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.
Subscribe to Journal
Get full journal access for 1 year
only $17.42 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Code and data processing scripts are available from the corresponding author upon request and at https://github.com/cdschar.
Rawlings, D. J., Metzler, G., Wray-Dutra, M. & Jackson, S. W. Altered B cell signalling in autoimmunity. Nat. Rev. Immunol. 17, 421–436 (2017).
Langefeld, C. D. et al. Transancestral mapping and genetic load in systemic lupus erythematosus. Nat. Commun. 8, 16021 (2017).
Chung, S. A. et al. Differential genetic associations for systemic lupus erythematosus based on anti-dsDNA autoantibody production. PLoS Genet. 7, e1001323 (2011).
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).
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).
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).
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).
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).
Manni, M. et al. Regulation of age-associated B cells by IRF5 in systemic autoimmunity. Nat. Immunol. 19, 407–419 (2018).
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).
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).
Banchereau, R. et al. Personalized immunomonitoring uncovers molecular networks that stratify lupus patients. Cell 165, 551–565 (2016).
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).
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).
Hewagama, A. & Richardson, B. The genetics and epigenetics of autoimmune diseases. J. Autoimmun. 33, 3–11 (2009).
Sanz, I., Wei, C., Lee, F. E. & Anolik, J. Phenotypic and functional heterogeneity of human memory B cells. Semin. Immunol. 20, 67–82 (2008).
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).
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).
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).
Kulis, M. et al. Whole-genome fingerprint of the DNA methylome during human B cell differentiation. Nat. Genet. 47, 746–756 (2015).
Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).
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).
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).
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).
Nagaoka, M. et al. The orphan nuclear receptor NR4A3 is involved in the function of dendritic cells. J. Immunol. 199, 2958–2967 (2017).
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).
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).
Lopez-Otin, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013).
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).
Mo, J. S. & Chae, S.-C. EPSTI1 polymorphisms are associated with systemic lupus erythematosus. Genes Genomics 39, 445–451 (2017).
Buchta, C. M. & Bishop, G. A. TRAF5 negatively regulates TLR signaling in B lymphocytes. J. Immunol. 192, 145–150 (2014).
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).
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).
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).
Chattopadhyay, S. et al. EGFR kinase activity is required for TLR4 signaling and the septic shock response. EMBO Rep. 16, 1535–1547 (2015).
ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012)..
Yu, B. et al. Epigenetic landscapes reveal transcription factors that regulate CD8+ T cell differentiation. Nat. Immunol. 18, 573–582 (2017).
Myouzen, K. et al. Regulatory polymorphisms in EGR2 are associated with susceptibility to systemic lupus erythematosus. Hum. Mol. Genet. 19, 2313–2320 (2010).
Jadhav, K. & Zhang, Y. Activating transcription factor 3 in immune response and metabolic regulation. Liver Res. 1, 96–102 (2017).
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).
Gilchrist, M. et al. Systems biology approaches identify ATF3 as a negative regulator of Toll-like receptor 4. Nature 441, 173–178 (2006).
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).
Palanichamy, A. et al. Novel human transitional B cell populations revealed by B cell depletion therapy. J. Immunol. 182, 5982–5993 (2009).
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).
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).
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).
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).
Gururajan, M. et al. Early growth response genes regulate B cell development, proliferation, and immune response. J. Immunol. 181, 4590–4602 (2008).
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).
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).
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
Hsu, F. et al. The UCSC known genes. Bioinformatics 22, 1036–1046 (2006).
Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).
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).
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).
Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008).
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).
Landt, S. G. et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).
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).
Supek, F., Bosnjak, M., Skunca, N. & Smuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One 6, e21800 (2011).
Barwick, B. G. et al. B cell activation and plasma cell differentiation are inhibited by de novo DNA methylation. Nat. Commun. 9, 1900 (2018).
Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011).
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).
Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).
Pohl, A. & Beato, M. bwtool: a tool for bigWig files. Bioinformatics 30, 1618–1619 (2014).
Carvalho, B. S. & Irizarry, R. A. A framework for oligonucleotide microarray preprocessing. Bioinformatics 26, 2363–2367 (2010).
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.
The authors declare no competing interests.
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
(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.
(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.
(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.
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.
(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 Figures 1–8
Patient cohort information
111 CpGs that stratify healthy control and SLE B cells
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
ATF3 target genes in SLE DN2 B cells
GEO accession numbers for genomics data associated with this study and sample group sizes for each cell type
PCR primers used in this study
About this article
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
Current Rheumatology Reports (2020)
Arthritis & Rheumatology (2020)
Naive- and Memory-like CD21low B Cell Subsets Share Core Phenotypic and Signaling Characteristics in Systemic Autoimmune Disorders
The Journal of Immunology (2020)
Seminars in Arthritis and Rheumatism (2020)