Three-dimensional genome restructuring across timescales of activity-induced neuronal gene expression

Abstract

Neuronal activation induces rapid transcription of immediate early genes (IEGs) and longer-term chromatin remodeling around secondary response genes (SRGs). Here, we use high-resolution chromosome-conformation-capture carbon-copy sequencing (5C-seq) to elucidate the extent to which long-range chromatin loops are altered during short- and long-term changes in neural activity. We find that more than 10% of loops surrounding select IEGs, SRGs, and synaptic genes are induced de novo during cortical neuron activation. IEGs Fos and Arc connect to activity-dependent enhancers via singular short-range loops that form within 20 min after stimulation, prior to peak messenger RNA levels. By contrast, the SRG Bdnf engages in both pre-existing and activity-inducible loops that form within 1–6 h. We also show that common single-nucleotide variants that are associated with autism and schizophrenia are colocalized with distinct classes of activity-dependent, looped enhancers. Our data link architectural complexity to transcriptional kinetics and reveal the rapid timescale by which higher-order chromatin architecture reconfigures during neuronal stimulation.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Identification of dynamic and invariant looping interactions across neuronal activity states.
Fig. 2: Activity-induced enhancers connected to distal target genes via looping interactions predict activity-stimulated expression.
Fig. 3: Unique topological motifs underlie the activity-dependent transcriptional response.
Fig. 4: IEGs form shorter and less complex loops than SRGs.
Fig. 5: Activity-induced loops form before and persist after peak mRNA levels of IEGs.
Fig. 6: Neuropsychiatric disease-associated SNVs colocalize with long-range activity-induced and -decommissioned looped enhancers.

Data availability

We have uploaded all data from this manuscript to the Gene Expression Omnibus (GEO) under accession number GSE131025.

Code availability

5C analysis code has been included as a supplementary zip file.

Change history

  • 23 July 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. 1.

    Flavell, S. W. & Greenberg, M. E. Signaling mechanisms linking neuronal activity to gene expression and plasticity of the nervous system. Annu. Rev. Neurosci. 31, 563–590 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Yap, E. L. & Greenberg, M. E. Activity-regulated transcription: bridging the gap between neural activity and behavior. Neuron 100, 330–348 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Greenberg, M. E. & Ziff, E. B. Stimulation of 3T3 cells induces transcription of the c-fos proto-oncogene. Nature 311, 433–438 (1984).

    CAS  PubMed  Google Scholar 

  4. 4.

    Muller, R., Bravo, R., Burckhardt, J. & Curran, T. Induction of c-fos gene and protein by growth factors precedes activation of c-myc. Nature 312, 716–720 (1984).

    CAS  PubMed  Google Scholar 

  5. 5.

    Curran, T. & Morgan, J. I. Superinduction of c-fos by nerve growth factor in the presence of peripherally active benzodiazepines. Science 229, 1265–1268 (1985).

    CAS  PubMed  Google Scholar 

  6. 6.

    Link, W. et al. Somatodendritic expression of an immediate early gene is regulated by synaptic activity. Proc. Natl Acad. Sci. USA 92, 5734–5738 (1995).

    CAS  PubMed  Google Scholar 

  7. 7.

    Lyford, G. L. et al. Arc, a growth factor and activity-regulated gene, encodes a novel cytoskeleton-associated protein that is enriched in neuronal dendrites. Neuron 14, 433–445 (1995).

    CAS  PubMed  Google Scholar 

  8. 8.

    Fowler, T., Sen, R. & Roy, A. L. Regulation of primary response genes. Mol. Cell 44, 348–360 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Tyssowski, K. M. et al. Different neuronal activity patterns induce different gene expression programs. Neuron 98, 530–546 e511 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Kawashima, T. et al. Synaptic activity-responsive element in the Arc/Arg3.1 promoter essential for synapse-to-nucleus signaling in activated neurons. Proc. Natl Acad. Sci. USA 106, 316–321 (2009).

    CAS  PubMed  Google Scholar 

  11. 11.

    Pintchovski, S. A., Peebles, C. L., Kim, H. J., Verdin, E. & Finkbeiner, S. The serum response factor and a putative novel transcription factor regulate expression of the immediate-early gene Arc/Arg3.1 in neurons. J. Neurosci. 29, 1525–1537 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Kim, T. K. et al. Widespread transcription at neuronal activity-regulated enhancers. Nature 465, 182–187 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Malik, A. N. et al. Genome-wide identification and characterization of functional neuronal activity-dependent enhancers. Nat. Neurosci. 17, 1330–1339 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Su, Y. et al. Neuronal activity modifies the chromatin accessibility landscape in the adult brain. Nat. Neurosci. 20, 476–483 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Schardin, M., Cremer, T., Hager, H. D. & Lang, M. Specific staining of human chromosomes in Chinese hamster x man hybrid cell lines demonstrates interphase chromosome territories. Hum. Genet. 71, 281–287 (1985).

