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Supplemental Figure and Table LegendsFigure S1. Cell cycle kinetic properties and marker gene expression of proliferating and quiescent NS cells and NS cells that have re-entered the cell cycle from quiescence.Removing BMP4 from quiescence medium (FGF2 only, (F)) or inhibiting BMP signaling by addition of soluble Noggin (EB+Nog) permits continued proliferation of NS cells, as determined by EdU incorporation and flow cytometric analysis of DNA content (A). Addition of Noggin, in the continued presence of BMP4, to cells already in quiescence (EBB+Nog) causes many NS cells to resume proliferation by day 3, although not to the same extent as when quiescence medium is replaced by EGF-containing proliferation medium (EBE) (A, bottom left panel). NS cells placed into quiescence medium rapidly exit the cell cycle and accumulate with a 2N DNA content in G0/G1 phase of the cell cycle, as determined by flow cytometric analysis (B). Immunocytochemistry shows that both quiescent and proliferating NS cells express NSC markers Sox2, Nestin and BLBP, whilst EGFR is specifically expressed in proliferating cells, and GFAP only detectable in quiescent cells (D). Neither quiescent nor proliferating NS cells express markers of astrocytic (S100β) or neuronal (βIII-Tubulin) differentiation (C). Following three days in quiescence medium, NS cells placed in proliferation medium begin to re-enter the cell cycle during the first day and between days 3 and 6, proliferate with similar kinetics to NS cells continuously cultured in proliferation medium (D). Immunocytochemistry shows that some cells retain immunoreactivity for GFAP until 4 days after replating from quiescence medium into proliferation medium (E). qPCR validation of 10/10 genes shown to be induced in RNAseq analysis of quiescent NS cells and 10/10 genes repressed in quiescence, in at least one of two independent experiments. Expression values are calculated with the ΔΔCt method, using 3 non-regulated control genes for normalization (F). Figure S2. Genes and Gene Ontology terms associated with quiescent adult tissue stem cells and NSCs are up-regulated in quiescent NSCs in vitro(A, B) Gene Ontology analysis of genes expressed by quiescent hair follicle (A) and hematopoietic stem cells (B). GO terms identical or highly related to those also enriched in cell-cycle arrested NSCs (see Fig. 1L)) are indicated by darker blue bars. GSEA analysis shows that genes that are enriched in NSCs FACS-sorted from the post-natal mouse brain sub-ependymal zone (C) or activated by FoxO3 in neurosphere cultures (E) show a highly significant tendency to be up-regulated in cell cycle arrested NSCs. Genes expressed specifically in FACS-sorted diencephalic astrocytes show only a weak tendency to be up-regulated in arrested NSCs (D).Figure S3. Characterisation and validation of active enhancers in quiescent and proliferating NSCs(A) Distances in kb of enhancers to closest gene transcription start site (TSS) are binned and plotted for all active enhancers in quiescent (A) and proliferating (B) NS cells. Most enhancers are within 100kb of a TSS. Representative quiescence-specific (qui1-qui7) and proliferation-specific (pro1-pro8) enhancers were cloned and tested for functional enhancer activity in luciferase reporter gene assays conducted in quiescent (C) and proliferating (D) NS cells. The y-axis values represent the normalized luciferase values, expressed as a fold change compated to a control plasmid containing the minimal promoter alone (denoted mp.). Asterisks denote significant induction of luciferase activity versus the control plasmid (t-test, p<0.05 n=3). GREAT was used to assess the Biological Processes enriched among genes associated with quiescent NS cell-specific enhancers. The x-axis values represent the binomial fdr q-values; the numbers in parentheses are the number of binomial region hits (E).Figure S4. NFI factor expression and ChIP-seq analysis in NS cells(A,B) Immunolabelling shows that NFIB is expressed at lower levels in quiescent NS cells than in proliferating cells, whilst NFIC is expressed at similar levels in both cell states. (C) Western blotting supports the immunolabelling data showing an up-regulation of NFIX and down-regulation of NFIA and NFIB in quiescent NS cells. We were unable to obtain an effective anti-NFIC antibody for use in western blotting. De novo motif analysis with MEME-chip application DREME shows the NFI consensus motif is most strongly enriched in NFI peaks in quiescent NS cells, as expected (D). 