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Diversification of memory B cells drives the continuous adaptation of secretory antibodies to gut microbiota

Nature Immunology volume 16, pages 880–888 (2015)Cite this article

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Abstract

Secretory immunoglobulin A (SIgA) shields the gut epithelium from luminal antigens and contributes to host-microbe symbiosis. However, how antibody responses are regulated to achieve sustained host-microbe interactions is unknown. We found that mice and humans exhibited longitudinal persistence of clonally related B cells in the IgA repertoire despite major changes in the microbiota during antibiotic treatment or infection. Memory B cells recirculated between inductive compartments and were clonally related to plasma cells in gut and mammary glands. Our findings suggest that continuous diversification of memory B cells constitutes a central process for establishing symbiotic host-microbe interactions and offer an explanation of how maternal antibodies are optimized throughout life to protect the newborn.

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Figure 1: The complexity of the microbiota determines IgA repertoire diversity but not gene segment use.
Figure 2: CDR3 sequences generated in monocolonized mice persist after complex colonization.
Figure 3: Convergence of microbiota in a semi-natural environment.
Figure 4: Adult mice maintain a stable clonal composition of their IgA repertoire over time.
Figure 5: Dynamics of human and mouse IgA repertoires during antibiotic treatment.
Figure 6: Mutation frequencies increase in mice and humans within clonally stable IgA repertoires.
Figure 7: Memory B cells recirculate between multiple compartments and give rise to plasma cells in mammary glands.
Figure 8: S. Typhimurium LPS–binding plasma cells are unrelated to B cell clones present before infection.

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Acknowledgements

We thank U. Kalinke (TWINCORE, Hannover, Germany), A. Krueger, I. Prinz, O. Schulz, S. Woltemate (Hannover Medical School, Hannover, Germany), M. Bemark (University of Gothenburg, Gothenburg, Sweden), B. Wabakken Hognestad and D. Ölsner (both at the Norwegian University of Life Sciences, Oslo, Norway), and R. Bharti (IKMB, University of Kiel, Kiel, Germany). This work was supported by the DFG Cluster of Excellence Inflammation at Interfaces (CL VIII to P.R., S.O. and S. Schreiber), the BMBF (grant SysINFLAME (TP3/4) to P.R.), the Israel Science Foundation (grant 270/09 to R.M.), the German Centre for Infection Research (DZIF), partner site Hannover-Braunschweig and Deutsche Forschungsgemeinschaft (grants PA921/4-1 (to O.P.) and SFB621-Z).

Author information

Author notes

  1. Cornelia Lindner and Irene Thomsen: These authors contributed equally to this work.

Authors and Affiliations

  1. Institute of Immunology, Hannover Medical School, Hannover, Germany

    Cornelia Lindner, Irene Thomsen, Benjamin Wahl, Milas Ugur, Maya K Sethi, Michaela Friedrichsen & Oliver Pabst

  2. Laboratory for Animal Science, Hannover Medical School, Hannover, Germany

    Anna Smoczek & André Bleich

  3. Department for Internal Medicine I, University Hospital Schleswig Holstein, Kiel, Germany

    Stephan Ott & Stefan Schreiber

  4. Pediatric Gastroenterology, Hannover Medical School, Hannover, Germany

    Ulrich Baumann

  5. Institute of Medical Microbiology and Hospital Epidemiology, Hannover Medical School, Hannover, Germany

    Sebastian Suerbaum

  6. Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein, Kiel, Germany

    Stefan Schreiber & Philip Rosenstiel

  7. INSERM UMR1163, Laboratory of Intestinal Immunity, Institut Imagine, Paris, France

    Valérie Gaboriau-Routhiau & Nadine Cerf-Bensussan

  8. The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel

    Helena Hazanov & Ramit Mehr

  9. Department of Food Safety and Infection Biology, Faculty of Veterinary Medicine and Biosciences, Norwegian University of Life Sciences, Oslo, Norway

    Preben Boysen

  10. Institute of Molecular Medicine, RWTH Aachen, Aachen, Germany

    Oliver Pabst

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Contributions

C.L. and I.T. performed experiments and analyzed data; M.U. performed cell-tracking experiments; B.W. generated tools for data analysis; M.F., M.K.S. and S. Suerbaum supported sequencing experiments; A.B. and A.S. performed colonization experiments; V.G.-R. and N.C.-B. provided samples from monocolonized mice; H.H. and R.M. helped with sequencing data analysis; P.B. performed cohousing experiments; S.O., U.B., S. Schreiber and P.R. provided human samples; and O.P. performed mouse surgery, designed the study and wrote the manuscript.

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Correspondence to Oliver Pabst.

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Integrated supplementary information

Supplementary Figure 1 Exponential Shannon index as a measure of immunoglobulin repertoire diversity.

(a) The IgA repertoire was sequenced in samples containing defined numbers of sorted IgA+ intestinal plasma cells (n = 4, pooled from two independent experiments). The maximum exponent Shannon was considered as 100% diversity. The exponent Shannon was determined for all samples, and varying numbers of sequences were used for analysis. Analysis of 4,000 sequences was sufficient to reach 95% of maximum diversity for all samples. 2,000 sequences were sufficient to comprehensively describe diversity in samples comprising 5,000 or fewer plasma cells. (b) The exponent Shannon was determined in samples containing 5,000 sorted IgA+ plasma cells either from single mice or pooled from two, four or six mice. Symbols represent independent experiments and analysis of 2,000 sequences each.

