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

doi:10.1007/s40484-020-0206-5

Quant. Biol.

2020, Vol. 8

Issue (2) : 119-129 https://doi.org/10.1007/s40484-020-0206-5

RESEARCH ARTICLE

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

Jianan Lin^1,², Zhengqing Ouyang¹(

)

¹. Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003, USA
². Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA

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Abstract

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.

Keywords RNA binding protein CLIP-seq RNA-seq knockdown RNA regulation

Corresponding Author(s): Zhengqing Ouyang

Online First Date: 10 June 2020 Issue Date: 13 July 2020

Cite this article:

Jianan Lin,Zhengqing Ouyang. Large-scale analysis of the position-dependent binding and regulation of human RNA binding proteins[J]. Quant. Biol., 2020, 8(2): 119-129.

URL:

https://academic.hep.com.cn/qb/EN/10.1007/s40484-020-0206-5
https://academic.hep.com.cn/qb/EN/Y2020/V8/I2/119

Fig.1 Diagram of the study design, data structure and analysis.

Fig.2 RNA decay regulators achieve functions by using different reference sites.

Fig.3 The translation termination site is important for the regulations of RNA decay related RBPs.

Fig.4 Examples of the four groups of RBPs’ regulations in gene expression.

Fig.5 Cell type-specific regulation of RBPs.

Fig.6 SND1’s binding pattern in K562 and HepG2 cell lines.

