Systematic identification of silencers in human cells

Abstract

The majority of the human genome does not encode proteins. Many of these noncoding regions contain important regulatory sequences that control gene expression. To date, most studies have focused on activators such as enhancers, but regions that repress gene expression—silencers—have not been systematically studied. We have developed a system that identifies silencer regions in a genome-wide fashion on the basis of silencer-mediated transcriptional repression of caspase 9. We found that silencers are widely distributed and may function in a tissue-specific fashion. These silencers harbor unique epigenetic signatures and are associated with specific transcription factors. Silencers also act at multiple genes, and at the level of chromosomal domains and long-range interactions. Deletion of silencer regions linked to the drug transporter genes ABCC2 and ABCG2 caused chemo-resistance. Overall, our study demonstrates that tissue-specific silencing is widespread throughout the human genome and probably contributes substantially to the regulation of gene expression and human biology.

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Fig. 1: Identification of silencers using the ReSE screen.
Fig. 2: Conserved and tissue-specific distribution of silencers.
Fig. 3: Unique signatures in silencer regions.
Fig. 4: Drug resistance regulated by silencer regions.
Fig. 5: Regulation of distal genes by silencers.

Data availability

The sequencing data are available at GEO under accession number GSE108536. Source data for Figs. 15 and Extended Data Figs. 2, 3, 6, 7, 9 and 10 are available with the paper.

References

  1. 1.

    Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).

  2. 2.

    Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

  3. 3.

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

  4. 4.

    Gerstein, M. B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012).

  5. 5.

    Neph, S. et al. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489, 83–90 (2012).

  6. 6.

    Djebali, S. et al. Landscape of transcription in human cells. Nature 489, 101–108 (2012).

  7. 7.

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

  8. 8.

    Roadmap Epigenomics, C. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  9. 9.

    Filion, G. J. et al. Systematic protein location mapping reveals five principal chromatin types in Drosophila cells. Cell 143, 212–224 (2010).

  10. 10.

    Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

  11. 11.

    Katainen, R. et al. CTCF/cohesin-binding sites are frequently mutated in cancer. Nat. Genet. 47, 818–821 (2015).

  12. 12.

    Lovén, J. et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell 153, 320–334 (2013).

  13. 13.

    Khurana, E. et al. Role of non-coding sequence variants in cancer. Nat. Rev. Genet. 17, 93–108 (2016).

  14. 14.

    Flavahan, W. A. et al. Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature 529, 110–114 (2016).

  15. 15.

    Melnikov, A. et al. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotech. 30, 271–277 (2012).

  16. 16.

    Kwasnieski, J. C., Fiore, C., Chaudhari, H. G. & Cohen, B. A. High-throughput functional testing of ENCODE segmentation predictions. Genome Res. 24, 1595–602 (2014).

  17. 17.

    Arnold, C. D. et al. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science 339, 1074–1077 (2013).

  18. 18.

    Zabidi, M. A. et al. Enhancer-core-promoter specificity separates developmental and housekeeping gene regulation. Nature 518, 556–559 (2015).

  19. 19.

    van Arensbergen, J. et al. Genome-wide mapping of autonomous promoter activity in human cells. Nat. Biotech. 35, 145–153 (2017).

  20. 20.

    Maston, G. A., Evans, S. K. & Green, M. R. Transcriptional regulatory elements in the human genome. Annu. Rev. Genomics Hum. Genet. 7, 29–59 (2006).

  21. 21.

    Siu, G., Wurster, A. L., Duncan, D. D., Soliman, T. M. & Hedrick, S. M. A transcriptional silencer controls the developmental expression of the CD4 gene. EMBO J. 13, 3570–3579 (1994).

  22. 22.

    Sawada, S., Scarborough, J. D., Killeen, N. & Littman, D. R. A lineage-specific transcriptional silencer regulates CD4 gene expression during T lymphocyte development. Cell 77, 917–929 (1994).

  23. 23.

    Zou, Y.-R. et al. Epigenetic silencing of CD4 in T cells committed to the cytotoxic lineage. Nat. Genet. 29, 332–336 (2001).

  24. 24.

