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BMC Genomics
2022 Jan 04;231:2. doi: 10.1186/s12864-021-08247-0.
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Developmental and Injury-induced Changes in DNA Methylation in Regenerative versus Non-regenerative Regions of the Vertebrate Central Nervous System.
Reverdatto S
,
Prasad A
,
Belrose JL
,
Zhang X
,
Sammons MA
,
Gibbs KM
,
Szaro BG
.
Abstract
BACKGROUND: Because some of its CNS neurons (e.g., retinal ganglion cells after optic nerve crush (ONC)) regenerate axons throughout life, whereas others (e.g., hindbrain neurons after spinal cord injury (SCI)) lose this capacity as tadpoles metamorphose into frogs, the South African claw-toed frog, Xenopus laevis, offers unique opportunities for exploring differences between regenerative and non-regenerative responses to CNS injury within the same organism. An earlier, three-way RNA-seq study (frog ONC eye, tadpole SCI hindbrain, frog SCI hindbrain) identified genes that regulate chromatin accessibility among those that were differentially expressed in regenerative vs non-regenerative CNS [11]. The current study used whole genome bisulfite sequencing (WGBS) of DNA collected from these same animals at the peak period of axon regeneration to study the extent to which DNA methylation could potentially underlie differences in chromatin accessibility between regenerative and non-regenerative CNS.
RESULTS: Consistent with the hypothesis that DNA of regenerative CNS is more accessible than that of non-regenerative CNS, DNA from both the regenerative tadpolehindbrain and frog eye was less methylated than that of the non-regenerative frog hindbrain. Also, consistent with observations of CNS injury in mammals, DNA methylation in non-regenerative frog hindbrain decreased after SCI. However, contrary to expectations that the level of DNA methylation would decrease even further with axotomy in regenerative CNS, DNA methylation in these regions instead increased with injury. Injury-induced differences in CpG methylation in regenerative CNS became especially enriched in gene promoter regions, whereas non-CpG methylation differences were more evenly distributed across promoter regions, intergenic, and intragenic regions. In non-regenerative CNS, tissue-related (i.e., regenerative vs. non-regenerative CNS) and injury-induced decreases in promoter region CpG methylation were significantly correlated with increased RNA expression, but the injury-induced, increased CpG methylation seen in regenerative CNS across promoter regions was not, suggesting it was associated with increased rather than decreased chromatin accessibility. This hypothesis received support from observations that in regenerative CNS, many genes exhibiting increased, injury-induced, promoter-associated CpG-methylation also exhibited increased RNA expression and association with histone markers for active promoters and enhancers. DNA immunoprecipitation for 5hmC in optic nerve regeneration found that the promoter-associated increases seen in CpG methylation were distinct from those exhibiting changes in 5hmC.
CONCLUSIONS: Although seemingly paradoxical, the increased injury-associated DNA methylation seen in regenerative CNS has many parallels in stem cells and cancer. Thus, these axotomy-induced changes in DNA methylation in regenerative CNS provide evidence for a novel epigenetic state favoring successful over unsuccessful CNSaxon regeneration. The datasets described in this study should help lay the foundations for future studies of the molecular and cellular mechanisms involved. The insights gained should, in turn, help point the way to novel therapeutic approaches for treating CNS injury in mammals.
