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      NUCOME: A comprehensive database of nucleosome organization referenced landscapes in mammalian genomes

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      BMC Bioinformatics

      BioMed Central

      Nucleosome, Database, Transcriptional regulation, MNase

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          Abstract

          Background

          Nucleosome organization is involved in many regulatory activities in various organisms. However, studies integrating nucleosome organization in mammalian genomes are very limited mainly due to the lack of comprehensive data quality control (QC) assessment and uneven data quality of public data sets.

          Results

          The NUCOME is a database focused on filtering qualified nucleosome organization referenced landscapes covering various cell types in human and mouse based on QC metrics. The filtering strategy guarantees the quality of nucleosome organization referenced landscapes and exempts users from redundant data set selection and processing. The NUCOME database provides standardized, qualified data source and informative nucleosome organization features at a whole-genome scale and on the level of individual loci.

          Conclusions

          The NUCOME provides valuable data resources for integrative analyses focus on nucleosome organization. The NUCOME is freely available at http://compbio-zhanglab.org/NUCOME.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12859-021-04239-9.

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          Most cited references 27

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          A barrier nucleosome model for statistical positioning of nucleosomes throughout the yeast genome.

          Most nucleosomes are well-organized at the 5' ends of S. cerevisiae genes where "-1" and "+1" nucleosomes bracket a nucleosome-free promoter region (NFR). How nucleosomal organization is specified by the genome is less clear. Here we establish and inter-relate rules governing genomic nucleosome organization by sequencing DNA from more than one million immunopurified S. cerevisiae nucleosomes (displayed at http://atlas.bx.psu.edu/). Evidence is presented that the organization of nucleosomes throughout genes is largely a consequence of statistical packing principles. The genomic sequence specifies the location of the -1 and +1 nucleosomes. The +1 nucleosome forms a barrier against which nucleosomes are packed, resulting in uniform positioning, which decays at farther distances from the barrier. We present evidence for a novel 3' NFR that is present at >95% of all genes. 3' NFRs may be important for transcription termination and anti-sense initiation. We present a high-resolution genome-wide map of TFIIB locations that implicates 3' NFRs in gene looping.
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            Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation

            Introduction Somatic stem cells replenish many tissues throughout life. In general, they have slow turnover and reside in specialized niches, protected from the environment, so that only a few are activated at a time. Thus, stem cells are a defense against aging, replacing cells lost through attrition. If the rejuvenating effect of stem cells were perfect, senescing cells would be replaced indefinitely; but even in highly regenerative tissues such as the skin, the gut, and the hematopoietic system, age-related decline in function is well established [1]. Still unclear are the effects of aging on the stem cells themselves, which could contribute to inferior tissue repair. Hematopoietic stem cells (HSCs) continuously replenish the blood and immune system throughout life. Data from mice support an age-related decline in stem cell function [1], suggesting that older HSCs are inadequate to cope with the demands of blood production. When limited numbers of aged hematopoietic progenitors are transplanted into young recipients under competitive conditions, they show an overall reduction in long-term repopulating potential [2]; in particular, lymphopoiesis is deficient, whereas myelopoiesis is enhanced [3,4]. Paradoxically, however, the total number of primitive progenitors has been reported to increase with age in the C57Bl/6 mice [2,5]. A recent study of aged hematopoietic stem and progenitor cells suggested that increased expression of particular proto-oncogenes may underlie some of these observed changes [4]. Although the previous studies varied widely, the findings provide compelling evidence for major age-related alterations in HSC function. To gain insight into the molecular mechanisms that underlie these deficits, we examined gene expression in HSC as a function of age on a genome-wide scale in normal and an early-aging p53 mutant strain. These data provide a comprehensive molecular portrait of aging in HSC, and show that stem cell aging mirrors the aging of other tissues, marked by a dramatic inflammatory response, stress responses, and substantial alterations in the regulation of chromatin structure. Results Phenotypically Defined HSCs Increase in Number with Age and Possess a Functional Defect The number of whole bone marrow (WBM) progenitors defined by cell surface markers in C57Bl/6 mice increases with age relative to total WBM cellularity [2,5]. To assess whether this property extends to HSCs purified using the side population (SP) cells defined by their ability to efflux Hoechst 33342 dye [6], we examined the SP cells in C57Bl/6 mice ranging from 2 to 21 mo of age. The results demonstrate a 9-fold increase in the number of SP cells with age (Figure 1A), with the most primitive SPlow cells [7] showing the greatest increase. These SP cells also exhibit the surface phenotype of HSC regardless of age (c-kitpos, lineageneg, and Sca-1pos [SParKLS]), consistent with their high degree of purity and homogeneity (Figures 1B and S1). Aged HSCs were uniformly CD48neg, which is one of the markers recently described to mark differentiated hematopoietic cells from both young and aged mice [8,9] (unpublished data). Thus, murine HSCs defined by multiple phenotypes increase 9-fold in WBM over approximately 2 y. The increase in cell number was not a result of greater proportion of S-phase HSCs, as determined by propidium iodide staining (Figure 1C), suggesting an alternative mechanism for the increase in HSC number. Figure 1 Aging HSC Phenotypes and Functional Alterations (A) Hoechst dye efflux by HSCs results in a SP (boxed) when viewed at two emission wavelengths. Comparison of the proportions of Sca-1–enriched SP cells from C57Bl/6 mice at 2 and 21 mo of age shows an approximate 9-fold increase with age. (B) Expression of the two canonical stem cell markers, c-Kit and Sca-1, does not change significantly between 2 and 21 mo of age within the lineage-negative (Lin−) SP population, indicating the SP cells remain remarkably phenotypically pure and homogeneous. (C) Cell cycle analysis by propidium iodide staining of 2- and 23-mo-old HSCs purified on the basis of SParKLS. (D) Limiting dilution functional assay of HSCs. In competitive repopulation experiments, there was little difference in HSC activity 4 wk after transplantation in young versus old HSCs. However, at 8 and 16 wk post-transplantation, 21-mo-old HSCs showed a reduced contribution compared to 2-mo-old control HSCs, depending on the donor cell dose (a single asterisk [*] indicates p ≤ 0.03; double asterisks [**] indicate p ≤ 0.09). Error bars represent one standard error. Limiting dilution bone marrow transplantation can measure the ability of HSCs to reconstitute recipients under competitive conditions and the functional purity of HSC [10]. HSCs (SParKLS) were therefore purified from either 2- or 21-mo-old mice and transplanted into lethally irradiated recipients, along with competitor bone marrow. Progeny from donor HSC were distinguished from competitor and recipient cells using the CD45 allelic system [11]. The proportion of peripheral blood progeny derived from purified young and old HSC was monitored at 4, 8, and 16 wk post-transplantation. Four weeks after transplantation, there was little difference in the contribution of young versus old HSCs (Figure 1D), but at 8 and 16 wk post-transplant, the contribution from the old HSCs was significantly reduced, but still multilineage (Figure S2). This finding argues that HSCs acquire a defect in long-term, but not short-term, repopulating potential with increasing age. This deficit represents roughly a 3-fold loss in functional activity per purified stem cell. With a 5- to 10-fold numerical increase in HSC, this indicates that the total stem cell activity remains fairly constant with age, which is consistent with other reports [2,12]. Gene Expression Changes in Aging HSCs by