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:

Aire controls gene expression in the thymic epithelium with ordered stochasticity

Abstract

The transcription factor Aire controls immunological tolerance by inducing the ectopic thymic expression of many tissue-specific genes, acting broadly by removing stops on the transcriptional machinery. To better understand Aire's specificity, we performed single-cell RNA-seq and DNA-methylation analysis of Aire-sufficient and Aire-deficient medullary epithelial cells (mTECs). Each of Aire's target genes was induced in only a minority of mTECs, independently of DNA-methylation patterns, as small inter-chromosomal gene clusters activated in concert in a proportion of mTECs. These microclusters differed between individual mice. Thus, our results suggest an organization of the DNA or of the epigenome that results from stochastic determinism but is 'bookmarked' and stable through mTEC divisions, which ensures more effective presentation of self antigens and favors diversity of self-tolerance between individuals.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Figure 1: Aire increases the repertoire and diversity of mTEC transcriptome.
Figure 2: scRNA-seq analysis of mTECs.
Figure 3: Summary of single-cell expression results.
Figure 4: Aire increases the intensity and frequency of otherwise rare transcripts.
Figure 5: Aire coordinates discrete interchromosomal gene networks.
Figure 6: Aire-dependent interchromosomal gene networks generate diverse and distinct mTEC subsets.
Figure 7: Little or no difference in the amount of CpG methylation at Aire-induced genes versus that at Aire-neutral genes.

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

Sequence Read Archive

References

  1. Peterson, P., Org, T. & Rebane, A. Transcriptional regulation by AIRE: molecular mechanisms of central tolerance. Nat. Rev. Immunol. 8, 948–957 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Anderson, M.S. et al. Projection of an immunological self shadow within the thymus by the aire protein. Science 298, 1395–1401 (2002).

    CAS  PubMed  Google Scholar 

  3. Hubert, F.X. et al. Aire regulates the transfer of antigen from mTECs to dendritic cells for induction of thymic tolerance. Blood 118, 2462–2472 (2011).

    CAS  PubMed  Google Scholar 

  4. Liston, A. et al. Aire regulates negative selection of organ-specific T cells. Nat. Immunol. 4, 350–354 (2003).

    CAS  PubMed  Google Scholar 

  5. Anderson, M.S. et al. The cellular mechanism of Aire control of T cell tolerance. Immunity 23, 227–239 (2005).

    CAS  PubMed  Google Scholar 

  6. Malchow, S. et al. Aire-dependent thymic development of tumor-associated regulatory T cells. Science 339, 1219–1224 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Waterfield, M. et al. The transcriptional regulator Aire coopts the repressive ATF7ip-MBD1 complex for the induction of immunotolerance. Nat. Immunol. 15, 258–265 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Org, T. et al. The autoimmune regulator PHD finger binds to non-methylated histone H3K4 to activate gene expression. EMBO Rep. 9, 370–376 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Koh, A.S. et al. Aire employs a histone-binding module to mediate immunological tolerance, linking chromatin regulation with organ-specific autoimmunity. Proc. Natl. Acad. Sci. USA 105, 15878–15883 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Giraud, M. et al. Aire unleashes stalled RNA polymerase to induce ectopic gene expression in thymic epithelial cells. Proc. Natl. Acad. Sci. USA 109, 535–540 (2012).

    CAS  PubMed  Google Scholar 

  11. Abramson, J., Giraud, M., Benoist, C. & Mathis, D. Aire's partners in the molecular control of immunological tolerance. Cell 140, 123–135 (2010).

    CAS  PubMed  Google Scholar 

  12. Gaetani, M. et al. AIRE-PHD fingers are structural hubs to maintain the integrity of chromatin-associated interactome. Nucleic Acids Res. 40, 11756–11768 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Oven, I. et al. AIRE recruits P-TEFb for transcriptional elongation of target genes in medullary thymic epithelial cells. Mol. Cell. Biol. 27, 8815–8823 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Giraud, M. et al. An RNAi screen for Aire cofactors reveals a role for Hnrnpl in polymerase release and Aire-activated ectopic transcription. Proc. Natl. Acad. Sci. USA 111, 1491–1496 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Villaseñor, J., Besse, W., Benoist, C. & Mathis, D. Ectopic expression of peripheral-tissue antigens in the thymic epithelium: probabilistic, monoallelic, misinitiated. Proc. Natl. Acad. Sci. USA 105, 15854–15859 (2008).

