255
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      N6-methyladenosine-dependent RNA structural switches regulate RNA–protein interactions

      , , , , ,
      Nature
      Springer Science and Business Media LLC

      Read this article at

      ScienceOpenPublisher
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          RNA-binding proteins control many aspects of cellular biology through binding single-stranded RNA binding motifs (RBM) 1-3 . However, RBMs can be buried within their local RNA structures 4-7 , thus inhibiting RNA-protein interactions. N 6-methyladenosine (m6A), the most abundant and dynamic internal modification in eukaryotic messenger RNA 8-19 , can be selectively recognized by the YTHDF2 protein to affect the stability of cytoplasmic mRNAs 15 , but how m6A achieves wide-ranging physiological significance needs further exploration. Here we show that m6A controls the RNA-structure-dependent accessibility of RBMs to affect RNA-protein interactions for biological regulation; we term this mechanism “m6A-switch”. We found that m6A alters the local structure in mRNA and long non-coding RNA (lncRNA) to facilitate binding of heterogeneous nuclear ribonucleoprotein C (hnRNP C), an abundant nuclear RNA-binding protein responsible for pre-mRNA processing 20-24 . Combining PAR-CLIP and m6A/MeRIP approaches enabled us to identify 39,060 m6A-switches among hnRNP C binding sites; and global m6A reduction decreased hnRNP C binding at 2,798 high confidence m6A-switches. We determined that these m6A-switch-regulated hnRNP C binding activities affect the abundance as well as alternative splicing of target mRNAs, demonstrating the regulatory role of m6A-switches on gene expression and RNA maturation. Our results illustrate how RNA-binding proteins gain regulated access to their RBMs through m6A-dependent RNA structural remodeling, and provide a new direction for investigating RNA-modification-coded cellular biology.

          Related collections

          Most cited references15

          • Record: found
          • Abstract: found
          • Article: not found

          Transcriptome-wide mapping of N(6)-methyladenosine by m(6)A-seq based on immunocapturing and massively parallel sequencing.

          N(6)-methyladenosine-sequencing (m(6)A-seq) is an immunocapturing approach for the unbiased transcriptome-wide localization of m(6)A in high resolution. To our knowledge, this is the first protocol to allow a global view of this ubiquitous RNA modification, and it is based on antibody-mediated enrichment of methylated RNA fragments followed by massively parallel sequencing. Building on principles of chromatin immunoprecipitation-sequencing (ChIP-seq) and methylated DNA immunoprecipitation (MeDIP), read densities of immunoprecipitated RNA relative to untreated input control are used to identify methylated sites. A consensus motif is deduced, and its distance to the point of maximal enrichment is assessed; these measures further corroborate the success of the protocol. Identified locations are intersected in turn with gene architecture to draw conclusions regarding the distribution of m(6)A between and within gene transcripts. When applied to human and mouse transcriptomes, m(6)A-seq generated comprehensive methylation profiles revealing, for the first time, tenets governing the nonrandom distribution of m(6)A. The protocol can be completed within ~9 d for four different sample pairs (each consists of an immunoprecipitation and corresponding input).
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A universal framework for regulatory element discovery across all genomes and data types.

            Deciphering the noncoding regulatory genome has proved a formidable challenge. Despite the wealth of available gene expression data, there currently exists no broadly applicable method for characterizing the regulatory elements that shape the rich underlying dynamics. We present a general framework for detecting such regulatory DNA and RNA motifs that relies on directly assessing the mutual information between sequence and gene expression measurements. Our approach makes minimal assumptions about the background sequence model and the mechanisms by which elements affect gene expression. This provides a versatile motif discovery framework, across all data types and genomes, with exceptional sensitivity and near-zero false-positive rates. Applications from yeast to human uncover putative and established transcription-factor binding and miRNA target sites, revealing rich diversity in their spatial configurations, pervasive co-occurrences of DNA and RNA motifs, context-dependent selection for motif avoidance, and the strong impact of posttranscriptional processes on eukaryotic transcriptomes.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A novel significance score for gene selection and ranking.

              When identifying differentially expressed (DE) genes from high-throughput gene expression measurements, we would like to take both statistical significance (such as P-value) and biological relevance (such as fold change) into consideration. In gene set enrichment analysis (GSEA), a score that can combine fold change and P-value together is needed for better gene ranking. We defined a gene significance score π-value by combining expression fold change and statistical significance (P-value), and explored its statistical properties. When compared to various existing methods, π-value based approach is more robust in selecting DE genes, with the largest area under curve in its receiver operating characteristic curve. We applied π-value to GSEA and found it comparable to P-value and t-statistic based methods, with added protection against false discovery in certain situations. Finally, in a gene functional study of breast cancer profiles, we showed that using π-value helps elucidating otherwise overlooked important biological functions. http://gccri.uthscsa.edu/Pi_Value_Supplementary.asp xy@ieee.org, cheny8@uthscsa.edu Supplementary data are available at Bioinformatics online.
                Bookmark

                Author and article information

                Journal
                Nature
                Nature
                Springer Science and Business Media LLC
                0028-0836
                1476-4687
                February 2015
                February 25 2015
                February 2015
                : 518
                : 7540
                : 560-564
                Article
                10.1038/nature14234
                3f778d02-5869-4253-809f-e00e39ba3958
                © 2015

                http://www.springer.com/tdm

                History

                Comments

                Comment on this article