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RNA polymerase II pausing regulates chromatin organization in erythrocytes

Nature Structural & Molecular Biology volume 30, pages 1092–1104 (2023)Cite this article

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Abstract

Chicken erythrocytes are nucleated cells often considered to be transcriptionally inactive, although the epigenetic changes and chromatin remodeling that would mediate transcriptional repression and the extent of gene silencing during avian terminal erythroid differentiation are not fully understood. Here, we characterize the changes in gene expression, chromatin accessibility, genome organization and chromatin nuclear disposition during the terminal stages of erythropoiesis in chicken and uncover complex chromatin reorganization at different genomic scales. We observe a robust decrease in transcription in erythrocytes, but a set of genes maintains their expression, including genes involved in RNA polymerase II (Pol II) promoter-proximal pausing. Erythrocytes exhibit a reoriented nuclear architecture, with accessible chromatin positioned towards the nuclear periphery together with the paused RNA Pol II. In erythrocytes, chromatin domains are partially lost genome-wide, except at minidomains retained around paused promoters. Our results suggest that promoter-proximal pausing of RNA Pol II contributes to the transcriptional regulation of the erythroid genome and highlight the role of RNA polymerase in the maintenance of local chromatin organization.

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Fig. 1: Chromatin remodeling during chicken erythropoiesis results in a reoriented nuclear architecture with accessible chromatin at the nuclear rim.
Fig. 2: Chicken erythrocytes show a dramatic decrease in transcription but continue expressing small RNAs and erythroid genes.
Fig. 3: Open chromatin in the chicken erythrocytes comprises paused promoters of silenced genes.
Fig. 4: Chicken erythrocytes have hypercompartmentalized genomes.
Fig. 5: Chicken erythrocytes present global TAD loss but retain structure at minidomains enriched in open chromatin.
Fig. 6: Minidomains contain and organize paused promoters in chicken erythrocytes.

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Data availability

All reads generated were mapped against the chicken genome version GRCg6a. All datasets generated in this study have been deposited as a super series at GEO with accession number GSE206194. Source data are provided with this paper.

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References

  1. Jayapal, S. R. et al. Down-regulation of Myc is essential for terminal erythroid maturation. J. Biol. Chem. 285, 40252–40265 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Popova, E. Y. et al. Chromatin condensation in terminally differentiating mouse erythroblasts does not involve special architectural proteins but depends on histone deacetylation. Chromosome Res. 17, 47–64 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ji, P., Yeh, V., Ramirez, T., Murata-Hori, M. & Lodish, H. F. Histone deacetylase 2 is required for chromatin condensation and subsequent enucleation of cultured mouse fetal erythroblasts. Haematologica 95, 2013–2021 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Zhao, B. et al. Nuclear condensation during mouse erythropoiesis requires Caspase-3-mediated nuclear opening. Dev. Cell 36, 498–510 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ji, P., Murata-Hori, M. & Lodish, H. F. Formation of mammalian erythrocytes: chromatin condensation and enucleation. Trends Cell Biol. 21, 409–415 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hartenstein, V. Blood cells and blood cell development in the animal kingdom. Annu. Rev. Cell Dev. Biol. 22, 677–712 (2006).

    Article  CAS  PubMed  Google Scholar 

  7. Koutzamani, E., Loborg, H., Sarg, B., Lindner, H. H. & Rundquist, I. Linker histone subtype composition and affinity for chromatin in situ in nucleated mature erythrocytes. J. Biol. Chem. 277, 44688–44694 (2002).

    Article  CAS  PubMed  Google Scholar 

  8. Bergman, M. G., Wawra, E. & Winge, M. Chicken histone H5 inhibits transcription and replication when introduced into proliferating cells by microinjection. J. Cell Sci. 91, 201–209 (1988).

    Article  CAS  PubMed  Google Scholar 

  9. Fan, L. & Roberts, V. A. Complex of linker histone H5 with the nucleosome and its implications for chromatin packing. Proc. Natl Acad. Sci. USA 103, 8384–8389 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zentgraf, H., Scheer, U. & Franke, W. W. Characterization and localization of the RNA synthesized in mature avian erythrocytes. Exp. Cell Res. 96, 81–95 (1975).

    Article  CAS  PubMed  Google Scholar 

  11. Baumann, R., Gotz, R. & Dragon, S. NTP pattern of avian embryonic red cells: role of RNA degradation and AMP deaminase/5′-nucleotidase activity. Am. J. Physiol. Regul. Integr. Comp. Physiol. 284, R771–R779 (2003).

