Landscape of monoallelic DNA accessibility in mouse embryonic stem cells and neural progenitor cells

A Corrigendum to this article was published on 26 May 2017

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Abstract

We developed an allele-specific assay for transposase-accessible chromatin with high-throughput sequencing (ATAC–seq) to genotype and profile active regulatory DNA across the genome. Using a mouse hybrid F1 system, we found that monoallelic DNA accessibility across autosomes was pervasive, developmentally programmed and composed of several patterns. Genetically determined accessibility was enriched at distal enhancers, but random monoallelically accessible (RAMA) elements were enriched at promoters and may act as gatekeepers of monoallelic mRNA expression. Allelic choice at RAMA elements was stable across cell generations and bookmarked through mitosis. RAMA elements in neural progenitor cells were biallelically accessible in embryonic stem cells but premarked with bivalent histone modifications; one allele was silenced during differentiation. Quantitative analysis indicated that allelic choice at the majority of RAMA elements is consistent with a stochastic process; however, up to 30% of RAMA elements may deviate from the expected pattern, suggesting a regulated or counting mechanism.

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Figure 1: Allele-specific ATAC–seq used to discover monoallelically accessible regulatory elements across the genome in mouse cells.
Figure 2: Distinct classes of monoallelic regulatory elements with different genomic locations.
Figure 3: RAMA elements are stable through mitosis and over many passages in the NPC state.
Figure 4: RME genes regulated at local promoter-proximal RAMA elements.
Figure 5: RAMA elements are accessible and marked by active and repressive marks in the ESC state.
Figure 6: Establishment of some RAMA elements cannot be explained by a stochastic model.

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Change history

  • 13 February 2017

    In the version of this article initially published online, there were two errors. In the section “Three classes of monoallelic elements” in the main text, "We classified all monoallelically accessible elements (1,966 elements)" should have read "1,964 elements." In the legend for Figure 5c, the number of elements open in ESCs should have been given as 234 instead of 35. The errors have been corrected in the print, PDF and HTML versions of this article.

References

  1. 1

    Chess, A. Random and non-random monoallelic expression. Neuropsychopharmacology 38, 55–61 (2013).

    CAS  Article  Google Scholar 

  2. 2

    Heard, E. & Disteche, C.M. Dosage compensation in mammals: fine-tuning the expression of the X chromosome. Genes Dev. 20, 1848–1867 (2006).

    CAS  Article  Google Scholar 

  3. 3

    Magklara, A. et al. An epigenetic signature for monoallelic olfactory receptor expression. Cell 145, 555–570 (2011).

    CAS  Article  Google Scholar 

  4. 4

    Chess, A. Mechanisms and consequences of widespread random monoallelic expression. Nat. Rev. Genet. 13, 421–428 (2012).

    CAS  Article  Google Scholar 

  5. 5

    Gendrel, A.V. et al. Developmental dynamics and disease potential of random monoallelic gene expression. Dev. Cell 28, 366–380 (2014).

    CAS  Article  Google Scholar 

  6. 6

    Gimelbrant, A., Hutchinson, J.N., Thompson, B.R. & Chess, A. Widespread monoallelic expression on human autosomes. Science 318, 1136–1140 (2007).

    CAS  Article  Google Scholar 

  7. 7

    Eckersley-Maslin, M.A. et al. Random monoallelic gene expression increases upon embryonic stem cell differentiation. Dev. Cell 28, 351–365 (2014).

    CAS  Article  Google Scholar 

  8. 8

    Jeffries, A.R. et al. Stochastic choice of allelic expression in human neural stem cells. Stem Cells 30, 1938–1947 (2012).

    Article  Google Scholar 

  9. 9

    Reinius, B. et al. Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA–seq. Nat. Genet. 48, 1430–1435 (2016).

    CAS  Article  Google Scholar 

  10. 10

    Jeffries, A.R. et al. Random or stochastic monoallelic expressed genes are enriched for neurodevelopmental disorder candidate genes. PLoS One 8, e85093 (2013).

