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

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STIMULATION REVEALS DOWN-REGULATION OF INFLAMMATORY PATHWAYS IN T AND B CELLS IN SLE VERSUS SJÖGREN’S SYNDROME Article Open access 15 December 2023 INTEGRATIVE TRANSCRIPTOME AND CHROMATIN

LANDSCAPE ANALYSIS REVEALS DISTINCT EPIGENETIC REGULATIONS IN HUMAN MEMORY B CELLS Article Open access 28 October 2020 SINGLE-CELL CHROMATIN ACCESSIBILITY AND TRANSCRIPTOMIC CHARACTERIZATION

OF BEHCET’S DISEASE Article Open access 17 October 2023 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

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microarray preprocessing. _Bioinformatics_ 26, 2363–2367 (2010). Article  CAS  Google Scholar  Download references 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. AUTHOR

INFORMATION AUTHORS AND AFFILIATIONS * 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 * 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 * 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 * Department of Hematology and Medical Oncology,

School of Medicine, Emory University, Atlanta, GA, USA Benjamin G. Barwick * Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, School of Medicine,

Emory University, Atlanta, GA, USA F. Eun-Hyung Lee Authors * Christopher D. Scharer View author publications You can also search for this author inPubMed Google Scholar * Emily L. Blalock

View author publications You can also search for this author inPubMed Google Scholar * Tian Mi View author publications You can also search for this author inPubMed Google Scholar * Benjamin

G. Barwick View author publications You can also search for this author inPubMed Google Scholar * Scott A. Jenks View author publications You can also search for this author inPubMed Google

Scholar * Tsuneo Deguchi View author publications You can also search for this author inPubMed Google Scholar * Kevin S. Cashman View author publications You can also search for this author

inPubMed Google Scholar * Bridget E. Neary View author publications You can also search for this author inPubMed Google Scholar * Dillon G. Patterson View author publications You can also

search for this author inPubMed Google Scholar * Sakeenah L. Hicks View author publications You can also search for this author inPubMed Google Scholar * Arezou Khosroshahi View author

publications You can also search for this author inPubMed Google Scholar * F. Eun-Hyung Lee View author publications You can also search for this author inPubMed Google Scholar * Chungwen

Wei View author publications You can also search for this author inPubMed Google Scholar * Iñaki Sanz View author publications You can also search for this author inPubMed Google Scholar *

Jeremy M. Boss View author publications You can also search for this author inPubMed Google Scholar 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. 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 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 * Received: 28 September 2018 * Accepted: 09 May 2019 * Published: 01 July 2019 * Issue Date: August 2019 * DOI:

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