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Bacterial induction of B cell senescence promotes age-related changes in the gut microbiota

Nature Cell Biology volume 25, pages 865–876 (2023)Cite this article

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Abstract

The elucidation of the mechanisms of ageing and the identification of methods to control it have long been anticipated. Recently, two factors associated with ageing—the accumulation of senescent cells and the change in the composition of gut microbiota—have been shown to play key roles in ageing. However, little is known about how these phenomena occur and are related during ageing. Here we show that the persistent presence of commensal bacteria gradually induces cellular senescence in gut germinal centre B cells. Importantly, this reduces both the production and diversity of immunoglobulin A (IgA) antibodies that target gut bacteria, thereby changing the composition of gut microbiota in aged mice. These results have revealed the existence of IgA-mediated crosstalk between the gut microbiota and cellular senescence and thus extend our understanding of the mechanism of gut microbiota changes with age, opening up possibilities for their control.

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Fig. 1: Bacterial induction of GC B cell senescence in ILFs.
Fig. 2: Bacteria-dependent increase in the number and size of ILFs with age.
Fig. 3: Bacterial induction of GC B cell senescence in Peyer’s patches.
Fig. 4: Age-related changes in gut microbiota and IgA.
Fig. 5: Age-related changes in IgA quantity and diversity.
Fig. 6: Age-related changes in B cells cause dysregulation of gut microbiota.
Fig. 7: LPS promotes B cell overgrowth and p16INK4a expression.

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Data availability

The SILVA 16S rRNA sequence database (version 138) (https://www.arb-silva.de/) was used for the 16S rRNA gene sequence analysis. scRNA-seq data, IgA repertoire analysis data (the IgA coding region sequence) and microbiome analysis data (the bacterial 16S rRNA gene sequence) generated in this study have been deposited in the DNA Data Bank of Japan (https://www.ddbj.nig.ac.jp) with the accession codes DRA015340, DRA015341 and DRA015346, respectively. In addition, processed data for scRNA-seq have been deposited in the DNA Data Bank of Japan with the accession code E-GEAD-583. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The code used for data analysis is publicly available from GitHub (https://github.com/KawamotoShimpei?tab=repositories).

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Acknowledgements

We thank D. Okuzaki and D. Motooka (Osaka University) for performing scRNA-seq analysis. We are grateful to members of the E.H. laboratory for helpful discussion during the preparation of this manuscript. This work was supported in part by grants from the Japan Agency for Medical Research and Development under grant numbers JP21gm5010001h0005, JP22gm1710004h0001, JP22zf0127008h0001 and JP22ama221114h0001 (to E.H.), JP22ama121025 (to K.K. and D.M.S.) and JP22gm1010009h0005 (to N.O.), the Japan Science and Technology Agency under grant numbers JPMJMS2022 (to E.H.) and JPMJER1902 (to S.F.), the Japan Society for the Promotion of Science under grant numbers JP22H00457 (to E.H.), JP20K07446 (to S.K.), JP22H03541 (to S.F.) and JP22H03540 (to N.O.), the Naito Foundation (to S.K.), the Food Science Institute Foundation (to S.F.) and the Mitsubishi Foundation (to E.H.). Some of the aged mice were provided by the Foundation for Biomedical Research and Innovation at Kobe through the National BioResource Project of the Ministry of Education, Culture, Sports, Science and Technology in Japan (to S.K.).

Author information

Author notes

  1. These authors contributed equally: Shimpei Kawamoto, Ken Uemura.

Authors and Affiliations

  1. Department of Molecular Microbiology, Research Institute for Microbial Diseases, Osaka University, Suita, Japan

    Shimpei Kawamoto, Ken Uemura, Nozomi Hori, Yusuke Konishi, Tomonori Matsumoto, Masae Suzuki, Tatsuyuki Matsudaira & Eiji Hara

  2. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Lena Takayasu & Wataru Suda

  3. Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Lena Takayasu

  4. Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan

    Kazutaka Katoh & Daron M. Standley

  5. National Institute of Infectious Diseases, Tokyo, Japan

    Yusuke Sakai

  6. Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan

    Takahiro Adachi

  7. Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan

    Naoko Ohtani

  8. Immunology Frontier Research Center, Osaka University, Suita, Japan

    Daron M. Standley & Eiji Hara

  9. Center for Infectious Disease Education and Research, Osaka University, Suita, Japan

    Daron M. Standley & Eiji Hara

  10. Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan

    Shinji Fukuda

  11. Kanagawa Institute of Industrial Science and Technology, Kawasaki, Japan

    Shinji Fukuda

  12. Transborder Medical Research Center, University of Tsukuba, Tsukuba, Japan

    Shinji Fukuda

  13. Laboratory for Regenerative Microbiology, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Shinji Fukuda

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  1. Shimpei Kawamoto
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Contributions

