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Regulation of chromatin accessibility and Zic binding at enhancers in the developing cerebellum

Nature Neuroscience volume 18, pages 647–656 (2015)Cite this article

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Abstract

To identify chromatin mechanisms of neuronal differentiation, we characterized chromatin accessibility and gene expression in cerebellar granule neurons (CGNs) of the developing mouse. We used DNase-seq to map accessibility of cis-regulatory elements and RNA-seq to profile transcript abundance across postnatal stages of neuronal differentiation in vivo and in culture. We observed thousands of chromatin accessibility changes as CGNs differentiated, and verified, using H3K27ac ChIP-seq, reporter gene assays and CRISPR-mediated activation, that many of these regions function as neuronal enhancers. Motif discovery in differentially accessible chromatin regions suggested a previously unknown role for the Zic family of transcription factors in CGN maturation. We confirmed the association of Zic with these elements by ChIP-seq and found, using knockdown, that Zic1 and Zic2 are required for coordinating mature neuronal gene expression patterns. Together, our data reveal chromatin dynamics at thousands of gene regulatory elements that facilitate the gene expression patterns necessary for neuronal differentiation and function.

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Figure 1: Postnatal cerebellar development involves extensive chromatin remodeling.
Figure 2: Opening DHS sites mark late-acting neuronal enhancers.
Figure 3: CRISPR-VP64–based activation confirms enhancer activity of late-opening DHS sites nearby Grin2c.
Figure 4: A subset of closing DHS sites mark early postnatal enhancer elements.
Figure 5: Postnatal closing DHS sites are enriched for embryonic hindbrain enhancer activity.
Figure 6: Zic1/2 binding is highly dynamic across postnatal cerebellar development.
Figure 7: Grin2c transcription is controlled by Zic1/2 stage-specific access to enhancer elements.
Figure 8: Zic1/2 promote the mature CGN transcriptional program.

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References

  1. Song, L. et al. Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. Genome Res. 21, 1757–1767 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Heintzman, N.D. et al. Histone modifications at human enhancers reflect global cell type–specific gene expression. Nature 459, 108–112 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Levine, M., Cattoglio, C. & Tjian, R. Looping back to leap forward: transcription enters a new era. Cell 157, 13–25 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gray, P.A. et al. Mouse brain organization revealed through direct genome-scale TF expression analysis. Science 306, 2255–2257 (2004).

    Article  CAS  PubMed  Google Scholar 

  5. Pattabiraman, K. et al. Transcriptional regulation of enhancers active in protodomains of the developing cerebral cortex. Neuron 82, 989–1003 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. West, A.E. & Greenberg, M.E. Neuronal activity-regulated gene transcription in synapse development and cognitive function. Cold Spring Harb. Perspect. Biol. 3, a005744 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Altman, J. & Bayer, S.A. Development of the Cerebellar System: In Relation to its Evolution, Structure, and Functions (CRC Press, 1997).

  8. Hatten, M.E. & Heintz, N. Mechanisms of neural patterning and specification in the developing cerebellum. Annu. Rev. Neurosci. 18, 385–408 (1995).

    Article  CAS  PubMed  Google Scholar 

  9. Ding, B. et al. Temporal regulation of nuclear factor one occupancy by calcineurin/NFAT governs a voltage-sensitive developmental switch in late maturing neurons. J. Neurosci. 33, 2860–2872 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kuhar, S.G. et al. Changing patterns of gene expression define four stages of cerebellar granule neuron differentiation. Development 117, 97–104 (1993).

    CAS  PubMed  Google Scholar 

  11. Thurman, R.E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Altman, J. Autoradiographic and histological studies of postnatal neurogenesis. 3. Dating the time of production and onset of differentiation of cerebellar microneurons in rats. J. Comp. Neurol. 136, 269–293 (1969).