    Taniuchi, I. et al. Differential requirements for runx proteins in CD4 repression and epigenetic silencing during T lymphocyte development. Cell 111, 621–633 (2002).

  25. 25.

    Taniuchi, I., Sunshine, M. J., Festenstein, R. & Littman, D. R. Evidence for distinct CD4 silencer functions at different stages of thymocyte differentiation. Mol. Cell 10, 1083–1096 (2002).

  26. 26.

    Pang, B. et al. Direct antigen presentation and gap junction mediated cross-presentation during apoptosis. J. Immunol. 183, 1083–1090 (2009).

  27. 27.

    Straathof, K. C. et al. An inducible caspase 9 safety switch for T-cell therapy. Blood 105, 4247–4254 (2005).

  28. 28.

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

  29. 29.

    Giresi, P. G., Kim, J., McDaniell, R. M., Iyer, V. R. & Lieb, J. D. FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin. Genome Res. 17, 877–885 (2007).

  30. 30.

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

  31. 31.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

  32. 32.

    Di, Y., Schafer, D. W., Cumbie, J. S. & Chang J. H. The NBP negative binomial model for assessing sifferential gene expression from RNA-seq. Stat. Appl. Genet. Mol. Biol. https://doi.org/10.2202/1544-6115.1637 (2011).

  33. 33.

    Visel, A. et al. ChIP–seq accurately predicts tissue-specific activity of enhancers. Nature 457, 854–858 (2009).

  34. 34.

    Bonn, S. et al. Tissue-specific analysis of chromatin state identifies temporal signatures of enhancer activity during embryonic development. Nat. Genet. 44, 148–156 (2012).

  35. 35.

    Dahlman, J. E. et al. Orthogonal gene knockout and activation with a catalytically active Cas9 nuclease. Nat. Biotechnol. 33, 1159–1161 (2015).

  36. 36.

    Konermann, S. et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517, 583–588 (2015).

  37. 37.

    Calderwood, D. A., Shattil, S. J. & Ginsberg, M. H. Integrins and actin filaments: reciprocal regulation of cell adhesion and signaling. J. Biol. Chem. 275, 22607–22610 (2000).

  38. 38.

    Shlyueva, D., Stampfel, G. & Stark, A. Transcriptional enhancers: from properties to genome-wide predictions. Nat. Rev. Genet. 15, 272–286 (2014).

  39. 39.

    Ernst, J. & Kellis, M. ChromHMM: automating chromatin state discovery and characterization. Nat. Methods 9, 215–216 (2012).

  40. 40.

    Yu, G., Wang, L. G. & He, Q. Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

  41. 41.

    Beck, D. B., Oda, H., Shen, S. S. & Reinberg, D. PR-Set7 and H4K20me1: at the crossroads of genome integrity, cell cycle, chromosome condensation, and transcription. Genes Dev. 26, 325–337 (2012).

  42. 42.

    Chantalat, S. et al. Histone H3 trimethylation at lysine 36 is associated with constitutive and facultative heterochromatin. Genome Res. 21, 1426–1437 (2011).

  43. 43.

    Ahn, S. H., Kim, M. & Buratowski, S. Phosphorylation of Serine 2 within the RNA polymerase II C-terminal domain couples transcription and 3′ end processing. Mol. Cell 13, 67–76 (2004).

  44. 44.

    Liu, T. et al. Cistrome: an integrative platform for transcriptional regulation studies. Genome Biol. 12, R83–R83 (2011).

  45. 45.

    Jiang, J. G., DeFrances, M. C., Machen, J., Johnson, C. & Zarnegar, R. The repressive function of AP2 transcription factor on the hepatocyte growth factor gene promoter. Biochem. Biophys. Res. Commun. 272, 882–886 (2000).

  46. 46.

    Eckert, D., Buhl, S., Weber, S., Jäger, R. & Schorle, H. The AP-2 family of transcription factors. Genome Biol. 6, 246 (2005).

  47. 47.

    Roth, C., Schuierer, M., Günther, K. & Buettner, R. Genomic structure and DNA binding properties of the human zinc finger transcriptional repressor AP-2rep (KLF12). Genomics 63, 384–390 (2000).

  48. 48.

    Bonev, B. & Cavalli, G. Organization and function of the 3D genome. Nat. Rev. Genet. 17, 661–678 (2016).