    CAS  PubMed  Google Scholar 

  16. 16.

    Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Beagan, J. A. & Phillips-Cremins, J. E. On the existence and functionality of topologically associating domains. Nat. Genet. 52, 8–16 (2020).

    CAS  PubMed  Google Scholar 

  21. 21.

    Phillips-Cremins, J. E. et al. Architectural protein subclasses shape 3D organization of genomes during lineage commitment. Cell 153, 1281–1295 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Phanstiel, D. H. et al. Static and dynamic DNA loops form AP-1-bound activation hubs during macrophage development. Mol. Cell 67, 1037–1048 e1036 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Gabriele, M. et al. YY1 Haploinsufficiency causes an intellectual disability syndrome featuring transcriptional and chromatin dysfunction. Am. J. Hum. Genet. 100, 907–925 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Gregor, A. et al. De novo mutations in the genome organizer CTCF cause intellectual disability. Am. J. Hum. Genet. 93, 124–131 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Hirayama, T., Tarusawa, E., Yoshimura, Y., Galjart, N. & Yagi, T. CTCF is required for neural development and stochastic expression of clustered Pcdh genes in neurons. Cell. Rep. 2, 345–357 (2012).

    CAS  PubMed  Google Scholar 

  26. 26.

    Yamada, T. et al. Sensory experience remodels genome architecture in neural circuit to drive motor learning. Nature 569, 708–713 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Beagan, J. A. et al. YY1 and CTCF orchestrate a 3D chromatin looping switch during early neural lineage commitment. Genome Res 27, 1139–1152 (2017).

  28. 28.

    Beagan, J. A. et al. Local genome topology can exhibit an incompletely rewired 3D-folding state during somatic cell reprogramming. Cell Stem. Cell 18, 611–624 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Kim, J. H. et al. 5C-ID: increased resolution chromosome-conformation-capture-carbon-copy with in situ 3C and double alternating primer design. Methods 142, 39–46 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Straughan, D. W., Neal, M. J., Simmonds, M. A., Collins, G. G. & Hill, R. G. Evaluation of bicuculline as a GABA antagonist. Nature 233, 352–354 (1971).

    CAS  PubMed  Google Scholar 

  31. 31.

    Narahashi, T., Moore, J. W. & Scott, W. R. Tetrodotoxin blockage of sodium conductance increase in lobster giant axons. J. Gen. Physiol. 47, 965–974 (1964).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Shepherd, J. D. & Huganir, R. L. The cell biology of synaptic plasticity: AMPA receptor trafficking. Annu. Rev. Cell Dev. Biol. 23, 613–643 (2007).

    CAS  PubMed  Google Scholar 

  33. 33.

    Bonev, B. et al. Multiscale 3D genome rewiring during mouse neural development. Cell 171, 557–572 e524 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Gilgenast, T. G. & Phillips-Cremins, J. E. Systematic evaluation of statistical methods for identifying looping interactions in 5C data. Cell Syst. 8, 197–211 e113 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Fernandez, L. R., Gilgenast, T. G. & Phillips-Cremins, J. E. 3DeFDR: Identifying cell type-specific looping interactions with empirical false discovery rate guided thresholding. Preprint at bioRxiv https://doi.org/10.1101/501056 (2018).

  36. 36.

    Joo, J. Y., Schaukowitch, K., Farbiak, L., Kilaru, G. & Kim, T. K. Stimulus-specific combinatorial functionality of neuronal c-fos enhancers. Nat. Neurosci. 19, 75–83 (2016).

    CAS  PubMed  Google Scholar 

  37. 37.

    Fulco, C. P. et al. Activity-by-contact model of enhancer-promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51, 1664–1669 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Schizophrenia Working Group of the Psychiatric Genomics Consortium Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. Mol. Autism 8, 21 (2017).

    Google Scholar 

  40. 40.

    Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Pers, T. H., Timshel, P. & Hirschhorn, J. N. SNPsnap: a web-based tool for identification and annotation of matched SNPs. Bioinformatics 31, 418–420 (2015).

    CAS  PubMed  Google Scholar 

  43. 43.

    Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Araujo, D. J. et al. Foxp1 in forebrain pyramidal neurons controls gene expression required for spatial learning and synaptic plasticity. J. Neurosci. 37, 10917–10931 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Sinning, A., Liebmann, L. & Hubner, C. A. Disruption of Slc4a10 augments neuronal excitability and modulates synaptic short-term plasticity. Front. Cell. Neurosci. 9, 223 (2015).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Krietenstein, N. et al. Ultrastructural details of mammalian chromosome architecture. Preprint at bioRxiv https://doi.org/10.1101/639922 (2019).

  47. 47.

    Luscher, C. & Malenka, R. C. NMDA receptor-dependent long-term potentiation and long-term depression (LTP/LTD). Cold Spring Harb. Perspect. Biol. 4, a005710 (2012).

  48. 48.

    Sun, J. H. et al. Disease-associated short tandem repeats co-localize with chromatin domain boundaries. Cell 175, 224–238.e15 (2018).

  49. 49.

    Norton, H. K. & Phillips-Cremins, J. E. Crossed wires: 3D genome misfolding in human disease. J. Cell Biol. 216, 3441–3452 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Pimentel, H., Bray, N. L., Puente, S., Melsted, P. & Pachter, L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat. Methods 14, 687–690 (2017).

    CAS  PubMed  Google Scholar 

  51. 51.

    Shepherd, J. D. et al. Arc/Arg3.1 mediates homeostatic synaptic scaling of AMPA receptors. Neuron 52, 475–484 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Langmead, B. Aligning short sequencing reads with Bowtie. Curr. Protoc. Bioinformatics Ch. 11, 11.17 (2010).

    Google Scholar 

  53. 53.

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

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Ramirez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Hnisz, D. et al. Activation of proto-oncogenes by disruption of chromosome neighborhoods. Science 351, 1454–1458 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Kim, J. H. et al. LADL: light-activated dynamic looping for endogenous gene expression control. Nat. Methods 16, 633–639 (2019).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Phillips-Cremins, J. E. Unraveling architecture of the pluripotent genome. Curr. Opin. Cell Biol. 28, 96–104 (2014).

    CAS  PubMed  Google Scholar 

  58. 58.

    Lajoie, B. R., van Berkum, N. L., Sanyal, A. & Dekker, J. My5C: web tools for chromosome conformation capture studies. Nat. Methods 6, 690–691 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Imakaev, M. et al. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat. Methods 9, 999–1003 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Wang, J., Vasaikar, S., Shi, Z., Greer, M. & Zhang, B. WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 45, W130–W137 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Arnold, M., Raffler, J., Pfeufer, A., Suhre, K. & Kastenmuller, G. SNiPA: an interactive, genetic variant-centered annotation browser. Bioinformatics 31, 1334–1336 (2015).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    International HapMap, C. et al. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).

    Google Scholar 

  69. 69.

    Genomes Project, C. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    Google Scholar 

Download references

Acknowledgements

We thank members of the Cremins laboratory for helpful discussions. J.E.P.-C. is a New York Stem Cell Foundation Robertson Investigator and an Alfred P. Sloan Foundation Fellow. This research was supported by the New York Stem Cell Foundation (J.E.P.-C.), the US National Institutes of Health (NIH) Director’s New Innovator Award from the National Institute of Mental Health (1DP2MH11024701, J.E.P.-C.), a National Institute of Mental Health grant (R01MH112766, J.D.S.), a Chan Zuckerberg Ben Barres Early Career Acceleration Award (J.D.S.), a 4D Nucleome Common Fund grant (1U01HL12999801, J.E.P.-C.), a joint NSF–NIGMS grant to support research at the interface of the biological and mathematical sciences (1562665, J.E.P.-C.), a Brain Research Foundation Fay Frank Seed Grant (J.E.P.-C.), a National Institute of Neurological Disorders and Stroke grant (R01NS114226, J.E.P.-C. and J.D.S.), and by National Science Foundation Graduate Research Fellowships under grant numbers DGE-1321851 (J.A.B.) and DGE-1321851 (L.R.F.).

Author information

Affiliations

Authors

Contributions

J.A.B., E.D.P., J.D.S., and J.E.P.-C. designed the experiments. E.D.P. isolated, cultured, treated, and fixed cells. J.A.B. performed RNA-seq, ChIP–seq, and 5C assays. J.A.B. and L.R.F. called loops on 5C data. J.A.B., K.R.T., and H.C. called loops on Hi-C data. J.A.B. analyzed the intersection of loop calls, ChIP–seq, gene expression, and disease-associated genome variants. M.H.G. performed LD score regression. J.A.B. and K.F. analyzed short-term activity-induction experiments. J.A.B., J.D.S., and J.E.P.-C. wrote the manuscript.

