suggest that hotspots are functional regulatory elements that define an extra layer of regulation of post-transcriptional regulatory networks.

CLIP-Seq and massively parallel functional analysis of the CELF6 RNA binding protein reveals a role in destabilizing synaptic gene mRNAs through interaction with 3’UTR elements in vivo

“…Antagonistic activity of RBPs on mRNA translation and stability has also been observed for CELF2 and HUR (44) and ELAVL1 and ZFP36 (45). Antagonistic activity of RBPs may be mediated by the proximity of binding sites in the 3'UTR, with specific knockdown of RBPs freeing access to other RBP binding sites and resulting in changes to mRNA levels, as has been found for UTRs containing AU-rich elements (46). In our analysis of CELF6 CLIP targets, we found several binding motifs showing enriched abundance in these UTRs for RBPs of different families.…”

GIGYF1/2-Driven Cooperation between ZNF598 and TTP in Posttranscriptional Regulation of Inflammatory Signaling

“…The two replicates of PAR-CLIP of ZNF598 (Garzia et al, 2017) were retrieved from Sequence Read Archive (SRA: SRP095894). Processing, mapping, normalization was done using the CLAP pipeline (Plass et al, 2017) and quantification of transcript binding affinity done as described in (Hansen et al, 2015). The datasets were processed with custom python scripts to trim low-quality base calls and remove 3 0 adapters.…”

Transite: A computational motif-based analysis platform that identifies RNA-binding proteins modulating changes in gene expression

“…An advantage of our matrix-based approach, however, is it retains positional hit information within the sequence and therefore facilitates the detection of closely spaced clusters of putative binding sites. Homotypic clusters of binding sites on DNA, for example, have been shown to be important for transcription factor binding [32], and have been postulated to be involved in RNA regulation [33,34], but a clear experimental demonstration of their general importance for RBP binding to RNA has not been unambiguously shown. Foreground sets in TSMA are defined by differential gene expression analysis of RNA-seq or microarray data sets, usually by selecting statistically significantly upregulated and downregulated genes.…”