Figure 1. Chromosome-wide overview of injury-induced changes in DNA methylation for regenerative (tadpole) vs. non-regenerative (post-metamorphic frog) hindbrain after spinal cord injury (SCI) for two representative chromosomes (180 Mb of Chr 2L, top; 104 Mb of Chr 9_10S, bottom) as revealed by whole genome bisulfite sequencing (WGBS). Tracks for regenerative tadpole and non-regenerative frog hindbrain are grouped separately (Tad, top; Frog, bottom). For each chromosome, the vertical scales, which indicate the level of methylation (5mC) in each methylation context (CpG, dark green; CHG, olive green; CHH, navy blue), were group-autoscaled across tadpole and frog SCI and controls to facilitate comparisons between injury conditions (SCI vs. control) and developmental stage (tadpole vs. frog). Methylation differences between SCI and control (ΔCpG, ΔCHG, ΔCHH) indicate log2(SCI 5mC/control 5mC). The resulting increased (>0) and decreased methylation (<0) levels are shown in light green vs. blue, above and below the horizontal axes, respectively. Changes in RNA expression between SCI and control are also indicated (ΔRNA-Seq Tad and Frog; log2(SCI/control), with red and blue indicating increased and decreased expression, respectively [11]; note, RNA-Seq and WGBS were performed on RNA and DNA, respectively, isolated from the very same animals. H3K4me3 peaks at gastrulation (st. 10.5, [41]) and the locations of annotated genes (gene models: Mayball [21; 88; 89]; X. laevis v. 9.1 [122]) are also indicated. For all three DNA methylation contexts (CpG, CHG, CHH), methylation levels increased between tadpole and frog stages pervasively across the entire chromosome, and SCI induced opposite, pervasive methylation responses (ΔCpG, ΔCHG, ΔCHH) in tadpole vs. frog [increased (light green) vs. decreased (light blue) methylation, respectively]. As illustrated in these two representative examples, similar patterns were seen for all chromosomes, with no overall differences between L and S homeologous chromosomes
Figure 2. Quantitation of DNA methylation across the genome. Fraction of C’s exhibiting methylation marks in each context (A, % total 5mC; B, %CpG; C, %CHG; D, %CHH), as determined by WGBS, were averaged (±SE) across three biological replicates (5 pooled tadpole and frog hindbrains, 6 pooled frog eyes). One way ANOVA indicated that methylation differed significantly across all groups compared (P < 0.002). Results of post hoc comparisons are indicated by the brackets above (Fisher LSD; *, P < 0.05). See the text for further details concerning differences among conditions
Fig. 3. View of DNA methylation tracks (CpG, CHH, CHG) in hindbrain after SCI for representative genes known to be differentially expressed in successful vs. unsuccessful CNSaxon regeneration [11]. Description of tracks and abbreviations are as in Fig. 1. Whereas changes in CHH and CHG methylation are pervasive across the genome, spanning both inter and intra genic regions, changes in CpG methylation are primarily confined to regions spanning the transcriptional start site (red boxes), where CpG methylation levels are generally lower than elsewhere.
Fig. 4. Degree of CpG methylation surrounding the transcriptional start site (TSS; ±2.0 kb) correlated with RNA expression for regenerative (tadpole) and non-regenerative (frog) hindbrain before and after SCI. Top, average level of CpG-methylation for different levels of gene expression (Q1 to Q4, representing the 25% most highly to least expressed genes, respectively from a total of 45,099 gene models in X. laevis v.9.1 [122]). (+ or -) Δ, indicates the injury-induced changes for genes in Q1. x-axis units, distance from the predicted transcription start site (TSS) in kilobases (kb); y-axis units (Methylation Density), number of 5mCs in a 50 bp bin x 1 million/total number of Cs. Bottom, heatmaps of CpG methylation for each quartile, clustered from highest to lowest. The degree of CpG-methylation exhibited the expected negative correlation with RNA expression, but for the top two quartiles, it increased in regenerative hindbrain and decreased in non-regenerative hindbrain after SCI. The degree of CpG methylation is indicated by the intensity of the color, as indicated to the right of each heatmap (Methylation Density)
Fig. 5. Numbers of genes having promoters (defined as 750 bp upstream to 250 bp downstream of the TSS) harboring differentially CpG-methylated regions (CpG-DMRs) for the various tissue- and injury-related comparisons. Bars indicate the total number of genes (from a total of 45,099 gene models in X. laevis v.9.1 [122]) that harbored such CpG-DMRs. Numbers above each bar indicate the fraction of such genes with DMRs >0 between the first vs. the second listed condition (hyper-methylated CpG DMR; black). CpG DMRs between uninjured regenerative vs. non-regenerative tissues (i.e., tadpolehindbrain and frog eye vs. frog hindbrain, respectively) were predominantly hypomethylated (CpG DMR < 0). With CNS injury (SCI or optic nerve crush (ONC)), CpG DMRs were predominantly hyper-methylated between injury vs. control conditions in regenerative CNS (i.e., tadpole SCI hindbrain and frog ONC eye), and hypo-methylated in non-regenerative CNS (i.e., frog SCI hindbrain)
