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Diversity, cellular origin and autoreactivity of antibody-secreting cell population expansions in acute systemic lupus erythematosus

Nature Immunology volume 16, pages 755–765 (2015)Cite this article

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Abstract

Acute systemic lupus erythematosus (SLE) courses with surges of antibody-secreting cells (ASCs) whose origin, diversity and contribution to serum autoantibodies remain unknown. Here, deep sequencing, proteomic profiling of autoantibodies and single-cell analysis demonstrated highly diversified ASCs punctuated by clones expressing the variable heavy-chain region VH4-34 that produced dominant serum autoantibodies. A fraction of ASC clones contained autoantibodies without mutation, a finding consistent with differentiation outside the germinal centers. A substantial ASC segment was derived from a distinct subset of newly activated naive cells of considerable clonality that persisted in the circulation for several months. Thus, selection of SLE autoreactivities occurred during polyclonal activation, with prolonged recruitment of recently activated naive B cells. Our findings shed light on the pathogenesis of SLE, help explain the benefit of agents that target B cells and should facilitate the design of future therapies.

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Figure 1: SLE flares are characterized by large polyclonal expansions of ASC populations.
Figure 2: Isotype distribution and SHM in patients with SLE and healthy vaccinated control subjects.
Figure 3: Over-representation of IGH VH4-34 among ASC clonal expansions in patients with SLE.
Figure 4: Contribution of naive cells to ASC populations in SLE flares.
Figure 5: Characterization of acN cells in SLE.
Figure 6: Clonality and connectivity of acN cells in SLE.
Figure 7: Serological consequence and cellular derivation of expanded ASC clones in SLE.
Figure 8: Autoreactivity of 9G4+ ASCs in SLE.

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Acknowledgements

We thank F. Stevenson (University of Southampton) for the 9G4 hybridoma; E. Meffre (Yale University) for expression vectors; and the blood donors and research coordinators involved in this study. Supported by the Autoimmunity Center of Excellence (U19 AI110483, 5P01AI078907 and 5R37AI049660) and the United States-Israel Binational Science Foundation (2013432).

Author information

Author notes

  1. Wan Cheung Cheung

    Present address: Present address: Biotherapeutic Technologies, Pfizer, Cambridge, Massachusetts, USA.,

Authors and Affiliations

  1. Department of Medicine, Division of Rheumatology, Emory University, Atlanta, Georgia, USA

    Christopher M Tipton, Asiya Chida, Travis Ichikawa, Jennifer Hom, Scott Jenks, Chungwen Wei & Iñaki Sanz

  2. Department of Medicine, Division of Allergy, Immunology and Rheumatology, University of Rochester Medical Center, Rochester, New York, USA

    Christopher F Fucile & Alexander F Rosenberg

  3. Cell Signaling Technology, Danvers, Massachusetts, USA

    Jaime Darce, Ivan Gregoretti & Sandra Schieferl

  4. Department of Dermatology, Emory University, Atlanta, Georgia, USA

    Ron J Feldman

  5. Bar-Ilan University, Ramat Gan, Israel

    Ramit Mehr

  6. Department of Pulmonology, Emory University, Atlanta, Georgia, USA

    F Eun-Hyung Lee

  7. Cell Signaling Technology, Danvers, Massachusetts, USA

    Wan Cheung Cheung

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Contributions

C.M.T. obtained most samples, conducted sample preparation and cell sorting, designed and conducted the NGS studies, analyzed and interpreted the data, helped to design figures and helped to write the manuscript; C.F.F. and A.F.R. wrote the programs used for NGS analysis, helped in data interpretation, produced visualization of the data, and helped to design and produce the figures; J.D., I.G., S.S. and W.C.C. conducted and analyzed the proteomics studies; A.C. conducted the experiments with single-cell monoclonal antibodies; T.I. conducted the ELISPOT experiments; J.H. obtained samples from patients with pemphigus, conducted some sequencing studies and helped with the analysis; S.J. helped with sequencing analysis; R.J.F. provided samples from patients with pemphigus; R.M. provided the IgTree program and aided in analysis; C.W. provided flow cytometry data and helped with its analysis; F.E.-H.L. provided the samples from vaccinated subjects and aided in data analysis; and I.S. designed and supervised the project, helped in experimental design and analysis, and wrote the manuscript.

Corresponding author

Correspondence to Iñaki Sanz.

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Competing interests

I.S. is a member of the Pfizer Visiting Professor Board and has consulted for Genentech on B cell--depleting agents, and F.E.-H.L. has grants from Genentech.

Integrated supplementary information

Supplementary Figure 1 Data-processing pipeline.