Supplementary Figure 2 Modulation of the microbiota by SPF colonization.

Germ-free mice were monocolonized with E. coli Nissle (ECN) for 4 weeks. Subsequently, biopsies of small intestine were taken and mice were gavaged with cecal content from donor SPF mice housed at the central animal facility at Hannover Medical School. After another 4 weeks, 16S rDNA was sequenced to analyze the bacterial composition in the small intestine and feces of recipient mice. Bacterial 16S rDNA sequences were analyzed with Qiime, an open source software package for the analysis of microbial communities. Qiime was also used to determine the diversity of the microbiota. Microbial diversity was comparable between donor and recipient mice (Shannon index calculated based on 2,236 sequences: donor SI, 6.181 ± 0.343; recipient SI, 5.835 ± 0.174; donor feces, 6.886 ± 0.094; recipient feces, 6.713 ± 0.110). Bar diagrams display families contributing more than 2% of all sequences. Data from two independent experiments were pooled.

Supplementary Figure 3 Separation of expanded and non-expanded components in the IgA repertoire.

Clusters were assigned a rank on the basis of abundance, and the frequency of all ranks was determined. Log(frequency) versus log(rank) diagrams are depicted for representative mice. The first component comprised low-frequency clones and showed power law characteristics (I), whereas the second component (II) comprised expanded clones that appeared as a long tail (gray symbols) in log(frequency) versus log(rank) diagrams. Diagrams represent, from top to bottom, (1) wild-type mice (n = 6) and MyD88/Trif double-deficient mice housed under SPF conditions at Hannover Medical School (n = 3), (2) germ-free mice (n = 7) and germ-free mice monocolonized for 4 weeks with ECN (n = 10; as described for Fig. 1), and (3) wild-type mice housed under SPF conditions at the Norwegian School of Veterinary Science (n = 5) or housed for 8 weeks together with wild-caught mice in an enriched environment (n = 5). Representative data from five independent experiments are displayed.

Supplementary Figure 4 Modulation of the microbiota by antibiotic treatment.

Biopsies of small intestine were obtained from 7- or 9-week-old C57BL/6 mice housed under SPF conditions. One week after surgery, mice received a cocktail of ampicillin and vancomycin in the drinking water, and this antibiotic administration continued for 4 weeks. Fecal samples were collected before and after antibiotic treatment to track changes in the microbiota by 16S rDNA sequencing. Microbial diversity was reduced after antibiotic treatment (Shannon index calculated based on 1,321 sequences: 6.526 ± 0.222 before and 3.973 ± 1.668 after treatment, mean ± s.d., n = 6). Of note, chloroplast sequences likely reflect food-derived sequences and are not part of the proper intestinal microbiota. We did not exclude these sequences from the analysis, in order to preserve the integrity of the data set. Operational taxonomic units generated by Qiime analysis of 16S rDNA sequences before and after antibiotic treatment were clustered using weighted UniFrac principle coordinate analysis. Data from two independent experiments are displayed.

Supplementary Figure 5 The intestinal IgA plasma cell repertoire mirrors the IgA memory B cell compartment.

(a) PP B cells, excluding plasma cells and germinal center cells (DAPI−CD19+CD138−GL7−), were sorted as CD80−CD73− (enriched in naive B cells) and memory B cells (CD80+CD73+). IgA plasma cells were sorted from intestinal lamina propria (IgA+CD138+ single cells). (b) IgA and IgM repertoire sequence information was obtained from sorted memory B cells and compared to the IgM repertoire of naïve B cells and the IgA repertoire of intestinal plasma cells. 1,000 sequences per sample were clustered with 95% CDR3 sequence identity and displayed as a network. Sample origin is indicated by color code. Asterisks mark clusters comprising gut plasma cells and IgA memory B cells. Morisita-Horn indices (MHI) were calculated comparing the IgA plasma cell repertoire to the PP B cell and memory B cell repertoires as indicated (n = 5). Data were pooled and represent three independent experiments.

Supplementary Figure 6 Memory B cells and plasma cells show frequent somatic mutations.

(a) PP B cells, PP memory B cells and lamina propria IgA plasma cells were isolated and sorted as described for Figure 7. IgA and IgM repertoires were analyzed, and the number of mutations was determined. Bars depict mutation frequencies for framework regions 1–3 (FR1–3) and complementarity-determining regions (CDRs) 1 and 2 (n = 3, mean + s.d.). (b) 4,000 sequences each from IgA plasma cells (IgA PCs) and IgA memory B cells were clustered with 95% CDR3 sequence identity. All CDR3 clusters present in both cell populations and represented by at least five sequences were identified, and the average number of mutations was determined for each cluster. Data for three representative animals out of six animals analyzed are displayed. Paired t-test was used for statistical analysis, **P < 0.01, ***P < 0.001. Data were pooled and represent three independent experiments.

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Lindner, C., Thomsen, I., Wahl, B. et al. Diversification of memory B cells drives the continuous adaptation of secretory antibodies to gut microbiota. Nat Immunol 16, 880–888 (2015). https://doi.org/10.1038/ni.3213

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