    Article  CAS  PubMed  Google Scholar 

  13. Gleeson, J.G., Lin, P.T., Flanagan, L.A. & Walsh, C.A. Doublecortin is a microtubule-associated protein and is expressed widely by migrating neurons. Neuron 23, 257–271 (1999).

    Article  CAS  PubMed  Google Scholar 

  14. Mellén, M., Ayata, P., Dewell, S., Kriaucionis, S. & Heintz, N. MeCP2 binds to 5hmC enriched within active genes and accessible chromatin in the nervous system. Cell 151, 1417–1430 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Hatten, M.E. Neuronal regulation of astroglial morphology and proliferation in vitro. J. Cell Biol. 100, 384–396 (1985).

    Article  CAS  PubMed  Google Scholar 

  16. Fogarty, M.P., Emmenegger, B.A., Grasfeder, L.L., Oliver, T.G. & Wechsler-Reya, R.J. Fibroblast growth factor blocks Sonic hedgehog signaling in neuronal precursors and tumor cells. Proc. Natl. Acad. Sci. USA 104, 2973–2978 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Fletcher, T.L., De Camilli, P. & Banker, G. Synaptogenesis in hippocampal cultures: evidence indicating that axons and dendrites become competent to form synapses at different stages of neuronal development. J. Neurosci. 14, 6695–6706 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Yashiro, K. & Philpot, B.D. Regulation of NMDA receptor subunit expression and its implications for LTD, LTP and metaplasticity. Neuropharmacology 55, 1081–1094 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Farrant, M., Feldmeyer, D., Takahashi, T. & Cull-Candy, S.G. NMDA-receptor channel diversity in the developing cerebellum. Nature 368, 335–339 (1994).

    Article  CAS  PubMed  Google Scholar 

  20. Ruppert, C., Goldowitz, D. & Wille, W. Proto-oncogene c-myc is expressed in cerebellar neurons at different developmental stages. EMBO J. 5, 1897–1901 (1986).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Eroglu, C. et al. Gabapentin receptor alpha2delta-1 is a neuronal thrombospondin receptor responsible for excitatory CNS synaptogenesis. Cell 139, 380–392 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Fernandez, C., Tatard, V.M., Bertrand, N. & Dahmane, N. Differential modulation of Sonic-hedgehog–induced cerebellar granule cell precursor proliferation by the IGF signaling network. Dev. Neurosci. 32, 59–70 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Heintzman, N.D. et al. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat. Genet. 39, 311–318 (2007).

    Article  CAS  PubMed  Google Scholar 

  24. Wang, Z. et al. Combinatorial patterns of histone acetylations and methylations in the human genome. Nat. Genet. 40, 897–903 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Mouse ENCODE Consortium. et al. An encyclopedia of mouse DNA elements (Mouse ENCODE). Genome Biol. 13, 418 (2012).

  26. Bao, D. et al. Cbln1 is essential for interaction-dependent secretion of Cbln3. Mol. Cell. Biol. 26, 9327–9337 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hirai, H. et al. Cbln1 is essential for synaptic integrity and plasticity in the cerebellum. Nat. Neurosci. 8, 1534–1541 (2005).

    Article  CAS  PubMed  Google Scholar 

  28. Suzuki, K., Sato, M., Morishima, Y. & Nakanishi, S. Neuronal depolarization controls brain-derived neurotrophic factor-induced upregulation of NR2C NMDA receptor via calcineurin signaling. J. Neurosci. 25, 9535–9543 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Perez-Pinera, P. et al. RNA-guided gene activation by CRISPR-Cas9-based transcription factors. Nat. Methods 10, 973–976 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Visel, A., Minovitsky, S., Dubchak, I. & Pennacchio, L.A. VISTA Enhancer Browser: a database of tissue-specific human enhancers. Nucleic Acids Res. 35, D88–D92 (2007).

    Article  CAS  PubMed  Google Scholar 

  31. Hambardzumyan, D. et al. AUF1 and Hu proteins in the developing rat brain: implication in the proliferation and differentiation of neural progenitors. J. Neurosci. Res. 87, 1296–1309 (2009).