1	J. D. Keene, (2007) RNA regulons: coordination of post-transcriptional events. Nat. Rev. Genet., 8, 533–543 https://doi.org/10.1038/nrg2111 pmid: 17572691
2	D. D. Licatalosi, , A. Mele, , J. J. Fak, , J. Ule, , M. Kayikci, , S. W. Chi, , T. A. Clark, , A. C. Schweitzer, , J. E. Blume, , X. Wang, , et al. (2008) HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature, 456, 464–469 https://doi.org/10.1038/nature07488 pmid: 18978773
3	D. I. Kao, , G. M. Aldridge, , I. J. Weiler, and W. T. Greenough, (2010) Altered mRNA transport, docking, and protein translation in neurons lacking fragile X mental retardation protein. Proc. Natl. Acad. Sci. USA, 107, 15601–15606 https://doi.org/10.1073/pnas.1010564107 pmid: 20713728
4	O. D. King, , A. D. Gitler, and J. Shorter, (2012) The tip of the iceberg: RNA-binding proteins with prion-like domains in neurodegenerative disease. Brain Res., 1462, 61–80 https://doi.org/10.1016/j.brainres.2012.01.016 pmid: 22445064
5	C. Lagier-Tourenne, , M. Polymenidou, and D. W. Cleveland, (2010) TDP-43 and FUS/TLS: emerging roles in RNA processing and neurodegeneration. Hum. Mol. Genet., 19, R46–R64 https://doi.org/10.1093/hmg/ddq137 pmid: 20400460
6	K. E. Lukong, , K. W. Chang, , E. W. Khandjian, and S. Richard, (2008) RNA-binding proteins in human genetic disease. Trends Genet., 24, 416–425 https://doi.org/10.1016/j.tig.2008.05.004 pmid: 18597886
7	J. König, , K. Zarnack, , N. M. Luscombe, and J. Ule, (2012) Protein-RNA interactions: new genomic technologies and perspectives. Nat. Rev. Genet., 13, 77–83 https://doi.org/10.1038/nrg3141 pmid: 22251872
8	M. Hafner, , M. Landthaler, , L. Burger, , M. Khorshid, , J. Hausser, , P. Berninger, , A. Rothballer, , M. Ascano, Jr, A. C. Jungkamp, , M. Munschauer, , et al. (2010) Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell, 141, 129–141 https://doi.org/10.1016/j.cell.2010.03.009 pmid: 20371350
9	J. König, , K. Zarnack, , G. Rot, , T. Curk, , M. Kayikci, , B. Zupan, , D. J. Turner, , N. M. Luscombe, and J. Ule, (2010) iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nat. Struct. Mol. Biol., 17, 909–915 https://doi.org/10.1038/nsmb.1838 pmid: 20601959
10	K. B. Cook, , T. R. Hughes, and Q. D. Morris, (2015) High-throughput characterization of protein-RNA interactions. Brief. Funct. Genomics, 14, 74–89 https://doi.org/10.1093/bfgp/elu047 pmid: 25504152
11	B. Chen, , J. Yun, , M. S. Kim, , J. T. Mendell, and Y. Xie, (2014) PIPE-CLIP: a comprehensive online tool for CLIP-seq data analysis. Genome Biol., 15, R18 https://doi.org/10.1186/gb-2014-15-1-r18 pmid: 24451213
12	T. Wang, , Y. Xie, and G. Xiao, (2014) dCLIP: a computational approach for comparative CLIP-seq analyses. Genome Biol., 15, R11 https://doi.org/10.1186/gb-2014-15-1-r11 pmid: 24398258
13	T. Wang, , B. Chen, , M. Kim, , Y. Xie, and G. Xiao, (2014) A model-based approach to identify binding sites in CLIP-Seq data. PLoS One, 9, e93248 https://doi.org/10.1371/journal.pone.0093248 pmid: 24714572
14	M. T. Lovci, , D. Ghanem, , H. Marr, , J. Arnold, , S. Gee, , M. Parra, , T. Y. Liang, , T. J. Stark, , L. T. Gehman, , S. Hoon, , et al. (2013) Rbfox proteins regulate alternative mRNA splicing through evolutionarily conserved RNA bridges. Nat. Struct. Mol. Biol., 20, 1434–1442 https://doi.org/10.1038/nsmb.2699 pmid: 24213538
15	S. Althammer, , J. González-Vallinas, , C. Ballaré, , M. Beato, and E. Eyras, (2011) Pyicos: a versatile toolkit for the analysis of high-throughput sequencing data. Bioinformatics, 27, 3333–3340 https://doi.org/10.1093/bioinformatics/btr570 pmid: 21994224
16	P. J. Uren, , E. Bahrami-Samani, , S. C. Burns, , M. Qiao, , F. V. Karginov, , E. Hodges, , G. J. Hannon, , J. R. Sanford, , L. O. Penalva, and A. D. Smith, (2012) Site identification in high-throughput RNA-protein interaction data. Bioinformatics, 28, 3013–3020 https://doi.org/10.1093/bioinformatics/bts569 pmid: 23024010
17	F. Comoglio, , C. Sievers, and R. Paro, (2015) Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data. BMC Bioinformatics, 16, 32 https://doi.org/10.1186/s12859-015-0470-y pmid: 25638391
18	D. L. Corcoran, , S. Georgiev, , N. Mukherjee, , E. Gottwein, , R. L. Skalsky, , J. D. Keene, and U. Ohler, (2011) PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data. Genome Biol., 12, R79 https://doi.org/10.1186/gb-2011-12-8-r79 pmid: 21851591
