Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

ZNF410 represses fetal globin by singular control of CHD4

Abstract

Known fetal hemoglobin (HbF) silencers have potential on-target liabilities for rational β-hemoglobinopathy therapeutic inhibition. Here, through transcription factor (TF) CRISPR screening, we identify zinc-finger protein (ZNF) 410 as an HbF repressor. ZNF410 does not bind directly to the genes encoding γ-globins, but rather its chromatin occupancy is concentrated solely at CHD4, encoding the NuRD nucleosome remodeler, which is itself required for HbF repression. CHD4 has two ZNF410-bound regulatory elements with 27 combined ZNF410 binding motifs constituting unparalleled genomic clusters. These elements completely account for the effects of ZNF410 on fetal globin repression. Knockout of ZNF410 or its mouse homolog Zfp410 reduces CHD4 levels by 60%, enough to substantially de-repress HbF while eluding cellular or organismal toxicity. These studies suggest a potential target for HbF induction for β-hemoglobin disorders with a wide therapeutic index. More broadly, ZNF410 represents a special class of gene regulator, a conserved TF with singular devotion to regulation of a chromatin subcomplex.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: ZNF410 is an HbF repressor.
Fig. 2: ZNF410 chromatin occupancy is restricted to two CHD4 elements with densely clustered motifs.
Fig. 3: ZNF410 represses HbF by activating CHD4.
Fig. 4: Zfp410-deficient mice are viable with unremarkable hematology.
Fig. 5: ZNF410-deficient human HSPCs de-repress HbF and retain repopulation potential.

Similar content being viewed by others

Data availability

The datasets generated during the current study are available from the indicated repositories when applicable or are included in this article. Datasets generated during the current study are available as follows. (1) RNA-seq of HUDEP-2 cells and CHD4 Δ6.7 kb HUDEP-2 cells edited at ZNF410 were deposited in NCBI’s Gene Expression Omnibus (GEO) and are accessible through GEO series accession number GSE166222. (2) CUT&RUN data for genomic ZNF410 chromatin occupancy are accessible through GEO series accession number GSE166221. (3) ATAC-seq data for HUDEP-2 cells are accessible through GEO series accession number GSE167298. Publicly available datasets referenced in this manuscript are available as follows. (1) Erythroid expression profiling datasets are available under accession numbers GSE53983, GSE54602, GSE22552 and E-MTAB-1035. (2) RNA-seq of HUDEP-2 cells edited at CHD4 is available from the NCBI SRA portal under accession number PRJNA496556, https://www.ncbi.nlm.nih.gov/sra. (3) ZNF410 and CHD4 expression values (TPM) across 54 human tissues were obtained from the GTEx Portal (https://gtexportal.org/home/). (4) Gene dependency scores for 558 cell lines were obtained from the Achilles Avana 20Q2 Public CERES dataset of the DepMap portal74. (5) MEL DNase sequencing data were obtained from the ENCODE project (dataset, ENCSR000CNN; file, ENCFF990ATO). Source data are provided with this paper.

Code availability

The scripts used for analysis of CUT&RUN experiments and motif mapping are provided in the Supplementary Methods. The motif-counting script is available at https://github.com/yao-qiuming/Vinjamur_NG2021.

References

  1. Orkin, S. H. & Bauer, D. E. Emerging genetic therapy for sickle cell disease. Annu. Rev. Med. 70, 257–271 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Piel, F. B., Steinberg, M. H. & Rees, D. C. Sickle cell disease. N. Engl. J. Med. 377, 302–305 (2017).

    Google Scholar 

  3. Weatherall, D. J. The evolving spectrum of the epidemiology of thalassemia. Hematol. Oncol. Clin. North Am. 32, 165–175 (2018).

    Article  PubMed  Google Scholar 

  4. Sankaran, V. G. et al. Human fetal hemoglobin expression is regulated by the developmental stage-specific repressor BCL11A. Science 322, 1839–1842 (2008).

