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      Dihydroceramide desaturase governs endoplasmic reticulum and lipid droplet homeostasis to promote glial function in the nervous system

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          Abstract

          Dihydroceramide desaturases convert dihydroceramides to ceramides in the last step of the de novo ceramide pathway. Reduction of DEGS1 dihydroceramide desaturase function in humans leads to the rare neurodegenerative disorder hypomyelinating leukodystrophy-18, but the exact mechanism that underlies the disease remains unclear. Through a forward genetic screen, we discovered that infertile crescent (ifc), the sole Drosophila dihydroceramide desaturase, governs central nervous system development and morphology. Expressed and genetically active most prominently in glia rather than neurons, ifc primarily controls nervous system development through a cell autonomous function in glia. Within the nervous system, loss of ifc results in ceramide depletion, dihydroceramide accumulation, and increased saturation of major membrane phospholipids. At the cellular level, loss of ifc leads to severe glial defects, particularly in cortex glia, including expansion of the endoplasmic reticulum, cell swelling, failure to enwrap neurons, and lipid droplet depletion. Our research supports a model in which inappropriate retention of dihydroceramide in the endoplasmic reticulum (ER) of glial cells drives ER expansion and cell swelling, disrupting glial function and leading to subsequent nervous system dysfunction. Given the conserved nature of the de novo ceramide biosynthesis pathway, our findings in the fly system suggest that dihydroceramide-triggered expansion of the ER in the membrane-rich glial cells may be the proximal cause of hypomyelinating leukodystrophy-18 in humans.

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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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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                02 January 2024
                : 2024.01.01.573836
                Affiliations
                [1 ]Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, St. Louis, MO 63110, USA
                [2 ]Department of Chemistry, Washington University in St. Louis, One Brookings Drive, St. Louis, MO 63130, USA
                [3 ]Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
                [4 ]Center for Proteomics, Metabolomics, and Isotope Tracing, Washington University in St. Louis, One Brookings Drive, St. Louis, MO 63130, USA
                [5 ]Division of Biological and Biomedical Systems, University of Missouri-Kansas City, Kansas City, MO 64110, USA
                Author notes

                Author Contributions

                BAW, HL, Yi Zhu, and JBS completed the genetic screen that identified the ifc alleles and completed the initial genetic characterization of these alleles. Yuqing Zhu, JD, and JBS completed all other experiments except for the metabolomic analyses, which were completed by KC and GJP. Yuqing Zhu and JBS wrote the manuscript and compiled figures with help from JD with figures. All authors read, commented on, and edited the manuscript.

                Article
                10.1101/2024.01.01.573836
                10802327
                38260379
                d52f27d2-f129-4a58-89f2-43b43aead25e

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

                History
                Funding
                This work was supported by grants from the National Institutes of Health (NS036570) to J.B.S., (NS122903) to H.L., and (R35 ES2028365) to G.J.P.
                Categories
                Article

                degs1,ifc,ceramide,glia,lipid droplet,endoplasmic reticulum,leukodystrophy,cns development,sphingolipids

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