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      Imp/IGF2BP levels modulate individual neural stem cell growth and division through myc mRNA stability

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          Abstract

          The numerous neurons and glia that form the brain originate from tightly controlled growth and division of neural stem cells, regulated systemically by important known stem cell-extrinsic signals. However, the cell-intrinsic mechanisms that control the distinctive proliferation rates of individual neural stem cells are unknown. Here, we show that the size and division rates of Drosophila neural stem cells (neuroblasts) are controlled by the highly conserved RNA binding protein Imp (IGF2BP), via one of its top binding targets in the brain, myc mRNA. We show that Imp stabilises myc mRNA leading to increased Myc protein levels, larger neuroblasts, and faster division rates. Declining Imp levels throughout development limit myc mRNA stability to restrain neuroblast growth and division, and heterogeneous Imp expression correlates with myc mRNA stability between individual neuroblasts in the brain. We propose that Imp-dependent regulation of myc mRNA stability fine-tunes individual neural stem cell proliferation rates.

          eLife digest

          The brain is a highly complex organ made up of huge numbers of different cell types that connect up to form a precise network. All these different cell types are generated from the repeated division of a relatively small pool of cells called neural stem cells. The division of these cells needs to be carefully regulated so that the correct number and type of nerve cells are produced at the right time and place. But it remains unclear how the division rate of individual neural stem cells is controlled during development.

          Controlling these divisions requires the activity of countless genes to be tightly regulated over space and time. When a gene is active, it is copied via a process called transcription into a single-stranded molecule known as messenger RNA (or mRNA for short). This molecule provides the instructions needed to build the protein encoded within the gene.

          Proteins are the functional building blocks of all cells. The conventional way of controlling protein levels is to vary the number of mRNA molecules made by transcription. Now, Samuels et al. reveal a second mechanism of determining protein levels in the brain, through regulating the stability of mRNA after it is transcribed.

          Samuels et al. discovered that a key regulatory protein called Imp controls the growth and division of individual neural stem cells in the brains of developing fruit flies. The experiments showed that Imp binds to mRNA molecules that contain the code for a protein called Myc, which is known to drive cell growth and division in many different cell types. Both human Imp and Myc have been implicated in cancer.

          Using a technique that images single molecules of mRNA, Samuels et al. showed that the Imp protein in stem cells stabilises the mRNA molecule coding for Myc. This means that when more Imp is present, more Myc protein gets produced. Thus, the level of Imp in each individual neural stem cell fine-tunes the rate at which the cell grows and divides: the higher the level of Imp, the larger the stem cell and the faster it divides.

          These findings underscore how important post-transcriptional processes are for regulating gene activity in the developing brain. The methods used in this study to study mRNA molecules in single cells also provide new insights that could not be derived from the average measurements of many cells. Similar methods could also be applied to other developmental systems in the future.

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          Most cited references 72

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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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              BEDTools: a flexible suite of utilities for comparing genomic features

              Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                14 January 2020
                2020
                : 9
                Affiliations
                [1 ]deptDepartment of Biochemistry The University of Oxford OxfordUnited Kingdom
                [2 ]deptMRC Laboratory for Molecular Cell Biology University College LondonUnited Kingdom
                New York University United States
                University of California, Los Angeles United States
                New York University United States
                Howard Hughes Medical Institute, University of Oregon United States
                Article
                51529
                10.7554/eLife.51529
                7025822
                31934860
                © 2020, Samuels et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome;
                Award ID: 105363/Z/14/Z
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome;
                Award ID: 096144/Z/17/Z
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome;
                Award ID: 209412/Z/17/Z
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000765, University College London;
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Developmental Biology
                Neuroscience
                Custom metadata
                Single molecule mRNA imaging uncovers post-transcriptional regulation of myc mRNA, via a cell-intrinsic mechanism allowing individualised control of neural stem cell proliferation during Drosophila brain development.

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