    Article  CAS  PubMed  Google Scholar 

  5. Masuda, T. et al. Transcription factors LRF and BCL11A independently repress expression of fetal hemoglobin. Science 351, 285–289 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sher, F. et al. Rational targeting of a NuRD subcomplex guided by comprehensive in situ mutagenesis. Nat. Genet. 51, 1149–1159 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Amaya, M. et al. Mi2β-mediated silencing of the fetal γ-globin gene in adult erythroid cells. Blood 121, 3493–3501 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Martyn, G. E. et al. Natural regulatory mutations elevate fetal globin via disruption of BCL11A or ZBTB7A binding. Nat. Genet. 50, 498–503 (2018).

    Article  CAS  PubMed  Google Scholar 

  9. Liu, N. et al. Direct promoter repression by BCL11A controls the fetal to adult hemoglobin switch. Cell 173, 430–442 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Xu, J. et al. Corepressor-dependent silencing of fetal hemoglobin expression by BCL11A. Proc. Natl Acad. Sci. USA 110, 6518–6523 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Maeda, T. et al. LRF is an essential downstream target of GATA1 in erythroid development and regulates BIM-dependent apoptosis. Dev. Cell 17, 527–540 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. An, X. et al. Global transcriptome analyses of human and murine terminal erythroid differentiation. Blood 123, 3466–3478 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Shi, L. et al. Developmental transcriptome analysis of human erythropoiesis. Hum. Mol. Genet. 23, 4528–4542 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Merryweather-Clarke, A. T. et al. Global gene expression analysis of human erythroid progenitors. Blood 117, e96–e108 (2011).

    Article  CAS  PubMed  Google Scholar 

  15. Kingsley, P. D. et al. Ontogeny of erythroid gene expression. Blood 121, e5–e13 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gautier, E.-F. et al. Comprehensive proteomic analysis of human erythropoiesis. Cell Rep. 16, 1470–1484 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Benanti, J. A., Williams, D. K., Robinson, K. L., Ozer, H. L. & Galloway, D. A. Induction of extracellular matrix-remodeling genes by the senescence-associated protein APA-1. Mol. Cell Biol. 22, 7385–7397 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Skene, P. J., Henikoff, J. G. & Henikoff, S. Targeted in situ genome-wide profiling with high efficiency for low cell numbers. Nat. Protoc. 13, 1006–1019 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).

    Article  CAS  PubMed  Google Scholar 

  20. GTEx Consortium. The Genotype–Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    Article  Google Scholar 

  21. Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Dempster, J. M. et al. Extracting biological insights from the Project Achilles genome-scale CRISPR screens in cancer cell lines. Preprint at bioRxiv https://doi.org/10.1101/720243 (2019).

  23. Low, J. K. K. et al. The nucleosome remodeling and deacetylase complex has an asymmetric, dynamic, and modular architecture. Cell Rep. 33, 108450 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Porcu, S. et al. The human β globin locus introduced by YAC transfer exhibits a specific and reproducible pattern of developmental regulation in transgenic mice. Blood 90, 4602–4609 (1997).

    Article  CAS  PubMed  Google Scholar 

  25. Gaensler, K. M., Kitamura, M. & Kan, Y. W. Germ-line transmission and developmental regulation of a 150-kb yeast artificial chromosome containing the human β-globin locus in transgenic mice. Proc. Natl Acad. Sci. USA 90, 11381–11385 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Liu, P. et al. Bcl11a is essential for normal lymphoid development. Nat. Immunol. 4, 525–532 (2003).

    Article  CAS  PubMed  Google Scholar 

  27. O’Shaughnessy-Kirwan, A., Signolet, J., Costello, I., Gharbi, S. & Hendrich, B. Constraint of gene expression by the chromatin remodelling protein CHD4 facilitates lineage specification. Development 142, 2586–2597 (2015).

    PubMed  PubMed Central  Google Scholar 

  28. McIntosh, B. E. et al. Nonirradiated NOD,B6.SCID Il2rγ−/−KitW41/W41 (NBSGW) mice support multilineage engraftment of human hematopoietic cells. Stem Cell Rep. 4, 171–180 (2015).

    Article  CAS  Google Scholar 

  29. Wu, Y. et al. Highly efficient therapeutic gene editing of human hematopoietic stem cells. Nat. Med. 25, 776–783 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. John, C. C. et al. Hydroxyurea dose escalation for sickle cell anemia in Sub-Saharan Africa. N. Engl. J. Med. 382, 2524–2533 (2020).