    Article  CAS  PubMed  Google Scholar 

  32. Vodopiutz, J. et al. Homozygous SALL1 mutation causes a novel multiple congenital anomaly-mental retardation syndrome. J. Pediatr. 162, 612–617 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Warming, S., Rachel, R.A., Jenkins, N.A. & Copeland, N.G. Zfp423 is required for normal cerebellar development. Mol. Cell. Biol. 26, 6913–6922 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Stergachis, A.B. et al. Developmental fate and cellular maturity encoded in human regulatory DNA landscapes. Cell 154, 888–903 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Sheffield, N.C. et al. Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions. Genome Res. 23, 777–788 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Shalizi, A. et al. A calcium-regulated MEF2 sumoylation switch controls postsynaptic differentiation. Science 311, 1012–1017 (2006).

    Article  CAS  PubMed  Google Scholar 

  37. Grinberg, I. et al. Heterozygous deletion of the linked genes ZIC1 and ZIC4 is involved in Dandy-Walker malformation. Nat. Genet. 36, 1053–1055 (2004).

    Article  CAS  PubMed  Google Scholar 

  38. Aruga, J. The role of Zic genes in neural development. Mol. Cell. Neurosci. 26, 205–221 (2004).

    Article  CAS  PubMed  Google Scholar 

  39. Borghesani, P.R. et al. BDNF stimulates migration of cerebellar granule cells. Development 129, 1435–1442 (2002).

    CAS  PubMed  Google Scholar 

  40. Kim, T.-H. et al. Broadly permissive intestinal chromatin underlies lateral inhibition and cell plasticity. Nature 506, 511–515 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Samstein, R.M. et al. Foxp3 exploits a pre-existent enhancer landscape for regulatory T cell lineage specification. Cell 151, 153–166 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Sotelo, C. Cellular and genetic regulation of the development of the cerebellar system. Prog. Neurobiol. 72, 295–339 (2004).

    Article  CAS  PubMed  Google Scholar 

  43. Aruga, J., Inoue, T., Hoshino, J. & Mikoshiba, K. Zic2 controls cerebellar development in cooperation with Zic1. J. Neurosci. 22, 218–225 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Aruga, J. et al. Mouse Zic1 is involved in cerebellar development. J. Neurosci. 18, 284–293 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zaret, K.S. & Carroll, J.S. Pioneer transcription factors: establishing competence for gene expression. Genes Dev. 25, 2227–2241 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ben-Arie, N. et al. Math1 is essential for genesis of cerebellar granule neurons. Nature 390, 169–172 (1997).

    Article  CAS  PubMed  Google Scholar 

  47. Flora, A., Klisch, T.J., Schuster, G. & Zoghbi, H.Y. Deletion of Atoh1 disrupts Sonic Hedgehog signaling in the developing cerebellum and prevents medulloblastoma. Science 326, 1424–1427 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. John, S. et al. Chromatin accessibility pre-determines glucocorticoid receptor binding patterns. Nat. Genet. 43, 264–268 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Camp, J.G. et al. Microbiota modulate transcription in the intestinal epithelium without remodeling the accessible chromatin landscape. Genome Res. 24, 1504–1516 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Thomas, J.O. & Thompson, R.J. Variation in chromatin structure in two cell types from the same tissue: a short DNA repeat length in cerebral cortex neurons. Cell 10, 633–640 (1977).

    Article  CAS  PubMed  Google Scholar 

  51. Song, L. & Crawford, G.E. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb. Protoc. 2010, pdb.prot5384 (2010).

  52. Kokubo, M. et al. BDNF-mediated cerebellar granule cell development is impaired in mice null for CaMKK2 or CaMKIV. J. Neurosci. 29, 8901–8913 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Tao, X., West, A.E., Chen, W.G., Corfas, G. & Greenberg, M.E. A calcium-responsive transcription factor, CaRF, that regulates neuronal activity-dependent expression of BDNF. Neuron 33, 383–395 (2002).