    Article  Google Scholar 

  11. 11

    Eckersley-Maslin, M.A. & Spector, D.L. Random monoallelic expression: regulating gene expression one allele at a time. Trends Genet. 30, 237–244 (2014).

    CAS  Article  Google Scholar 

  12. 12

    Schimenti, J. Monoallelic gene expression in mice: who? When? How? Why? Genome Res. 11, 1799–1800 (2001).

    CAS  Article  Google Scholar 

  13. 13

    Gendrel, A.V., Marion-Poll, L., Katoh, K. & Heard, E. Random monoallelic expression of genes on autosomes: parallels with X-chromosome inactivation. Semin. Cell Dev. Biol. 56, 100–110 (2016).

    CAS  Article  Google Scholar 

  14. 14

    Buenrostro, J.D., Giresi, P.G., Zaba, L.C., Chang, H.Y. & Greenleaf, W.J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    CAS  Article  Google Scholar 

  15. 15

    Keane, T.M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011).

    CAS  Article  Google Scholar 

  16. 16

    Giorgetti, L. et al. Structural organization of the inactive X chromosome in the mouse. Nature 535, 575–579 (2016).

    CAS  Article  Google Scholar 

  17. 17

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

    Article  Google Scholar 

  18. 18

    Frost, J. et al. The effects of culture on genomic imprinting profiles in human embryonic and fetal mesenchymal stem cells. Epigenetics 6, 52–62 (2011).

    CAS  Article  Google Scholar 

  19. 19

    Humpherys, D. et al. Epigenetic instability in ES cells and cloned mice. Science 293, 95–97 (2001).

    CAS  Article  Google Scholar 

  20. 20

    Kadauke, S. et al. Tissue-specific mitotic bookmarking by hematopoietic transcription factor GATA1. Cell 150, 725–737 (2012).

    CAS  Article  Google Scholar 

  21. 21

    Naumova, N. et al. Organization of the mitotic chromosome. Science 342, 948–953 (2013).

    CAS  Article  Google Scholar 

  22. 22

    Hsiung, C.C.S. et al. Genome accessibility is widely preserved and locally modulated during mitosis. Genome Res. 25, 213–225 (2015).

    CAS  Article  Google Scholar 

  23. 23

    Guo, Y. et al. CTCF/cohesin-mediated DNA looping is required for protocadherin α promoter choice. Proc. Natl. Acad. Sci. USA 109, 21081–21086 (2012).

    CAS  Article  Google Scholar 

  24. 24

    Tasic, B. et al. Promoter choice determines splice site selection in protocadherin α and γ pre-mRNA splicing. Mol. Cell 10, 21–33 (2002).

    CAS  Article  Google Scholar 

  25. 25

    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  Article  Google Scholar 

  26. 26

    Brookes, E. et al. Polycomb associates genome-wide with a specific RNA polymerase II variant, and regulates metabolic genes in ESCs. Cell Stem Cell 10, 157–170 (2012).

    CAS  Article  Google Scholar 

  27. 27

    Bernstein, B.E. et al. A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell 125, 315–326 (2006).

    CAS  Article  Google Scholar 

  28. 28

    Voigt, P., Tee, W.W. & Reinberg, D. A double take on bivalent promoters. Genes Dev. 27, 1318–1338 (2013).

    CAS  Article  Google Scholar 

  29. 29

    Lyons, D.B. et al. Heterochromatin-mediated gene silencing facilitates the diversification of olfactory neurons. Cell Rep. 9, 884–892 (2014).

    CAS  Article  Google Scholar 

  30. 30

    Farago, M. et al. Clonal allelic predetermination of immunoglobulin-κ rearrangement. Nature 490, 561–565 (2012).

    CAS  Article  Google Scholar 

  31. 31

    Mostoslavsky, R et al. Asynchronous replication and allelic exclusion in the immune system. Nature 414, 221–225 (2001).