S.K. and E.H. designed the experiments, analysed the data and wrote the manuscript. S.K. and K.U. performed most of the experiments. N.H., Y.S. and T. Matsudaira helped with the immunostaining analysis. M.S. helped to perform the animal experiments. L.T., Y.K., K.K., T. Matsumoto and W.S. helped with the bioinformatics analysis. N.O. analysed the survival curves of p16/p21 DKO mice. T.A., D.M.S. and S.F. analysed the data. E.H. oversaw the projects.

Corresponding authors

Correspondence to Shimpei Kawamoto or Eiji Hara.

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Extended data

Extended Data Fig. 1 Gut microbiota-dependent accumulation of senescent cells in the ileum of aged female mice.

a-c, Representative images of non-invasive (upper) or ex vivo (lower) BLI of SPF or GF p16-luc mice (female) at 6 or 20 months of age are shown. The colour bars indicate the radiance with minimum and maximum threshold values (a). The bioluminescence intensity emitted from the central abdomen (b) or ileum (c). The areas used for the bioluminescence measurement are shown as the dotted squares (a). The sample size (n) represents the number of biologically independent animals (b, c, n = 5). Data are presented as mean values ± s.e.m. Statistical significance was determined with two-way ANOVA followed by Šídák’s multiple comparisons test (b, c). All experiments were repeated at least twice, independently, with similar results. NS, not significant. Numerical data is available in source data.

Source data

Extended Data Fig. 2 Expression of senescence-associated genes in ileal GC B cells of aged mice.

a, UMAP plot gathering all cells from ileal lamina propria from 6 or 20 M SPF or GF p16-luc mice, in which all cells are clustered and color-coded by cell types (upper panel). B cells and activated B cells were further subdivided into three clusters (lower panel). b, UMAP plots showing subdivided B cells collected from 6 M or 20 M SPF or GF p16-luc mice, with Cdkn2a-, Cdkn1a-, Aicda-, Bcl6-, Fas- or Tnfrsf13b-expressing cells represented by red dots. c, Heat map showing differential expression levels of genes classified as SASP factors in these three B-cell clusters classified in the bottom row of panel a. Blue or red intensity indicates a negative or positive z-score, respectively. Note that a series of SASP factors are highly expressed in activated B cells where cells with high expression of Cdkn2a are present.

Extended Data Fig. 3 Establishment of an immunohistochemical staining method for p16INK4a in ageing mice.

a, Immunohistochemical images of isolated lymphoid follicles (ILFs) from the ileum of 15 M WT or p16/p21-DKO mice stained with antibodies against B220 (green) or p16INK4a (red). b, Immunohistochemical images of lungs, mesenteric lymph nodes, spleen, liver, and colon of 3 M or 15 M WT and 15 M p16/21-DKO mice, stained with antibody against p16INK4a (red). Scale bars, 20 μm. All experiments were repeated three times, independently, with similar results.

Extended Data Fig. 4 Accumulation of p16INK4a expressing cells in other tissues with ageing.

a, The bioluminescence imaging with a lowered threshold for the data in Fig. 1a shows an increase in signal with ageing in mesenteric lymph nodes, spleen, liver and colon. The colour bars indicate the radiance with minimum and maximum threshold values. b and c, The bioluminescence intensity emitted from the lungs, mesenteric lymph nodes, spleen, liver, and colon of 6 M or 20 M SPF or GF mice (b) or the expression level of p16INK4a in those tissues (c). The sample size (n) represents the number of biologically independent animals (b, c, n = 5). Data are presented as mean values ± s.e.m. Statistical significance was determined with two-way ANOVA followed by Šídák’s multiple comparisons test (b, c). All experiments were repeated at least twice, independently, with similar results. NS, not significant. Numerical data is available in source data.

Source data

Extended Data Fig. 5 Gating strategy of B cells in flow cytometry.

Total lymphocytes isolated from Peyer’s patches or lamina propria of small intestine were gated on a forward scatter (FSC)/side scatter (SSC) plot and then gated on the Zombie-NIR– population to remove dead cells. These cells were further gated for the B cell subsets of interest, namely IgA plasma cells (B220– IgA+), germinal center (GC) B cells (B220+ PNA+ FAS+), or non-GC B cells (B220+ PNA– FAS–). Data were analyzed using FlowJo software, and percentages in the figure represent the frequency in the parent population.