    Article  CAS  PubMed  Google Scholar 

  54. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Boyle, A.P., Guinney, J., Crawford, G.E. & Furey, T.S. F-Seq: a feature density estimator for high-throughput sequence tags. Bioinformatics 24, 2537–2538 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Trapnell, C., Pachter, L. & Salzberg, S.L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Huang, D.W., Sherman, B.T. & Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    CAS  Google Scholar 

  60. Machanick, P. & Bailey, T.L. MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics 27, 1696–1697 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Kharchenko, P.V., Tolstorukov, M.Y. & Park, P.J. Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat. Biotechnol. 26, 1351–1359 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Landt, S.G. et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Paige, S.L. et al. A temporal chromatin signature in human embryonic stem cells identifies regulators of cardiac development. Cell 151, 221–232 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the Duke Genome Sequencing and Analysis Core for sequencing the RNA-seq, ChIP-seq and DNase-seq libraries. This work was supported by the Duke Institute for Brain Sciences, and US National Institutes of Health grants 1R21NS084336 (A.E.W. and G.E.C.) and R01DA036865 (C.A.G. and G.E.C).

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Authors and Affiliations

  1. Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, USA

    Christopher L Frank

  2. Center for Genomic and Computational Biology, Duke University Medical Center, Durham, North Carolina, USA

    Christopher L Frank, Lingyun Song, Christopher M Vockley, Alexias Safi, Charles A Gersbach & Gregory E Crawford

  3. Department of Neurobiology, Duke University Medical Center, Durham, North Carolina, USA

    Fang Liu, Ranjula Wijayatunge, Matthew T Biegler, Marty G Yang & Anne E West

  4. Department of Cell Biology, Duke University Medical Center, Durham, North Carolina, USA

    Christopher M Vockley

  5. Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA

    Charles A Gersbach

  6. Department of Pediatrics, Division of Molecular Genetics, Duke University Medical Center, Durham, North Carolina, USA

    Gregory E Crawford

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Contributions

A.E.W., G.E.C., C.L.F., R.W. and F.L. designed the study. F.L., C.L.F., R.W., L.S. and A.S. performed DNase-seq and ChIP-seq experiments. M.T.B., M.G.Y., C.M.V. and C.A.G. designed and performed targeted enhancer function assays. F.L. performed Zic knockdown experiments. C.L.F. performed all bioinformatic analyses. C.L.F., A.E.W. and G.E.C. wrote the manuscript with input from all authors.

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Correspondence to Gregory E Crawford or Anne E West.

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Integrated supplementary information

Supplementary Figure 1 DNase-seq captures reproducible and dynamic chromatin accessibility profiles from in vivo cerebellum.

(a) Pairwise density scatterplots between biological triplicate DNase-seq libraries for P7, P14, and P60 cerebellum. Pearson’s r displayed for each pairwise comparison. (b) The distribution of gene features all DHS peak calls (F-seq, P < 0.05) map to is highly similar to other mouse tissues profiled, including heart, kidney, and liver. Preference given to promoter regions (< 2 kb from annotated TSS), then exonic, intronic, downstream, and intergenic. (c) MA plot outputs from DESeq analyses (fold-change over average normalized DNase-seq signal) with significantly differential DHS sites highlighted in red (opened across development) and blue (closed). These analyses produce the numbers listed in Fig. 1c. (d) Presence vs. absence overlap of DHS sites between timepoints to visualize magnitude of changes. The union of replicate peak calls were overlapped between timepoints. Note this analysis is susceptible to thresholding issues and does not take into account variance between replicates as the DESeq approach does.

Supplementary Figure 2 CGNs are the predominant cell type of the cerebellum and best associated with in vivo chromatin signals.