70% of all NFI peaks contain one or more NFI motif within 100bp of the peak summit. (E). (F) NFI ChIP-seq peaks are broadly distributed in the genome, with ~80% within 100kb of a gene TSS.Figure S5. Knockdown and over-expression of NFIX demonstrate that it is both necessary and sufficient for several aspects of NS cells quiescence. Lentiviral transduction with an shRNA specifically targeting Nfix causes significant depletion of nuclear NFIX in NS cells treated with quiescence medium for one, two and three days (EB day 1 to EB day 3) (A-C). Western blotting is quiescent NS cells shows total cellular NFIX is depleted (D). Flow cytometric analysis of DNA content shows that a significant fraction of NFIX-depleted NSCs continue to proliferate when treated with quiescence medium, compared to control shRNA expressing cells (E). NS cells electroporated with NFIX or a control GFP vector do not express markers of neuronal (βIII-tubulin) or astrocytic (s100β) differentiation (F). Microarray analysis shows that a large proportion (48%) of genes regulated in quiescent NS cells compared to proliferating NS cells are also de-regulated by NFIX over-expression and/or knockdown (G). A significant fraction of genes specifically induced in NS cell quiescence are positively regulated by NFIX (induced by NFIX gain of function and/or down-regulated by Nfix knockdown), 71% of which are associated with one or more NFI ChIP-seq peak (H). Similarly, a significant fraction of genes induced in proliferating NS cells are negatively regulated by NFIX (repressed in NFIX over-expression and/or up-regulated by Nfix knockdown), although a smaller number of these (43%) are NFI-bound, suggesting that negative regulation may be less direct (I). qPCR validation of 6/6 genes shown to be down-regulated (J) and 5/5 genes shown to be up-regulated (K) in the microarray analysis of cells in quiescence medium expressing Nfix shRNA for one, two and three days, in both of two independent experiments. Similarly, 5/5 NFIX-induced (L) and NFIX-repressed (M) genes were validated by qPCR analysis. Expression values in J-M are calculated with the ΔΔCt method, using 3 non-regulated control genes for normalization (E).Figure S6. A dominant negative NFI (DN-NFI) construct partially blocks induction of NS cell quiescence.(A-C) qPCR analysis of NFI target genes induced by NFIX and/or NFIA and/or NFIC shows that the activity of all three of these NFI factors is partially blocked by co-expression of DN-NFI. Numbers above selected bars show the percentage reduction in gene expression in cells expressing DN-NFI together with each NFI factor, compared to the NFI factor alone. (D) Venn diagram showing the significant overlap of genes induced in quiescent NS cells in microarray experiments with genes repressed by DN-NFI (p<2.2x10-16, hypergeometric test). (F) Gene Ontology analysis of genes that are both induced in quiescent NSCs and repressed by DN-NFI. (E) Venn diagram showing the significant overlap of genes repressed in quiescent NSCs with genes induced by DN-NFI (p<2.2x10-16, hypergeometric test). (G) Gene Ontology analysis of genes that are repressed in quiescent NSCs and induced by DN-NFI. (I,J) qPCR validation of 9/9 genes shown to be down-regulated (H) and 9/9 genes shown to be up-regulated (I) in the microarray analysis of cells expressing DN-NFI, in two independent experiments. Expression values in A-C and H-I are calculated with the ΔΔCt method, using 3 non-regulated control genes for normalization.Table S1. RNA-seq profiling of genes de-regulated in BMP4-treated quiescent NSCs.Ensembl gene identifiers, official gene symbol, normalized transcript count (FPKM) in proliferating NSCs, FPKM in quiescent NSCs, Log2 fold change (quiescent/proliferating NSCs) and p-value (Cuffdiff t-test) for genes significantly up- and down-regulated in quiescent NSCs.Table S2. Genomic locations of active enhancers in