    CAS  Article  Google Scholar 

  32. 32

    Ensminger, A.W. & Chess, A. Bidirectional promoters regulate the monoallelically expressed Ly49 NK receptors. Immunity 21, 2–3 (2004).

    CAS  Article  Google Scholar 

  33. 33

    Chess, A., Simon, I., Cedar, H. & Axel, R. Allelic inactivation regulates olfactory receptor gene expression. Cell 78, 823–834 (1994).

    CAS  Article  Google Scholar 

  34. 34

    Storey, J.D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

    CAS  Article  Google Scholar 

  35. 35

    Conti, L. et al. Niche-independent symmetrical self-renewal of a mammalian tissue stem cell. PLoS Biol. 3, e283 (2005).

    Article  Google Scholar 

  36. 36

    Mrxuqdo, Q.H.W., Iurp, V. & Wkurxjksxw, K. Cutadapt removers adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Google Scholar 

  37. 37

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

    CAS  Article  Google Scholar 

  38. 38

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

    CAS  Article  Google Scholar 

  39. 39

    Boeva, V. et al. Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics 28, 423–425 (2012).

    CAS  Article  Google Scholar 

  40. 40

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

    Article  Google Scholar 

  41. 41

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

    CAS  Article  Google Scholar 

  42. 42

    Elston, R.C. & Forthofer, R. Testing for Hardy–Weinberg equilibrium in small samples. Soc. Int. Biometric 33, 536–542 (2016).

    Article  Google Scholar 

  43. 43

    Juan, D. et al. Epigenomic co-localization and co-evolution reveal a key role for 5hmC as a communication hub in the chromatin network of ESCs. Cell Rep. 14, 1246–1257 (2016).

    CAS  Article  Google Scholar 

  44. 44

    Shen, L., Shao, N., Liu, X. & Nestler, E. ngs.plot: quick mining and visualization of next-generation sequencing data by integrating genomic databases. BMC Genomics 15, 284 (2014).

    Article  Google Scholar 

Download references

Acknowledgements

We thank J. Dekker for sharing Hi-C data before publication and for feedback, and J. Pritchard, A. Urban and members of our laboratories for advice. This work was supported by the US National Institutes of Health (NIH; P50-HG007735 to H.Y.C. and W.J.G.). The Heard labratory is “Equipe labellisée par la Ligue Nationale Contre le Cancer” and is supported by a European Research Council (ERC) Advanced Investigator Award; Labex DEEP (ANR-11-LBX-0044) part of the IDEX Idex PSL (ANR-10-IDEX-0001-02 PSL).

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Authors

Contributions

J.X., A.C.C., E.H. and H.Y.C. conceived the project. J.X. wrote the allele-specific ATAC–seq analysis pipeline and did most of the data analysis, and A.C.C. performed all experiments, generated ATAC–seq libraries and did some data analysis. A.-V.G., M.A. and E.H. provided cell lines and conceptual input. J.L. and R.T. helped design statistical analysis and permutations for the allele-specific ATAC–seq pipeline. W.J.G. provided insight and helped design experiments. H.Y.C. supervised the project and wrote the paper with J.X. and A.C.C.

Corresponding author

Correspondence to Howard Y Chang.

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Competing interests

H.Y.C. and W.J.G. are co-founders of Epinomics. Stanford University has filed a patent on ATAC–seq, on which H.Y.C. and W.J.G. are inventors.

Integrated supplementary information

Supplementary Figure 1 Evaluation of allelically informative accessible regions and monoallelic sites.

