Epigenetic programming underpins B cell dysfunction in human SLE


Systemic lupus erythematosus (SLE) is characterized by the expansion of extrafollicular pathogenic B cells derived from newly activated naive cells. Although these cells express distinct markers, their epigenetic architecture and how it contributes to SLE remain poorly understood. To address this, we determined the DNA methylomes, chromatin accessibility profiles and transcriptomes from five human B cell subsets, including a newly defined effector B cell subset, from subjects with SLE and healthy controls. Our data define a differentiation hierarchy for the subsets and elucidate the epigenetic and transcriptional differences between effector and memory B cells. Importantly, an SLE molecular signature was already established in resting naive cells and was dominated by enrichment of accessible chromatin in motifs for AP-1 and EGR transcription factors. Together, these factors acted in synergy with T-BET to shape the epigenome of expanded SLE effector B cell subsets. Thus, our data define the molecular foundation of pathogenic B cell dysfunction in SLE.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Epigenetic states of B cell subsets identify cell type relationships and differentiation hierarchies.
Fig. 2: Resting naive B cells are epigenetically distinct in subjects with SLE.
Fig. 3: DNA methylation status stratifies B cell subsets from healthy controls and subjects with SLE.
Fig. 4: DN2 B cells in SLE have a chromatin conformation driven by TLR and RTK signaling pathways.
Fig. 5: Chromation accessibility in DN2 B cells is driven by T-BET, AP-1 and EGR transcription factors.
Fig. 6: SLE transcription factor networks correlate with disease-specific transcriptomes.
Fig. 7: SLE DN2 B cells display activation of ATF3-regulated stress response pathways.

Data availability

The data that support the findings of this study are available from the NCBI Gene Expression Omnibus (GEO) under accession GSE118256 and are detailed in Supplementary Table 5.

Code availability

Code and data processing scripts are available from the corresponding author upon request and at https://github.com/cdschar.


  1. 1.

    Rawlings, D. J., Metzler, G., Wray-Dutra, M. & Jackson, S. W. Altered B cell signalling in autoimmunity. Nat. Rev. Immunol. 17, 421–436 (2017).

    CAS  Article  Google Scholar 

  2. 2.

    Langefeld, C. D. et al. Transancestral mapping and genetic load in systemic lupus erythematosus. Nat. Commun. 8, 16021 (2017).

    CAS  Article  Google Scholar 

  3. 3.

    Chung, S. A. et al. Differential genetic associations for systemic lupus erythematosus based on anti-dsDNA autoantibody production. PLoS Genet. 7, e1001323 (2011).

    CAS  Article  Google Scholar 

  4. 4.

    William, J., Euler, C., Christensen, S. & Shlomchik, M. J. Evolution of autoantibody responses via somatic hypermutation outside of germinal centers. Science 297, 2066–2070 (2002).

    CAS  Article  Google Scholar 

  5. 5.

    Tipton, C. M. et al. Diversity, cellular origin and autoreactivity of antibody-secreting cell population expansions in acute systemic lupus erythematosus. Nat. Immunol. 16, 755–765 (2015).

    CAS  Article  Google Scholar 

  6. 6.

    Jenks, S. A. et al. Distinct effector B cells induced by unregulated Toll-like receptor 7 contribute to pathogenic responses in systemic lupus erythematosus. Immunity 49, 725–739 (2018).

    CAS  Article  Google Scholar 

  7. 7.

    Hao, Y., O’Neill, P., Naradikian, M. S., Scholz, J. L. & Cancro, M. P. A B-cell subset uniquely responsive to innate stimuli accumulates in aged mice. Blood 118, 1294–1304 (2011).

    CAS  Article  Google Scholar 

  8. 8.

    Rubtsov, A. V. et al. Toll-like receptor 7 (TLR7)-driven accumulation of a novel CD11c+ B-cell population is important for the development of autoimmunity. Blood 118, 1305–1315 (2011).

    CAS  Article  Google Scholar 

  9. 9.

    Manni, M. et al. Regulation of age-associated B cells by IRF5 in systemic autoimmunity. Nat. Immunol. 19, 407–419 (2018).

    CAS  Article  Google Scholar 

  10. 10.

    Poovassery, J. S. & Bishop, G. A. Type I IFN receptor and the B cell antigen receptor regulate TLR7 responses via distinct molecular mechanisms. J. Immunol. 189, 1757–1764 (2012).

    CAS  Article  Google Scholar 

  11. 11.