(a) Immunostaining of different cell types present in the developing cerebellum. Nuclei stained with Hoechst (blue), granule neurons with anti-Zic (red), astrocytes with Glial Fibrillary Acidic Protein (GFAP) (green), and Purkinje neurons with calbindin D28k (yellow). White scale bar is 100 µm. (b) Table of cell type abundances in the cerebellar cortex. (c,e,f) Beanplots of normalized P60 DNase-seq, P60 H3K27ac, and P56 H3K4me3 (ENCODE) signal present at promoter regions (< 2 kb from TSS) of all “highly” expressed genes at P60 (FPKM > 1), “lowly” expressed genes (FPKM between 0 and 1), and cell type enriched gene sets. Cell type enriched gene sets obtained from; see Methods. Black line marks median value. Significance between cell type enriched sets assessed by Mann-Whitney U test. GN = granule neurons, PC = Purkinje cells, BG = Bergmann glia. (d) Granule neuron-enriched genes contain the greatest fraction that are both differentially expressed and nearby opened and closed DHS sites across P7 to P60 development. Arrows indicate percent of genes that are matched between genes nearest opened/closed DHS sites and cell-type enriched gene lists restricted to differentially expressed genes (RNA-seq FDR < 0.05) only. Overlap for all differentially expressed genes between P7 and P60 marked with black arrow. The distribution of percent overlaps obtained by randomly sampling 10,000 times from all expressed genes is displayed in gray (P < 0.0001).

Supplementary Figure 3 Cultured granule neurons model chromatin accessibility changes in a homogeneous and synchronized population.

(a) Immunostaining of purified granule neuron precursors (GNPs) immediately following isolation, and after 3 or 7 days in culture. Loss of Ki67 marker of proliferation (blue), gain of Microtubule Associated protein 2 (MAP2) marker of neuronal dendrites (green) and gain of synaptophysin synapse marker (red) demonstrate neuronal differentiation in culture. This experiment was performed once. (b) Density scatterplots between triplicate replicate DNase-seq normalized read counts for three timepoints of cultured CGNs. Pearson’s correlation (r) in white. (c) MA plot outputs from DESeq analyses (fold-change over average normalized DNase-seq signal) with significantly opened (red) and closed (blue) DHS sites highlighted (FDR < 0.05). (d) Number of opened, closed, and total DHS sites identified between GNPs, +3DIV, and +7DIV (FDR < 0.05). (e) Distribution of opened, closed, and all identified DHS sites relative to genic features.

Supplementary Figure 4 Cultured granule neurons increase temporal resolution for early postnatal chromatin accessibility changes in the cerebellum.

(a) Heatmap of P7 to P60 closed (n = 5503) and opened (n = 4053) DHS sites with z-score normalized signal from each CGN culture and in vivo timepoint. DHS sites (rows) hierarchically clustered by Euclidean distance. (b) UCSC browser image highlighting two DHS sites found in an intron of the Grin2b gene that display opening, then closing dynamics in cultured neurons - events not seen with in vivo cerebellar timepoints. (c) qRT-PCR Grin2b and Grin2c expression levels across cultured CGN and in vivo development show brief Grin2b induction and attenuated increase in Grin2c expression in cultured neurons. Error bars = s.e.m. Expression normalized to Gapdh levels.

Supplementary Figure 5 Robust transcriptional changes shape postnatal cerebellar development.

(a) Dendrogram of P7, P14, and P60 cerebellum biological triplicate RNA-seq expression. Jensen-Shannon (JS) distance between samples on x-axis. (b) Volcano plots of pairwise gene expression changes between P7, P14, and P60 cerebellum RNA-seq. Significantly differential transcript abundances are highlighted in color and totaled in each direction (FDR < 0.05). (c) Four clusters of gene expression changes produced by JS distance to expression vectors. The top 100 gene expression profiles are displayed (gray) along with the most enriched gene ontologies (GO) for each cluster. Red lines mark average gene expression profile in each cluster. (d-g) Grin2c, Mycn, Cacna2d3, and Igfbp2 gene expression profiles displayed as density of mapped RNA-seq reads across exons. These four genes exemplify the four cluster profiles in c. Pink highlighted regions enlarged on right to show dynamics of DNase-seq mapped read densities at gene promoters for P7, P14, and P60 timepoints. Y-axis fixed for each gene.