19	M. J. Moore, , C. Zhang, , E. C. Gantman, , A. Mele, , J. C. Darnell, and R. B. Darnell, (2014) Mapping Argonaute and conventional RNA-binding protein interactions with RNA at single-nucleotide resolution using HITS-CLIP and CIMS analysis. Nat. Protoc., 9, 263–293 https://doi.org/10.1038/nprot.2014.012 pmid: 24407355
20	A. Shah, , Y. Qian, , S. M. Weyn-Vanhentenryck, and C. Zhang, (2017) CLIP Tool Kit (CTK): a flexible and robust pipeline to analyze CLIP sequencing data. Bioinformatics, 33, 566–567 pmid: 27797762
21	F. Erhard, , L. Dölken, , L. Jaskiewicz, and R. Zimmer, (2013) PARma: identification of microRNA target sites in AGO-PAR-CLIP data. Genome Biol., 14, R79 https://doi.org/10.1186/gb-2013-14-7-r79 pmid: 23895117
22	J. Lin, , Y. Zhang, , W. N. Frankel, and Z. Ouyang, (2019) PRAS: Predicting functional targets of RNA binding proteins based on CLIP-seq peaks. PLOS Comput. Biol., 15, e1007227 https://doi.org/10.1371/journal.pcbi.1007227 pmid: 31425505
23	G. Rot, , Z. Wang, , I. Huppertz, , M. Modic, , T. Lenče, , M. Hallegger, , N. Haberman, , T. Curk, , C. von Mering, and J. Ule, (2017) High-resolution RNA maps suggest common principles of splicing and polyadenylation regulation by TDP-43. Cell Reports, 19, 1056–1067 https://doi.org/10.1016/j.celrep.2017.04.028 pmid: 28467899
24	E. T. Wang, , A. J. Ward, , J. M. Cherone, , J. Giudice, , T. T. Wang, , D. J. Treacy, , N. J. Lambert, , P. Freese, , T. Saxena, , T. A. Cooper, , et al. (2015) Antagonistic regulation of mRNA expression and splicing by CELF and MBNL proteins. Genome Res., 25, 858–871 https://doi.org/10.1101/gr.184390.114 pmid: 25883322
25	E. L. Van Nostrand, , G. A. Pratt, , A. A. Shishkin, , C. Gelboin-Burkhart, , M. Y. Fang, , B. Sundararaman, , S. M. Blue, , T. B. Nguyen, , C. Surka, , K. Elkins, , et al. (2016) Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods, 13, 508–514 https://doi.org/10.1038/nmeth.3810 pmid: 27018577
26	E. L. Van Nostrand, , P. Freese, , G. A. Pratt, , X. Wang, , X. Wei, , R. Xiao, , S. M. Blue, , J.-Y. Chen, , N. A. L. Cody, , D. Dominguez, , et al. (2018) A large-scale binding and functional map of human RNA binding proteins. bioRxiv, 179648
27	M. I. Love, , W. Huber, and S. Anders, (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol., 15, 550 https://doi.org/10.1186/s13059-014-0550-8 pmid: 25516281
28	J. L. Wagnon, , M. Briese, , W. Sun, , C. L. Mahaffey, , T. Curk, , G. Rot, , J. Ule, and W. N. Frankel, (2012) CELF4 regulates translation and local abundance of a vast set of mRNAs, including genes associated with regulation of synaptic function. PLoS Genet., 8, e1003067 https://doi.org/10.1371/journal.pgen.1003067 pmid: 23209433
29	C. A. Chénard, and S. Richard, (2008) New implications for the QUAKING RNA binding protein in human disease. J. Neurosci. Res., 86, 233–242 https://doi.org/10.1002/jnr.21485 pmid: 17787018
30	R. Gherzi, , K.-Y. Lee, , P. Briata, , D. Wegmüller, , C. Moroni, , M. Karin, and C.-Y. Chen, (2004) A KH domain RNA binding protein, KSRP, promotes ARE-directed mRNA turnover by recruiting the degradation machinery. Mol. Cell, 14, 571–583 https://doi.org/10.1016/j.molcel.2004.05.002 pmid: 15175153
31	S. Lebedeva, , M. Jens, , K. Theil, , B. Schwanhäusser, , M. Selbach, , M. Landthaler, and N. Rajewsky, (2011) Transcriptome-wide analysis of regulatory interactions of the RNA-binding protein HuR. Mol. Cell, 43, 340–352. PMID:21723171 https://doi.org/10.1016/j.molcel.2011.06.008
32	J. R. Hogg, and S. P. Goff, (2010) Upf1 senses 3′ UTR length to potentiate mRNA decay. Cell, 143, 379–389
33	The ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature, 489,57–74
34	C. A. Davis, , B. C. Hitz, , C. A. Sloan, , E. T. Chan, , J. M. Davidson, , I. Gabdank, , J. A. Hilton, , K. Jain, , U. K. Baymuradov, , A. K. Narayanan, , et al. (2018) The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res., 46, D794–D801 https://doi.org/10.1093/nar/gkx1081 pmid: 29126249

[1]	QB-20206-OF-OYZQ_suppl_1	Download
[2]	Supplementary_Materials 1	Download
[3]	Supplementary_Materials 2	Download

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