    Ulff-Moller, C. J. et al. Twin DNA methylation profiling reveals flare-dependent interferon signature and B cell promoter hypermethylation in systemic lupus erythematosus. Arthritis Rheumatol. 70, 878–890 (2018).

    CAS  Article  Google Scholar 

  12. 12.

    Banchereau, R. et al. Personalized immunomonitoring uncovers molecular networks that stratify lupus patients. Cell 165, 551–565 (2016).

    CAS  Article  Google Scholar 

  13. 13.

    Scharer, C. D. et al. ATAC-seq on biobanked specimens defines a unique chromatin accessibility structure in naive SLE B cells. Sci. Rep. 6, 27030 (2016).

    CAS  Article  Google Scholar 

  14. 14.

    Javierre, B. M. et al. Changes in the pattern of DNA methylation associate with twin discordance in systemic lupus erythematosus. Genome Res. 20, 170–179 (2010).

    CAS  Article  Google Scholar 

  15. 15.

    Hewagama, A. & Richardson, B. The genetics and epigenetics of autoimmune diseases. J. Autoimmun. 33, 3–11 (2009).

    CAS  Article  Google Scholar 

  16. 16.

    Sanz, I., Wei, C., Lee, F. E. & Anolik, J. Phenotypic and functional heterogeneity of human memory B cells. Semin. Immunol. 20, 67–82 (2008).

    CAS  Article  Google Scholar 

  17. 17.

    Wirths, S. & Lanzavecchia, A. ABCB1 transporter discriminates human resting naive B cells from cycling transitional and memory B cells. Eur. J. Immunol. 35, 3433–3441 (2005).

    CAS  Article  Google Scholar 

  18. 18.

    Barwick, B. G., Scharer, C. D., Bally, A. P. R. & Boss, J. M. Plasma cell differentiation is coupled to division-dependent DNA hypomethylation and gene regulation. Nat. Immunol. 17, 1216–1225 (2016).

    CAS  Article  Google Scholar 

  19. 19.

    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 

  20. 20.

    Kulis, M. et al. Whole-genome fingerprint of the DNA methylome during human B cell differentiation. Nat. Genet. 47, 746–756 (2015).

    CAS  Article  Google Scholar 

  21. 21.

    Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).

    CAS  Article  Google Scholar 

  22. 22.

    Scharer, C. D., Barwick, B. G., Guo, M., Bally, A. P. R. & Boss, J. M. Plasma cell differentiation is controlled by multiple cell division-coupled epigenetic programs. Nat. Commun. 9, 1698 (2018).

    Article  Google Scholar 

  23. 23.

    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).

    CAS  Article  Google Scholar 

  24. 24.

    Yoon, H. S. et al. ZBTB32 is an early repressor of the CIITA and MHC class II gene expression during B cell differentiation to plasma cells. J. Immunol. 189, 2393–2403 (2012).

    CAS  Article  Google Scholar 

  25. 25.

    Nagaoka, M. et al. The orphan nuclear receptor NR4A3 is involved in the function of dendritic cells. J. Immunol. 199, 2958–2967 (2017).

    CAS  Article  Google Scholar 

  26. 26.

    Ashouri, J. F. & Weiss, A. Endogenous Nur77 is a specific indicator of antigen receptor signaling in human T and B cells. J. Immunol. 198, 657–668 (2017).

    CAS  Article  Google Scholar 

  27. 27.

    Gross, I., Bassit, B., Benezra, M. & Licht, J. D. Mammalian Sprouty proteins inhibit cell growth and differentiation by preventing Ras activation. J. Biol. Chem. 276, 46460–46468 (2001).

    CAS  Article  Google Scholar 

  28. 28.

    Lopez-Otin, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013).

    CAS  Article  Google Scholar 

  29. 29.

    Li, T. et al. Identification of epithelial stromal interaction 1 as a novel effector downstream of Kruppel-like factor 8 in breast cancer invasion and metastasis. Oncogene 33, 4746–4755 (2014).

    CAS  Article  Google Scholar 

  30. 30.

    Mo, J. S. & Chae, S.-C. EPSTI1 polymorphisms are associated with systemic lupus erythematosus. Genes Genomics 39, 445–451 (2017).

    CAS  Article  Google Scholar 

  31. 31.

    Buchta, C. M. & Bishop, G. A. TRAF5 negatively regulates TLR signaling in B lymphocytes. J. Immunol. 192, 145–150 (2014).

    CAS  Article  Google Scholar 

  32. 32.

