Analysis of the B cell receptor repertoire in six immune-mediated diseases

Abstract

B cells are important in the pathogenesis of many, and perhaps all, immune-mediated diseases. Each B cell expresses a single B cell receptor (BCR)1, and the diverse range of BCRs expressed by the total B cell population of an individual is termed the ‘BCR repertoire’. Our understanding of the BCR repertoire in the context of immune-mediated diseases is incomplete, and defining this could provide new insights into pathogenesis and therapy. Here, we compared the BCR repertoire in systemic lupus erythematosus, anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis, Crohn’s disease, Behçet’s disease, eosinophilic granulomatosis with polyangiitis, and immunoglobulin A (IgA) vasculitis by analysing BCR clonality, use of immunoglobulin heavy-chain variable region (IGHV) genes and—in particular—isotype use. An increase in clonality in systemic lupus erythematosus and Crohn’s disease that was dominated by the IgA isotype, together with skewed use of the IGHV genes in these and other diseases, suggested a microbial contribution to pathogenesis. Different immunosuppressive treatments had specific and distinct effects on the repertoire; B cells that persisted after treatment with rituximab were predominately isotype-switched and clonally expanded, whereas the inverse was true for B cells that persisted after treatment with mycophenolate mofetil. Our comparative analysis of the BCR repertoire in immune-mediated disease reveals a complex B cell architecture, providing a platform for understanding pathological mechanisms and designing treatment strategies.

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Fig. 1: Differences in isotype use, IGHV gene use and clonality between IMDs.
Fig. 2: Class switching in IMDs.
Fig. 3: Effects of therapy on the BCR repertoire.

Data availability

Sequencing data are available from the EGA (accession numbers in Supplementary Table 3)

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Acknowledgements

This work was supported by the Wellcome Trust (grants WT106068AIA and 083650/Z/07/Z), the EU H2020 project SYSCID (grant 733100), the UK Medical Research Council (program grant MR/L019027) and the UK National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre. We thank the patients who participated in this study; V. Morrison; A. Reynolds; all NIHR Cambridge BioResource staff and volunteers; the Cambridge NIHR BRC Cell Phenotyping Hub (particularly A. Petrunkina Harrison, N. S.Yarkoni, E. Perez, S. McCallum and C. Bowman); F. Alberici, N. Noor and other members of the Addenbrooke’s Vasculitis and Gastroenterology services; N. E. Urquijo for discussions about network subsampling, and P. Naydenova; and G. Manferrari. We are grateful to J. A. Todd and D. M. Tarlinton for reading the manuscript.

Author information

R.J.M.B.-R. and K.G.C.S. planned the study. R.J.M.B.-R. performed BCR amplification and analysed sequencing data. F.M. analysed clinical data and L.B., D.C.P. and S.M.F. performed immunophenotyping. E.F.M., J.C.L., D.C.T., S.M.F., D.R.W.J. and P.A.L. contributed to sample collection and clinical data generation, and P.K. contributed to sample processing. R.J.M.B.-R., P.A.L., E.F.M., J.C.L., D.C.T. and K.G.C.S. provided intellectual contributions to analyses. R.J.M.B.-R. and K.G.C.S. wrote the manuscript. All authors edited the manuscript.

Correspondence to R. J. M. Bashford-Rogers or K. G. C. Smith.

Ethics declarations

Competing interests

R.J.M.B.-R., P.K. and K.G.C.S. are all named on a patent associated with the methodologies in this paper. S.M.F. is a current employee of GlaxoSmithKline, and holds shares in GlaxoSmithKline. R.J.M.B.-R. is a consultant for Imperial College London and VHSquared. P.K. is an employee and holder of shares in Kymab Ltd. D.R.W.J. is a recipient of a research grant from Roche and Genentech. K.G.C.S. is a co-founder of Rheos Medicines and K.G.C.S., P.A.L. and E.F.M. are co-founders of PredictImmune.