Corresponding authors

Correspondence to Jason D. Shepherd or Jennifer E. Phillips-Cremins.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Hyejung Won and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Mapping genome folding across neural activity states.

a, Representative immunofluorescence images of DAPI (blue), MAP2 (green), PSD95 (magenta) signal across conditions. Results were consistent across 2 culture batches, 4 total 5C replicates, 3 RNA-seq replicates, and H3K27ac ChIP replicate analyzed. b,c, Fold change vs amplitude plots of RNA-seq data comparing the Bic vs Untreat conditions (b) and TTX vs Untreat conditions (c). d, Interaction frequency heatmaps of 1-3 Mb regions surrounding the Fos, Arc, Neurexin-1, and Neuroligin-3 genes (labeled in green) across embryonic stem (ES) cells, neural progenitor cells (NPCs), and cortical neurons (CNs) (data analyzed from Bonev+ 2017). e, Interaction frequency heatmaps of the regions presented in (a) across tetrodotoxin-treated (TTX), untreated, and bicuculline-treated (Bic) DIV16 cortical neurons.

Extended Data Fig. 2 Activity-induced loops are not present earlier in cortical neuron differentiation.

a, Zoom-in heatmaps of critical loops presented throughout the paper. From left to right the columns are Obs/Exp heatmaps of HiC (Bonev et al.) data from 1) embryonic stem (ES) cells, 2) neural progenitor cells (NPC), 3) cortical neurons (CN), followed by 5C interaction score heatmaps across the 4) TTX, 5) untreated, and 6) BIC treated conditions. Genes of interest in each zoom window, Figure panels where same loop is further analyzed, and loop classification are listed on left.

Extended Data Fig. 3 5C data correlateions cluster by condition.

a, b, Pearson’s correlation coefficients of background-normalized contact frequencies (‘observed/expected’) at activity-induced loops (a) and activity-invariant loops (b) across each pair of replicates. The N = 4 independent biological replicates for each condition were then hierarchically clustered based on correlation results.

Extended Data Fig. 4 Activity-induced and activity-invariant loops are reproducible across condition replicates.

a, Zoom-in interaction score heatmaps from each of the 12 5C replicates generated for critical loops presented throughout the paper. Genes of interest in each zoom window, Figure panels where same loop is further analyzed, and loop classification are listed on left.

Extended Data Fig. 5 Identifying dynamic looping across neural activity states.

a, Diagram of 5C processing pipeline used to call significant constitutive and dynamic loops (bottom right) starting from 5C interaction frequency counts for all pairs of 4 kb genomic bins within queried regions across 4 replicates (from two litter/culture batches) of each condition (top left). First the local domain background signal is quantified using a donut expected model (Rao + 2014) and removed from the interaction frequency signal. Probabilistic modeling converts these expected-normalized interaction frequencies to an ‘interaction score’ (bottom left). For a bin-bin pair to be classified as looping, its interaction score must fall above a given ‘significance threshold’. For a looping bin-bin pair to be classified as ‘Bic-only’ the minimum interaction score of the Bic replicates must exceed the maximum interaction score of the four TTX replicates by a given ‘difference threshold’ (Supplementary Methods). Looping pixels not classified as Bic- or TTX-only are classified as constitutive (top right). Bin-bin pairs of the same class are then grouped into clusters if they are directly adjacent; clusters below a selected size threshold are removed from looping classification (bottom right). See Methods for more details. b, Scatterplot of the background-normalized contact frequency (‘Observed/Expected’) counts of looping-classified pixels in TTX and Bic conditions.

Extended Data Fig. 6 Quantifying activity-dependent regulatory elements.

a, Spearman’s correlation coefficients for terms included in models (Fig. 2f-i). b, c, Results of promoter-only (b) and promoter plus nearest enhancer (c) models for only genes that form loops to classified enhancers within 5C regions. N = 45 genes analyzed. d, R2 values of models presented in (b-c). e, Coefficients of each explanatory variable term in models presented in (b-c). t-statistic p-values and standard errors represented via stars and error bars, respectively. fh, Acetylation heatmaps, pileups of classified activity-induced (f), activity-decommissioned (g), invariant (h) enhancers.