Microarray Analysis To identify transcriptional changes in aged HSCs that correlate with the observed functional deficit, we examined the expression of more than 14,000 genes, using Affymetrix MOE430a microarrays and HSCs purified from 2-, 6-, 12-, and 21-mo-old mice. A quadratic trend line (parabola) was fit for each gene over the 19-mo test period, which showed that the genes generally either increased or decreased in expression in a time-dependent fashion. We used a linear contrast model based on the entire observation course to determine which genes had the largest changes in expression over time. This revealed 1,600 genes that were up-regulated at 21 mo (“Up-with-Age” group), and 1,500 that were down-regulated (“Down-with-Age” group), which is summarized as a heat map in Figure 2. A small hand-picked list is shown in Table 1; the entire list of differentially expressed genes is supplemented in Tables S1 and S2, and a searchable database of all genes on the array can be found at http://rd.plos.org/pbio.0050201. Expression changes of a subset of these genes were validated by real-time quantitative PCR in duplicate on independently purified HSC (Figure S3). We also compared transcriptional profiles for WBM versus HSCs to identify HSC-specific transcripts; surprisingly, only a modest overlap of genes was found with those that were up-regulated or down-regulated with age, suggesting that the HSC-specific transcriptional programs remain relatively stable as the organism ages (Figure 2). A remarkable overlap was found between genes up- and down-regulated with age in this study and a previous study of HSC aging, with the top ten genes being identical [4]. Figure 2 Gene Expression in HSC throughout Aging The heat maps show expression levels for four different gene lists, with the degree of overlap among the lists indicated on the right. Color intensity indicates level of expression, where blue signifies low expression and red signifies high expression. Each column delineates the mean expression at 2, 6, 12, and 21 mo, and each row represents a given gene within each gene list. “Expressed in HSCs” refers to genes derived from a comparison of HSCs versus WBM. Table 1 Selected Genes Differentially Expressed during HSC Aging Gene Ontology Categories Enriched for Age-Induced or Age-Repressed Genes We next sought to identify biological processes that were enriched in age-induced or age-repressed genes, compared to chance alone. For this purpose, we used Gene Ontology (GO; http://www.geneontology.org) to group genes on the basis of a particular biological process [13], and identified GO categories that were enriched with statistical significance by a method previously reported [14]. When applied to the Up-with-Age gene list, the analysis revealed a large number of enriched categories that have been linked to aging in general, such as NO-mediated signal transduction, the stress response (protein folding), and the inflammatory response, whereas categories enriched for Down-with-Age genes often included those involved in the preservation of genomic integrity, such as chromatin remodeling and DNA repair (Figure 3A) (the entire GO results can be found at http://rd.plos.org/pbio.0050201). Figure 3 Gene Ontology Analysis (A) Fold enrichment over chance for selected GO categories of the Up-with-Age (red) and Down-with-Age (blue) gene lists. Bars without asterisks, p-value ≤ 0.05. Triple asterisks (***) indicate p ≤ 0.005. The number of genes found within each gene list and found on the entire array are shown for each GO category. (B) GO-timer T 1/2-max for selected GO categories as a function of density over time. Areas of color correspond to the time at which a GO category is undergoing the most rapid up-regulation (red) or down-regulation (blue). It is important to note that after a given GO category T 1/2-max, the expression remains up-regulated (red) or down-regulated (blue). NF-κB and P-Selectin Are Activated in Aged HSCs A link between aging and inflammation has been demonstrated in several vertebrate models and in humans [15], and we found evidence for the age-dependent regulation of several stress-related genes in HSCs. One of the most highly up-regulated of these genes expresses P-selectin, a cell surface adhesion molecule that serves as a marker for physiological stress states, including inflammation [16], aging [17], and cardiovascular disease [18]. P-selectin expression in HSCs, was of particular interest because it mediates the leukocyte–vascular endothelium interaction important for leukocyte extravasation [16] and therefore has implications for HSC migration. Flow cytometric analysis demonstrated increasing levels of P-selectin on the surface of HSCs isolated from 24- to 28-mo-old mice (21%–81%, Figure 4A), in contrast to scant levels (3%) on HSCs from young mice. Figure 4 Up-Regulation of P-selectin Cell Surface Expression and NF-κB Localization in Aged HSCs. (A) An increasing percentage of HSCs express P-selectin (SelP) when examined by FACS, ranging from 3% (2-mo-old HSCs) to 81% (28-mo-old HSCs). FITC, fluorescein isothiocyanate; PE, phycoerythrin. (B and C) HSCs stained with anti-p65 NF-kB antibody (red) and DAPI (blue). Two-month-old HSCs contain approximately 3% nuclear-localized p65; however, at 22 mo, approximately 71% show nuclear-localized p65. We hypothesized that the p65 isoform of NF-κB, which transcriptionally regulates P-selectin [19] would be activated in aged HSCs. To test this, we purified HSCs from 2- and 22-mo-old mice, and examined them for p65 localization by immunofluorescence. In contrast to only 3% of 2-mo-old HSCs, 71% of 22-mo-old HSCs showed enhanced nuclear localization of p65 protein (Figure 4B and 4C). These results implicate NF-κB activation as the mechanism of increased P-selectin expression in aged HSCs, most likely reflecting a time-dependent rise in