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Metagenomic immunoglobulin sequencing reveals IgA coating of microbial strains in the healthy human gut

Nature Microbiology volume 10, pages 112–125 (2025)Cite this article

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Abstract

IgA, the primary human antibody secreted from the gut mucosa, shapes the intestinal microbiota. Methodological limitations have hindered defining which microbial strains are targeted by IgA and the implications of binding. Here we develop a technique, metagenomic immunoglobulin sequencing (MIg-seq), that provides strain-level resolution of microbes coated by IgA and use it to determine IgA coating levels for 3,520 gut microbiome strains in healthy human faeces. We find that both health and disease-associated bacteria are targeted by IgA. Microbial genes are highly predictive of IgA binding levels; in particular, mucus degradation genes are correlated with high binding, and replication rates are significantly reduced for microbes bound by IgA. We demonstrate that IgA binding is more correlated with host immune status than traditional relative abundance measures of microbial community composition. This study introduces a powerful technique for assessing strain-level IgA binding in human stool, paving the way for deeper understanding of IgA-based host–microbe interactions.

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Fig. 1: Metagenomic immunoglobulin sequencing (MIg-seq) is a useful and robust technique.
Fig. 2: IgA binding levels are consistent across healthy humans.
Fig. 3: Taxon-specific microbial genes are associated with high IgA targeting.
Fig. 4: IgA binding impacts in situ microbial replication rates.
Fig. 5: IgA binding is integrated with other components of the immune system.

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Data availability

The data supporting the findings of this study are available within the paper and its Supplementary Information. Metagenomic reads are available on the NCBI Short Read Archive via BioProject accession PRJNA1049470. The protocol is available at https://doi.org/10.17504/protocols.io.5jyl8pk6dg2w/v1. Additional data, including genomes, genes and additional tables of processed data are available on Zenodo at https://doi.org/10.5281/zenodo.11154974 ref. 66. This manuscript uses the following public databases: Pfam v.32.0, KOfam v.103, CARD v.3.2.5 and CAzy dbCAN v.11.

Code availability

Custom code, including code used to develop machine learning models, is available at https://github.com/MrOlm/MIGSeq_code ref. 67.

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Acknowledgements

We thank L. H. Lam, H. Maeker, J. Fessler, M. Carter and K. C. Huang for helpful discussions during the preparation of this manuscript.

Support for the project was provided by National Institutes of Health grant F32DK128865 (M.R.O.), National Institutes of Health training grant T32 AI007328-30 (M.R.O.), the Colleen and Robert D. Hass fund (S.P.S.), and National Institutes of Health grants T32DK007056, K08DK134856 (S.P.S.), R01DK085025 (J.L.S.) and DP1AT009892 (J.L.S.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. J.L.S. is a Chan Zuckerberg Biohub Investigator.

This publication includes data generated at the UC San Diego IGM Genomics Center utilizing an Illumina NovaSeq 6000 that was purchased with funding from a National Institutes of Health SIG grant (#S10 OD026929). This publication includes data generated at the Stanford Shared FACS Facility (NIH S10 Shared Instrument Grant 1S10OD026831-01).

Author information

Author notes

  1. Matthew R. Olm

    Present address: University of Colorado, Boulder, CO, USA

  2. These authors contributed equally: Matthew R. Olm, Sean P. Spencer.

Authors and Affiliations

  1. Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA

    Matthew R. Olm, Sean P. Spencer, Tadashi Takeuchi, Evelyn Lemus Silva & Justin L. Sonnenburg

  2. Division of Gastroenterology and Hepatology, Stanford School of Medicine, Stanford, CA, USA

    Sean P. Spencer

  3. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Justin L. Sonnenburg

  4. Center for Human Microbiome Studies, Stanford University School of Medicine, Stanford, CA, USA

    Justin L. Sonnenburg

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Contributions

M.R.O., S.P.S. and J.L.S. designed the study. S.P.S. led the development of the MIg-seq protocol. M.R.O. performed bioinformatic analysis. M.R.O., S.P.S., T.T. and E.L.S. performed wet lab experiments. M.R.O. wrote the manuscript and all authors contributed to manuscript revisions.

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Correspondence to Justin L. Sonnenburg.

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Nature Microbiology thanks Gregory Donaldson, Oliver Pabst, Emma Slack and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Effectiveness of magnetic cell sorting for enriching IgA+ cells.

Aggregated (A) and sample-level (B) fraction of bacterial cells bound by IgA as assessed by bacterial flow cytometry. IgA+ (blue) is the result of magnetic cell sorting, unsorted (orange) is the native unsorted sample, and unstained (green) is the unsorted fraction without the Anti-Human IgA APC antibody added. Boxplots show the minimum and maximum (whiskers), median (center line), and interquartile range (box bounds); whiskers extend up to 1.5 times the interquartile range. n = 38 biological replicates.

Extended Data Fig. 2 Gating strategy for MIg-Seq protocol.

The preliminary gate to identify IgA-positive cells was SSC and SYBR Green, with SYBR-positive gate drawn based on preliminary experiments using a sample without SYBR green (SYBR stains all bacteria). SYBR-positive cells were then analyzed for IgA staining, with an IgA-positive gate drawn based on a sample without IgA staining. Representative flow plots are shown for a preliminary experiment of a single sample (#1037) that is unstained and without SYBR (A), staining and unsorted (B), and stained and sorted (C).

Extended Data Fig. 3 MIg-Seq assay antibody binding validation.