    Article  CAS  PubMed  Google Scholar 

  12. Cameron, I. L. & Prescott, D. M. RNA and protein metabolism in the maturation of the nucleated chicken erythrocyte. Exp. Cell Res. 30, 609–612 (1963).

    Article  CAS  PubMed  Google Scholar 

  13. Beacon, T. H. & Davie, J. R. The chicken model organism for epigenomic research. Genome 64, 476–489 (2021).

    Article  CAS  PubMed  Google Scholar 

  14. Mota-Gomez, I. & Lupianez, D. G. A (3D-nuclear) space odyssey: making sense of Hi-C maps. Genes (Basel) 10, 415 (2019).

    Article  CAS  PubMed  Google Scholar 

  15. Jerkovic, I. & Cavalli, G. Understanding 3D genome organization by multidisciplinary methods. Nat. Rev. Mol. Cell Biol. 22, 511–528 (2021).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Arzate-Mejia, R. G., Josue Cerecedo-Castillo, A., Guerrero, G., Furlan-Magaril, M. & Recillas-Targa, F. In situ dissection of domain boundaries affect genome topology and gene transcription in Drosophila. Nat. Commun. 11, 894 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lupianez, D. G. et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161, 1012–1025 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Andrey, G. & Mundlos, S. The three-dimensional genome: regulating gene expression during pluripotency and development. Development 144, 3646–3658 (2017).

    Article  CAS  PubMed  Google Scholar 

  22. Nora, E. P. et al. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell 169, 930–944 e22 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Schwarzer, W. et al. Two independent modes of chromatin organization revealed by cohesin removal. Nature 551, 51–56 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Guo, Y. et al. CRISPR inversion of CTCF sites alters genome topology and enhancer/promoter function. Cell 162, 900–910 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bonev, B. et al. Multiscale 3D genome rewiring during mouse neural development. Cell 171, 557–572 e24 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. van Steensel, B. & Furlong, E. E. M. The role of transcription in shaping the spatial organization of the genome. Nat. Rev. Mol. Cell Biol. 20, 327–337 (2019).

    PubMed  PubMed Central  Google Scholar 

  27. Rowley, M. J. et al. Evolutionarily conserved principles predict 3D chromatin organization. Mol. Cell 67, 837–852 e7 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hug, C. B., Grimaldi, A. G., Kruse, K. & Vaquerizas, J. M. Chromatin architecture emerges during zygotic genome activation independent of transcription. Cell 169, 216–228 e19 (2017).

    Article  CAS  PubMed  Google Scholar 

  29. Chen, H. Y., Sun, J. M., Hendzel, M. J., Rattner, J. B. & Davie, J. R. Changes in the nuclear matrix of chicken erythrocytes that accompany maturation. Biochem. J. 320, 257–265 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Grdisa, M. & White, M. K. Molecular and biochemical events during differentiation of the HD3 chicken erythroblastic cell line. Int. J. Biochem. Cell Biol. 35, 422–431 (2003).

    Article  CAS  PubMed  Google Scholar 

  31. Chen, X. et al. ATAC-see reveals the accessible genome by transposase-mediated imaging and sequencing. Nat. Methods 13, 1013–1020 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Solovei, I. et al. LBR and lamin A/C sequentially tether peripheral heterochromatin and inversely regulate differentiation. Cell 152, 584–598 (2013).

    Article  CAS  PubMed  Google Scholar 

  33. Core, L. & Adelman, K. Promoter-proximal pausing of RNA polymerase II: a nexus of gene regulation. Genes Dev. 33, 960–982 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Peterlin, B. M., Brogie, J. E. & Price, D. H. 7SK snRNA: a noncoding RNA that plays a major role in regulating eukaryotic transcription. Wiley Interdiscip. Rev. RNA 3, 92–103 (2012).

    Article  CAS  PubMed  Google Scholar 

  35. Brogie, J. E. & Price, D. H. Reconstitution of a functional 7SK snRNP. Nucleic Acids Res. 45, 6864–6880 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Fishman, V. et al. 3D organization of chicken genome demonstrates evolutionary conservation of topologically associated domains and highlights unique architecture of erythrocytes’ chromatin. Nucleic Acids Res. 47, 648–665 (2019).