Additional information

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

Peer review information Nature thanks Felix Breden, George Georgiou and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Overview of BCR repertoire strategy.

a, Schematic diagram of the strategy for BCR repertoire analysis. b, Schematic diagram of the BCR sequencing strategy. In the reverse transcription (RT) step, the primer anneals to the constant region of the BCR mRNA to generate cDNA with a random 12-nucleotide barcode. This barcode can be computationally used to reduce PCR amplification biases after sequencing. The product is cleaned and PCR-amplified using multiple primers that bind to the FR1 region of the IgH genes along with a universal sequence complementary to the end of the reverse-transcription primer. c, Gating strategy for sorting B cell subsets from healthy donor PBMCs by flow cytometry.

Extended Data Fig. 2 Effects of B cell subset and age on the BCR repertoire.

a, Frequencies of isotype use from BCR sequencing data from sorted naive B cells (CD19+IgD+CD27); CD19+CD27IgD- B cells; IgD+ memory, B1 and marginal-zone B cells (CD19+CD27+IgD+); IgD memory B cells (CD19+CD27+IgDCD38); and plasmablasts (CD19+CD27+IgDCD38+) from 19 healthy individuals. b, Mean CDR3 lengths (left) and mean SHM per BCR (right) from cell-sorted B cell populations from healthy individuals (n = 19). c, Plasmablast frequency per patient in peripheral blood at enrolment as a percentage of CD19+ B cells. d, Distribution of patient ages within this study, grouped by disease. eg, Correlations of the BCR repertoire in PBMCs with age in healthy individuals for the mean number of somatic hypermutations per BCR per bp (e), the percentage of BCRs per isotype (f) and percentage size of the largest cluster per sample (g). For b, c, P values were calculated by two-way ANOVA; *FDR < 0.05, **FDR < 0.005, ***FDR < 0.0005 (determined by the Šidák method). For eg, P values were calculated by two-sided Wilcoxon test; *P <0.05, **P < 0.005, ***P < 0.0005, all other values not significant. Raw P values are provided in Supplementary Table 4. For all box plots, box lines show the 25th, 50th and 75th percentiles; whiskers show the upper and lower quartiles.

Extended Data Fig. 3 Changes in the BCR repertoire with age and changes in isotype use with disease.

a, Correlation of P values obtained using age as a covariate versus those obtained when age was excluded from the analysis across 178 BCR features (calculated by two-way ANOVA). Grey dotted lines indicate the threshold of significance after accounting for multiple testing (FDR < 0.05, determined by the Šidák method). b, Analyses in which statistical significance was discordant (that is, below the threshold for significance without accounting for age and above the threshold when age was used as a covariate, or vice versa; purple points in a). c, Analyses in which statistically significant P values were decreased by more than 1.5-fold after using age as a covariate. d, Percentage of normalized isotype use (unique VDJ sequences per isotype) for BCR repertoires in PBMCs per disease. e, Normalized transcript levels of the IgE immunoglobulin constant region between disease groups, from transcriptomic data. n = 58 for healthy individuals and n = 23, n = 33, n = 13, n = 10, n = 8, n = 11 and n = 37 for patients with AAV (MPO+), AAV (PR3+), Behçet’s disease, Crohn’s disease, EGPA, IgAV and SLE, respectively. f, IgE titre between healthy individuals (n = 4) and patients with EGPA (n = 5). For d, e, P values were calculated by two-way ANOVA; *FDR < 0.05, **FDR < 0.005, ***FDR < 0.0005 (determined by the Šidák method). For all box plots, box lines show the 25th, 50th and 75th percentiles; whiskers show the upper and lower quartiles.

Extended Data Fig. 4 Changes in IGHV gene use with disease.