  49. 49.

    Krijger, P. H. L. & de Laat, W. Regulation of disease-associated gene expression in the 3D genome. Nat. Rev. Mol. Cell Biol. 17, 771–782 (2016).

  50. 50.

    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).

  51. 51.

    Wang, Y. et al. The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions. Genome Biol. 19, 151 (2018).

  52. 52.

    Heidari, N. et al. Genome-wide map of regulatory interactions in the human genome. Genome Res. 24, 1905–1917 (2014).

  53. 53.

    Javierre, B. M. et al. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384.e1319 (2016).

  54. 54.

    Sanyal, A., Lajoie, B. R., Jain, G. & Dekker, J. The long-range interaction landscape of gene promoters. Nature 489, 109–113 (2012).

  55. 55.

    Hart, T., Komori, H. K., LaMere, S., Podshivalova, K. & Salomon, D. R. Finding the active genes in deep RNA-seq gene expression studies. BMC Genomics 14, 778 (2013).

  56. 56.

    Muerdter, F. et al. Resolving systematic errors in widely used enhancer activity assays in human cells. Nat. Methods 15, 141–149 (2018).

  57. 57.

    Wang, Y. M. et al. Correlation between DNase I hypersensitive site distribution and gene expression in HeLa S3 cells. PLoS ONE 7, e42414 (2012).

  58. 58.

    Bell, O., Tiwari, V. K., Thomä, N. H. & Schübeler, D. Determinants and dynamics of genome accessibility. Nat. Rev. Genet. 12, 554–564 (2011).

  59. 59.

    Evers, B. et al. CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes. Nat. Biotechnol. 34, 631–633 (2016).

  60. 60.

    Morgens, D. W., Deans, R. M., Li, A. & Bassik, M. C. Systematic comparison of CRISPR/Cas9 and RNAi screens for essential genes. Nat. Biotechnol. 34, 634–636 (2016).

  61. 61.

    Wijdeven, R. H. et al. Genome-wide identification and characterization of novel factors conferring resistance to topoisomerase II poisons in cancer. Cancer Res. 75, 4176–4187 (2015).

  62. 62.

    Korkmaz, G. et al. Functional genetic screens for enhancer elements in the human genome using CRISPR-Cas9. Nat. Biotechnol. 34, 192–198 (2016).

  63. 63.

    Boettcher, M. et al. Dual gene activation and knockout screen reveals directional dependencies in genetic networks. Nat. Biotechnol. 36, 170–170 (2018).

  64. 64.

    Liao, H.-K. et al. In vivo target gene activation via CRISPR/Cas9-mediated trans-epigenetic modulation. Cell 171, 1495–1507.e1415 (2017).

  65. 65.

    Matharu, N. et al. CRISPR-mediated activation of a promoter or enhancer rescues obesity caused by haploinsufficiency. Science 363, eaau0629 (2019).

  66. 66.

    Komor, A. C., Badran, A. H. & Liu, D. R. CRISPR-based technologies for the manipulation of eukaryotic genomes. Cell 168, 20–36 (2017).

  67. 67.

    Ma, H. et al. Correction of a pathogenic gene mutation in human embryos. Nature 548, 413–419 (2017).

  68. 68.

    Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

  69. 69.

    Pang, B. et al. Drug-induced histone eviction from open chromatin contributes to the chemotherapeutic effects of doxorubicin. Nat. Commun. 4, 1908 (2013).

  70. 70.

    Giresi, P. G. & Lieb, J. D. Isolation of active regulatory elements from eukaryotic chromatin using FAIRE (Formaldehyde Assisted Isolation of Regulatory Elements). Methods 48, 233–239 (2009).

  71. 71.

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

  72. 72.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

  73. 73.

    Ji, X., Li, W., Song, J., Wei, L. & Liu, X. S. CEAS: cis-regulatory element annotation system. Nucleic Acids Res. 34, W551–W554 (2006).

  74. 74.

    Zhu, L. J. et al. ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinf. 11, 237 (2010).

  75. 75.

    Giardine, B. et al. Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 15, 1451–1455 (2005).

  76. 76.