    Article  CAS  PubMed  Google Scholar 

  37. Kruse, K., Hug, C. B., Hernandez-Rodriguez, B. & Vaquerizas, J. M. TADtool: visual parameter identification for TAD-calling algorithms. Bioinformatics 32, 3190–3192 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hou, C., Dale, R. & Dean, A. Cell type specificity of chromatin organization mediated by CTCF and cohesin. Proc. Natl Acad. Sci. USA 107, 3651–3656 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Singh, A., Compe, E., Le May, N. & Egly, J. M. TFIIH subunit alterations causing xeroderma pigmentosum and trichothiodystrophy specifically disturb several steps during transcription. Am. J. Hum. Genet. 96, 194–207 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Fong, Y. W. & Zhou, Q. Stimulatory effect of splicing factors on transcriptional elongation. Nature 414, 929–933 (2001).

    Article  CAS  PubMed  Google Scholar 

  42. Jani, D. et al. Functional and structural characterization of the mammalian TREX-2 complex that links transcription with nuclear messenger RNA export. Nucleic Acids Res. 40, 4562–4573 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Morera, D. et al. RNA-Seq reveals an integrated immune response in nucleated erythrocytes. PLoS ONE 6, e26998 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Gilbert, N. et al. Formation of facultative heterochromatin in the absence of HP1. EMBO J. 22, 5540–5550 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zhao, B. et al. Disruption of erythroid nuclear opening and histone release in myelodysplastic syndromes. Cancer Med. 8, 1169–1174 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lefevre, C., Bondu, S., Le Goff, S., Kosmider, O. & Fontenay, M. Dyserythropoiesis of myelodysplastic syndromes. Curr. Opin. Hematol. 24, 191–197 (2017).

    Article  CAS  PubMed  Google Scholar 

  47. Kabat, D. & Attardi, G. Synthesis of chicken hemoglobins during erythrocyte differentiation. Biochim. Biophys. Acta 138, 382–399 (1967).

    Article  CAS  PubMed  Google Scholar 

  48. Hildebrand, E. M. & Dekker, J. Mechanisms and functions of chromosome compartmentalization. Trends Biochem. Sci. 45, 385–396 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Penagos-Puig, A. & Furlan-Magaril, M. Heterochromatin as an important driver of genome organization. Front. Cell Dev. Biol. 8, 579137 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Li, D. et al. Heterochromatin rewiring and domain disruption-mediated chromatin compaction during erythropoiesis. Nat. Struct. Mol. Biol. 30, 463–474 (2023).

    Article  CAS  PubMed  Google Scholar 

  51. Rao, S. S. P. et al. Cohesin loss eliminates all loop domains. Cell 171, 305–320 e24 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Fudenberg, G. et al. Formation of chromosomal domains by loop extrusion. Cell Rep. 15, 2038–2049 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Khoury, A. et al. Constitutively bound CTCF sites maintain 3D chromatin architecture and long-range epigenetically regulated domains. Nat. Commun. 11, 54 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Barutcu, A. R., Blencowe, B. J. & Rinn, J. L. Differential contribution of steady-state RNA and active transcription in chromatin organization. EMBO Rep. 20, e48068 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Zhang, S. et al. RNA polymerase II is required for spatial chromatin reorganization following exit from mitosis. Sci. Adv. 7, eabg8205 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Levine, M. Paused RNA polymerase II as a developmental checkpoint. Cell 145, 502–511 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Gariglio, P., Bellard, M. & Chambon, P. Clustering of RNA polymerase B molecules in the 5′ moiety of the adult β-globin gene of hen erythrocytes. Nucleic Acids Res. 9, 2589–2598 (1981).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Murphy, Z. C. et al. Regulation of RNA polymerase II activity is essential for terminal erythroid maturation. Blood 138, 1740–1756 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. St Paul, M., Paolucci, S., Barjesteh, N., Wood, R. D. & Sharif, S. Chicken erythrocytes respond to Toll-like receptor ligands by up-regulating cytokine transcripts. Res. Vet. Sci. 95, 87–91 (2013).

    Article  Google Scholar 

  60. Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21.29.1–21.29.9 (2015).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Ramirez, F. et al. High-resolution TADs reveal DNA sequences underlying genome organization in flies. Nat. Commun. 9, 189 (2018).