a, Changes in IGHV gene use between unexpanded and expanded clones. Heat map of the difference in the frequency of each IGHV gene between health and disease within BCRs from IgM+IgD+ or isotype-switched (IgA1, IgA2, IgG1, IgG2, IgG3, IgG4 or IgE) unexpanded clones (containing fewer than three unique BCRs) or expanded clones (containing three or more unique BCRs per clone). Only genes that occurred at a higher frequency than 0.1% are shown. IGHV genes are ordered according to amino acid similarity, as in Fig. 2. b, Frequencies of IGHV4-34 BCRs with autoreactive AVY and NHS motifs compared between healthy individuals and disease groups, separated by BCR type: IgM+IgD+SHM, IgM+IgD+SHM+ and IgMIgD BCR sequences (defined in a). c, Heat maps showing the mean SHM per BCR (top) and relative mean CDR3 lengths (bottom) per isotype per disease from total PBMC B cells. d, Distribution of the mean CDR3 lengths per IGHV gene in healthy individuals (n = 32). Each point represents the mean CDR3 length for an individual for unmutated IgD or IgM BCRs (left) and class-switched BCRs (right). Instances in which IGHV genes were represented by fewer than 10 BCRs in an individual are excluded. For ad, n = 32 for healthy individuals and n = 20, n = 34, n = 12, n = 10, n = 23, n = 10 and n = 13 for patients with AAV (MPO+), AAV (PR3+), EGPA, SLE, Crohn’s disease, IgAV and Behçet’s disease, respectively. P values were calculated by two-way ANOVA. Orange squares indicate significantly higher, and blue squares significantly lower, corresponding gene frequency between healthy individuals and disease. FDR was determined by the Šidák method.

Extended Data Fig. 5 Network subsampling methods for preserving repertoire structure.

a, Schematic diagram of the cluster-vertex migration in the CC algorithm. b, Maximum cluster sizes between true (unsampled) networks and subsampled networks of 2,000 clones by the tree subsampling methods. c, Comparison of representative networks from each group of patients at diagnosis. The patient samples are represented across the three sampling methods. Each vertex represents a unique sequence, and the relative vertex size is proportional to the number of identical reads. Edges join vertices that differ by single nucleotide non-indel differences and clusters are collections of related, connected vertices. Networks comprise a subsample of 2,000 clones, using the corresponding subsampling method. Each vertex is represented by a pie chart that indicates the percentage of each isotype, in which blue represents IgD or IgM, red IgA1 or IgA2, yellow IgG1 or IgG2, green IgG3 and grey IgE.

Extended Data Fig. 6 BCR repertoire clonality between diseases.

a, b, Box plots of the clonal expansion index (a) and the clonal diversification index (b) for BCR repertoires in PBMCs per disease. c, Plots of the percentage of clones per sample per disease that are greater than clone size (C). Clone size is defined as the number of unique VDJ sequences that are clonally related. For each disease, the mean percentage is indicated by the dark blue line, and the upper and lower interquartile ranges by the light blue areas. Overlaid in grey is the equivalent for healthy individuals. Differences in read depth were accounted for by subsampling 5,000 clones from each repertoire and determining the mean of 20 repeats. As a disease comparison, we show the distribution for CLL. d, Box plots of the percentage of clones that have more than 10, 20, 30, 40 or 50 unique VDJs per disease. Differences in read depth were accounted for by subsampling 5,000 clones and determining the mean of 20 repeats. For a, b, d, P values were calculated by two-way ANOVA; *FDR < 0.05, **FDR < 0.005, ***FDR < 0.0005 (determined by the Šidák method). n = 32 for healthy individuals and n = 20, n = 34, n = 12, n = 10, n = 23, n = 10 and n = 13 for patients with AAV (MPO+), AAV (PR3+), EGPA, SLE, Crohn’s disease, IgAV and Behçet’s disease, respectively. For all box plots, box lines show the 25th, 50th and 75th percentiles; whiskers show the upper and lower quartiles.

Extended Data Fig. 7 BCR repertoire similarity between diseases and estimation of CSR.