    Article  CAS  PubMed  Google Scholar 

  31. Ippolito, G. C. et al. Dendritic cell fate is determined by BCL11A. Proc. Natl Acad. Sci. USA 111, E998–E1006 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Tsang, J. C. et al. Single-cell transcriptomic reconstruction reveals cell cycle and multi-lineage differentiation defects in Bcl11a-deficient hematopoietic stem cells. Genome Biol. 16, 178 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Luc, S. et al. Bcl11a deficiency leads to hematopoietic stem cell defects with an aging-like phenotype. Cell Rep. 16, 3181–3194 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Khaled, W. T. et al. BCL11A is a triple-negative breast cancer gene with critical functions in stem and progenitor cells. Nat. Commun. 6, 5987 (2015).

    Article  CAS  PubMed  Google Scholar 

  35. Benitez, C. M. et al. An integrated cell purification and genomics strategy reveals multiple regulators of pancreas development. PLoS Genet. 10, e1004645 (2014).

  36. Maeda, T. Regulation of hematopoietic development by ZBTB transcription factors. Int. J. Hematol. 104, 310–323 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Yu, X. et al. Disruption of the MBD2–NuRD complex but not MBD3–NuRD induces high level HbF expression in human erythroid cells. Haematologica 104, 2361–2371 (2019).

  38. Steinberg, M. H. Fetal hemoglobin in sickle cell anemia. Blood 136, 2392–2400 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Vinjamur, D. S., Bauer, D. E. & Orkin, S. H. Recent progress in understanding and manipulating haemoglobin switching for the haemoglobinopathies. Br. J. Haematol. 180, 630–643 (2018).

    Article  CAS  PubMed  Google Scholar 

  40. Lan, X. et al. ZNF410 uniquely activates the NuRD component CHD4 to silence fetal hemoglobin expression. Mol. Cell 81, 239–254 (2021).

    Article  CAS  PubMed  Google Scholar 

  41. Krönke, J. et al. Lenalidomide causes selective degradation of IKZF1 and IKZF3 in multiple myeloma cells. Science 343, 301–305 (2014).

    Article  PubMed  Google Scholar 

  42. Lu, G. et al. The myeloma drug lenalidomide promotes the cereblon-dependent destruction of Ikaros proteins. Science 343, 305–309 (2014).

    Article  CAS  PubMed  Google Scholar 

  43. Lu, B. et al. A transcription factor addiction in leukemia imposed by the MLL promoter sequence. Cancer Cell 34, 970–981 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Gillespie, M. A. et al. Absolute quantification of transcription factors reveals principles of gene regulation in erythropoiesis. Mol. Cell 78, 960–974 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Jones, W. D. et al. De novo mutations in MLL cause Wiedemann–Steiner syndrome. Am. J. Hum. Genet. 91, 358–364 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Sifrim, A. et al. Distinct genetic architectures for syndromic and nonsyndromic congenital heart defects identified by exome sequencing. Nat. Genet. 48, 1060–1065 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Weiss, K. et al. De novo mutations in CHD4, an ATP-dependent chromatin remodeler gene, cause an intellectual disability syndrome with distinctive dysmorphisms. Am. J. Hum. Genet. 99, 934–941 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Layat, E., Probst, A. V. & Tourmente, S. Structure, function and regulation of transcription factor IIIA: from Xenopus to Arabidopsis. Biochim. Biophys. Acta 1829, 274–282 (2013).

    Article  CAS  PubMed  Google Scholar 

  49. Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018).

    Article  CAS  PubMed  Google Scholar 

  50. Ezer, D., Zabet, N. R. & Adryan, B. Homotypic clusters of transcription factor binding sites: a model system for understanding the physical mechanics of gene expression. Comput. Struct. Biotechnol. J. 10, 63–69 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Kurita, R. et al. Establishment of immortalized human erythroid progenitor cell lines able to produce enucleated red blood cells. PLoS ONE 8, e59890 (2013).

  52. Vinjamur, D. S. & Bauer, D. E. Growing and genetically manipulating human umbilical cord blood-derived erythroid progenitor (HUDEP) cell lines. Methods Mol. Biol. 1698, 275–284 (2018).