Steps for processing data from multiple sorted B cell populations derived from a single individual at a single time point are shown. Parameters used are indicated to the right of the corresponding box. These steps are implemented by a combination of publicly available software (fastq-joiner), public resources (IMGT/HighV-quest), and custom software written in Perl and Matlab by the authors. After raw reads are joined, the sequences are each confirmed to meet a length threshold of 200bp and are eliminated if they contain more than 0, 10, or 15 bp that are less than Q10, Q20, and Q30 respectively. A subset of 150,000 sequences from the total is randomly chosen to relieve computational stress and these sequences are aligned with IMGT.org's HighV-Quest tool. Alignment results are filtered by removing “unproductive” and “unknown” sequences. Custom software is then used to identify clusters of sequences based on the clonal identification metric (see methods). 50,000 sequences from the final set are then randomly chosen, again to relieve computational stress of the downstream analyses, to retain similar number of sequences in each data set, and for display purposes. All of IMGT/HighV-quest's data is retained through the process and used for mutation calculations and alignment analyses.

Supplementary Figure 2 Validation of identity thresholds for clonality identification metric.

Grouped lineages of ASCs between sample replicates, cell populations and individual subjects are shown under different analytical requirements: (a) Circos plots are used to display connectivity between: a single CD138- ASC sample split into 3 separate fractions; a CD138+ ASC sample from the same individual (FLU 4, gray); and one CD138- ASC sample from a different individual (FLU 1, blue). Percentages in the upper left corner of each Circos plot indicate the level of CDR3 sequence similarity used to assign membership within the same clone. The two right-hand plots also require identical junction regions, as identified by the 3bp before and after the VH-D split and the 3bp before and after the D-JH split. The clone labeled 1 in the 85% threshold plot is split into multiple clones (1a, 1b, and 1c) when the identical junction requirement is used. (b) Plot of lineage sizes of two replicates of CD138- ASCs from FLU 4 based on 85% shows a high correlation between the sizes of the largest clones in each data set. Deviation from similar sized clones within the pair of data sets primarily occurs in small clones, which would be more prone to reflect sampling and/or sequencing error. The high correlation of large clones within replicates further validates the ability to identify expanded clones in the circulating B cell and ASC populations. Blue numbers indicate number of sequences per clone (non-log) for very small clones.

Source data

Supplementary Figure 3 Effect of the requirement for identical V-D and D-J junctions on the clonal identification metric.

(a) Alignment of a sample of VH sequences from the top FLU 4 CD138- ASC clone defined in Supplementary Fig. 2a using a requirement of 85% HCDR3 identity. Consistent with a shared clonal origin, this alignment demonstrates both shared as well as unique mutations in a step-wise fashion within VH rearrangements that share a highly conserved HCDR3 including conserved VH-D junction. However, the added requirement of complete conservation of both the VH-D and D-JH junction splits these sequences into 3 separate clones. Red indicates base differences from germline VH4-38 and differences within the clonally related HCDR3 sequences. (b) Alignments of example sequences from each of the lineages in a, focusing on CDR3 and surrounding region to illustrate the degree of HCDR3 and junctional conservation.

Source data

Supplementary Figure 4 Distribution of lineage sizes for 13 samples.

Lineages are lined up in size order from bottom to top along the extent of the y-axis representing 100% of all the sequences. Horizontal lines delineate the individual lineages. The x-axis is the normalized lineage size (percentage of the total number of sequences). Identical sequences are included in lineage composition.

Supplementary Figure 5 Distribution of lineage sizes for 13 samples.

Lineages are lined up in size order from bottom to top along the extent of the y-axis representing 100% of all the sequences. Horizontal lines delineate the individual lineages. The x-axis is the normalized lineage size (percentage of the total number of sequences). Identical sequences are included in lineage composition.

Supplementary Figure 6 VH4-34+ ASC clonal lineages at multiple time points following a flare.

Time points are from patient SLE-3 at early flare and 5, 6, 7 and 8 weeks thereafter. Each vertical bar represents the repertoire of ASC (CD138− and CD138+) at a single time point, with lineages ordered from smallest to largest, and the y-axis representing the cumulative % of sequences. The largest 10 VH4-34 clones found at active flare are indicated by colored curves connecting corresponding lineages at multiple time points. All but one of these were found at all five time points and in all cases, the abundance of these VH4-34 clones within the most expanded clones diminishes beyond the early flare.

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Supplementary Text and Figures

Supplementary Figures 1–6, Supplementary Table, and Supplementary Note (PDF 7191 kb)

Supplementary Data Set 1

Bioinformatics pipeline for the analysis of NGS data. (ZIP 834 kb)

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Tipton, C., Fucile, C., Darce, J. et al. Diversity, cellular origin and autoreactivity of antibody-secreting cell population expansions in acute systemic lupus erythematosus. Nat Immunol 16, 755–765 (2015). https://doi.org/10.1038/ni.3175

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