    Russell Knode, L. M. et al. Age-associated B cells express a diverse repertoire of VH and Vκ genes with somatic hypermutation. J. Immunol. 198, 1921–1927 (2017).

    CAS  Article  Google Scholar 

  33. 33.

    Wiestner, A. et al. ZAP-70 expression identifies a chronic lymphocytic leukemia subtype with unmutated immunoglobulin genes, inferior clinical outcome, and distinct gene expression profile. Blood 101, 4944–4951 (2003).

    CAS  Article  Google Scholar 

  34. 34.

    Veleeparambil, M. et al. Constitutively bound EGFR-mediated tyrosine phosphorylation of TLR9 is required for its ability to signal. J. Immunol. 200, 2809–2818 (2018).

    CAS  Article  Google Scholar 

  35. 35.

    Chattopadhyay, S. et al. EGFR kinase activity is required for TLR4 signaling and the septic shock response. EMBO Rep. 16, 1535–1547 (2015).

    CAS  Article  Google Scholar 

  36. 36.

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

  37. 37.

    Yu, B. et al. Epigenetic landscapes reveal transcription factors that regulate CD8+ T cell differentiation. Nat. Immunol. 18, 573–582 (2017).

    CAS  Article  Google Scholar 

  38. 38.

    Myouzen, K. et al. Regulatory polymorphisms in EGR2 are associated with susceptibility to systemic lupus erythematosus. Hum. Mol. Genet. 19, 2313–2320 (2010).

    CAS  Article  Google Scholar 

  39. 39.

    Jadhav, K. & Zhang, Y. Activating transcription factor 3 in immune response and metabolic regulation. Liver Res. 1, 96–102 (2017).

    Article  Google Scholar 

  40. 40.

    Juilland, M. et al. CARMA1- and MyD88-dependent activation of Jun/ATF-type AP-1 complexes is a hallmark of ABC diffuse large B-cell lymphomas. Blood 127, 1780–1789 (2016).

    CAS  Article  Google Scholar 

  41. 41.

    Gilchrist, M. et al. Systems biology approaches identify ATF3 as a negative regulator of Toll-like receptor 4. Nature 441, 173–178 (2006).

    CAS  Article  Google Scholar 

  42. 42.

    Glasmacher, E. et al. A genomic regulatory element that directs assembly and function of immune-specific AP-1–IRF complexes. Science 338, 975–980 (2012).

    CAS  Article  Google Scholar 

  43. 43.

    Palanichamy, A. et al. Novel human transitional B cell populations revealed by B cell depletion therapy. J. Immunol. 182, 5982–5993 (2009).

    CAS  Article  Google Scholar 

  44. 44.

    Leadbetter, E. A. et al. Chromatin–IgG complexes activate B cells by dual engagement of IgM and Toll-like receptors. Nature 416, 603–607 (2002).

    CAS  Article  Google Scholar 

  45. 45.

    Lau, C. M. et al. RNA-associated autoantigens activate B cells by combined B cell antigen receptor/Toll-like receptor 7 engagement. J. Exp. Med. 202, 1171–1177 (2005).

    CAS  Article  Google Scholar 

  46. 46.

    Gomez-Martin, D., Diaz-Zamudio, M., Galindo-Campos, M. & Alcocer-Varela, J. Early growth response transcription factors and the modulation of immune response: implications towards autoimmunity. Autoimmun. Rev. 9, 454–458 (2010).

    CAS  Article  Google Scholar 

  47. 47.

    Oh, Y. K., Jang, E., Paik, D. J. & Youn, J. Early growth response-1 plays a non-redundant role in the differentiation of B cells into plasma cells. Immune Netw. 15, 161–166 (2015).

    Article  Google Scholar 

  48. 48.

    Gururajan, M. et al. Early growth response genes regulate B cell development, proliferation, and immune response. J. Immunol. 181, 4590–4602 (2008).

    CAS  Article  Google Scholar 

  49. 49.

    Price, M. J., Patterson, D. G., Scharer, C. D. & Boss, J. M. Progressive upregulation of oxidative metabolism facilitates plasmablast differentiation to a T-independent antigen. Cell Rep. 23, 3152–3159 (2018).

    CAS  Article  Google Scholar 

  50. 50.

    Ho, H. H., Antoniv, T. T., Ji, J. D. & Ivashkiv, L. B. Lipopolysaccharide-induced expression of matrix metalloproteinases in human monocytes is suppressed by IFN-γ via superinduction of ATF-3 and suppression of AP-1. J. Immunol. 181, 5089–5097 (2008).