    Article  CAS  PubMed  Google Scholar 

  53. Giarratana, M. C. C. et al. Proof of principle for transfusion of in vitro-generated red blood cells. Blood 118, 5071–5079 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Shalem, O. et al. Genome-scale CRISPR–Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

    Article  CAS  PubMed  Google Scholar 

  55. Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR–Cas9. Nat. Biotechnol. 34, 184–191 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Morgens, D. W. et al. Genome-scale measurement of off-target activity using Cas9 toxicity in high-throughput screens. Nat. Commun. 8, 15178 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Canver, M. C. et al. BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis. Nature 527, 192–197 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Schoonenberg, V. A. C. et al. CRISPRO: identification of functional protein coding sequences based on genome editing dense mutagenesis. Genome Biol. 19, 169 (2018).

    Article  Google Scholar 

  61. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 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 

  63. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  65. Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  Google Scholar 

  70. Davis, C. A. et al. The Encyclopedia of DNA Elements (ENCODE): data portal update. Nucleic Acids Res. 46, D794–D801 (2018).

    Article  CAS  PubMed  Google Scholar 

  71. Garber, M. et al. Identifying novel constrained elements by exploiting biased substitution patterns. Bioinformatics 25, i54–i62 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    Article  CAS  PubMed  Google Scholar 

  74. DepMap, Broad. DepMap 20Q2 Public. figshare https://doi.org/10.6084/m9.figshare.12280541.v4 (2020).

Download references

Acknowledgements

We thank R. Kurita and Y. Nakamura for sharing HUDEP-2 cells (Department of Research and Development, Central Blood Institute, Blood Service Headquarters, Japanese Red Cross Society and Cell Engineering Division, RIKEN BioResource Research Center, Faculty of Medicine, University of Tsukuba); R. Mathieu and the HSCI-BCH Flow Cytometry Facility, supported by the Harvard Stem Cell Institute and the NIH (U54DK110805) for assistance with flow cytometry; Z. Herbert from the Molecular Biology Core Facilities at the Dana-Farber Cancer Institute for assistance with sequencing; Y. Fujiwara and M. Nguyen from the BCH Mouse Embryonic Stem Cell and Gene Targeting Core (supported by the NIH, NIDDK Center of Excellence in Molecular Hematology (U54DK110805)) for assistance with transgenic mouse generation; J. Doench for assistance with CRISPR screening; S. Henikoff for sharing protein A–MNase for CUT&RUN experiments; J. Bonanno for technical assistance; and S. Orkin, C. Brendel, N. Liu, D. Seruggia, N. Dharia and members of the Bauer laboratory for helpful discussions. D.S.V. was supported by the Cooley’s Anemia Foundation Research Fellowship award; L.P. was supported by the NHGRI (R00HG008399 and R35HG010717); D.E.B. was supported in part by a Sponsored Research Agreement from Sanofi, NHLBI (DP2HL137300 and P01HL032262) and the Burroughs Wellcome Fund.

Author information

Authors and Affiliations

Authors

Contributions

D.E.B. conceived the study. D.E.B., D.S.V. and M.H. designed experiments. D.S.V., C.M., J.Z. and M.H. performed experiments. Q.Y., M.A.C. and L.P. designed computational analyses. D.S.V., Q.Y. and M.A.C. performed data analysis. C.R., K.L. and S.A.W. provided the 3× NLS-Cas9 protein. D.E.B., D.S.V. and Q.Y. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Daniel E. Bauer.

Ethics declarations

Competing interests

D.E.B. and D.S.V. are co-inventors on a patent related to ZNF410 disruption. The authors declare no other competing interests.

Additional information

Peer review information Nature Genetics thanks Swee Lay Thein and the other, anonymous, reviewers 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 ZNF410 targeted HUDEP-2 cells display normal cell growth and elevated γ-globin expression.