Fig. 6. Tissue- and Injury-related changes in CpG methylation across the TSS (-2 kb – +2 kb) for all genes in the genome (N = 45,099 gene models), correlated with differential gene expression. Top and bottom panels illustrate average density and heatmaps of CpG methylation across the TSS for genes in each differential expression grouping. Whether a gene fell into the RNA Up, RNA Down, or no significant change category was determined by RNA-seq from RNA samples previously collected [11] from the same animals analyzed here by WGBS. Comparisons indicate changes between the first vs. second conditions (e.g., negative differential methylation and RNA Up indicate hypo-methylation and significantly increased RNA expression, respectively, in the first vs. the second condition, etc.). Injury vs. control heatmaps for non-regenerative CNS (frog hindbrain) resembled those generated by comparisons between regenerative vs. non-regenerative CNS (tadpole and frog eye vs. frog hindbrain, respectively) and the opposite of what was seen with CNS injury in the two regenerative CNS regions (i.e., tadpole SCI hindbrain and frog ONC eye). x-axis units, as in Fig. 4; y-axis units, Δ5mC within a 50 bp bin x 1 million/total number of Cs
Fig. 7. Heatmaps of active histone marks (H3K4me3 and H3K27ac) across the TSS, relative to quartiles of RNA expression (A) and Promoter CpG-DMR’s (B) in regenerative CNS tissues after SCI (left) and ONC (right). A, the presence of active marks declined with decreasing levels of RNA expression (Q1 to Q4, most to least). B, active histone marks were more highly represented among DMRs exhibiting increased methylation with injury (DMR Up) than among those exhibiting decreased methylation (DMR DN). See Fig. 5 for total numbers of DMR-bearing genes in each category. x-axis units as in Fig. 4; y-axis units, number of mapped reads in a 50 bp bin x 1 million/total number of mapped reads
Fig. 8. Gene Ontologies (Metascape [116]) for genes exhibiting increased CpG-DMRs after SCI in tadpole (CNS axon regenerative) hindbrain. The twenty highest ranking categories (i.e., lowest probability (P) of arising by chance) are listed separately for up-regulated (top) and down-regulated (bottom) genes. Up-regulated genes were highly enriched for categories representing genes associated with DNA replication, repair, and covalent modification (red boxes). Injury-induced genes that decreased in expression had many genes associated with physiological functions and developmental functions (see text). Total numbers of genes in the top and bottom panels were 738 and 460, respectively
Fig. 9. In optic nerve injury, changes in 5-hydroxymethylcytosine are distinct from those of CpG 5mC. Chr 2L and 9_10S: as was seen for SCI in tadpolehindbrain (Fig. 1, illustrated for the same two chromosomes), ONC-induced increased CpG methylation (ΔCpG MS ONC: log2(MS-CpG ONC/MS-CpG control eye)) pervasively across the entire chromosome (light green vs light blue for increased vs. decreased CpG MS, respectively). In contrast, 5hmC (Δ5hmC ONC) exhibited both increases and decreases spread across the chromosome. ina.s and nefm.s, two neuronal intermediate filament genes that increase with ONC in retinal ganglion cells, illustrate the complexity of 5-hydroxymethylation. Red boxes, regions encompassing the TSS marked by active histone marks (H3K4me3, H3K27ac). Green box, region encompassing the TSS, that exhibited increased CpG methylation (ΔCpG MS ONC, light green). 1, blue box, in ina.s, region near the TSS exhibiting increased 5hmC with injury (Δ5hmC ONC, olive green), which mostly flanked that marked for increased CpG 5mC. Note, nefm.s, exhibited little to no 5hmC marks in the corresponding region. Blue ellipses: 2, 5hmC marks at the borders of intron 2 of ina.s. 3, 5hmC marking integrated adenoviral retroviral sequences in ina.s. 4, 5hmC marking the template strand of a region of exon 3 of nefm that is highly enriched for repetitive glutamates. See text for more details
Fig. 10. Additional examples of 5hmC vs CpG 5mC for representative genes changing in expression during ONC. Track labels, as well as red, green, and blue boxes, are as in Fig. 10. Magenta boxes, examples of increased H3K27ac marks induced by ONC. All genes exhibited the same behavior with respect to CpG as illustrated previously for ezh2.L, vim.L, idh1.L and jarid2.S in tadpolehindbrain after SCI (Fig. 3). Changes in 5hmC (Δ5hmC ONC: olive green, up; blue green, down) across the TSS were generally not congruent with those of CpG 5mC marks (blue boxes vs. green boxes, respectively). Blue ellipse, vim.L, an integrated adenoviral sequence marked by 5hmC
Fig. 11. Distributions of CpG 5mC surrounding the transcriptional start site (TSS; ± 2.0 kb) to ONC differed markedly from that of 5hmC. A, Although less pronounced than in tadpole SCI, the density of CpG DMRs across the TSS follows the same pattern as for tadpole SCI, with CpG 5mC DMRs increasing for the top two quartiles of RNA expression with injury. Units and labels are as in Fig. 4. B, In contrast, 5hmC showed no such response, and overall was markedly different from the pattern of CpG 5mC (see text for details). Units and labels are as in Fig. 7
Ananthakrishnan,
Dynamic regulation of middle neurofilament RNA pools during optic nerve regeneration.