    CAS  Article  Google Scholar 

  51. 51.

    Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    Article  Google Scholar 

  52. 52.

    Hsu, F. et al. The UCSC known genes. Bioinformatics 22, 1036–1046 (2006).

    CAS  Article  Google Scholar 

  53. 53.

    Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).

    CAS  Article  Google Scholar 

  54. 54.

    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).

    CAS  Article  Google Scholar 

  55. 55.

    Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  56. 56.

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

    Article  Google Scholar 

  57. 57.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  Article  Google Scholar 

  58. 58.

    Landt, S. G. et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).

    CAS  Article  Google Scholar 

  59. 59.

    Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    CAS  Article  Google Scholar 

  60. 60.

    Supek, F., Bosnjak, M., Skunca, N. & Smuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One 6, e21800 (2011).

    CAS  Article  Google Scholar 

  61. 61.

    Barwick, B. G. et al. B cell activation and plasma cell differentiation are inhibited by de novo DNA methylation. Nat. Commun. 9, 1900 (2018).

    Article  Google Scholar 

  62. 62.

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

    CAS  Article  Google Scholar 

  63. 63.

    Feng, H., Conneely, K. N. & Wu, H. A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Res. 42, e69 (2014).

    CAS  Article  Google Scholar 

  64. 64.

    Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).

    CAS  Article  Google Scholar 

  65. 65.

    Pohl, A. & Beato, M. bwtool: a tool for bigWig files. Bioinformatics 30, 1618–1619 (2014).

    CAS  Article  Google Scholar 

  66. 66.

    Carvalho, B. S. & Irizarry, R. A. A framework for oligonucleotide microarray preprocessing. Bioinformatics 26, 2363–2367 (2010).

    CAS  Article  Google Scholar 

Download references


We thank the members of the Boss and Sanz laboratories for critical reading of the manuscript, the New York University Genome Technology Center for Illumina sequencing, the Yerkes Genomics Core for RNA-seq library preparation, the Emory Pediatrics Flow Cytometry core for flow cytometry isolation of cell subsets and the Emory Integrated Genetics and Computational Core for Bioanalyzer and sequencing library quality control. This work was supported by NIH grants U19 AI110483 to J.M.B. and I.S., P01 AI125180 to I.S., F.E.-H.L. and J.M.B., RO1 AI113021 to J.M.B., F31 AI112261 to B.G.B., and T32 GM008490 to J.M.B.

Author information




C.D.S. and E.L.B. designed and performed experiments, analyzed the data and wrote the manuscript; B.G.B. and T.M. analyzed data; D.G.P. performed ATAC-seq; S.A.J. performed PD-1 and ATF3 phenotyping; T.D., K.S.C. and S.L.H. sorted and prepared cDNA for validation cohorts; B.E.N., F.E.-H.L. and C.W. provided cell sorting and biobanking expertise and performed sample preparation; A.K. evaluated cohort clinical data; and I.S. and J.M.B. designed experiments, wrote the manuscript and oversaw the project.

Corresponding authors

Correspondence to Iñaki Sanz or Jeremy M. Boss.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information. Laurie Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 aN and DN2 B cell subsets are expanded in subjects with SLE.

(a) Schematic showing gating strategy used to define B cell subsets. (b) Flow cytometry data for a representative HC and SLE subject from one experiment. Sample sizes for each cell type can be found in Supplementary Table 5. (c) Bar plot showing the frequency of each cell subset defined in a between HC and SLE subjects from one experiment. Each subject is denoted by a dot and the mean ±SD is shown. Significance determined by two-tailed Student’s t-test.

Supplementary Figure 2 Sequencing and QC of B cell subsets.

(a) Schematic and workflow of cell isolation and processing. (b) Annotation of data sets collected for each subject and cell type for each of the three genomic assays performed. (c) Bar plot showing the conversion efficiency of methylated and unmethylated DNA methylation libraries. (d) Representative histogram showing distance between paired-end reads for ATAC-seq data from one experiment. Similar results were obtained from all ATAC-seq samples. (e) Density plots of transcript expression for all RNA-seq libraries with the detection threshold annotated.

Supplementary Figure 3 Progressive upregulation of gene sets associated with B cell differentiation.