Extended Data Fig. 6 Changes in gut bacterial composition in mice during ageing.

a, An attempt was made to build a model predicting Faith’s phylogenetic diversity (PD) based on various parameters by means of an LME model. Scatter plot of Faith’s PD diversity in each age with linear regression trend lines. Male and female-derived samples are marked in different colours. Of the two parameters ‘Sex’ and ‘Age’, only ‘Age’ was evaluated as a valid parameter (P (>|z|) =0.016*), and finally the equation of LME model was constructed with only ‘Age’ as a valid parameter (P (>|z|) =0.003**). b, Principal Coordinate Analysis (PCoA) plots of the Bray-Curtis distance show changes in gut bacterial composition during ageing process (3, 6, 12, 18, and 24 M). PCoA plots for each age group are shown on the right. The statistical significance judged by PERMANOVA are shown at the top. c, Bar plots showing the phylogenetic composition of the gut microbiota of mice of different ages at genus level. The bacterial genera with statistically significant difference between 3 M and 24 M are marked with an asterisk. Blue (decrease) and red (increase) at 24 M. d, Heat map showing age-related changes of bacterial genera. The degree of blue- or yellow-colour intensity indicates a negative or positive z-score, respectively. The bacterial genera with statistically significant difference between 3 M and 24 M are marked with an asterisk. Blue (decrease) and red (increase) at 24 M. e and f, An age prediction model based on changes in gut microbiota was built using machine learning-based analysis. Scheme of machine learning-based analysis (e). Model building by machine learning and evaluation of model accuracy is repeated 1,000 times (e) and all evaluation results are shown (f). Statistical significance was determined with Wilcoxon matched-paired singed rank test (c, d). * p < 0.05, ** p < 0.01, *** p < 0.005, **** p < 0.001. Numerical data and exact P values are available in source data.

Source data

Extended Data Fig. 7 Identification of bacteria with statistically significant changes throughout ageing using cross-nested differential abundance analysis.

Microbiome Multivariable Association with Linear Models (MaAsLin) was used to identify the OTUs that changed statistically significantly with age, considering each random variable (individual, sex, rearing cage, and parent), and 84 OTUs were finally identified as statistically significant OTUs. Heat map showing age-related changes in the 84 OTUs identified as statistically significant bacteria using MaAsLin. The degree of blue- or yellow-colour intensity indicates a negative or positive z-score, respectively. The OTU number and name of the bacterial taxonomy when the corresponding sequence is classified by SILVA are appended to the right-hand side. The OTUs and taxonomies that overlap with the top 50 most important bacteria for building age prediction models for mice using machine learning-based analysis of gut microbiota composition (random forest regression) in Fig. 4d are highlighted by red colour. Note that 35 OTUs (70%) out of the 50 OTUs identified by machine learning overlapped with those identified by MaAsLin.

Extended Data Fig. 8 Survival curves of p16-KO, p21-KO and p16/21-DKO mice.

Survival curves were measured for WT mice (n = 10), p16/p21-DKO mice (n = 19), p16-KO mice (n = 24) and p21-KO mice (n = 13) under SPF environment. Note that p16/p21-DKO, p16-KO and p21-KO mice start to die around 12–15 months of age. In particular, p16/p21-DKO mice showed a severe phenotype, mainly cancer, and all individuals died by 17 months of age.

Extended Data Fig. 9 Graphical model of IgA−mediated crosstalk between the gut microbiota and B cell senescence.

In young mice, GC B cells induced by the gut microbiota are appropriately selected in the PPs and differentiate into IgA plasma cells, secrete sufficient amounts of gut bacteria-specific IgA into the gut lumen to regulate the gut microbiota and maintain symbiosis (left). However, during the ageing process, cellular senescence is induced in the GC B cells of PPs and ILFs by continuous stimulation of the gut microbiota, leading to a decrease in the quantity and quality of IgA produced. This provokes changes in the gut microbiota known as dysbiosis (right).

Supplementary information

Reporting Summary

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Supplementary Tables

Supplementary Table 1. Mouse breeding conditions. Supplementary Table 2. Mouse information used for Fig. 4d. Supplementary Table 3. Primers used in this study.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

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Unprocessed western blot.

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Unprocessed western blot.

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Kawamoto, S., Uemura, K., Hori, N. et al. Bacterial induction of B cell senescence promotes age-related changes in the gut microbiota. Nat Cell Biol 25, 865–876 (2023). https://doi.org/10.1038/s41556-023-01145-5

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