(a) Comparison of allelic read assignment for technical replicates, biological replicates and different clones. Pearson’s correlation coefficients were calculated from allelic counts for the 129 and Cast alleles. (b) Comparison of two statistical methods for evaluating allelic bias based on the binomial distribution and permutation method. Cumulative curves for P value (adjusted by the Benjamini–Hochberg procedure) from the X chromosome and chromosome 5 are shown in red and blue, respectively. The permutation method was compared to the exact binomial test. The right panel is a zoomed-out view of the left panel. The difference between the dark and light curves shows that the permutation method is more stringent on the autosomes, but more sensitive on the X chromosome. (c) Scatterplot showing the number of usable reads versus the number of allelically informative accessible regions. The number of allelically informative regions is close to stable when the number of usable sequencing reads is more than 40 million. The number of allelically informative accessible regions in ESCs is lower than for NPC clones with the same number of usable reads (red points). Blue points show downsampled data from a highly sequenced line (NPC XY14). (d) Scatterplot of TSS enrichment score versus the number of allelically informative accessible regions. There is no significant correlation between them (r = –0.186, P = 0.461). Red indicates the two ESC lines. (e) Simulation to determine the number of reads needed to identify monoallelic peaks in NPCs. 6–100 million usable reads were randomly sampled from a deeply sequenced NPC clone (NPC XY14). The number of monoallelically accessible regions dramatically increases up to 40 million reads where it plateaus. (f) Number of monoallelic peaks identified by ATAC–seq on the Cast X chromosome, 129 X chromosome, Cast autosomes and 129 autosomes in each cell line analyzed.

Supplementary Figure 2 Examples of genome-specific monoallelic regulatory elements and chromHMM annotation of accessible sites.

(a) Example of a 129-specific site located at the Mpp7 gene promoter on chromosome 18. 129 reads are shown in pink, and Cast reads are shown in blue. (b) Example of a Cast-specific site located at the AK043958 gene promoter on chromosome 4. 129 reads are shown in pink, and Cast reads are shown in blue. The peak to the left is biallelic, as indicated by pile-up of 129 (pink) and Cast (blue) reads, while the peak on the right is Cast specific. (c) Proportion of 129-specific, Cast-specific and RAMA elements located at distal elements (>2 kb from a TSS), promoter elements of expressed genes and promoter elements of unexpressed genes (<2 kb from a TSS). For each set of elements, control sets are size matched and matched for peak enrichment. (d) Chromatin states identified by chromHMM using available histone modification ChIP–seq data in NPCs. Enrichment for the histone modifications labeled on the x axis is represented in blue. Interpretations of each state are labeled on the right. (e) Allelically accessible regions (accessible regions for which ten clones have sufficient allelically informative information after removing CNV regions) show the same distribution of chromatin states as all accessible regions across the genome in NPCs.

Supplementary Figure 3 Genome-specific monoallelic sites and RAMA elements are stable across passages and during mitosis.

(ac) Scatterplots showing the d score for Cast- and 129-specific monoallelically accessible regions in PX + 10 versus PX + 0 for NPC XX2, XX4 and XY14. (d) Distribution of changes in d score (max d score – min d score) across passages PX + 0, PX + 5, and PX + 10 for NPC clones XX2, XX4 and XY14. As a control, the changes in d score are compared to three replicates from a single clone at the same passage (NPC XX 1). (e) Distribution of changes in d score (max d score – min d score) across passages PX + 0, PX + 5, and PX + 10 for NPC clones XX2, XX4 and XY14 for genome-specific monoallelic elements. (f) Tracks showing an example RAMA element at the promoter of RME gene Fam149a across passages in NPC XX 4. 129 reads are shown in magenta, and Cast reads are shown in blue. (g) Scatterplot showing the number of reads under all ATAC–seq peaks in mitotic and asynchronous NPCs (XX 1). All peaks are shown in black, and RAMA elements are shown in red.

Supplementary Figure 4 RME genes are regulated at local promoter-proximal RAMA elements.