    Karolchik, D., Hinrichs, A. S. & Kent, W. J. The UCSC genome browser. Curr. Protoc. Bioinformatics 28, 1.4 (2009).

  77. 77.

    Zhou, X. & Wang, T. Using the Wash U epigenome browser to examine genome-wide sequencing data. Curr. Protoc. Bioinformatics 40, 10.10 (2012).

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Acknowledgements

We thank J. Gruber, X. Li, E. Monte, K. Van Bortle, C. Jiang and M. Shi at Stanford University, and L. Janssen, Y. Li, L. Hamoen and A. Henic at Leiden University Medical Center, for many helpful discussions and inputs regarding the manuscript. This work was supported by the National Institutes of Health (NIH; nos. U54HG006996 and UM1HG009442) to M.P.S. This work used the Genome Sequencing Service Center of Stanford Center for Genomics and Personalized Medicine Sequencing Center, supported by NIH grant award nos. S10OD020141 and S10OD025212. B.P. was partially supported by the LUMC Gisela Thier Fellowship and KWF/Alpe Young Investigator Grant no. 11707.

Author information

B.P. and M.P.S. conceived the project. B.P. designed and performed experiments and analyses. B.P. and M.P.S. wrote the manuscript.

Correspondence to Michael P. Snyder.

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Competing interests

M.P.S. is a founder and member of the science advisory board of Personalis, SensOmics, Qbio, January, Mirvie and Filtricine, and a science advisory board member of Genapsys and Epinomics.

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Extended data

Extended Data Fig. 1 ReSE screen results from K562 cells.

(a) The genome is split into bins of 1000 bp. For each bin, the number of reads found in each sample is counted. All regions were included. Spearman correlation was used to calculate the correlation efficiency among all 4 samples. (b) ReSE screen results from two biological replicate experiments in K562 cells. Each circle represents one tested fragment. The y-axis indicates the FDR of enrichment of the ReSE fragments after induction of apoptosis compared with untreated control cells. The size of the circle indicates the fold enrichment of ReSE fragment counts of apoptosis-induction samples over untreated control after normalization. The red line indicates the cutoff value of FDR at 0.01.

Extended Data Fig. 2 Luciferase assays to determine the repressive activity of regions from K562 cells.

Silencer regions ranked on the bottom of the list based on the FDR value (a) or random regions from the screen (b) were cloned by PCR from the genomic DNA of K562 cells, and then inserted upstream of the promoter of a luciferase reporter plasmid pGL4.53. The 293T cells were used as the control cell line to control for cell-type dependent repressive activity, and the empty pGL4.53 plasmid was used as the control for the baseline luciferase activity. The Y-axis represents the percentage of luciferase activity compared to the pGL4.53 empty plasmids in the respective cells (n = 3 biological independent samples; the bars show the mean value ± S.E.M; *P value < 0.05, calculated using two-sided Student’s t test). All exact P values are provided in the Source Data. Source Data

Extended Data Fig. 3 Gene up-regulation by removing silencers from endogenous regions in K562 cells.

Three silencers from (Fig. 1e) are in the intron regions of genes HRH1 (a), SYNE2 (b) and CDH23 (c). Paired CRISPR guide RNAs were designed to target both sides of the selected silencer to remove it from the endogenous locus. Single clones were selected and verified by PCR (upper panel), which is the representative result of 2 experiments. The expression of the respective genes was quantified by qPCR (lower panel). (n = 3 biological replicates of the same clones; the bars show the mean value ± S.D.; *P value < 0.05, calculated using two-sided Student’s t test). All exact P values are provided in the Source Data. The blots were cropped. Full scans of the blots are shown in Source Data Full Gel Extended Data Fig. 3. Source data

Extended Data Fig. 4 ReSE screen results from K562 cells differentiated with PMA.