Supplementary Figure 6 Cultured CGNs model transcription of early postnatal development.

(a) Volcano plots of pairwise gene expression changes between freshly isolated GNPs, +3DIV, and +7DIV RNA-seq. Significantly differential transcript abundances are highlighted in color and totaled in each direction (FDR < 0.05, n = 3 independent CGN cultures). (b) Principal components analysis (PCA) for all RNA-seq samples in PC1 and PC2 axes. PC1 separation is likely driven by developmental gene expression changes and places cultured neuron timepoints before P14 cerebellum.

Supplementary Figure 7 Opening of DHS sites is highly correlated with H3K27ac deposition, especially at non-promoter sites.

(a) Percentage of opened, closed, and all DHS sites identified in this study that overlap ENCODE histone mark ChIP-seq peak calls from P56 cerebellum. Opened and closed DHS sites are separated into promoter-located and distal. (b) Scatterplot of P7 to P60 cerebellum fold change in normalized DNase-seq signal (x-axis) vs. normalized H3K27ac signal (y-axis). Pearson’s correlation displayed above (r). (c) Same plot as b but density of data points linked to color intensity. A poorly correlated but substantial subset of DHS sites was identified (black box) and found to contain 72% promoter-located sites. (d) Subset of scatterplot points in b mapping to known promoters. (e) Subset of scatterplot points in b mapping outside known promoters.

Supplementary Figure 8 Transcription factor motifs enriched in dynamic DHS sites.

Top 10 MEME-ChIP Discriminative DNA motif discovery (DREME) results are listed for each of four sets of differential DHS sites: P7 to P60 opened, GNPs to +7DIV opened, P7 to P60 closed, and GNPs to +7DIV closed. Custom 3rd order Markov model backgrounds were used, generated from all DHS sites identified in vivo or in culture. Transcription factor (TF) matches to identified motifs are listed that have E-values < 1 as is default for the TOMTOM motif comparison tool. Similar TFs found both in vivo and in cultured neurons are highlighted in yellow, while Zic motif matches highlighted in pink.

Supplementary Figure 9 Zic knockdown and antibody validation.

(a) Relative qRT-PCR measures of Zic1 and Zic2 expression in vector control (pLKO), Zic1 shRNA, Zic2 shRNA, and combination Zic1+Zic2 shRNAs infected CGN cultures at +7DIV. Expression normalized to β-actin. (n = 6 independent infections for Zic1 or Zic2 KD, n = 10 for vector control, and n = 8 for Zic1+2 KD) (b) Western blot for Zic1, Zic2 and β-actin (loading control) in vector control cultured CGNs (Con), Zic1 knockdown cultured CGNs (Zic1KD), Zic2 knockdown cultured CGNs (Zic2KD), combination Zic1+Zic2 knockdown cultured CGNs (Zic1/2KD), P60 whole cerebellum (Cb), and P60 cortex (non-Cb) which does not express detectable Zic1/2. Antibody used is identical to that used for ChIP-seq experiments. This experiment was performed once. (c) ChIP-seq quality control measures for Zic antibody used on P7 cerebellum, P60 cerebellum, P60 cortex (which does not express detectable Zic1/2), and P60 cerebellum IgG control. Correlations are plotted between reads mapping to positive and negative reference genome strands over varying shift lengths. Successful ChIP-seq experiments for punctate transcription factors should give a strong peak of cross-strand correlation at a mean library insert size greater than ~100 bp. Phantom peak correlation is marked with blue dotted line and other possible maxima are marked with red dotted lines. NSC = normalized strand coefficient, RSC = relative strand correlation, Qtag = quality score based on NSC and RSC values, as described in.