a, The maximum clone sizes (as a percentage of unique VDJ sequences of a given isotype in the largest clone divided by the total number of unique BCRs of that isotype) for BCR repertoires from PBMCs per disease across isotypes. For box plots, box lines show the 25th, 50th and 75th percentiles; whiskers show the upper and lower quartiles. b, Global repertoire dissimilarity measures between disease groups. Heat map showing the global repertoire dissimilarity measures between disease groups on the basis of a combination of three main BCR features (isotype frequency, clonal expansion index and clonal diversification index) and determining joint differences between groups (MANOVA test using disease group and age as covariables). The light and dark orange squares indicate significant differences between corresponding disease groups (FDR < 0.05 and FDR < 0.005, respectively, determined by Šidák method). n = 32 for healthy individuals and n = 20, n = 34, n = 12, n = 10, n = 23, n = 10 and n = 13 for patients with AAV (MPO+), AAV (PR3+), EGPA, SLE, Crohn’s disease, IgAV and Behçet’s disease, respectively. c, The sequence of B cell isotype expression is defined by the order of constant regions on the chromosome. Possible class-switching events are depicted by the arrows between constant regions. d, Schematic diagram of class-switching types that are detectable from the sequencing data. The differences from c are a result of the ambiguity of isotype between IgA1 and IgA2, and IgG1 and IgG2, in the isotype-specific sequencing, and splicing of IgD from IgM-containing transcripts. Possible class-switching events are represented by the arrows between constant regions. e, Several unique RNA sequences with identical antigen-binding regions (V–D–J) but different constant regions represent instances of class switching. f, Schematic diagram of the subsampling of BCR repertoires to generate the relative class-switch event frequency. This is the frequency of unique VDJ regions that are expressed as two isotypes (that is, from more than one B cell, one of which has undergone CSR), and determined as the proportion of unique BCRs that are present as both isotypes (IgX and IgY) within a random subsample of 8,000 BCRs, from which the mean of 1,000 repeats was generated. This provides information on the frequency of BCRs that are observed to be associated with any two isotypes (class-switching events), and accounts for total read depth, but not for differences in the relative frequencies of BCRs per isotype. For a, n = 32 for healthy individuals and n = 54, n = 12, n = 10, n = 23, n = 10 and n = 13 for patients with AAV, EGPA, SLE, Crohn’s disease, IgAV and Behçet’s disease, respectively. P values were calculated by two-way ANOVA, *FDR < 0.05, **FDR < 0.005, ***FDR < 0.0005 (determined by the Šidák method).

Extended Data Fig. 8 Differences in CSR estimation between diseases.

a, Box plots of the proportion of class-switching events between isotypes for each autoimmune disease. b, Box plots of the proportion of class-switching events between autoimmune diseases across isotypes for BCR repertoires in PBMCs by subsampling the total repertoire. P values were calculated by two-way ANOVA; *FDR < 0.05, **FDR < 0.005, ***FDR < 0.0005 (determined by the Šidák method). n = 32 for healthy individuals and n = 54, n = 12, n = 10, n = 23, n = 10 and n = 13 for patients with AAV, EGPA, SLE, Crohn’s disease, IgAV and Behçet’s disease, respectively. For all box plots, box lines show the 25th, 50th and 75th percentiles; whiskers show the upper and lower quartiles. c, Phylogenetic trees of representative clonal expansions of B cells from patients, demonstrating CSR events. Each vertex is represented by a pie chart that indicates the percentage of each isotype, in which blue represents IgD or IgM, red IgA1 or IgA2, yellow IgG1 or IgG2, green IgG3 and grey IgE. Branch lengths are estimated by maximum parsimony, and the BCRs with the lowest number of somatic hypermutations are indicated (denoted ‘BCRs closest to germline’).

Extended Data Fig. 9 Normalized differences in CSR estimation between diseases and IgE clonal features.

a, Schematic diagram of subsampling of BCR repertoires to generate the per-isotype normalized class-switch event frequencies (defined as the frequency of unique VDJ regions expressed as two isotypes, normalizing for differences in isotype frequencies). To account for differences in the proportions of the isotypes, BCRs from each isotype were randomly subsampled to a fixed depth of 200 reads, and the proportion of unique VDJ sequences present between each pair of isotypes was counted. The mean of 1,000 repeats was generated. b, Box plots of the proportion of the per-isotype normalized class-switch event frequencies between isotypes for each autoimmune disease. P values were calculated by two-way ANOVA; *FDR < 0.05, **FDR < 0.005, ***FDR < 0.0005 (determined by the Šidák method). n = 32 for healthy individuals and n = 54, n = 12, n = 10, n = 23, n = 10 and n = 13 for patients with AAV, EGPA, SLE, Crohn’s disease, IgAV and Behçet’s disease, respectively. c, Box plots of the mean cluster sizes per patient per isotype as a percentage of BCRs per isotype, comparing IgE-associated clones with non-IgE-associated clones for each disease. d, The proportion of VDJ sequences per isotype that are observed also as other isotypes for each disease. P values were calculated by two-sided Wilcoxon test; *FDR < 0.05, **FDR < 0.005, ***FDR < 0.0005 (determined by the Šidák method). n = 32 for healthy individuals and n = 54, n = 12, n = 10, n = 23, n = 10 and n = 13 for patients with AAV (MPO+), AAV (PR3+), EGPA, SLE, Crohn’s disease, IgAV and Behçet’s disease, respectively. For all box plots, box lines show the 25th, 50th and 75th percentiles; whiskers show the upper and lower quartiles.