(a) Cumulative plot of the log2 (RPKM) values from RNA–seq data for RME genes with promoter RAMA elements (green) and RME genes with a non-RAMA element in the TSS region (yellow). (b) Cumulative plot showing the peak enrichment score for promoter RAMA elements located at RME genes (dark green) and non-RME genes (light green). (c) RT–PCR followed by Sanger sequencing for RME gene Bag3 in NPC lines XY7-10. Colored circles indicate the allelic status of the promoter ATAC–seq peak in that line. Red boxes outline the informative SNP at each locus. (d) Tracks showing allele-specific ATAC–seq at the noncoding RNA Vaultrc5, which is not detected by allelic RNA–seq due to poly(A) selection. 129 reads are shown in pink, and Cast reads are shown in blue. (e) Tracks showing allele-specific ATAC–seq at the noncoding RNA Mir34b, which is not detected by allelic RNA–seq due to poly(A) selection. 129 reads are shown in pink, and Cast reads are shown in blue. (f) Permutation of RNA–seq d score versus d score of all accessible elements within 2–10 kb of RME genes. Matched with Figure 4h, left. (g) Permutation of RNA–seq d score of nearest gene versus d score of distal RAMA elements. Matched with Figure 4h, left. (h) Density plot showing the correlation coefficient for distal RAMA elements and RNA–seq d score of the nearest gene compared to permutated values. (i) Density plot showing the correlation coefficient for ATAC–seq d score of accessible element and RNA–seq d score of the nearest gene for promoter and distal genetic MA separately. (j) Tracks showing allele-specific ATAC–seq at the Arc locus. The Arc promoter element is RAMA, while the upstream enhancer is biallelically accessible. 129 reads are shown in pink, and Cast reads are shown in blue.

Supplementary Figure 5 Chromatin signature of RAMA elements during differentiation from ESCs to NPCs.

(a) Histone modification signal from ESC ChIP–seq data at promoter RAMA elements that are accessible in ESCs. (b) Histone modification signal from NPC ChIP–seq data at promoter RAMA elements that are accessible in ESCs. (c) Histone modification signal from ESC ChIP–seq data at distal RAMA elements that are not accessible in ESCs. (d) Histone modification signal from NPC ChIP–seq data at distal RAMA elements that are not accessible in ESCs. (e) Histone modification signal from ESC ChIP–seq data at distal RAMA elements that are accessible in ESCs. (f) Histone modification signal from NPC ChIP–seq data at distal RAMA elements that are accessible in ESCs. (g) Chromatin states identified by chromHMM using histone modification ChIP–seq data from ESCs. Enrichment for histone modifications labeled on the x axis is represented in blue. Interpretations of each state are labeled on the right. (h) Chromatin states identified by chromHMM using Pol II and PRC complex ChIP–seq data from ESCs. Enrichment for transcription factors labeled on the x axis is represented in blue. Interpretations of each state are labeled on the right. (i) ChIP–qPCR for H3K4me3 and H3K9me3 at six RAMA promoters in XX1 ESCs.

Supplementary Figure 6 Test of the stochastic model of RAMA establishment in NPCs.

(a) Distribution of Pact (n accessible alleles/n total alleles) for promoter-distal (blue) and promoter-proximal (black) RAMA elements. (b) Distribution of P value by likelihood test for all RAMA elements. (c) Example RAMA site at the promoter of Mettl24 that has fewer monoallelic (1) clones than expected based on its Pact of 0.81. (d) Allelic ATAC–seq signal in 16 clones at the RAMA promoter element at Pde7b (related to Fig. 6h). Pink, 129 monoallelic; blue, Cast monoallelic; gray, biallelically accessible; black, not accessible; white, masked in CNV region. (d) Allelic ATAC–seq signal in 16 clones at the RAMA promoter element at Slc27a6 (related to Fig. 6i). Pink, 129 monoallelic; blue, Cast monoallelic; gray, biallelically accessible; black, not accessible; white, masked in CNV region.

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Xu, J., Carter, A., Gendrel, AV. et al. Landscape of monoallelic DNA accessibility in mouse embryonic stem cells and neural progenitor cells. Nat Genet 49, 377–386 (2017). https://doi.org/10.1038/ng.3769

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