Supplementary Figure 10 Zic ChIP-seq peak overlaps and replicate correlations.

(a) Venn diagram of overlap between P7 and P60 Zic ChIP-seq peaks (union of replicate peak calls used for each timepoint). (b) Scatterplots of pairwise Zic ChIP-seq replicates with Pearson correlation (r) displayed. Note the correlations are much higher between biological replicates of the same developmental stage than between P7 and P60 cerebellum.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10 (PDF 4587 kb)

Supplementary Methods Checklist (PDF 419 kb)

DNase-seq replicate correlations.

Contains table of pairwise Pearson correlation coefficients between all DNase-seq samples presented in this study. Additional tabs show correlations split between DNase-seq samples from in vivo cerebellum and cultured CGNs. Correlations were calculated between log-normalized total DNase-seq read counts present in the union of all top 100,000 peak calls. (XLS 46 kb)

In vivo differential DHS sites.

Contains all significantly differential DHS sites (FDR < 0.05, 250 bp windows) identified with DESeq pairwise comparisons between in vivo cerebellum timepoints with mm9 coordinates, normalized DHS cut counts, fold-change, and test statistics. Tabs indicate each pairwise comparison. (XLS 7597 kb)

Cultured CGN differential DHS sites.

Contains all significantly differential DHS sites (FDR < 0.05, 250 bp windows) identified with DESeq pairwise comparisons between cultured CGN timepoints with mm9 coordinates, normalized DHS cut counts, fold-change, and test statistics. Tabs indicate each pairwise comparison. (XLS 10035 kb)

RNA-seq comparisons across in vivo and cultured CGN development.

Contains all pairwise RNA-seq differential expression results between in vivo and cultured CGN timepoints, with gene names, mm9 coordinates, FPKM values (pooled and for individual replicates), and test statistics. (XLS 41341 kb)

RNA-seq expression clusters.

Contains top 100 genes from RNA-seq expression clustering presented in Supplementary Figure 5 (4 clusters) along with gene ontology enrichment statistics. (XLS 109 kb)

Expression changes in vicinity of Grin2c.

Contains P7 cerebellum, P60 cerebellum, Zic1 shRNA knockdown, Zic2 shRNA knockdown, and +7DIV vector control FPKM RNA-seq expression values, mm9 coordinates, and test statistics for 21 annotated genes in vicinity of the Grin2c gene. (XLS 55 kb)

DNase-seq and RNA-seq BDNF exposure changes.

Contains all DHS site windows called significantly opened or closed (FDR < 0.05) between +7DIV CGNs and +7DIV supplemented with BDNF for 3 days. Also has all genes tested for differential expression between those conditions. (XLS 7648 kb)

VISTA database elements.

Contains all VISTA database tested elements that overlap DHS sites that close or open going from P7 to P60 cerebellum in vivo (FDR < 0.05 from all pairwise comparisons) with mm9 coordinates, nearest genes, VISTA ID number, and location(s) of VISTA-reported E11.5 staining. Also contains list of all VISTA hindbrain-localized elements that overlap any DHS site identified in P7 cerebellum (108 elements) with mm9 coordinates and nearest genes. (XLS 56 kb)

Zic ChIP-seq differential peaks between P7 and P60.

Contains all Zic1/2 ChIP-seq peaks that exhibit significantly increased/decreased binding affinity when comparing P7 to P60 cerebellum, with mm9 coordinates, signal per condition, and test statistics. (XLS 6236 kb)

Zic1/2 knockdown RNA-seq.

Contains significantly differential gene names, mm9 coordinates, FPKM values, and test statistics (FDR < 0.10) for Zic1 and Zic2 shRNA knockdown RNA-seq experiments. (XLS 78 kb)

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Frank, C., Liu, F., Wijayatunge, R. et al. Regulation of chromatin accessibility and Zic binding at enhancers in the developing cerebellum. Nat Neurosci 18, 647–656 (2015). https://doi.org/10.1038/nn.3995

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