Extended Data Fig. 7 Assessing activity-dependent regulation using murine HiC (Bonev et al.33) loop calls.

a, Expression (TPM) of the transcripts whose promoters intersect each looping class. P-values presented calculated using two-tailed Wilcoxon signed-rank test. b, Expression (log2(TPM)) of the genes whose promoters fall opposite activity-induced (class 2) and activity-decommissioned (class 3) enhancers in genome-wide cortical neuron loops, original data from Bonev et al.33. Number of genes in each class (a, b) listed as N = above boxes. Boxes in a-b range from lower to upper quartile with median line, whiskers extend to min/max data point within 1.5*interquartile range. c, Number of loops called in HiC data obtained from embryonic stem cells (ES), neural progenitor cells (NPCs), and cortical neurons (CN) (Bonev et al.33). c,d, Interaction frequency heatmaps (top) and thresholded loop calls (bottom) for a ~2.5 Mb region surrounding the Synaptotagmin1 Syt1 gene. d,e, Expression (log2(TPM)) of the genes whose promoters fall opposite activity-induced (class 2) and activity-decommissioned (class 3) enhancers in genome-wide cortical neuron loops, original data from Bonev et al.33. Number of genes in each class listed above boxes. The remaining gene ontology terms passing the FDR < 0.05 threshold for class 2 (a) which could not be presented in Fig. 3. N = 2139 Class 2 genes, enrichment calculated using Webgestalt65. f, (Left) Gene ontology enrichment ratios for class 3 genes parsed by expression into activity downregulated (Bic/TTX < 2/3), activity invariant (5/6 < Bic/TTX < 6/5), and activity upregulated (Bic/TTX > 3/2) groups. (Right) Genes found in the ‘regulation of trans-synaptic signaling’ and ‘synapse organization’ GO terms enriched in activity downregulated class 3 genes.

Extended Data Fig. 8 Expression of Bdnf transcripts.

a, Depiction of the 12 RefSeq transcript isoforms of the Bdnf gene, above which we annotate the 8 promoters as in Hong et al., Neuron, 2008. b, Expression strip plots of each Bdnf isoform, organized in columns by shared promoter. N = 3, mean lines plotted. c, Boxplots overlaid by strip plots of count of opposing looping anchors that contain an activity-dependent enhancer for rapid immediate early genes (rIEGs, as defined as rPRGs in Tyssowski et al.9), translation-independent SRGs (tiSRGs, defined as dPRGs in Tyssowski et al.9), translation-dependent SRGs (tdSRGs, defined as SRGs in Tyssowski et al.9), and all genes. Boxes range from lower to upper quartile with median line, whiskers extend to min/max data point within 1.5*interquartile range.

Extended Data Fig. 9 Verification of the eRNA signature captures enhancer activity dynamics.

a, Genome browser view of ~50 kb window surrounding the Fos gene. Rows from top to bottom present: 1) RNA signal in active neurons from Kim et al. 12, 2) RNA signal in inactive neurons from Kim et al. 12, 3) RNA signal from neurons in the Bic condition, 4) RNA signal from neurons in the TTX condition, 5) H3K27ac ChIP-seq signal from neurons in the Bic condition, 6) H3K27ac ChIP-seq signal from neurons in the TTX condition. b, RNA-seq signatures at enhancers near Fos across 0, 5, 20, 60, and 360 minutes of acute neuron activation.

Extended Data Fig. 10 Foxp1 and Slc4a10 fall opposite disease-associated variants in conserved classified loops.

a, Number of loops called in HiC data obtained from human fetal cortical plate (CP) and germinal zone (GZ) tissue (Won et al.41). b, Interaction frequency heatmap (left) and thresholded loop calls (right) of the 2.5 Mb region surrounding the Bdnf gene in human cortical plate (CP) fetal tissue. ce, Human (c) and mouse (d) interaction frequency heatmaps of a 2 Mb region surrounding the Foxp1 gene. The expression of the looping Foxp1 isoform labeled in green in (d) is plotted in (e). fh, Human (f) and mouse (g) interaction frequency heatmaps of a < 2 Mb region surrounding the Slc4a10 gene (green), followed by expression of its 5 expressed isoforms (h). N = 3 RNA-seq replicates in (e,h), mean lines plotted.

Supplementary information

Supplementary Information

Supplementary Tables 1, 2, 5 and 7.

Reporting Summary

Supplementary Table

Supplemental Tables 3, 4, 6, 8–19.

Supplementary Software

Custom 5C Analysis Scripts.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Beagan, J.A., Pastuzyn, E.D., Fernandez, L.R. et al. Three-dimensional genome restructuring across timescales of activity-induced neuronal gene expression. Nat Neurosci 23, 707–717 (2020). https://doi.org/10.1038/s41593-020-0634-6

Download citation

Further reading