Trancriptome-Wide Applications of Protein Occupancy Profile Sequencing (POP-seq)

Abstract

Dynamic protein-RNA interactions regulate RNA metabolism and alter cellular physiology by altering key regulatory processes such as capping, splicing, polyadenylation, and localization. Several high throughput methods have been developed to detect protein-RNA interactions, but they often exhibit biases due to the inherent limitations of crosslinking-based approaches. We propose Protein Occupancy Profile-Sequencing (POP-seq), a phase separation-based method that does not require crosslinking to detect protein occupancy transcriptome wide. In this study, we employed POP-seq to examine the unbiased regulatory protein-RNA interactions in the following cancer cell lines: K562, HepG2, A549, MCF7, Jurkat, and HEK293. In our preliminary analysis, we performed a comparison of the POP-seq identified interactions using two protocols, one involving UV crosslinking (UPOP-seq) and the other with no-crosslinking (NPOP-seq), in K562 and HepG2 cells. This comparative analysis of two protocol showed >70% overlapping genes detected by both approaches in the two cell lines. Most of these peaks were mapped to intronic regions of the protein coding gene. Concurrently, we also implemented this crosslinking free approach on two leukemia cell lines: Jurkat and K562. Differential analysis shows higher binding activity in Jurkat compared to K562 with majority of the peaks spanned over intronic protein coding region followed by SINE and LINE. Differential proximal binding analysis shows that SE events followed by A3SS events plays a major role in alternative splicing suggesting enriched regions plays vital role in cellular functions including post-transcriptional regulation of gene expression. Motif analysis shows clinically relevant significant motif enrichment of POP-seq identified peaks. This study was further expanded by adding three human additional cell lines: MCF7, A459, and HEK293. Differential peak analysis across cell lines revealed a closer association between A549 and MCF7 cells based on the normalized POP-seq peaks per gene. We observed that genes associated with differential peaks between cell lines exhibited enrichment for crucial cellular functions, particularly in the post-transcriptional regulation of gene expression. Our analysis unveiled a notable enrichment of specific motifs within the identified peaks obtained from POP-seq. These overrepresented motifs were significantly linked to somatic variation, phenotypic variation (Phenvar), clinical variation (Clinvar), GWAS, and allele-specific expression (ASE), with a preferential abundance of the motifs on the C and G bases. Additionally, our alternative splicing analysis revealed that POP-seq detected protein-RNA interactions that substantially contributed to splicing events in certain cell line pairs, while their impact was less pronounced in others. Overall, our study offers the first extensive dataset of protein-RNA interaction maps across the transcriptome in multiple cell lines, utilizing a crosslinking-free approach. This valuable resource not only provides comprehensive insights into regulatory interactions but also opens new possibilities for applying this method in primary tissues to detect and study protein-RNA interactions in a broader biological context.