2008, Pubmed,
Xenbase
Ananthakrishnan,
Dynamic regulation of middle neurofilament RNA pools during optic nerve regeneration.
2008,
Pubmed
,
Xenbase
Anderson,
Increased expression and localization of the RNA-binding protein HuD and GAP-43 mRNA to cytoplasmic granules in DRG neurons during nerve regeneration.
2003,
Pubmed
Avci,
Thyroid hormone triggers the developmental loss of axonal regenerative capacity via thyroid hormone receptor α1 and krüppel-like factor 9 in Purkinje cells.
2012,
Pubmed
Avraham,
Leptin induces neuroprotection neurogenesis and angiogenesis after stroke.
2011,
Pubmed
Bachman,
5-Hydroxymethylcytosine is a predominantly stable DNA modification.
2014,
Pubmed
Bagci,
Autocrine semaphorin 3A signaling promotes glioblastoma dispersal.
2009,
Pubmed
Bahar Halpern,
Paradoxical role of DNA methylation in activation of FoxA2 gene expression during endoderm development.
2014,
Pubmed
Barker,
MethyLock: DNA Demethylation Is the Epigenetic Key to Axon Regeneration.
2017,
Pubmed
Beattie,
Metamorphosis alters the response to spinal cord transection in Xenopus laevis frogs.
1990,
Pubmed
,
Xenbase
Beaver,
Continued neurogenesis is not a pre-requisite for regeneration of a topographic retino-tectal projection.
2001,
Pubmed
,
Xenbase
Belrose,
Comparative gene expression profiling between optic nerve and spinal cord injury in Xenopus laevis reveals a core set of genes inherent in successful regeneration of vertebrate central nervous system axons.
2020,
Pubmed
,
Xenbase
Berry,
Chromatin modification and epigenetic control in functional nerve regeneration.
2020,
Pubmed
Bogdanović,
DNA methylation and the preservation of cell identity.
2017,
Pubmed
Bolger,
Trimmomatic: a flexible trimmer for Illumina sequence data.
2014,
Pubmed
Bowes,
Xenbase: a Xenopus biology and genomics resource.
2008,
Pubmed
,
Xenbase
Carpenter,
Methylcytosine and normal cytosine deamination by the foreign DNA restriction enzyme APOBEC3A.
2012,
Pubmed
Chan,
Physical and functional interactions between hnRNP K and PRMT family proteins.
2009,
Pubmed
Chang,
Identification of the methylation preference region in heterogeneous nuclear ribonucleoprotein K by protein arginine methyltransferase 1 and its implication in regulating nuclear/cytoplasmic distribution.
2011,
Pubmed
Chatterjee,
Genome-wide methylation sequencing of paired primary and metastatic cell lines identifies common DNA methylation changes and a role for EBF3 as a candidate epigenetic driver of melanoma metastasis.
2017,
Pubmed
Chung,
Coordinated genomic control of ciliogenesis and cell movement by RFX2.
2014,
Pubmed
,
Xenbase
Cingolani,
Intronic non-CG DNA hydroxymethylation and alternative mRNA splicing in honey bees.
2013,
Pubmed
Deaton,
CpG islands and the regulation of transcription.
2011,
Pubmed
Dubey,
Reexpression of vimentin in differentiated neuroblastoma cells enhances elongation of axonal neurites.
2004,
Pubmed
Eagleson,
Fate of the anterior neural ridge and the morphogenesis of the Xenopus forebrain.
1995,
Pubmed
,
Xenbase
Edwards-Faret,
Cellular response to spinal cord injury in regenerative and non-regenerative stages in Xenopus laevis.
2021,
Pubmed
,
Xenbase
Ehrlich,
DNA cytosine methylation and hydroxymethylation at the borders.
2014,
Pubmed
Elurbe,
Regulatory remodeling in the allo-tetraploid frog Xenopus laevis.
2017,
Pubmed
,
Xenbase
Feng,
A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data.