Extended Data Fig. 10 Effects of therapy on the BCR repertoire.

ac, Percentage of BCRs per isotype (a), mean SHM per BCR per isotype (b) and clonal expansion indices (c) of samples that were taken from patients with AAV and SLE at diagnosis (red, untreated), and after 3 months of induction therapy with MMF (blue) or RTX (green). For AAV, the patients per group were: untreated, n = 42; MMF, n = 5; RTX, n = 5; and for SLE: untreated, n = 11; MMF, n = 6; RTX, n = 9. d, Percentage of BCRs shared between samples that were taken from patients with AAV at diagnosis and 3 or 12 months after induction therapy (blue); BCRs shared between repertoires from the same RNA tube (red); and BCRs shared between unrelated patient samples. Zero overlap was found between unrelated samples, whereas there was a significantly higher overlap between BCRs shared between repertoires from the same RNA tube compared to BCRs shared between AAV samples taken at diagnosis and 3 or 12 months after induction therapy. This suggests that the overlap measurements yield realistic and normalized values at this sampling depth. e, The percentages of persistent BCRs shared between diagnosis and 3 months after induction therapy, split between patients who received different therapies. P values were calculated by two-sided Wilcoxon test. For all box plots, box lines show the 25th, 50th and 75th percentiles; whiskers show the upper and lower quartiles.

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Supplementary Data 3

IGHV gene frequency differences between diseases. Boxplots of IGHV gene frequencies and d) summary heatmap of each IgHV gene frequency difference between healthy individuals for all IGHV genes and each autoimmune disease patient group within BCRs from IgM+D+SHM- BCR sequences, largely derived from naive B cells; IgM+D+SHM+ BCR sequences in which SHM is evidence of antigenic stimulation; and IgM-D- BCR sequences, all isotype-switched IgA/IgG/E. Orange squares indicate significantly higher, and blue squares significantly lower, corresponding gene frequency between healthy individuals and disease. IGHV genes are ordered according to amino acid similarity, indicated by the IGHV gene amino acid similarity tree. FDR determined by Šidák method. P-values calculated by two-sided by ANOVA and * denotes FDR <0.05, ** <0.005, *** <0.0005, where FDR determined by Šidák method. Boxplots show the 25th, 50th and 75th percentiles; whiskers show upper and lower quartiles. n=32, 18, 32, 12, 10, 23, 10 and 13 for healthy, AAV MP0+, AAV PR3+, EGPA, SLE, CD IgAV and Behçet’s patients respectively.

Supplementary Data 5

BCR repertoire networks for each patient.

Supplementary Data 6

Representative BCR repertoire networks for disease groups (i) at diagnosis and (ii) during therapy (3 months post-induction therapy with MMF or RTX). BCR networks of representative samples from each disease, with CLL included to show a malignant expanded clone for comparison. The networks cannot be corrected for age, so age of the patient is shown next to each. Each vertex represents a unique sequence, where relative vertex size is proportional to the number of identical reads. Edges join vertices that differ by single nucleotide non-indel differences and clusters are collections of related, connected vertices. Networks were generated using the clone subsampling method (see Supplementary discussion file 4). The ages of the individuals is provided above each plot. Each vertex is represented by a pie chart indicating the percentage of each isotype, where blue = IgD/M, red = IgA1/2, yellow = IgG1/2, green = IgG3, and grey = IgE.

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This file contains Supplementary Tables 1-7.

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Bashford-Rogers, R.J.M., Bergamaschi, L., McKinney, E.F. et al. Analysis of the B cell receptor repertoire in six immune-mediated diseases. Nature 574, 122–126 (2019). https://doi.org/10.1038/s41586-019-1595-3

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