HUDEP-2 cells were edited at ZNF410 or AAVS1 (control) using sgRNA:Cas9 RNP electroporation. a, Efficient editing was achieved with all five ZNF410 targeting gRNAs (n = 1 for each gRNA). Cells were cultured in EDM2 for 8 days and (b) cell count and (c) viability data were recorded on alternate days for five individual gRNAs targeting ZNF410 (sg 1-5, n = 3 for sg 1, n = 1 for sg 2-5) in comparison to mock (n = 3) and AAVS1 (control, n = 3) targeted cells. Data are presented as mean values and error bars are standard deviation. d, Robust γ-globin induction was obtained with all five ZNF410 targeting gRNAs (n = 1 for each gRNA) in comparison to mock (n = 1) and AAVS1 (control, n = 1) targeted cells. e, HUDEP-2/Cas9 cells nontransduced (mock, n = 3) or transduced with ZNF410 targeting sgRNA (n = 4) assayed on day 9 of erythroid differentiation with RT-qPCR for HBE1 (p = 0.1426, ns), HBB (p = 0.0353) and HBA (p = 0.0122) expression. All values are relative to Catalase expression (endogenous control) and expressed as fold change relative to mock for each biological replicate. Data are presented as mean values and error bars are standard deviation. Statistically significant differences were determined using paired Student’s t-test comparing ZNF410 targeted cells to mock. f, Three HUDEP-2 ZNF410 biallelic KO clones were generated using paired genomic cleavages that delete the entire coding sequence. Clones were generated in two successive steps. In the first step a HUDEP-2 clone heterozygous for ZNF410 deletion was isolated. The ZNF410 null allele in this clone is designated Null cl. allele 1 and its sequence is shown on the left. In the second step this heterozygous ZNF410 deletion clone was retargeted and three biallelic ZNF410 null clones were isolated with the sequences of the second ZNF410 null allele in each clone shown on the right of the figure.

Extended Data Fig. 2 ZNF410 targeted primary erythroblasts display elevated fetal hemoglobin expression in normal and sickle cell patient derived donor cells.

a, Immunoblot showing protein expression of ZNF410, CHD4, and members of the HbF repressive NuRD subcomplex - GATAD2A, MTA2, MBD2, HDAC2 and RBBP4 - in ZNF410 targeted primary erythroblasts on Day 11 of erythroid culture. GAPDH included as loading control. b, Growth curve of ZNF410 targeted primary erythroblasts (n = 3) compared to mock (n = 3) and safe control (n = 3) targeted cells over 18 days of erythroid differentiation culture. Data are presented as mean values and error bars are standard deviation. c, HBG1/2 (p < 0.05), HBE1 (ns), HBB (p < 0.01) and HBA1/2 (ns) globin gene expression measured by RT-qPCR in ZNF410 targeted (n = 3) compared to Mock (n = 3) and safe control targeted (n = 3) primary erythroblasts. Catalase was used as the endogenous normalization control. Values are displayed relative to the mean of mock samples. Statistical tests compare ZNF410 targeted and mock samples, ns not significant. Data are presented as mean values and error bars are standard deviation. d, ZNF410 targeted (n = 1) by RNP electroporation of Cas9 and sgRNA in CD34 + HSPCs from a sickle cell disease patient and subsequently differentiated to erythroid cells in vitro. At the end of erythroid culture (day 18), HbF level was measured by hemoglobin HPLC and compared to mock (n = 1) and safe control (n = 1) targeted cells.

Source data

Extended Data Fig. 3 Absent ZNF410 chromatin occupancy.

a-c, ɑ-like and -like globin gene clusters and GALNT18 intron 1 with a cluster of 6 ZNF410 motifs indicating absence of ZNF410 occupancy in representative CUT&RUN control IgG (n = 9) and anti-HA (n = 7) in HUDEP-2 cells over-expressing HA-tagged ZNF410, control IgG (n = 1) and anti-ZNF410 (n = 1) in HUDEP-2 cells, and control IgG (n = 2) and anti-ZNF410 (n = 2) in CD34 + HSPC derived erythroid precursors. Positions of ZNF410 motifs (red rectangles), accessible chromatin by representative ATAC-seq in HUDEP-2 cells (gray peaks, n = 3) and DNA sequence conservation by SiPhy rate.