2014,
Pubmed
Forehand,
Anatomical and behavioral recovery from the effects of spinal cord transection: dependence on metamorphosis in anuran larvae.
1982,
Pubmed
Gaspar,
DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data.
2017,
Pubmed
Gervasi,
Sequence and expression patterns of two forms of the middle molecular weight neurofilament protein (NF-M) of Xenopus laevis.
1997,
Pubmed
,
Xenbase
Gervasi,
Increased expression of multiple neurofilament mRNAs during regeneration of vertebrate central nervous system axons.
2003,
Pubmed
,
Xenbase
Gibbs,
Metamorphosis and the regenerative capacity of spinal cord axons in Xenopus laevis.
2011,
Pubmed
,
Xenbase
Gibbs,
Regeneration of descending projections in Xenopus laevis tadpole spinal cord demonstrated by retrograde double labeling.
2006,
Pubmed
,
Xenbase
Gibbs,
Tracing Central Nervous System Axon Regeneration in Xenopus.
2018,
Pubmed
,
Xenbase
Guillaumet-Adkins,
Hypermethylation of the alternative AWT1 promoter in hematological malignancies is a highly specific marker for acute myeloid leukemias despite high expression levels.
2014,
Pubmed
Guo,
Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain.
2014,
Pubmed
Gupta,
Developmental enhancers are marked independently of zygotic Nodal signals in Xenopus.
2014,
Pubmed
,
Xenbase
Herb,
Reversible switching between epigenetic states in honeybee behavioral subcastes.
2012,
Pubmed
Hochstein,
Epigenetic status of an adenovirus type 12 transgenome upon long-term cultivation in hamster cells.
2007,
Pubmed
Holt,
Cell movements in Xenopus eye development.
1980,
Pubmed
,
Xenbase
Iskandar,
Folate regulation of axonal regeneration in the rodent central nervous system through DNA methylation.
2010,
Pubmed
James-Zorn,
Xenbase: expansion and updates of the Xenopus model organism database.
2013,
Pubmed
,
Xenbase
Jang,
CpG and Non-CpG Methylation in Epigenetic Gene Regulation and Brain Function.
2017,
Pubmed
Jankowski,
Sox11 transcription factor modulates peripheral nerve regeneration in adult mice.
2009,
Pubmed
Joshi,
The MDM4/MDM2-p53-IGF1 axis controls axonal regeneration, sprouting and functional recovery after CNS injury.
2015,
Pubmed
Kakebeen,
Chromatin accessibility dynamics and single cell RNA-Seq reveal new regulators of regeneration in neural progenitors.
2020,
Pubmed
,
Xenbase
Karimi,
Xenbase: a genomic, epigenomic and transcriptomic model organism database.
2018,
Pubmed
,
Xenbase
Karp,
Design and analysis issues in quantitative proteomics studies.
2007,
Pubmed
Karp,
Experimental and statistical considerations to avoid false conclusions in proteomics studies using differential in-gel electrophoresis.
2007,
Pubmed
Kimura,
Exogenous expression of mouse Dnmt3 induces apoptosis in Xenopus early embryos.
2002,
Pubmed
,
Xenbase
Kriaucionis,
The nuclear DNA base 5-hydroxymethylcytosine is present in Purkinje neurons and the brain.
2009,
Pubmed
Krueger,
Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications.
2011,
Pubmed
Kyono,
DNA methylation dynamics underlie metamorphic gene regulation programs in Xenopus tadpole brain.
2020,
Pubmed
,
Xenbase
Kyono,
Developmental and Thyroid Hormone Regulation of the DNA Methyltransferase 3a Gene in Xenopus Tadpoles.
2016,
Pubmed
,
Xenbase
Kyono,
Liganded Thyroid Hormone Receptors Transactivate the DNA Methyltransferase 3a Gene in Mouse Neuronal Cells.
2016,
Pubmed
Langmead,
Fast gapped-read alignment with Bowtie 2.
2012,
Pubmed
Lee,
Differential landscape of non-CpG methylation in embryonic stem cells and neurons caused by DNMT3s.
2017,
Pubmed
Lee,
Transcriptional and Epigenomic Regulation of Adipogenesis.
2019,
Pubmed
Lee-Liu,
Genome-wide expression profile of the response to spinal cord injury in Xenopus laevis reveals extensive differences between regenerative and non-regenerative stages.
2014,
Pubmed
,
Xenbase
Li,
DNA methylation in mammals.