    Article  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  Google Scholar 

  64. Bhardwaj, V. et al. snakePipes: facilitating flexible, scalable and integrative epigenomic analysis. Bioinformatics 35, 4757–4759 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  66. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  67. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Raudvere, U. et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191–W198 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Rubin, A. J. et al. Lineage-specific dynamic and pre-established enhancer-promoter contacts cooperate in terminal differentiation. Nat. Genet. 49, 1522–1528 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  71. Akdemir, K. C. & Chin, L. HiCPlotter integrates genomic data with interaction matrices. Genome Biol. 16, 198 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Kruse, K., Hug, C. B., Hernandez-Rodriguez, B. & Vaquerizas, J. M. TADtool: visual parameter identification for TAD-calling algorithms. Bioinformatics 32, 3190–3192 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Chen, X. et al. ATAC-see reveals the accessible genome by transposase-mediated imaging and sequencing. Nat. Methods 13, 1013–1020 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

A.P.-P. is supported by CONAHCyT scholarship no. 822335. This work was supported by PAPIIT grants no. IN207319 and no. IN210323 and by CONAHCyT grants no. 303068, no. 15758 and no. 137721. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank F. Recillas-Targa for kind donation of the HD3 erythroblast cell line. We thank the Molecular Biology Unit, the Microscopy Unit and the Bioinformatics Unit at the IFC, in particular R. Rincón-Heredia and A. Cesar Poot-Hernández for advice on image processing and genomic data analysis, respectively. We thank the Animal Facility at the IFC, in particular C. Rivera-Cerecedo and H. Malagón-Rivera. We thank the Supercomputing Unit at LANCIS, in particular R. García-Herrera for access to the computer cluster. We thank R. Saldaña-Meyer and M. Escamilla Del Arenal for helpful discussion on the results obtained in this work.

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Authors and Affiliations

  1. Departamento de Genética Molecular, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico

    Andrés Penagos-Puig, Sherlyn Claudio-Galeana, Aura Stephenson-Gussinye, Karina Jácome-López, Amaury Aguilar-Lomas, Rosario Pérez-Molina & Mayra Furlan-Magaril

  2. Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden

    Xingqi Chen

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Contributions

A.P.-P. and M.F.-M. designed the project, analyzed and discussed the data, and wrote the manuscript with comments from all authors. A.P.-P. performed and analyzed ATAC-see, ATAC-seq and Hi-C experiments and immunostainings. S.C.-G. performed and analyzed the RNA-seq and ChIP–seq as well as the ChIP–qPCR experiments and western blots. A.S.-G. performed and analyzed the initial Hi-C and RT–qPCR experiments. A.A.-L. and K.J.-L. helped with data analysis, X.C. provided the Tn5 ATTO 590N enzyme and R.P.-M. helped with experiments and provided laboratory assistance to all authors. All authors contributed to the discussion of the results.

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Correspondence to Mayra Furlan-Magaril.

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Extended data

Extended Data Fig. 1 Validation of ATAC-see and chromatin reorientation in aRBC.

a, Imaging of open chromatin by ATAC-see (red) and DAPI (blue, nuclei) in chicken fibroblasts. A negative control of ATAC-see in presence of EDTA 50 mM shows no incorporation of the fluorophore. Representative images from two independent experiments are shown. b, ATAC-see signal average intensity per nucleus in chicken erythrocytes. Boxplots with median (horizontal line), mean (+), 25th and 75th percentiles (boxes), and whiskers denoting the 10th and 90th percentile from 50 nuclei from two independent experiments are shown. P values based on one-way ANOVA followed by Tukey’s post-hoc test are indicated. c, Representative images from two ATAC-see experiments in aRBC. Brightest pixels enriched in ATAC-see (red) or DAPI (blue) are shown on the right. d, Pearson’s correlation score between ATAC-see, DAPI or H3K9me3 signal intensity and the distance to the nuclear rim (n = 50 nuclei from two independent replicates). Scatter dot plots show mean values +/− SD. e, Fluorescence imaging of Lamin B1 (green) and DAPI. In every case, maximal projections of three-dimensional (3D) stacks are presented. Representative images from two replicates are shown. Scale bar = 5 µm.