Extended Data Fig. 4 ZNF410 chromatin occupancy.

a, The third most enriched peak for ZNF410 binding (following CHD4 promoter and -6 kb enhancer) by CUT&RUN with anti-HA antibody in HUDEP-2 cells over-expressing ZNF410-HA was at TIMELESS intron 1. Representative CUT&RUN control IgG (n = 9) and anti-HA (n = 7) in HUDEP-2 cells over-expressing HA-tagged ZNF410, control IgG (n = 1) and anti-ZNF410 (n = 1) in HUDEP-2 cells, and control IgG (n = 2) and anti-ZNF410 (n = 2) in CD34 + HSPC derived erythroid precursors. Positions of ZNF410 motifs (red rectangles), accessible chromatin by representative ATAC-seq in HUDEP-2 cells (gray peaks, n = 3), DNA sequence conservation by SiPhy rate, and repetitive elements from RepeatMasker. b, A total of 5 peaks were identified by CUT&RUN with anti-ZNF410 antibody in HUDEP-2 cells. The top 4 peaks were at the CHD4 promoter or -6 kb enhancer, the fifth was at DPY19L3 intron 5. c, A total of 5 peaks were identified by CUT&RUN with anti-ZNF410 antibody in CD34 + HSPC derived erythroid precursors. All 5 peaks were at the CHD4 promoter or -6 kb enhancer. d, Peak of ZNF410 occupancy at DPY19L3 intron 5 in HUDEP-2 cells.

Extended Data Fig. 5 ZNF410 represses HbF by activating CHD4.

a, Comparison of genes downregulated in ZNF410 and CHD4 mutant cells by GSEA (b) LC3-I/II and GAPDH (control) immunoblot in unedited parental and ZNF410 null HUDEP-2 cells (left panel) and mock and ZNF410 targeted primary erythroblasts (c) Correlation of ZNF410 and CHD4 expression across 54 human tissues from GTEx (Pearson r = 0.77, p < 0.0001) (d) CHD4 expression in ZNF410 targeted (n = 3) compared to mock (n = 1) and AAVS1 (n = 1) targeted control HUDEP-2 cells. Data are mean values, error bars are standard deviation. e, Cas9 paired cleavages with CHD4-proximal-gRNA-1 and CHD4-distal-gRNA-1 (CHD4 Δ 6.7 kb) or CHD4-proximal-gRNA-1 and CHD4-distal-gRNA-2 (CHD4 Δ 6.9 kb) were used to generate HUDEP-2 clones with biallelic deletions spanning both of the ZNF410 binding regions upstream of CHD4. Positions of ZNF410 motifs (red rectangles) and accessible chromatin by ATAC-seq (gray peaks) (f) CHD4 expression in CHD4 Δ 6.9 kb clones (n = 3) compared to HUDEP-2 cells (n = 1) (left panel) and HbF level measured by hemoglobin HPLC in CHD4 Δ 6.7 kb (n = 1) and Δ 6.9 kb (#2 and #3, n = 2) clones compared to HUDEP-2 cells (n = 1) (right panel). g, CHD4 Δ 6.9 kb clones and HUDEP-2 cells were subjected to AAVS1 (negative control), ZNF410 and ZBTB7A targeting using RNP electroporation of 3X-NLS-Cas9 and sgRNA. Left panel, editing efficiency measured by indel frequency in HUDEP-2 cells (n = 1) and CHD4 Δ 6.9 kb clones (n = 3) targeted with ZNF410 or ZBTB7A sgRNAs. The shaded portion of the bar represents the percentage of indels resulting in frameshift (fs) alleles. The white portion of the bar represents in-frame indels. Right panel, HBG expression relative to total β-like globin (HBG + HBB) in HUDEP-2 cells (n = 1) and CHD4 Δ 6.9 kb clones (n = 3) targeted with AAVS1 (negative control), ZNF410 or ZBTB7A sgRNAs. h, HBG expression relative to total β-like globin (HBG + HBB) in CHD4 Δ 6.9 kb clone 3 (n = 1) subjected to ZNF410, BCL11A and ZBTB7A targeting using RNP electroporation of 3xNLS-Cas9 and sgRNA compared to mock (n = 1) cells. i, CBX6 expression in mock, AAVS1 and ZNF410 targeted HUDEP-2 cells (n = 2 for mock and control, n = 3 for ZNF410 targeted) and CHD4 Δ 6.7 kb HUDEP-2 cells (n = 3 for mock, control and ZNF410 targeted). Catalase was used as the endogenous normalization control. CBX6 expression in targeted cells is shown relative to expression in mock cells. Data are mean values, error bars are standard deviation.