2014,
Pubmed
Lister,
Global epigenomic reconfiguration during mammalian brain development.
2013,
Pubmed
Lister,
Human DNA methylomes at base resolution show widespread epigenomic differences.
2009,
Pubmed
Liu,
The EZH2- H3K27me3-DNMT1 complex orchestrates epigenetic silencing of the wwc1 gene, a Hippo/YAP pathway upstream effector, in breast cancer epithelial cells.
2018,
Pubmed
Liu,
Heterogeneous nuclear ribonucleoprotein K, an RNA-binding protein, is required for optic axon regeneration in Xenopus laevis.
2012,
Pubmed
,
Xenbase
Loh,
Comprehensive mapping of 5-hydroxymethylcytosine epigenetic dynamics in axon regeneration.
2017,
Pubmed
Luo,
Dynamic DNA methylation: In the right place at the right time.
2018,
Pubmed
Madrid,
DNA methylation and hydroxymethylation have distinct genome-wide profiles related to axonal regeneration.
2021,
Pubmed
Martinez-De Luna,
Müller glia reactivity follows retinal injury despite the absence of the glial fibrillary acidic protein gene in Xenopus.
2017,
Pubmed
,
Xenbase
Mellén,
5-hydroxymethylcytosine accumulation in postmitotic neurons results in functional demethylation of expressed genes.
2017,
Pubmed
Meng,
Traumatic Brain Injury Induces Genome-Wide Transcriptomic, Methylomic, and Network Perturbations in Brain and Blood Predicting Neurological Disorders.
2017,
Pubmed
Neri,
Genome-wide analysis identifies a functional association of Tet1 and Polycomb repressive complex 2 in mouse embryonic stem cells.
2013,
Pubmed
Norsworthy,
Sox11 Expression Promotes Regeneration of Some Retinal Ganglion Cell Types but Kills Others.
2017,
Pubmed
Pauwels,
Maternal Methyl-Group Donor Intake and Global DNA (Hydroxy)Methylation before and during Pregnancy.
2016,
Pubmed
Perlson,
Vimentin-dependent spatial translocation of an activated MAP kinase in injured nerve.
2005,
Pubmed
Perrone-Bizzozero,
Post-transcriptional regulation of GAP-43 rnRNA levels during neuronal differentiation and nerve regeneration.
1991,
Pubmed
Pinney,
Mammalian Non-CpG Methylation: Stem Cells and Beyond.
2014,
Pubmed
Pollema-Mays,
Expression of DNA methyltransferases in adult dorsal root ganglia is cell-type specific and up regulated in a rodent model of neuropathic pain.
2014,
Pubmed
Popov,
Identification of new regulators of embryonic patterning and morphogenesis in Xenopus gastrulae by RNA sequencing.
2017,
Pubmed
,
Xenbase
Quigley,
Rfx2 Stabilizes Foxj1 Binding at Chromatin Loops to Enable Multiciliated Cell Gene Expression.
2017,
Pubmed
,
Xenbase
Quinlan,
BEDTools: a flexible suite of utilities for comparing genomic features.
2010,
Pubmed
Raj,
Thyroid Hormone Induces DNA Demethylation in Xenopus Tadpole Brain.
2020,
Pubmed
,
Xenbase
Ramírez,
deepTools2: a next generation web server for deep-sequencing data analysis.
2016,
Pubmed
Rasmussen,
Role of TET enzymes in DNA methylation, development, and cancer.
2016,
Pubmed
Raymond,
Regeneration: New Neurons Wire Up.
2016,
Pubmed
Robinson,
Integrative genomics viewer.
2011,
Pubmed
Rodger,
Characterisation of DNA methylation changes in EBF3 and TBC1D16 associated with tumour progression and metastasis in multiple cancer types.
2019,
Pubmed
Session,
Genome evolution in the allotetraploid frog Xenopus laevis.
2016,
Pubmed
,
Xenbase
Sifuentes,
Rapid, Dynamic Activation of Müller Glial Stem Cell Responses in Zebrafish.
2016,
Pubmed
Smith,
Promoter DNA Hypermethylation and Paradoxical Gene Activation.
2020,
Pubmed
Szaro,
Regulation in the neural plate of Xenopus laevis demonstrated by genetic markers.
1985,
Pubmed
,
Xenbase
Szaro,
Immunocytochemical identification of non-neuronal intermediate filament proteins in the developing Xenopus laevis nervous system.