inflammation. Timing of Gene Induction/Repression in Aging HSCs The time course of data allowed us to examine the timing of changes in age-regulated gene expression. We determined when the trend line for each given gene achieved half its maximum change over the full time course (T 1/2-max), then grouped the genes by GO category and plotted the results for those categories that had a significant enrichment in the previous analysis (Figure 3A), creating a GO-timer. As shown in Figure 3B, genes that participate in NO-mediated signal transduction were the first to be up-regulated during HSC aging, followed closely by those contributing to the stress response and the regulation of lymphocyte proliferation. Inflammatory-response genes were not activated until late in the aging process, after up-regulation of NF-κB signaling, strengthening our hypothesis that inflammation exerts a strong influence on HSC aging through stimulation of the NF-κB pathway. Complete GO-timer results can be found at http://rd.plos.org/pbio.0050201. Centers-of-Regulated Expression Analysis In Saccharomyces cerevisiae, the chromatin regulatory factor Sir2, a NAD-dependent histone deacetylase, suppresses recombination and silences transcription at multiple genomic loci [20]; its loss is associated genomic instability and aging. Since genes involved in chromatin remodeling and transcriptional silencing were excessively down-regulated in our GO enrichment analysis, we predicted global dysregulation of transcriptional activity. We reasoned that this would be evidenced by finding regions of chromosomes in which genes that were physically clustered together changed coordinately with age. To test this idea, genes were ordered by their chromosomal position, and age-induced and age-repressed genes were mapped using a density-based statistical approach. The result was a single curve across each chromosome, with peaks representing regions of coordinate up-regulation, and valleys regions of coordinate down-regulation (Figure 5A). Chromosomal loci with significant coordinate changes in gene expression were identified as centers of regulated expression (COREs; Figure 5A, red lines). Using this method, we found more than 100 such COREs among the 19 mouse autosomes (Table S3). Importantly, there were twice as many CORE peaks as there were valleys, indicating a predominance of a loss of transcriptional silencing throughout the genome. Figure 5 Density Plots of Coordinately Regulated Gene Expression for Chromosomes 4, 7, 10, and 13. (A) The black line represents the local density of coordinate regulation for all unique microarray probes. A positive value (peak) indicates a region where there are a greater number of up-regulated genes, whereas a negative value (valley) corresponds to a region of several down-regulated genes. The red vertical line indicates a CORE that extends beyond the threshold of significance (blue and red lines; p 2-fold), which simply conveys a degree of difference (contrast) in gene expression over time. Gene Ontology analyses. To investigate the biological significance of the gene lists described above, we used GO (http://www.geneontology.org). GO is a controlled vocabulary that describes gene biological roles and is arranged in a quasi-hierarchical structure from more general terms to the more specific. After mapping each gene in the two lists to the GO tree structure, we determined the number of genes at or below any given node in the GO hierarchy and the amount of statistically significant enrichment (Fisher exact p-value) for each GO node relative to chance observation, using a previously developed procedure [54]. To assess the emergence and disappearance of enriched GO categories, we defined the time of half-maximal expression change (T 1/2-max) for each gene in each category over the time interval. For genes whose maximal expression values were outside the 2- to 21-mo interval, the T 1/2-max was determined as the intermediate expression value between the expression at 2 and 21 mo. For genes whose extreme expression values were within the interval, the T 1/2-max was determined as the intermediate expression value between the expression at 2 mo and that extrema. Genes were grouped by GO category, yielding reliable estimates of time of induction and reduction for a given biological process. We conducted this analysis separately for the Up-with-Age and Down-with-Age gene lists. CORE analysis method. To identify COREs, we obtained the genome coordinates of the Affymetrix MOE430A array from the MM5 build of the UCSC Genome Browser. To compare the locations of age-induced or age-repressed genes, we divided all genes into two disjoint classes based on the sign of the 21-mo versus 2-mo contrast. Redundant probe sets were removed by grouping all probe sets by Entrez Gene annotation. Because not all probe sets for a single Entrez Gene identifier have the same sign for the contrast score, the sign of the mean value was assigned to the Entrez Gene identifier. To compare the positions of these locations, we constructed a Gaussian kernel density estimate by chromosomal position for genes that increased with age and genes that decreased with age. We then calculated a ratio of these density estimates for the two groups. This ratio represents the density of genes that increase (peak) or decrease (valley) with age. A permutation test was performed to estimate the p-value where gene locations were randomly swapped along each chromosome, maintaining gene density but randomizing direction of regulation (up/down), Density estimate ratios were calculated based on 1,000 random permutations. This calculation enabled us to estimate a threshold of statistical significance such that peaks and valleys (high densities of age-induced and age-repressed genes) exceeding the 0.025 and 0.975 permutation-based quantiles were judged to be statistically