    PubMed  PubMed Central  Google Scholar 

  16. Taubert, R., Schwendemann, J. & Kyewski, B. Highly variable expression of tissue-restricted self-antigens in human thymus: implications for self-tolerance and autoimmunity. Eur. J. Immunol. 37, 838–848 (2007).

    CAS  PubMed  Google Scholar 

  17. Derbinski, J. et al. Promiscuous gene expression patterns in single medullary thymic epithelial cells argue for a stochastic mechanism. Proc. Natl. Acad. Sci. USA 105, 657–662 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Pinto, S. et al. Overlapping gene coexpression patterns in human medullary thymic epithelial cells generate self-antigen diversity. Proc. Natl. Acad. Sci. USA 110, E3497–E3505 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Venanzi, E.S., Melamed, R., Mathis, D. & Benoist, C. The variable immunological self: genetic variation and nongenetic noise in Aire-regulated transcription. Proc. Natl. Acad. Sci. USA 105, 15860–15865 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013).

    CAS  PubMed  Google Scholar 

  21. Elowitz, M.B., Levine, A.J., Siggia, E.D. & Swain, P.S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).

    CAS  PubMed  Google Scholar 

  22. Ozbudak, E.M. et al. Regulation of noise in the expression of a single gene. Nat. Genet. 31, 69–73 (2002).

    CAS  PubMed  Google Scholar 

  23. Kalmar, T. et al. Regulated fluctuations in nanog expression mediate cell fate decisions in embryonic stem cells. PLoS Biol. 7, e1000149 (2009).

    PubMed  PubMed Central  Google Scholar 

  24. Shalek, A.K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Ross, I.L., Browne, C.M. & Hume, D.A. Transcription of individual genes in eukaryotic cells occurs randomly and infrequently. Immunol. Cell Biol. 72, 177–185 (1994).

    CAS  PubMed  Google Scholar 

  27. Wu, A.R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).

    CAS  PubMed  Google Scholar 

  28. Kharchenko, P.V., Silberstein, L. & Scadden, D.T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Gardner, J.M. et al. Deletional tolerance mediated by extrathymic Aire-expressing cells. Science 321, 843–847 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Sansom, S.N. et al. Population and single-cell genomics reveal the Aire dependency, relief from Polycomb silencing and distribution of self-antigen expression in thymic epithelia. Genome Res. 24, 1918–1931 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Klein, L. et al. Shaping of the autoreactive T-cell repertoire by a splice variant of self protein expressed in thymic epithelial cells. Nat. Med. 6, 56–61 (2000).

    CAS  PubMed  Google Scholar 

  32. Anderson, A.C. et al. High frequency of autoreactive myelin proteolipid protein-specific T cells in the periphery of naive mice: mechanisms of selection of the self-reactive repertoire. J. Exp. Med. 191, 761–770 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Keane, P., Ceredig, R. & Seoighe, C. Promiscuous mRNA splicing under the control of AIRE in medullary thymic epithelial cells. Bioinformatics 31, 986–990 (2015).

    CAS  PubMed  Google Scholar 

  34. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    CAS  PubMed  Google Scholar 

  35. Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

    CAS  PubMed  Google Scholar 

  36. Bodenhofer, U., Kothmeier, A. & Hochreiter, S. APCluster: an R package for affinity propagation clustering. Bioinformatics 27, 2463–2464 (2011).

    CAS  PubMed  Google Scholar 

  37. Johnnidis, J.B. et al. Chromosomal clustering of genes controlled by the aire transcription factor. Proc. Natl. Acad. Sci. USA 102, 7233–7238 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Derbinski, J. et al. Promiscuous gene expression in thymic epithelial cells is regulated at multiple levels. J. Exp. Med. 202, 33–45 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Su, A.I. et al. Large-scale analysis of the human and mouse transcriptomes. Proc. Natl. Acad. Sci. USA 99, 4465–4470 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. van der Maaten, L. & Hinton, G. Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  41. Gu, H. et al. Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling. Nat. Protoc. 6, 468–481 (2011).

    CAS  PubMed  Google Scholar 

  42. Ball, M.P. et al. Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat. Biotechnol. 27, 361–368 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Org, T. et al. AIRE activated tissue specific genes have histone modifications associated with inactive chromatin. Hum. Mol. Genet. 18, 4699–4710 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Raser, J.M. & O'Shea, E.K. Control of stochasticity in eukaryotic gene expression. Science 304, 1811–1814 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Tao, Y. et al. AIRE recruits multiple transcriptional components to specific genomic regions through tethering to nuclear matrix. Mol. Immunol. 43, 335–345 (2006).

