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      Role of UPF1-LIN28A interaction during early differentiation of pluripotent stem cells

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          Abstract

          UPF1 and LIN28A are RNA-binding proteins involved in post-transcriptional regulation and stem cell differentiation. Most studies on UPF1 and LIN28A have focused on the molecular mechanisms of differentiated cells and stem cell differentiation, respectively. We reveal that LIN28A directly interacts with UPF1 before UPF1-UPF2 complexing, thereby reducing UPF1 phosphorylation and inhibiting nonsense-mediated mRNA decay (NMD). We identify the interacting domains of UPF1 and LIN28A; moreover, we develop a peptide that impairs UPF1-LIN28A interaction and augments NMD efficiency. Transcriptome analysis of human pluripotent stem cells (hPSCs) confirms that the levels of NMD targets are significantly regulated by both UPF1 and LIN28A. Inhibiting the UPF1-LIN28A interaction using a CPP-conjugated peptide promotes spontaneous differentiation by repressing the pluripotency of hPSCs during proliferation. Furthermore, the UPF1-LIN28A interaction specifically regulates transcripts involved in ectodermal differentiation. Our study reveals that transcriptome regulation via the UPF1-LIN28A interaction in hPSCs determines cell fate.

          Abstract

          UPF1 and LIN28A are RNA-binding proteins involved in post-transcriptional regulation and cell differentiation. Here, authors report that they interact with each other via specific domains and regulate ectodermal specialization of human pluripotent stem cells.

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          Most cited references58

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          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.
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            HISAT: a fast spliced aligner with low memory requirements.

            HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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              StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.

              Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.
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                Author and article information

                Contributors
                chshpark@hanyang.ac.kr
                jwhwang@hanyang.ac.kr
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                2 January 2024
                2 January 2024
                2024
                : 15
                : 158
                Affiliations
                [1 ]Graduate School of Biomedical Science & Engineering, Hanyang University, ( https://ror.org/046865y68) Seoul, Korea
                [2 ]Hanyang Institute of Bioscience and Biotechnology, Hanyang University, ( https://ror.org/046865y68) Seoul, Korea
                Author information
                http://orcid.org/0000-0002-6432-8078
                http://orcid.org/0000-0003-3510-9295
                http://orcid.org/0000-0002-2290-1649
                Article
                44600
                10.1038/s41467-023-44600-5
                10762078
                38167913
                6088459a-9096-4a1f-91fd-b745c20d2484
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 13 February 2023
                : 21 December 2023
                Categories
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                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                rna quality control,embryonic stem cells,ectoderm
                Uncategorized
                rna quality control, embryonic stem cells, ectoderm

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