significant at an estimated p-value of 0.05. Immunoglobulin assays. Purified cells were sorted into lysis/PCR buffer, and PCR was performed as previously reported [24]. For GL RT-PCR, approximately 20,000 HSCs from either young or old mice were sorted into HBSS, and RNA was isolated by the RNAqueous kit (Ambion). RT-PCR was performed with an oligo-dT primer and SuperScript II (Invitrogen, http://www.invitrogen.com) followed by 50 cycles of PCR. RT-PCR fragments were purified, cloned into the Topo 2.1 vector (Invitrogen), and sequenced. IgH recombination primers are previously published [24]. IgK GL transcript primers include 5′-CTTCAGTGAGGAGGGTTTTTG-3′ (forward 1), 5′-ACTATGAAAATCAGCAGTTCTC-3′ (forward 2), and 5′-CGTTCATACTCGTCCTTGGTC-3′ (reverse). p53 Gene Ontology analysis. To assess the age-related expression differences between the p53+/m and p53+/− mice, genes with best-fitting trend lines (R 2 > 0.50) from the WT HSC aging time course were selected, and a predicted age (in months) was extrapolated for each gene based on the level of expression for both the p53+/m and p53+/− 12-mo-old mice. Genes were grouped on the basis of GO for both phenotypes and the categories with a significant shift in age (Wilcoxon t-test) between the p53+/m and p53+/− mice were identified by a p-value ≤ 0.05 and a median aged difference of greater than 1 mo. Mice used in these experiments have been back-crossed onto the C57Bl/6 background for four or more generations. Datasets All data can be downloaded from our Web site http://rd.plos.org/pbio.0050201. In addition, all microarray data files have been deposited in the Gene Expression Omnibus (accession number GSE6503). Supporting Information Figure S1 Fluorescence Activated Cell Sorter Analysis of SP Cell Lineage Expression Young (2 Mo) and Old (21 Mo) SP cells express very low levels of differentiated cell surface lineage markers (Gr-1, Mac-1, B220, Ter119, CD4, and CD8). (492 KB PDF) Click here for additional data file. Figure S2 Fluorescence Activated Cell Sorter Analysis of Peripheral Blood Contribution of HSC from 21-Mo-Old Mice after Transplant HSC from old mice reconstitute all three lineages of the peripheral blood including myeloid (Gr-1 and Mac-1), B cell (B220), and T cell (CD4,8) at both 4 and 16 wks post-transplant. (704 KB PDF) Click here for additional data file. Figure S3 Real-Time PCR of selected Up-Regulated HSC Aging Genes Error bars represent standard error from two separate experiments. mRNA was purified from HSC sorted independently from the HSC used in the microarray studies. (441 KB PDF) Click here for additional data file. Figure S4 CD19 and IL-7r Are Not Expressed on HSC and PCR-Based IgH Recombination Assay (A) Young (red line) and old (green line) HSC do not express CD19 or IL7r compared to WBM (black line) on the basis of fluorescence activated cell sorting (FACS). (B) The presence of DNA by a-actin (“A”), IgH GL locus (“Ig”), and recombined locus (“R”) have been examined using PCR in several populations including spleenocytes, B cells (B220+ Mac-1−), myeloid cells (Mac-1+ B220−), 2-mo-old HSC, 21-mo-old HSC, and 21-mo-old myeloid cells. No recombination was detected in any HSC. (2.3 MB PDF) Click here for additional data file. Figure S5 Single HSC Methylcellulose Assays Single HSC from WT, p53 +/−, and p53 +/m 12-mo-old mice were sorted into 96-well plates containing methylcellulose (M3434; Stem Cell Technologies, http://www.stemcell.com) and allowed to form colonies for 14 d. p53 +/m HSC were found to give rise to significantly smaller colonies (a single asterisk [*] indicates p-value ≤ 0.004) when ten colonies were pooled and the average number of cells per colony was determined for 60 colonies (n = 6) for each genotype. All three genotypes formed colonies at approximately the same frequency as shown in the table based on the percent of wells containing a colony (96-well plate). (484 KB PDF) Click here for additional data file. Table S1 Up-with-Age in HSC Gene List (311 KB XLS) Click here for additional data file. Table S2 Down-with-Age in HSC Gene List (292 KB XLS) Click here for additional data file. Table S3 Table for COREs (245 KB XLS) Click here for additional data file. Table S4 Genes Up in p53+/m Compared to p53+/− HSC (125 KB XLS) Click here for additional data file. Table S5 Genes Up in p53+/− Compared to p53+/m HSC (105 KB XLS) Click here for additional data file. Table S6 Gene Ontology Enrichment Results for Up in p53+/m HSC (58 KB XLS) Click here for additional data file. Table S7 Gene Ontology Enrichment Results for Up in p53+/− HSC (77 KB XLS) Click here for additional data file. Table S8 Gene Ontology Table of Age Differences between p53+/− and p53+/m HSC (24 KB XLS) Click here for additional data file. Accession Numbers Entrez Gene (http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene) ID accession numbers for the genes discussed in this paper are App (11820), Blm (12144), CatnB (12387), Cct6a (12466), CD150 (6504), CD19 (12478), CD45 (19264), CD48 (12506), c-Kit (16590), Clu (12759), Cox2 (19225), Ctsb (13030), Ctsc (13032), Ctss (13040), Dnaja1 (15502), Dnaja2 (56445), Dnaja2 (56445), Dnajb10 (56812), Dnajb6 (23950), Dnajc3 (19107), Dnmt3b (13436), Dnmt3b (13436), Eng (13805), Hdac1 (433759), Hdac5 (15184), Hdac6 (15185), Hdac6 (15185), Hspa5 (14828), Hspa8 (15481), Hspca (15519), Icam1 (15894), IgH (111507), IgK (243469), Il-7r (16197), Lmna (16905), Madh4 (17128), p53 (22059), p65 (19697), Pml (18854), Rad52 (19365), Runx1 (12394), Sca-1 (110454), Selp (25651), Sirt2 (64383), Sirt3 (64384), Sirt7 (209011), Smarca4 (20586), Smarcb1 (20587), Spnb2 (20742), Tlr4 (21898), Xab2 (67439), and Xrcc1 (22594).
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              MeCP2 binds to non-CG methylated DNA as neurons mature, influencing transcription and the timing of onset for Rett syndrome.

