Poster abstracts
Poster number 61 submitted by Mansi Srivastava
Transcriptome-wide high-throughput mapping of protein-RNA occupancy profiles using POP-seq
Mansi Srivastava (BioHealth Informatics, School of Informatics and Computing, IUPUI), Gayathri Panangipalli (BioHealth Informatics, School of Informatics and Computing, IUPUI), Rajneesh Srivastava (BioHealth Informatics, School of Informatics and Computing, IUPUI), Neel Sangani (BioHealth Informatics, School of Informatics and Computing, IUPUI), Hunter Gill (BioHealth Informatics, School of Informatics and Computing, IUPUI), Sarath Chandra Janga (BioHealth Informatics, School of Informatics and Computing, IUPUI)
Abstract:
Interaction between RNA-binding proteins (RBPs) and RNA is critical for post-transcriptional regulatory processes1. Existing high throughput methods based on crosslinking of the protein-RNA are reported to contribute to biases in the resulting protein occupancy profiles2–6. We present Protein Occupancy Profile-Sequencing (POP-seq), a phase separation-based method that does not require crosslinking thus providing unbiased protein occupancy profiles on whole cell transcriptomes. In order to compare the robustness of identified protein occupied sites, POP-seq was implemented in two phases: the first phase comprised of UV crosslinking and no-crosslinking approaches on K562 and HepG2 cells, resulting ~200,000 peaks detected across the protocols. The results were further cross-validated with the publicly available ENCODE eCLIP profile for scores of RBPs in the two cell lines. Our analysis reveals that majority of detected genes (>70%) overlap between the two approaches indicating the reproducible nature of the generated interaction maps in the two cell lines. We observed an abundance of binding sites on the intronic region of the genomic location (~40%) for both the cell lines with maximum number of POP-seq peaks detected on protein-coding genes (>85%). Cross-validation with the ENCODE eCLIP profile of RBPs demonstrated relatively higher recall for UV-crosslinking (~24% and 17%) compared to no-crosslinking approach in K562 and HepG2 cells respectively, indicating the robustness of both approaches. In the second phase, we expanded POP-seq on multiple cell lines (MCF7, A549, Jurkat and HEK293) using the no-crosslinking approach resulting in ~60,000 reproducible peaks between replicates and are currently investigating their significance across cell types. Altogether, our data supports POP-seq as a robust and cost-effective method enabling comprehensive mapping and understanding of post-transcriptional regulatory networks.
References:
1.Hentze, M. W., Castello, A., Schwarzl, T. & Preiss, T. A brave new world of RNA-binding proteins. Nat. Rev. Mol. Cell Biol. 19, 327–341 (2018).
2.Baltz, A. G. et al. The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts. Mol. Cell 46, 674–690 (2012).
3.Schueler, M. et al. Differential protein occupancy profiling of the mRNA transcriptome. Genome Biol. 15, R15 (2014).
4.Silverman, I. M. et al. RNase-mediated protein footprint sequencing reveals protein-binding sites throughout the human transcriptome. Genome Biol. 15, R3 (2014).
5.Queiroz, R. M. L. et al. Comprehensive identification of RNA-protein interactions in any organism using orthogonal organic phase separation (OOPS). Nat. Biotechnol. 37, 169–178 (2019).
6.Trendel, J. et al. The Human RNA-Binding Proteome and Its Dynamics during Translational Arrest. Cell 176, 391-403.e19 (2019).
Keywords: transcriptome, phase-separation, POP-seq