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      Qki activates Srebp2-mediated cholesterol biosynthesis for maintenance of eye lens transparency

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          Abstract

          Defective cholesterol biosynthesis in eye lens cells is often associated with cataracts; however, how genes involved in cholesterol biosynthesis are regulated in lens cells remains unclear. Here, we show that Quaking (Qki) is required for the transcriptional activation of genes involved in cholesterol biosynthesis in the eye lens. At the transcriptome level, lens-specific Qki-deficient mice present downregulation of genes associated with the cholesterol biosynthesis pathway, resulting in a significant reduction of total cholesterol level in the eye lens. Mice with Qki depletion in lens epithelium display progressive accumulation of protein aggregates, eventually leading to cataracts. Notably, these defects are attenuated by topical sterol administration. Mechanistically, we demonstrate that Qki enhances cholesterol biosynthesis by recruiting Srebp2 and Pol II in the promoter regions of cholesterol biosynthesis genes. Supporting its function as a transcription co-activator, we show that Qki directly interacts with single-stranded DNA. In conclusion, we propose that Qki-Srebp2–mediated cholesterol biosynthesis is essential for maintaining the cholesterol level that protects lens from cataract development.

          Abstract

          Eye lens cells are highly enriched in cholesterol that sustains lens transparency, and disruption of cholesterol biosynthesis leads to cataracts. The authors show that cholesterol biosynthesis regulated by Qki is essential for maintenance of membrane integrity of lens cells and proper protein folding.

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          Most cited references112

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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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            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/.
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              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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                Author and article information

                Contributors
                jhu3@mdanderson.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                21 May 2021
                21 May 2021
                2021
                : 12
                : 3005
                Affiliations
                [1 ]GRID grid.240145.6, ISNI 0000 0001 2291 4776, Department of Cancer Biology, , The University of Texas MD Anderson Cancer Center, ; Houston, TX USA
                [2 ]GRID grid.240145.6, ISNI 0000 0001 2291 4776, Cancer Biology Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, ; Houston, TX USA
                [3 ]GRID grid.216938.7, ISNI 0000 0000 9878 7032, State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Protein Science, College of Life Sciences, Nankai University, ; Tianjin, China
                [4 ]GRID grid.413087.9, ISNI 0000 0004 1755 3939, Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, ; Shanghai, China
                [5 ]GRID grid.240145.6, ISNI 0000 0001 2291 4776, Department of Bioinformatics and Computational Biology, , The University of Texas MD Anderson Cancer Center, ; Houston, TX USA
                [6 ]GRID grid.468198.a, ISNI 0000 0000 9891 5233, Clinical Science Division, , H. Lee Moffitt Cancer Center & Research Institute, ; Tampa, FL USA
                [7 ]GRID grid.27255.37, ISNI 0000 0004 1761 1174, Department of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, , Shandong University, ; Jinan, Shandong China
                [8 ]GRID grid.64924.3d, ISNI 0000 0004 1760 5735, Cancer Research Institute of Jilin University, The First Hospital of Jilin University, ; Jilin, China
                [9 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Department of Oncology, Affiliated Sixth People’s Hospital, , Shanghai Jiaotong University, ; Shanghai, China
                [10 ]GRID grid.240145.6, ISNI 0000 0001 2291 4776, Neuroscience Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, ; Houston, TX USA
                Author information
                http://orcid.org/0000-0001-6813-648X
                http://orcid.org/0000-0003-4200-2859
                http://orcid.org/0000-0002-6625-0147
                http://orcid.org/0000-0002-7706-7540
                http://orcid.org/0000-0001-9760-2013
                Article
                22782
                10.1038/s41467-021-22782-0
                8139980
                34021134
                11391eb4-1222-44da-a5b2-c7b52bcbc0e4
                © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2021

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 7 April 2020
                : 23 March 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100004917, Cancer Prevention and Research Institute of Texas (Cancer Prevention Research Institute of Texas);
                Award ID: RP120348 and RP170002
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                sterols,chaperones,lens diseases
                Uncategorized
                sterols, chaperones, lens diseases

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