Source data

Extended Data Fig. 6 Zfp410 is the conserved mouse ortholog of ZNF410.

a, CUT&RUN performed in mouse erythroleukemia (MEL) cells using anti-Zfp410 antibody (n = 3) and IgG control (n = 3). The third most enriched Zfp410 peak (following Chd4 promoter and Chd4 -6 kb enhancer) was at the Hist1h2bl promoter. No Zfp410 motifs were identified at this locus, which overlaps accessible chromatin (DNase-seq, gray peaks). b, Diagram of the Zfp410 gene trap allele. A targeting cassette including splice acceptor site upstream of LacZ was inserted into Zfp410 intron 5 thus disrupting full-length expression. Schema obtained along with mouse ES cells from EuMMCR, Germany. c, Exon and domain structure of mouse Zfp410. d, Mouse embryonic (βh1 and εy) and adult β-major/minor globin gene expression measured by RT-qPCR in Zfp410 Gt/Gt (n = 5) mouse E14.5 fetal liver erythroid cells compared to heterozygous (n = 4) and wildtype (n = 5) control animals. e, Weight was measured at indicated time points over the course of 15 weeks for wildtype male (+/+ (M), n = 1), Zfp410 heterozygous male (+/Gt (M), n = 2), Zfp410 homozygous male (Gt/Gt (M), n = 2), Zfp410 heterozygous female (+/Gt (F), n = 5) and Zfp410 homozygous female (Gt/Gt (F), n = 1) mice. Data are presented as mean values and error bars are standard deviation. f, Peripheral blood hematological parameters for wildtype (n = 1), Zfp410 + /Gt (n = 7) and Zfp410 Gt/Gt (n = 3) mice, with normal ranges for hemoglobin, mean corpuscular volume (MCV), reticulocyte, white blood cell (WBC), neutrophil and platelet count shown by dotted lines.

Extended Data Fig. 7 ZNF410 is dispensable for hematopoietic repopulation and erythropoiesis.

CD34 + HSPCs from donor 3 were edited by RNP electroporation targeting ZNF410, BCL11A or ZBTB7A and infused to NBSGW mice or subject to in vitro erythroid differentiation. a, Indel frequency at ZNF410, BCL11A and ZBTB7A was quantified in input cells 4 days after electroporation, and in engrafted total or sorted cells at bone marrow (BM) harvest. The percentage of frameshift alleles is represented in gray and the percentage of in-frame alleles is represented in white. b, Comparison of engraftment assessed by human CD45 + staining compared to total CD45 + cells in xenografts of ZNF410 (n = 4), BCL11A (n = 3) and ZBTB7A (n = 3) edited and mock control (n = 4) CD34 + HSPCs. Each symbol represents one mouse. c,d, Erythroid maturation, evaluated based on CD71 and CD235a immunophenotype and enucleation frequency, was assessed on day 18 of in vitro erythroid culture in safe control (n = 4), ZNF410 (n = 2), BCL11A (n = 2) and ZBTB7A (n = 2) targeted primary erythroblasts.

Extended Data Fig. 8 Flow cytometry gating strategy for xenograft experiment.

Hierarchy of FACS gates and representative plots for each gate are shown for a representative control (mock) transplanted bone marrow sample. The first gate was plotted to delineate the cell population of interest (POI) and avoid debris. The second and third gates were plotted to exclude doublets. Values in plots are for respective gates.

Supplementary information

Source data

Source Data Extended Data Fig. 2

Unprocessed western blots.

Source Data Extended Data Fig. 5

Unprocessed western blots.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vinjamur, D.S., Yao, Q., Cole, M.A. et al. ZNF410 represses fetal globin by singular control of CHD4. Nat Genet 53, 719–728 (2021). https://doi.org/10.1038/s41588-021-00843-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-021-00843-w

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing