13
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A ubiquitous GC content signature underlies multimodal mRNA regulation by DDX3X

      Preprint
      research-article

      Read this article at

      Bookmark
          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

          The road from transcription to protein synthesis is paved with many obstacles, allowing for several modes of post-transcriptional regulation of gene expression. A fundamental player in mRNA biology is DDX3X, an RNA binding protein that canonically regulates mRNA translation. By monitoring dynamics of mRNA abundance and translation following DDX3X depletion, we observe stabilization of translationally suppressed mRNAs. We use interpretable statistical learning models to uncover GC content in the coding sequence as the major feature underlying RNA stabilization. This result corroborates GC content-related mRNA regulation detectable in other studies, including hundreds of ENCODE datasets and recent work focusing on mRNA dynamics in the cell cycle. We provide further evidence for mRNA stabilization by detailed analysis of RNA-seq profiles in hundreds of samples, including a Ddx3x conditional knockout mouse model exhibiting cell cycle and neurogenesis defects. Our study identifies a ubiquitous feature underlying mRNA regulation and highlights the importance of quantifying multiple steps of the gene expression cascade, where RNA abundance and protein production are often uncoupled.

          Related collections

          Most cited references43

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Fast gapped-read alignment with Bowtie 2.

              As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
                Bookmark

                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                23 November 2023
                : 2023.05.11.540322
                Affiliations
                [1 ]Department of Cell and Tissue Biology, UCSF, San Francisco, United States
                [2 ]Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, United States
                [3 ]Department of Cell Biology, Duke University Medical Center, Durham, United States
                [4 ]Duke Regeneration Center, Duke University Medical Center, Durham, United States
                [5 ]Department of Neurobiology, Duke University Medical Center, Durham, United States
                [6 ]Duke Institute for Brain Sciences, Duke University Medical Center, Durham, United States
                [7 ]Helen Diller Family Comprehensive Cancer Center, San Francisco, United States
                [8 ]Human Technopole, Milan, Italy
                [9 ]Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA 94158, USA
                [10 ]Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
                Author notes
                [#]

                These Authors contributed equally to this work

                Author contributions

                Z.J. performed SLAM-seq and provided support for analysis of stabilized mRNAs. A.X. performed cell cycle analysis along the degron time course. S.V. performed Ribo-seq and RNA-seq. M.H. and D.L.S. provided crucial input with the Ddx3x mouse model dataset. S.N.F. supervised the initial part of the project, secured funding, and provided significant input, together with all authors, in writing the manuscript. L.C. conceived the project, performed all computational analyses, prepared the figures, and wrote the manuscript, with support from F.D. and all Authors.

                Author information
                http://orcid.org/0000-0002-3536-079X
                http://orcid.org/0000-0001-5938-561X
                http://orcid.org/0000-0002-2507-401X
                http://orcid.org/0009-0005-4948-4805
                http://orcid.org/0000-0002-3430-2782
                http://orcid.org/0000-0001-9189-844X
                http://orcid.org/0000-0002-9965-9694
                http://orcid.org/0000-0002-5600-0988
                Article
                10.1101/2023.05.11.540322
                10197686
                37214951
                174a28b1-51ec-4e0b-a079-c79453a851f8

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

                History
                Categories
                Article

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content252

                Cited by1

                Most referenced authors1,585