(a) ReSE screen results from two biological replicate experiments in K562 cells treated with PMA. Each circle represents one tested fragment. The y-axis indicates the FDR of the enrichment of the ReSE fragments after induction of apoptosis compared with untreated control cells. The size of the circle indicates the fold enrichment of the ReSE fragment counts of the apoptosis-induction samples over the untreated control after normalization. The red line indicates the cutoff value of FDR at 0.01. (b) RNA-seq experiments were performed on the cell subsamples from (a). DEseq2 was used to calculate the genes deregulated in the cells with PMA treatment compared to the non-treated K562 cells. Around 4666 genes were changed at least 2 fold with the adjusted P value less than 0.01 during the PMA treatment (red dots). Two biological replicates were included for each condition. (c) Comparison of silencer regions identified from K562 cells and K562 cells differentiated by PMA. The overlapping was not random as determined by permutation tests (n = 20,000, adjust P value = 0.00005).

Extended Data Fig. 5 ReSE screen results from HepG2 cells.

(a) The genome is split into bins of 1000 bp. For each bin, the number of reads found in each sample is counted. All regions were included. Spearman correlation was used to calculate the correlation efficiency among all 4 samples. (b) ReSE screen results from two biological replicate experiments in HepG2 cells. Each circle represents one tested fragment. The y-axis indicates the FDR of the enrichment of the ReSE fragments after induction of apoptosis compared with the untreated control cells. The size of the circle indicates the fold enrichment of ReSE fragment counts of the apoptosis-induction samples over the untreated control after normalization. The red line indicates the cutoff value of FDR at 0.01.

Extended Data Fig. 6 Silencer regions identified from HepG2 cells.

(a) Luciferase assays to determine the repressive activity of silencers from K562 and HepG2 cells. Silencer regions were cloned by PCR from the genomic DNA of K562 cells into a luciferase reporter plasmid pGL4.53. The 293T cells were used as the control cell line and the empty pGL4.53 plasmid was used as the control for the baseline luciferase activity. The Y-axis represents the percentage of luciferase activity compared to the pGL4.53 empty plasmids in the respective cells. Part of the data from K562 bottom ranked silencers were also used in Extended Data Fig. 2 (n = 3 biological independent samples; the bars show the mean value ± S.E.M; *P value < 0.05, calculated using two-sided Student’s t test). (b) Distribution of the significantly enriched silencer regions from HepG2 cells in the genome. The pie chart indicates the distribution of silencers in genomic features. The bar plot indicates the distribution of silencers in the overlapping annotation features in the genome. All exact P values are provided in the Source Data. Source Data

Extended Data Fig. 7 Unique signatures in silencer regions from HepG2 cells.

(a) Distribution of silencer regions in the respective chromatin states in HepG2 cells. (Txn, transcription; Repressed, Polycomb repressed; lo, low signal; CNV, copy number variation). Colored wedges indicate the chromatin states that are significantly enriched compared to the library background distribution (P value < 0.001, one-sided binomial test). (b) Association of histone modifications with silencers in HepG2 cells. The y-axis indicates the significance of the enrichment of histone modifications with silencer fragments. The size of the circle indicates the ratio of silencers covered by the respective histone modification (scale, 0-1). The enrichment analysis is based on permutation tests using 20,000 random permutations. The P values were calculated and multiple comparison corrections were computed using the Benjamini-Hochberg procedure. The red line shows the cutoff of the adjusted P value < 0.05. (c) Association of transcription factors with silencers in HepG2 cells. The y-axis indicates the significance of the enrichment of transcription factors with silencer fragments. The size of the circle indicates the ratio of silencers covered by the respective transcription factor (scale, 0-1). The enrichment analysis is based on permutation tests using 20,000 random permutations. The P values were calculated and multiple comparison corrections were computed using the Benjamini-Hochberg procedure. The red line shows the cutoff of the adjusted P value < 0.05. All exact P values are provided in the Source Data. Source Data

Extended Data Fig. 8 Additional top motifs present in the silencer regions from K562 or HepG2 cells.

Related to Fig. 3d,e,f. Top motifs identified in the silencers regions from K562 (a) or HepG2 (b) cells are shown.

Extended Data Fig. 9 Additional silencer knockout clones from K562 and 293T cells.