quiescent and proliferating NS cells.Genomic co-ordinates (mm9) of enhancers, Ensembl transcript, identifiers, Ensembl gene identifiers and official gene symbols of the closest Ensembl v61 protein coding gene’s transcription start site (TSS), and the distance to the TSS (base pairs). Worksheets contain, sequentially, all active enhancers in quiescent NS cells, all active enhancers in proliferating NS cells, quiescence-specific enhancers, proliferation-specific enhancers and pan-NS cell enhancers.Table S3. Candidate enhancers tested in luciferase assays.Genomic co-ordinates (mm9) and primer sequences for the seven quiescent NS cell-specific (denoted qui1-qui7) and eight proliferating NS cell-specific (pro1-pro8) enhancers cloned and tested in luciferase assays.Table S4. Candidate NFIX target genes in quiescent NSCsEnsembl gene identifiers and official gene symbols for genes from the microarray analyses that are up-regulated in quiescent NSCs and induced by NFIX over-expression and/or down-regulated by knockdown of Nfix and are associated with one or more NFI ChIP-seq peak in an active enhancer or promoter region.Supplemental Materials and MethodsNS cell cultureNS5 cells were cultured according to standard methods PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Db250aTwvQXV0aG9yPjxZZWFyPjIwMDU8L1llYXI+PFJl
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ADDIN EN.CITE.DATA (Conti et al. 2005) with the following minor modification: cells were plated onto uncoated tissue culture plastic with the addition of laminin (Sigma) at 2?g/ml to the medium. To induce quiescence 35-65,000 cells/cm2 were plated into normal proliferation medium (EGF and FGF2, both at 10ng/ml (Peprotech)) and after 16hrs fresh NSC medium was added without EGF and with BMP4 (R&D systems) at 50ng/ml and FGF2 at 20ng/ml. For re-activation, after at least 3 days in BMP4-containing medium, cells were passaged with Accutase (Sigma) and plated into proliferation medium at a density of 35-65,000 cells/cm2. Proliferation was assessed with anti-Ki67 immunostaining (BD Pharmingen 550609, 1:200) and with Click-IT EdU detection (Invitrogen) after 4hr exposure to EdU, according to the manufacturers instructions. BMP signaling was inhibited by the addition of Noggin (R&D systems) at 250ng/ml. Neurogenesis was induced according to Spiliotopoulos et al (2009) and cells were fixed at 10 days and stained with mouse anti-βIII-tubulin (Sigma T3952, 1:1000). Rabbit anti-NFIA (Abcam ab11988, 1:500), Rabbit anti-NFIB (Sigma-Atlas HPA003956, 1:400), Rabbit anti-NFIC (Abnova MRCMB010, 1:400) and rabbit anti-NFIX (Abcam ab101341, 1:500) were used to detect NFI proteins. ImageJ was used to quantify intensity of nuclear staining using cells fixed, stained and imaged in parallel for each condition. NS cells were stained with rat anti-GFAP (Invitrogen 13-0300, 1:1000), goat anti-Sox2 (Santa Cruz SC17320, 1:500), goat anti-EGFR (R&D AF1280, 1:400), rabbit anti-BLBP (Millipore, AB9558, 1:200), mouse anti-Nestin (Millipore MAB353, 1:200) and mouse-anti-S100? (Sigma, SAB1402349, 1:1000). Rabbit anti-GFP (Abcam A6455, 1:1000) was used to detect electroporated NS cells. For quantitative PCR analysis of gene expression, Taqman gene expression assays (Life Technologies) were used, and relative gene expression calculated with the ΔΔCt method, using at least three non-changing control genes for normalization.Flow cytometric analysis of proliferationNS cells were trypsinized and fixed in 70% ice-cold ethanol. DNA was stained with Propidium Iodide (Molecular Probes, 50?g/ml) in the presence of RNAseA (40?g/ml) and cells were analyzed on a BD FACS Calibur. Cell cycle phase distributions were calculated with Watson’s pragmatic model within the Flowjo software package.Chromatin immunoprecipitationNSCs were fixed sequentially with di(N-succimidyl) glutarate and 1% formaldehyde in phosphate-buffered saline and then lysed, sonicated and immunoprecipitated as describe previously PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DYXN0cm88L0F1dGhvcj48WWVhcj4yMDExPC9ZZWFyPjxS