    CAS  PubMed  Google Scholar 

  46. Gill, J., Malin, M., Hollander, G.A. & Boyd, R. Generation of a complete thymic microenvironment by MTS24+ thymic epithelial cells. Nat. Immunol. 3, 635–642 (2002).

    CAS  PubMed  Google Scholar 

  47. Zhao, R. et al. Gene bookmarking accelerates the kinetics of post-mitotic transcriptional re-activation. Nat. Cell Biol. 13, 1295–1304 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Le Borgne, M. et al. The impact of negative selection on thymocyte migration in the medulla. Nat. Immunol. 10, 823–830 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Merkenschlager, M., Benoist, C. & Mathis, D. Evidence for a single-niche model of positive selection. Proc. Natl. Acad. Sci. USA 91, 11694–11698 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Krueger, F. & Andrews, S.R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank S. Mostafavi for advice on computational analysis; M. Anderson (University of California, San Francisco) for the Aire-GFP line; and K. Hattori, G. Buruzula, and K. Waraska for help with mice, sorting and sequencing. Supported by the US National Institutes of Health (DK060027; and a Children's Hospital in Pediatric Gastroenterology training grant for M.M.) and Boehringer Ingelheim Fonds (D.Z.).

Author information

Authors and Affiliations

Authors

Contributions

M.M., data collection, data analysis and manuscript writing; D.Z., data analysis and manuscript writing; D.M., manuscript writing; C.B. data analysis and manuscript writing.

Corresponding authors

Correspondence to Diane Mathis or Christophe Benoist.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Less increase in frequency of expression of Aire-activated genes than predicted from sampling statistics.

We modeled the change in frequency of cells expressing a given gene resulting from the increase in its per-cell expression level. For each Aire-induced gene Gi we randomly picked, from the general distribution of Aire-neutral genes, 50 genes whose mean expression levels in positive cells matched the levels of Gi in Aire KO and WT cells, respectively, and we calculated the average pairwise difference in expression frequency. The distribution of these “simulated Aire-induction” changes (grey dots) matched the general distribution of intensity vs frequency of Fig. 4D but was very different from the corresponding changes for Aire-induced genes in the presence or absence of Aire (red dots), where equal changes in intensity led to far smaller increases in frequency (KS test p<10-15).

Supplementary Figure 2 Absence of co-expressed microclusters among Aire-neutral genes.

A) Gene-gene correlations were computed for a set of Aire-neutral genes (expression-matched with Aire-induced genes in Fig. 5A) and clustered by affinity propagation as in Fig. 5A. B) Direct comparison of the size and mean intra-cluster correlation for Aire-WT MEC microclusters for Aire-induced genes (red dots) and Aire-neutral genes (from S2A; black dots).

Supplementary Figure 3 Locally clustered Aire-induced genes are co-expressed in individual mTECs.

Representative raw read pileups for a few cells at locally clustered gene families in the A) Sprr locus on chromosome 3 and B) Mup locus on chromosome 4. Only one exon per gene was detected because the SCS technique only tags polyA-proximal sequences.

Supplementary Figure 4 MBD1 motifs are not over-methylated or over-represented at Aire-induced loci.

A) Distribution of methylation frequencies at CpGs in TCGCA (MBD1 binding site) motifs and non-TCGCA sites in Aire-induced and Aire-neutral promoters. Most CpGs in all four groups were unmethylated (<10%), and three exceptions of Aire-induced promoters containing a methylated TCGCA motif are indicated. B) Overall profiles of expression level and fold change in Aire WT/KO of genes with promoters that contain a TCGCA motif (regardless of methylation status) and those that do not. Table at bottom shows counts and proportions of TCGCA motifs in Aire-induced and neutral loci. C) Volcano plot showing MBD1’s effect on MEC transcriptome (all genes in black) overlaid with Aire-induced genes (red). Numbers show how many genes from each group fall in the two sectors of the plot.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 (PDF 574 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meredith, M., Zemmour, D., Mathis, D. et al. Aire controls gene expression in the thymic epithelium with ordered stochasticity. Nat Immunol 16, 942–949 (2015). https://doi.org/10.1038/ni.3247

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ni.3247

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