Related to Fig. 4c,d,e,f. Additional clones where silencer was knocked out from the ABCC2 gene (a) or ABCG2 gene (c) in K562 cells were generated, and confirmed by PCR. The expressions of ABCC2 gene (b) and ABCG2 gene (d) of the respective cells were quantified by qPCR. Clones where silencer was knocked out from the ABCC2 gene (e) or ABCG2 gene (g) in 293T cells were also generated, and confirmed by PCR. The expressions of ABCC2 gene (f) and ABCG2 gene (h) of the respective cells were quantified by qPCR (For all qPCR experiments, n = 3 biological replicates of the same clones; the bars show the mean value ± S.D.; *P value < 0.05, calculated using two-sided Student’s t test. All PCR experiments are the representative results of 2 experiments). (i and j) Silencer knockout clones (from Extended Data Fig. 3) were used as additional control knockout clones, since the targeted deletion regions were completely different from the ABCC2 or ABCG2 silencer regions. The expression of ABCC2 (i) and ABCG2 (j) genes of these clones was quantified by qPCR. The expression data of all individual clones were grouped as the control set, and compared to that of all the ABCC2 or ABCG2 silencer knockout clones (for each individual clone, n = 3 biological replicates of the same clones; the bars show the mean value ± S.D.). The ABCC2 and ABCG2 genes were significantly up-regulated in ABCC2 silencer or ABCG2 silencer knockout clones respectively (*P value < 0.05, calculated using one-sided Student’s t test). All exact P values are provided in the Source Data. The blots were cropped. Full scans of the blots are shown in Source Data Full Gel Extended Data Fig. 9. Source Data

Extended Data Fig. 10 Long-range interactions of silencers with distal genes.

(a) Related to Fig. 5d. Direct interactions of silencers with the promoters of distal genes as identified by the 5C data from K562 cells. The red arrow indicates the silencer region, and the blue arrow shows the interaction region of genes. Interaction arcs are highlighted in blue. (b) Related to Fig. 5e. K562 RNA-seq data is shown for genes that interact with silencers as identified by the 5C data from K562 cells. Source Data

Supplementary information

Supplementary Information

Supplementary Figs. 1–11 and Source Data Full Gel for Supplementary Fig. 3

Reporting Summary

Supplementary Table 1

Silencers identified from K562 cells using FDR cutoff of 0.01. MAGeCK algorithm based on a negative binomial model was used to identify significantly enriched silencers. Genomic location, FDR and fold enrichment are listed.

Supplementary Table 2

Silencers identified from K562 cells treated with PMA using FDR cutoff of 0.01. MAGeCK algorithm based on a negative binomial model was used to identify significantly enriched silencers. Genomic location, FDR and fold enrichment are listed.

Supplementary Table 3

Silencers identified from HepG2 cells using FDR cutoff of 0.01. MAGeCK algorithm based on a negative binomial model was used to identify significantly enriched silencers. Genomic location, FDR and fold enrichment are listed.

Supplementary Table 4

Pathway enrichment of genes that harbor silencers within 10 kb of TSS of genes in K562 cells. Canonical pathways enriched in gene sets containing silencers were calculated using Ingenuity Pathway Analysis.

Supplementary Table 5

Pathway enrichment of genes that harbor silencers within 10 kb of TSS of genes in HepG2 cells. Canonical pathways enriched in gene sets containing silencers were calculated using Ingenuity Pathway Analysis.

Supplementary Table 6

Pathway enrichment of genes that harbor silencers in the promoter (1 kb surrounding TSS) and the body of genes in K562 cells. Canonical pathways enriched in gene sets containing silencers were calculated using Ingenuity Pathway Analysis.

Supplementary Table 7

Pathway enrichment of genes that harbor silencers in the promoter (1 kb surrounding TSS) and the body of genes in HepG2 cells. Canonical pathways enriched in gene sets containing silencers were calculated using Ingenuity Pathway Analysis.

Supplementary Table 8

Promoter regions interacting with silencer regions identified by ReSE in K562 cells. Capture Hi-C data profiling interactions with 31,253 promoter regions from human primary blood cells were used. The interaction regions (oe) were intersected with silencers identified from K562 cells to identify potential genes.

Supplementary Table 9

Related information on tested regions. All tested regions and associated primers are listed.

Supplementary Dataset 1

Statistical source data for Supplementary Fig. 10.

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Pang, B., Snyder, M.P. Systematic identification of silencers in human cells. Nat Genet 52, 254–263 (2020). https://doi.org/10.1038/s41588-020-0578-5

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