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ADDIN EN.CITE.DATA (Castro et al. 2011) using material from ~5x106 cells per sample. Immunoprecipitations were with rabbit anti-H3K27ac (Abcam, ab4729, 4?g per ChIP sample), rabbit anti-p300 (Santa Cruz, sc-585, 3?g per ChIP sample) or goat anti-NFI (Santa Cruz, sc-30918, 6?g per ChIP sample).ChIP-seq data generation and processingDNA libraries were prepared from 10ng of immunoprecipitated DNA according to the standard Illumina ChIP-seq protocol for quiescent NSC H3K27ac ChIP, quiescent NSC p300 ChIP, quiescent NSC NFI ChIP, quiescent NSC input DNA, proliferating NSC H3K27ac ChIP, proliferating NSC p300 ChIP and proliferating NSC input DNA. Libraries were sequenced with the Genome Analyzer IIx (Illumina). The raw reads for p300, H3K27ac and NFI in quiescent NSC and p300 and H3K27ac in proliferating NSCs were mapped to the mouse genome (mm9 including random chromosomes) with Bowtie ADDIN EN.CITE <EndNote><Cite><Author>Langmead</Author><Year>2010</Year><RecNum>1953</RecNum><DisplayText>(Langmead 2010)</DisplayText><record><rec-number>1953</rec-number><foreign-keys><key app="EN" db-id="2dvswp9tce5sp2edrptxssr6epezvpwexrpd">1953</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Langmead, B.</author></authors></contributors><auth-address>Johns Hopkins University, Baltimore, Maryland, USA.</auth-address><titles><title>Aligning short sequencing reads with Bowtie</title><secondary-title>Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.]</secondary-title><alt-title>Curr Protoc Bioinformatics</alt-title></titles><periodical><full-title>Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.]</full-title><abbr-1>Curr Protoc Bioinformatics</abbr-1></periodical><alt-periodical><full-title>Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.]</full-title><abbr-1>Curr Protoc Bioinformatics</abbr-1></alt-periodical><pages>Unit 11 7</pages><volume>Chapter 11</volume><edition>2010/12/15</edition><keywords><keyword>Algorithms</keyword><keyword>Consensus Sequence</keyword><keyword>Genome</keyword><keyword>Genomics/*methods</keyword><keyword>Sequence Alignment/*methods</keyword><keyword>Sequence Analysis, DNA/*methods</keyword><keyword>*Software</keyword></keywords><dates><year>2010</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>1934-340X (Electronic)
1934-3396 (Linking)</isbn><accession-num>21154709</accession-num><urls><related-urls><url>;(Langmead 2010) version 0.12.5. For each cell condition an input chromatin sample was mapped in the same way. The number of uniquely mapped reads is in quiescent NSC was 28.0 million for p300, 38.0 million for H3K27ac, 24.9 million for NFI and 27.3 million for input. In proliferating NSC 18.8 million p300, 33.5 million H3K27ac and 31.0 million input unique reads were mapped. H3K27ac datasets were processed further with SICER ADDIN EN.CITE <EndNote><Cite><Author>Zang</Author><Year>2009</Year><RecNum>1934</RecNum><DisplayText>(Zang et al. 2009)</DisplayText><record><rec-number>1934</rec-number><foreign-keys><key app="EN" db-id="2dvswp9tce5sp2edrptxssr6epezvpwexrpd">1934</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Zang, C.</author><author>Schones, D. E.</author><author>Zeng, C.</author><author>Cui, K.</author><author>Zhao, K.</author><author>Peng, W.</author></authors></contributors><auth-address>Department of Physics, The George Washington University, Washington, DC 20052, USA.</auth-address><titles><title>A clustering approach for identification of enriched domains from histone modification ChIP-Seq data</title><secondary-title>Bioinformatics</secondary-title><alt-title>Bioinformatics</alt-title></titles><periodical><full-title>Bioinformatics</full-title><abbr-1>Bioinformatics</abbr-1></periodical><alt-periodical><full-title>Bioinformatics</full-title><abbr-1>Bioinformatics</abbr-1></alt-periodical><pages>1952-8</pages><volume>25</volume><number>15</number><edition>2009/06/10</edition><keywords><keyword>*Chromatin Immunoprecipitation</keyword><keyword>Cluster Analysis</keyword><keyword>Computational Biology/*methods</keyword><keyword>Genome</keyword><keyword>Histones/*chemistry</keyword><keyword>Protein Structure, Tertiary</keyword><keyword>Sequence Analysis, DNA</keyword></keywords><dates><year>2009</year><pub-dates><date>Aug 1</date></pub-dates></dates><isbn>1367-4811 (Electronic)
1367-4803 (Linking)</isbn><accession-num>19505939</accession-num><work-type>Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't
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ADDIN EN.CITE.DATA (Zhang et al. 2008)? version 2.0.9 to define bound regions for p300 and NFI. As this tool is very sensitive to the unbalance number of reads in the real and the input set, we decided to reduce the larger dataset by randomly sampling reads with a custom python script (balanceBAMFiles.py). For this process we only considered a maximum of two overlapping reads, discarding the rest. To correct for sampling bias we generated 10 different random samples on which we ran MACS specifying the shift size to 90, q value to 1e-5 and leaving the rest of parameters as default. We subsequently collapsed the 10 different peak calling result for each set using another custom script (aggregatePeaksFromSubsampling.py) which reports only overlapping peaks in at least 9 of the 10 lists.ChIP-seq data generated in this study have been deposited in the European Nucleotide Archive’s Sequence Read Archive under accession number ERP002084, and are also available via ArrayExpress under accession number E-MTAB-1423.Reviewers can access these data prior to publication via the ArrayExpress website using Username: Reviewer_E-MTAB-1423 and Password: oVyQQPE7 All custom scripts used are available upon request. The ChIP-seq datasets for H3K4me1, H3K4me2, H3K4me3, H3K27me3, H3K9me3 and H3K36me3 in neural progenitors were retrieved from GEO repository with accession number GSE8024 and GSE11172. These dataset were processed with SICER in the way as described above using as input the whole cell extract sample provided in GSE8024. For all these modifications we used as window size 100 and as gap size 1000 for all except H3K4me2 where the selected value is 500.Normalization of ChIP-seq signalAs strength signal for each factor binding or histone modification we use the q-value from MACS or the score from SICER, respectively. In order to have comparable values between factors/modification and conditions we re-scaled each signal to the range [0,1]. A very small proportion of enriched regions, peaks or islands, have a much higher score than the rest. To ensure that these regions do not mask the contribution of the lower ranking peaks we decided not to divide by the maximum observed score value but by the expected maximum value if the distribution of the score would be linear. We did so by fitting a linear regression model using regions ranked 1001-2000 to predict the values for the 1000 highest scoring regions. Then we used the model to predict the expected value of the single highest scoring region, and used this value to perform the scaling.Definition of active enhancersIn order to define active enhancer regions in quiescent and proliferating NSCs, we used the p300 peaks as reference, selecting peaks whose summit is included within a H3K27ac island. We removed promoter-proximal peaks whose summit is closer than 2kb to the transcription start site of any gene in ENSEMBL version 61 annotated as known protein-coding. We used the q-value reported by MACS for each p300 peak as the enhancer score. We divided these enhancer sets into quiescence-specific, proliferation- specific or pan-NS cell sets by considering the H3K27ac and p300 signal in both cell states. For H3K27ac, we used SICER to call differentially enriched regions. Quiescence-specific enhancers are defined as p300 peaks that are only called in quiescent NS cells, that fall within an H3K27ac island that is specifically enriched in quiescence, and vice versa for proliferation-specific enhancers. Pan-NS cell enhancers are defined by the consistent presence of p300 peaks in the two cell states that fall within a non-differentially enriched H3K27ac island.Validation of candidate enhancers by luciferase reporter gene assaysSeven quiescence-specific enhancers and seven proliferation-specific enhancers that were within 50kb of the TSS of a gene up- or down-regulated in quiescent NS cells, respectively, were selected. PCR primers were designed to flank the p300 peak region defined by MACS (Supplemental Table S3) and the amplified regions were cloned upstream of the β-globin minimal promoter driving the firefly luciferase reporter gene. Luciferase assays were conducted as described previously PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DYXN0cm88L0F1dGhvcj48WWVhcj4yMDExPC9ZZWFyPjxS