Source data

Extended Data Fig. 2 Characterization of the erythroid transcriptome.

a, Spearman correlation heatmap of RNA-seq replicates. b, Venn diagram of expressed genes in fibroblasts and erythroid cells. c, Relative expression of GAPDH and HBBA measured by RT-qPCR, triplicates from two biological replicates are presented. For aRBC and fibroblasts this means independent samples from two different animals, for eRBC this means two independent RBC collection from approximately 20 embryos, and for erythroblasts this means two independent cell cultures. Data is presented as means +/− SD. P values indicated are based on one-way ANOVA analysis followed by Tukey’s post-hoc test d, Expression levels of the erythroid genes EPB41 and EPB42 measured by RNA-seq (up; n = 3 independent RNA-seq libraries) and RT-qPCR (down: n= same as c). Data is presented as means +/− SD with P values based on one-way ANOVA followed by Tukey’s post-hoc test. e, EU incorporation measured by fluorescence intensity after 6 hours of incubation with EU (n = 50 nuclei from three independent Click-iT experiments). P values based on one-way ANOVA analysis followed by Kruskal-Wallis post hoc test are presented. f, MA plots of differentially expressed genes between eRBC and erythroblasts, and aRBC and erythroblasts. g, GO terms enriched in upregulated genes from f. Analysis was performed using Fisher’s exact test and the default multiple-hypotheses testing method (g:SCS) on gProfiler.

Source data

Extended Data Fig. 3 Chicken erythrocytes retain accessible paused promoters.

a, Spearman correlation heatmap of ATAC-seq replicates. b, Example of the preserved accessibility of the promoter of CTCF despite no transcriptional activity detected by RNA-seq. c, Representative images of DAPI and RNA pol II Ser2-P immunofluorescence from two replicates. Maximal projections of three-dimensional (3D) stacks are presented. Scale bar = 5 µm. Average signal intensity of RNA pol II Ser5-P (d,) and Ser2-P (e,) signal average intensity per nucleus in chicken erythrocytes. Boxplots with median (horizontal line), mean (+), 25th and 75th percentiles (boxes), and whiskers denoting the 10th and 90th percentile from 50 nuclei from two independent experiments are shown. P values based on one-way ANOVA followed by Tukey’s post-hoc test are presented. Pearson’s correlation score between RNA Pol II Ser-5 (f,) and Ser-2 (g,) signal intensity and the distance to the nuclear rim (n = 50 nuclei from two independent replicates). Scatter dot plots show mean values +/− SD. h, RNA pol II Ser5-Pand DAPI intensity is presented as a function of distance to the nuclear periphery. Individual tracks for 50 nuclei are presented in lighter colors and the mean intensity is presented in bold. i, RNA pol II Ser2-P and DAPI intensity is presented as a function of distance to the nuclear periphery. Individual tracks for 50 nuclei are presented in lighter colors and the mean intensity is presented in bold.

Source data

Extended Data Fig. 4 ChIP-qPCR confirms RNA Pol II Ser-5 occupancy of promoters of genes. that are not expressed in aRBC.

a, ATAC-seq, RNA Pol II Ser5-P ChIP-seq, and RNA-seq tracks of HTATSF1, GTF2H5, PCID2, and SOX2. b, ChIP-qPCR against RNA pol II Ser5-P. ATAC-seq peaks at the promoter of HTATSF1, GTF2H5 and PCID2 were analyzed. Fold enrichment was calculated against amplification in the promoter of SOX2. SOX2 enrichment was calculated against input. Mean +/− SD from 4 replicates is presented. Statistical differences against a mock control were calculated with a two-tailed t-test.

Source data

Extended Data Fig. 5 Erythroid mini domains retain local structure around open chromatin and paused genes.

a, Pearson correlation heatmap between Hi-C replicates at 50 Kb resolution. b, Hi-C contact matrices from eRBC and aRBC (3: 24.5–28 Mb) and examples of mini domains identified. ATAC-seq and RNA-seq tracks are plotted below. c, Western blot for CTCF and SMC1 proteins for all cell types. Histone 3 (H3) was used as the loading control. Representative results from two experiments are presented. d, RNA-seq RPKM count inside chromatin domains or a set of random regions of the same size in fibroblasts (n = 2,373 domains), erythroblasts (n = 2,562), eRBC (n = 916), and aRBC (903). Boxplots show median (horizontal line), mean (+), 25th and 75th percentiles (boxes), and whiskers denote the 1st and 99th percentiles. P values based on one-way ANOVA followed by Kruskal-Wallis post hoc test are stated.

Source data

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Source data for the western blot presented in Extended Data Fig. 5.

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Penagos-Puig, A., Claudio-Galeana, S., Stephenson-Gussinye, A. et al. RNA polymerase II pausing regulates chromatin organization in erythrocytes. Nat Struct Mol Biol 30, 1092–1104 (2023). https://doi.org/10.1038/s41594-023-01037-0

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  • DOI: https://doi.org/10.1038/s41594-023-01037-0

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