              Epigenetic mechanisms, such as DNA methylation, regulate transcriptional programs to afford the genome flexibility in responding to developmental and environmental cues in health and disease. A prime example involving epigenetic dysfunction is the postnatal neurodevelopmental disorder Rett syndrome (RTT), which is caused by mutations in the gene encoding methyl-CpG binding protein 2 (MeCP2). Despite decades of research, it remains unclear how MeCP2 regulates transcription or why RTT features appear 6-18 months after birth. Here we report integrated analyses of genomic binding of MeCP2, gene-expression data, and patterns of DNA methylation. In addition to the expected high-affinity binding to methylated cytosine in the CG context (mCG), we find a distinct epigenetic pattern of substantial MeCP2 binding to methylated cytosine in the non-CG context (mCH, where H = A, C, or T) in the adult brain. Unexpectedly, we discovered that genes that acquire elevated mCH after birth become preferentially misregulated in mouse models of MeCP2 disorders, suggesting that MeCP2 binding at mCH loci is key for regulating neuronal gene expression in vivo. This pattern is unique to the maturing and adult nervous system, as it requires the increase in mCH after birth to guide differential MeCP2 binding among mCG, mCH, and nonmethylated DNA elements. Notably, MeCP2 binds mCH with higher affinity than nonmethylated identical DNA sequences to influence the level of Bdnf, a gene implicated in the pathophysiology of RTT. This study thus provides insight into the molecular mechanism governing MeCP2 targeting and sheds light on the delayed onset of RTT symptoms.
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                Author and article information

                Contributors
                gfliu0806@tongji.edu.cn
                yzhang@tongji.edu.cn
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                13 June 2021
                13 June 2021
                2021
                : 22
                Affiliations
                GRID grid.24516.34, ISNI 0000000123704535, Institute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Science and Technology, , Tongji University, ; 1239 Siping Road, Shanghai, 200092 China
                Article
                4239
                10.1186/s12859-021-04239-9
                8201709
                34120586
                © The Author(s) 2021

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31970642
                Award ID: 31721003
                Award ID: 32030022
                Award Recipient :
                Funded by: National Key Research and Development Program of China
                Award ID: 2017YFA0102600
                Award ID: 2016YFA0100400
                Award Recipient :
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                © The Author(s) 2021

                Bioinformatics & Computational biology

                nucleosome, database, transcriptional regulation, mnase

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