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ADDIN EN.CITE.DATA (McLean et al. 2010)?. Specifically, we used the basal plus extension definition of regulatory for all known protein coding genes from ENSEMBL v61. For each gene then we computed the score contributed by quiescence-specific and proliferation-specific enhancers as follows:,meaning that for each enhancer (e) in the the domain (D) we sum the score of that enhancer relative to the distance to TSS of the gene in the given domain. As, by definition, the enhancers cannot be closer than 2kb from the TSS we normalized the distance dividing by 2000. As before, the genes upregulated in quiescent NSCs tend to have a higher score from quiescence-specific than proliferation-specific enhancers. We also conducted 100 random permutations of the enhancer labels and again the observed values are significantly different from the expected values (Wilcoxon rank sum test p < 2.2E-16).Motif analysis To identify motifs over-represented in the active enhancer regions we used three tools that are based on different approaches, MEME-ChIP ADDIN EN.CITE <EndNote><Cite><Author>Machanick</Author><Year>2011</Year><RecNum>1956</RecNum><DisplayText>(Machanick and Bailey 2011)</DisplayText><record><rec-number>1956</rec-number><foreign-keys><key app="EN" db-id="2dvswp9tce5sp2edrptxssr6epezvpwexrpd">1956</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Machanick, P.</author><author>Bailey, T. L.</author></authors></contributors><auth-address>Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Queensland, Australia.</auth-address><titles><title>MEME-ChIP: motif analysis of large DNA datasets</title><secondary-title>Bioinformatics</secondary-title><alt-title>Bioinformatics</alt-title></titles><periodical><full-title>Bioinformatics</full-title><abbr-1>Bioinformatics</abbr-1></periodical><alt-periodical><full-title>Bioinformatics</full-title><abbr-1>Bioinformatics</abbr-1></alt-periodical><pages>1696-7</pages><volume>27</volume><number>12</number><edition>2011/04/14</edition><keywords><keyword>Algorithms</keyword><keyword>Binding Sites</keyword><keyword>*Chromatin Immunoprecipitation</keyword><keyword>DNA/chemistry/metabolism</keyword><keyword>Genomics</keyword><keyword>Internet</keyword><keyword>Protein Binding</keyword><keyword>*Regulatory Elements, Transcriptional</keyword><keyword>Sequence Analysis, DNA</keyword><keyword>*Software</keyword><keyword>Transcription Factors/*metabolism</keyword></keywords><dates><year>2011</year><pub-dates><date>Jun 15</date></pub-dates></dates><isbn>1367-4811 (Electronic)
1367-4803 (Linking)</isbn><accession-num>21486936</accession-num><work-type>Research Support, N.I.H., Extramural
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ADDIN EN.CITE.DATA (Thomas-Chollier et al. 2012) ?. GADEM and peak-motifs had as input 200 bases centred in the peak summit, while MEME-ChIP is limited to the central 100 bases. In all cases the simple repeats were masked using DustMasker ADDIN EN.CITE <EndNote><Cite><Author>Morgulis</Author><Year>2006</Year><RecNum>1954</RecNum><DisplayText>(Morgulis et al. 2006)</DisplayText><record><rec-number>1954</rec-number><foreign-keys><key app="EN" db-id="2dvswp9tce5sp2edrptxssr6epezvpwexrpd">1954</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Morgulis, A.</author><author>Gertz, E. M.</author><author>Schaffer, A. A.</author><author>Agarwala, R.</author></authors></contributors><auth-address>National Center for Biotechnology Information, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20894 USA.</auth-address><titles><title>A fast and symmetric DUST implementation to mask low-complexity DNA sequences</title><secondary-title>Journal of computational biology : a journal of computational molecular cell biology</secondary-title><alt-title>J Comput Biol</alt-title></titles><periodical><full-title>Journal of computational biology : a journal of computational molecular cell biology</full-title><abbr-1>J Comput Biol</abbr-1></periodical><alt-periodical><full-title>Journal of computational biology : a journal of computational molecular cell biology</full-title><abbr-1>J Comput Biol</abbr-1></alt-periodical><pages>1028-40</pages><volume>13</volume><number>5</number><edition>2006/06/27</edition><keywords><keyword>Genome, Human/*genetics</keyword><keyword>Humans</keyword><keyword>*Pattern Recognition, Automated</keyword><keyword>*Sequence Alignment</keyword><keyword>*Sequence Analysis, DNA</keyword><keyword>*Software</keyword></keywords><dates><year>2006</year><pub-dates><date>Jun</date></pub-dates></dates><isbn>1066-5277 (Print)