A) Schematic of assay design for multistep magnet cell sorting, highlighting the motivation of using irrelevant magnetic beads to assess for non-specific bacterial binding to the magnetic beads. B) Stool was stained with Anti-Human IgA APC antibody and then with either anti-APC + magnetic beads as an experimental condition or anti-PE + magnetic beads as an irrelevant control prior to magnetic enrichment and analysis of the positive fraction via flow cytometry. C) DNA concentration in the positive fractions was obtained using Qubitâ„¢ dsDNA Quantification Assay Kit.

Extended Data Fig. 4 Phylum-Level Relative Abundance of Native and IgA+ Metagenomes Across 30 Samples.

Phylum-level relative abundance of native metagenomes (left) and IgA+ fraction metagenomes (right) for the 30 samples used in this study.

Extended Data Fig. 5 Comparative Analysis of IgA-Bound Bacteria and Microbiome Shifts Post-Dietary Intervention.

A) Boxplots comparing the overall fraction of bacteria bound by IgA in native samples (as assessed by bacterial flow cytometry) in baseline samples versus after dietary intervention of increased consumption of fermented foods (left) or fibre (right). Lines connect samples from the same subjects over time. P-value from Wilcoxon signed-rank test. For fermented and fibre groups, n = 4, 7; p = 0.25, 0.38, respectively. B) Violin plot of weighted UniFrac distance between the same subjects in baseline vs. intervention samples (top) and weighted UniFrac distance between different subjects (bottom; this represents the UniFrac distances between samples taken from unrelated subjects at either timepoint). P-values from comparing the distribution of IgA+ vs native weighted UniFrac distances using two-sided Wilcoxon rank-sum test; n = the number of comparisons considered in each the IgA+ and native fractions. C) Principal component analysis of weighted UniFrac distance between all metagenomes in this study. Lines connect native and IgA+ metagenomes from the same stool sample. D) Violin plot comparing the weighted UniFrac distance between samples from the same subjects (top) and the absolute abundance of the difference in the total fraction of bacteria bound by IgA (bottom). Normalization of differences performed using min-max scaling prior to comparison. P-value from two-sided Wilcoxon signed-rank test. Boxplots show the minimum and maximum (whiskers), median (center line), and interquartile range (box bounds); whiskers extend up to 1.5 times the interquartile range.

Extended Data Fig. 6 Concatenated Gene Phylogeny of Microbial Strains with Relative IgA Binding Abundance.

A concatenated gene phylogenetic tree of all microbial strains detected in at least 5 samples. Stacked bar plots on the right display the relative abundance of IgA binding categories for each detection. The total bar length represents the total number of detections. Black stars mark taxa that appear to have substantially different IgA coating than their phylogenetic neighbors based on manual inspection.

Extended Data Fig. 7 Association of Microbial Functions with IgA+ Probability Scores and Statistical Significance.

For each microbial function (dot), the association of that function with IgA+ probability score (x-axis; median IgA+ probability score of microbes encoding function - median IgA+ probability score of microbes not encoding function) versus the p-value of the association of the function with IgA+ probability score (two-sided Wilcoxon rank-sum test with FDR correction). Horizontal dotted line at p = 0.05. Each phylum is treated independently.

Extended Data Fig. 8 Comparison of iRep in IgA+ vs. Native Fractions Across Phyla and Genera.

For each phylum (A) and genus (B) with at least 5 measurements, the difference in iRep in IgA+ vs. native fractions of all species detected within that phylum or genus. Negative values indicate IgA is associated with reduced replication rates. P-values from post hoc two-sided pairwise test for multiple comparisons of mean rank sums (Dunn’s test). For Firmucutes_A, Bacteroidota, Firmicutes, and Actinobacteria, n = 144, 32, 10, and 8, respectively. All exact p-values > 0.05 are shown in the figure. For genera, in order from top to bottom, n = 5, 6, 19, 8, 6, 5, 5, 6, 5, 7, and 6. Boxplots show the minimum and maximum (whiskers), median (center line), and interquartile range (box bounds); whiskers extend up to 1.5 times the interquartile range.

Extended Data Fig. 9 Significant Correlations Between IgA-Associated Microbial Genes and Human Health/Microbiome Function Metrics.

A correlation analysis between the 986 microbial genes significantly associated with IgA binding and 9 measurements of human health and microbiome function. The x-axis indicated the number of significant (p < 0.05) correlations identified after false discovery rate correction.

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2.

Reporting Summary

Supplementary Table 1

Information on the DNA extraction concentration, sequencing depth, IgA sorting information and metadata for all 38 samples.

Supplementary Table 2

Microbial relative abundance and IgA+ probabilities for all samples.

Supplementary Table 3

Statistics for IgA binding enrichment among phyla and genus-level taxonomic groups.

Supplementary Table 4

Statistics for association of microbial annotations with IgA binding.

Supplementary Table 5

Paired iRep values for all species detected in both IgA+ and native samples.

Supplementary Table 6

Statistical associations between each phylum and each immune metric.

Supplementary Table 7

Statistical associations between each gene that is significantly positively associated with IgA+ probability score and each immune metric.

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Olm, M.R., Spencer, S.P., Takeuchi, T. et al. Metagenomic immunoglobulin sequencing reveals IgA coating of microbial strains in the healthy human gut. Nat Microbiol 10, 112–125 (2025). https://doi.org/10.1038/s41564-024-01887-4

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