(a) Volcano plot of DAR and DEG comparing DN2 vs. aN B cells from HC (left) and SLE (right). The number of differential features is indicated. DEG and DAR represent features with >=2-fold change and FDR <0.05 as determined by edgeR. (b) GSEA plots of gene sets displayed in Fig. 1d depicting the enrichment for HC (top) and SLE (bottom) cell types. (c) Bar plot of gene expression levels for the indicated gene. Data represent mean ±SD. (d) Genome plot showing the accessibility and DNA methylation levels at the PRDM1 locus. The location of DAR and DML is highlighted with a box. (e) Genome plot of the indicated locus showing the accessibility pattern for each cell type. The location of DAR is highlighted with a box. Data from d-e represent the mean for each cell type from one experiment.

Supplementary Figure 4 Coordinated changes in accessibility and gene expression in rN B cells.

(a) Bar plot of gene expression levels for the indicated gene. Data represent mean ±SD. * indicates DEG between SLE and HC (>=2-fold change and FDR < 0.05) as determined by edgeR. (b) Genome plot showing the accessibility and DNA methylation levels at the IFI44 locus. Boxed region contains a DAR and DML between SLE and HC. Data represent the mean for each cell type from one experiment. See also Fig. 2.

Supplementary Figure 5 Gene expression and chromatin accessibility changes in DN2 cells are shared with aN.

(a) Bar plot of gene expression levels for the indicated gene. Data represent mean ±SD. For each indicated gene, a genome plot (top) showing the accessibility of the locus and bar plot of gene expression (bottom) at loci that are shared with HC (b) or unique to SLE DN2 B cells (c). DAR between DN2 and SM are highlighted in a box. Gene expression data represent mean ±SD. Genome plot data for b-c represent the mean for each cell type from one experiment. T-BET binding in GM12878 B cells is previously reported1. See also Fig. 4.

Supplementary Figure 6 The ABC signature is enriched in both SLE and HC DN2 B cells.

GSEA of the comparing the HC DN2 versus HC SM (top), SLE DN2 versus SLE SM (middle), or SLE DN2 versus HC DN2 (bottom) for enrichment with ABC datasets. Gene set comparing (a) ABC versus young follicular B cells (FoB)2, (b) ABC versus old FoB2, and (c) old ABC versus old FoB3. FDR < 0.05 was considered significant using the Benjamini-Hochberg correction on the P-value derived from permutation testing.

Supplementary Figure 7 DN2 and aN B cells have similar transcription factor accessibility footprints.

Histogram of accessibility for the indicated range surrounding (a) T-BET, (b) AP-1, (c) EGR, and (d) NF-κB motifs in the indicated B cell subset (columns). For each B cell subset the HC and SLE sample is shown. rppm, reads per peak per million. See also Fig. 5b.

Supplementary Figure 8 Transcription factor and gene set enrichment in SLE.

(a) Heatmap of normalized enrichment score (NES) calculated by GSEA for pathways up regulated in all SLE cell types (left) or within each cell type (right). For each gene set the NES for each cell type compared to the HC counterpart is annotated. See also Fig. 6b. (b) Venn diagram showing the overlap of ChIP-seq peaks for ATF3 (top) and EGR1 (bottom) from the ENCODE Consortium1 with DAR between HC and SLE B cells. * indicates P-value <0.0001 based on randomly permuting the DAR 10,000 times. (c) Bar plot of gene expression levels for the indicated gene. Data represent mean ±SD. * indicates DEG between SLE and HC (>=2-fold change and FDR <0.05) as determined by edgeR. See also Fig. 6e. (d) Network diagram depicting the gene sets targeted by each EGR factor. Line thickness is scaledSLE DN2 B cells have activation of to the significance as determined by Fisher’s Exact test. See also Fig. 6g.

Supplementary information

Supplementary Information

Supplementary Figures 1–8

Reporting Summary

Supplementary Table 1

Patient cohort information

Supplementary Table 2

111 CpGs that stratify healthy control and SLE B cells

Supplementary Table 3

Genes with peaks that are specific to healthy control or SLE DN2 B cells, or shared between healthy control and SLE DN2 B cells as compared to isotype-switched memory B cells

Supplementary Table 4

ATF3 target genes in SLE DN2 B cells

Supplementary Table 5

GEO accession numbers for genomics data associated with this study and sample group sizes for each cell type

Supplementary Table 6

PCR primers used in this study

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Scharer, C.D., Blalock, E.L., Mi, T. et al. Epigenetic programming underpins B cell dysfunction in human SLE. Nat Immunol 20, 1071–1082 (2019). https://doi.org/10.1038/s41590-019-0419-9

Download citation

Further reading


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