1066-5277 (Linking)</isbn><accession-num>16796549</accession-num><work-type>Research Support, N.I.H., Intramural</work-type><urls><related-urls><url>;(Morgulis et al. 2006)?. Consistently, the three tools report the NFI, bHLH and Sox binding motifs as the most over-represented motifs. In order to corroborate these results we scanned the enhancer region (2kb around the p300 summit), to identify the position of these motifs. We decided to use the PWM reported by MEME-ChIP whose motif is shown in Fig. 4A-C. A motif match is defined by the MATCHTM metric ADDIN EN.CITE <EndNote><Cite><Author>Kel</Author><Year>2003</Year><RecNum>1952</RecNum><DisplayText>(Kel et al. 2003)</DisplayText><record><rec-number>1952</rec-number><foreign-keys><key app="EN" db-id="2dvswp9tce5sp2edrptxssr6epezvpwexrpd">1952</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kel, A. E.</author><author>Gossling, E.</author><author>Reuter, I.</author><author>Cheremushkin, E.</author><author>Kel-Margoulis, O. V.</author><author>Wingender, E.</author></authors></contributors><auth-address>BIOBASE GmbH, Halchtersche Str. 33, D-38304 Wolfenbuttel, Germany. ake@biobase.de</auth-address><titles><title>MATCH: A tool for searching transcription factor binding sites in DNA sequences</title><secondary-title>Nucleic acids research</secondary-title><alt-title>Nucleic Acids Res</alt-title></titles><periodical><full-title>Nucleic acids research</full-title><abbr-1>Nucleic Acids Res</abbr-1></periodical><alt-periodical><full-title>Nucleic acids research</full-title><abbr-1>Nucleic Acids Res</abbr-1></alt-periodical><pages>3576-9</pages><volume>31</volume><number>13</number><edition>2003/06/26</edition><keywords><keyword>Algorithms</keyword><keyword>Binding Sites</keyword><keyword>Internet</keyword><keyword>Regulatory Sequences, Nucleic Acid</keyword><keyword>Sequence Analysis, DNA/*methods</keyword><keyword>*Software</keyword><keyword>Transcription Factors/*metabolism</keyword><keyword>User-Computer Interface</keyword></keywords><dates><year>2003</year><pub-dates><date>Jul 1</date></pub-dates></dates><isbn>1362-4962 (Electronic)
0305-1048 (Linking)</isbn><accession-num>12824369</accession-num><work-type>Research Support, Non-U.S. Gov't</work-type><urls><related-urls><url>;(Kel et al. 2003)? with a score of at least 0.9. The enrichment of each motif is assessed fitting a density function to the distribution of position with matches (Fig. 4D-F). A enhancer is called to include a motif if there is a match within the p300 peak coordinates of that enhancer.Generation and analysis of microarray and RNAseq dataFor microarray analysis RNA from three biological replicates per conditions was prepared and hybridized to Illumina Mouseref-8 v2.0 beadchips according to the manufacturers specifications. Normalization and statistical analysis were carried out with GeneSpring software (Agilent). Probes were considered de-regulated if there was >=1.5 fold differential expression with Benjamini-Hochberg-corrected p-value <0.05 (t-test). Generation of RNA-seq data will be described elsewhere (manuscript in preparation). We obtained a total of 48.3 million pairs 75bp paired-end reads for quiescent NSCs and 98.8 million pairs for proliferating NSCs. We first filtered out the first 11 bases of all reads using FASTX-toolkit version 0.0.13 (see URLs). We then mapped the filtered reads to the mm9 mouse genome using TopHat PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5UcmFwbmVsbDwvQXV0aG9yPjxZZWFyPjIwMTI8L1llYXI+