1988,
Pubmed
,
Xenbase
Szaro,
Spatial and temporal expression of phosphorylated and non-phosphorylated forms of neurofilament proteins in the developing nervous system of Xenopus laevis.
1989,
Pubmed
,
Xenbase
Szaro,
Specific changes in axonally transported proteins during regeneration of the frog (Xenopus laevis) optic nerve.
1985,
Pubmed
,
Xenbase
Takai,
5-Hydroxymethylcytosine plays a critical role in glioblastomagenesis by recruiting the CHTOP-methylosome complex.
2014,
Pubmed
Takasawa,
DNA hypermethylation enhanced telomerase reverse transcriptase expression in human-induced pluripotent stem cells.
2018,
Pubmed
Taylor,
Is the capacity for optic nerve regeneration related to continued retinal ganglion cell production in the frog?
1989,
Pubmed
,
Xenbase
Thorvaldsdóttir,
Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration.
2013,
Pubmed
Thyagarajan,
Post-transcriptional control of neurofilaments in development and disease.
2007,
Pubmed
Tibshirani,
Cytoplasmic sequestration of FUS/TLS associated with ALS alters histone marks through loss of nuclear protein arginine methyltransferase 1.
2015,
Pubmed
Toth,
Ndel1 promotes axon regeneration via intermediate filaments.
2008,
Pubmed
Trapnell,
Differential analysis of gene regulation at transcript resolution with RNA-seq.
2013,
Pubmed
Trapnell,
Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.
2012,
Pubmed
Tripathi,
Meta- and Orthogonal Integration of Influenza "OMICs" Data Defines a Role for UBR4 in Virus Budding.
2015,
Pubmed
VandenBosch,
Epigenetics in neuronal regeneration.
2020,
Pubmed
Vasanthakumar,
5-hydroxymethylcytosine in cancer: significance in diagnosis and therapy.
2015,
Pubmed
Venkatesh,
Developmental Chromatin Restriction of Pro-Growth Gene Networks Acts as an Epigenetic Barrier to Axon Regeneration in Cortical Neurons.
2018,
Pubmed
Venkatesh,
Epigenetic profiling reveals a developmental decrease in promoter accessibility during cortical maturation in vivo.
2016,
Pubmed
Viré,
The Polycomb group protein EZH2 directly controls DNA methylation.
2006,
Pubmed
Vize,
Xenopus genomic data and browser resources.
2017,
Pubmed
,
Xenbase
Wang,
Overexpression of Sox11 promotes corticospinal tract regeneration after spinal injury while interfering with functional recovery.
2015,
Pubmed
Wen,
Whole-genome analysis of 5-hydroxymethylcytosine and 5-methylcytosine at base resolution in the human brain.
2014,
Pubmed
Wen,
Genomic distribution and possible functions of DNA hydroxymethylation in the brain.
2014,
Pubmed
Weng,
An Intrinsic Epigenetic Barrier for Functional Axon Regeneration.
2017,
Pubmed
Weng,
Epigenetic regulation of axonal regenerative capacity.
2016,
Pubmed
Wilson,
Regeneration in the Xenopus tadpole optic nerve is preceded by a massive macrophage/microglial response.
1992,
Pubmed
,
Xenbase
Yadav,
Chromatin plasticity: A versatile landscape that underlies cell fate and identity.
2018,
Pubmed
Yaoita,
A correlation of thyroid hormone receptor gene expression with amphibian metamorphosis.
1990,
Pubmed
,
Xenbase
Zhao,
The return of phosphorylated and nonphosphorylated epitopes of neurofilament proteins to the regenerating optic nerve of Xenopus laevis.
1994,
Pubmed
,
Xenbase
Zhao,
Xefiltin, a new low molecular weight neuronal intermediate filament protein of Xenopus laevis, shares sequence features with goldfish gefiltin and mammalian alpha-internexin and differs in expression from XNIF and NF-L.
1997,
Pubmed
,
Xenbase
Zhao,
Xefiltin, a Xenopus laevis neuronal intermediate filament protein, is expressed in actively growing optic axons during development and regeneration.
1997,
Pubmed
,
Xenbase
Ziller,
Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing.
2015,
Pubmed
Ziller,
Genomic distribution and inter-sample variation of non-CpG methylation across human cell types.
2011,
Pubmed
de Mendoza,
The emergence of the brain non-CpG methylation system in vertebrates.
2021,
Pubmed