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Epigenetic programming underpins B cell dysfunction in human SLE

Nature Immunology volume 20, pages 1071–1082 (2019)Cite this article

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Abstract

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.

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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.

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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.

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Acknowledgements

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.

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Authors and Affiliations

  1. Department of Microbiology and Immunology, School of Medicine, Emory University, Atlanta, GA, USA

    Christopher D. Scharer, Tian Mi, Dillon G. Patterson, Sakeenah L. Hicks & Jeremy M. Boss

  2. Division of Rheumatology, Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA

    Emily L. Blalock, Scott A. Jenks, Tsuneo Deguchi, Kevin S. Cashman, Bridget E. Neary, Arezou Khosroshahi, Chungwen Wei & Iñaki Sanz

  3. Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA

    Emily L. Blalock, Scott A. Jenks, Tsuneo Deguchi, Kevin S. Cashman, Bridget E. Neary, Arezou Khosroshahi, F. Eun-Hyung Lee & Iñaki Sanz

  4. Department of Hematology and Medical Oncology, School of Medicine, Emory University, Atlanta, GA, USA

    Benjamin G. Barwick

  5. Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA

    F. Eun-Hyung Lee

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Contributions

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.

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Correspondence to Iñaki Sanz or Jeremy M. Boss.

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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

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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

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