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ADDIN EN.CITE.DATA (Trapnell et al. 2012)? version 1.3.0 to estimate expression level of the genes defined in ENSEMBL version 61, specifying in addition the following parameters: --frag-bias-correct --upper-quartile-norm –multi-read-correct. Finally we used Cuffdiff, included in the Cufflinks package, to perform the differential expression analysis of the quiescent NSC and proliferating NSC datasets. As differentially expressed genes we take those that have a p-value lower than or equal to 0.05 and excluded genes with a reported FPKM of zero in either cell state.Mice and ImmunohistochemistryTo characterize expression patterns of NFI factors in the Dentate Gyrus NSCs, we used wildtype MF1 mice. To test the function of Nfix in post-natal neurogenesis we used mice carrying an allele of Nfix that lacks exon 2 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DYW1wYmVsbDwvQXV0aG9yPjxZZWFyPjIwMDg8L1llYXI+
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ADDIN EN.CITE.DATA (Campbell et al. 2008). We have previously demonstrated that these mice do not produce NFIX protein PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DYW1wYmVsbDwvQXV0aG9yPjxZZWFyPjIwMDg8L1llYXI+
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dGU+
ADDIN EN.CITE.DATA (Campbell et al. 2008). Postnatal pups were transcardially perfused with 0.9% saline, followed by 4% paraformaldehyde (PFA), then postfixed in 4% PFA at 4C and 40?m vibratome sections were cut for immunohistochemistry. Immunohistochemistry was performed on free-floating sections in 10% normal donkey serum and 0.1% Triton-x100 according to standard protocols. Antibodies used were rabbit-anti-NFIX (Abcam ab101341, 1:500 or Geneka, 16021118, 1:2000), rat anti-GFAP (Invitrogen 13-0300, 1:500), chicken-anti-GFAP (Invitrogen, 131-17719, 1:600), mouse-anti-S100? (Sigma, SAB1402349, 1:1000), mouse-anti-Ki67 (BD Pharmingen, 550609, 1:200), rabbit-anti-Ki67 (Novocastra NCL0Ki67p, 1:200), mouse-anti-MCM2 (BD Transduction Laboratories, 1:50), rat anti-BrdU (AbD Serotec OBT0030CX, 1:100) and mouse anti-Nestin (Millipore MAB353, 1:200). All work with laboratory mice was conducted according to the relevant national and international guidelines and regulations.URLs FASTX-toolkit, References ADDIN EN.REFLIST Campbell CE, Piper M, Plachez C, Yeh YT, Baizer JS, Osinski JM, Litwack ED, Richards LJ, Gronostajski RM. 2008. The transcription factor Nfix is essential for normal brain development. BMC Dev Biol 8: 52.Castro DS, Martynoga B, Parras C, Ramesh V, Pacary E, Johnston C, Drechsel D, Lebel-Potter M, Garcia LG, Hunt C et al. 2011. A novel function of the proneural factor Ascl1 in progenitor proliferation identified by genome-wide characterization of its targets. Genes Dev 25: 930-945.Conti L, Pollard SM, Gorba T, Reitano E, Toselli M, Biella G, Sun Y, Sanzone S, Ying QL, Cattaneo E et al. 2005. Niche-independent symmetrical self-renewal of a mammalian tissue stem cell. PLoS Biol 3: e283.Kel AE, Gossling E, Reuter I, Cheremushkin E, Kel-Margoulis OV, Wingender E. 2003. MATCH: A tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res 31: 3576-3579.Langmead B. 2010. Aligning short sequencing reads with Bowtie. Curr Protoc Bioinformatics Chapter 11: Unit 11 17.Li L. 2009. GADEM: a genetic algorithm guided formation of spaced dyads coupled with an EM algorithm for motif discovery. J Comput Biol 16: 317-329.Machanick P, Bailey TL. 2011. MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics 27: 1696-1697.McLean CY, Bristor D, Hiller M, Clarke SL, Schaar BT, Lowe CB, Wenger AM, Bejerano G. 2010. GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol 28: 495-501.Morgulis A, Gertz EM, Schaffer AA, Agarwala R. 2006. A fast and symmetric DUST implementation to mask low-complexity DNA sequences. J Comput Biol 13: 1028-1040.Thomas-Chollier M, Herrmann C, Defrance M, Sand O, Thieffry D, van Helden J. 2012. RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets. Nucleic Acids Res 40: e31.Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L. 2012. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7: 562-578.Zang C, Schones DE, Zeng C, Cui K, Zhao K, Peng W. 2009. A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics 25: 1952-1958.Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W et al. 2008. Model-based analysis of ChIP-Seq (MACS). Genome Biol 9: R137. ................
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