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Accurate identification and quantification of commensal microbiota bound by host immunoglobulins

View ORCID ProfileMatthew A. Jackson, Claire Pearson, Nicholas E. Ilott, Kelsey E. Huus, Ahmed N. Hegazy, Jonathan Webber, B. Brett Finlay, Andrew J. Macpherson, Fiona Powrie, Lilian H. Lam
doi: https://doi.org/10.1101/2020.08.19.257501
Matthew A. Jackson
1Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
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  • For correspondence: matthew.jackson@kennedy.ox.ac.uk
Claire Pearson
1Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
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Nicholas E. Ilott
1Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
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Kelsey E. Huus
2Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
3Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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Ahmed N. Hegazy
1Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
4Department of Gastroenterology, Infectiology, and Rheumatology, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany, Deutsches Rheumaforschungszentrum Berlin (DRFZ), an Institute of the Leibniz Association and Berlin Institute of Health (BIH), Berlin, Germany
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Jonathan Webber
1Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
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B. Brett Finlay
2Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
3Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
5Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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Andrew J. Macpherson
6Maurice Müller Laboratories, Department of Biomedical Research, University of Bern, 3008 Bern, Switzerland
7University Clinic of Visceral Surgery and Medicine, Inselspital, 3010 Bern, Switzerland
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Fiona Powrie
1Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
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Lilian H. Lam
1Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
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Abstract

Background Identifying which taxa are targeted by immunoglobulins can uncover important host-microbe interactions. Immunoglobulin binding of commensal taxa can be assayed by sorting bound bacteria from samples and using amplicon sequencing to determine their taxonomy, a technique most widely applied to study Immunoglobulin A (IgA-Seq). Previous experiments have scored taxon binding in IgA-Seq datasets by comparing abundances in the IgA bound and unbound sorted fractions. However, as these are relative abundances, such scores are influenced by the levels of the other taxa present and represent an abstract combination of these effects. Diversity in the practical approaches of prior studies also warrants benchmarking of the individual stages involved. Here, we provide a detailed description of the design strategy for an optimised IgA-Seq protocol. Combined with a novel scoring method for IgA-Seq datasets that accounts for the aforementioned effects, this platform enables accurate identification and quantification of commensal gut microbiota targeted by host immunoglobulins.

Results Using germ-free and Rag1−/− mice as negative controls, and a strain-specific IgA antibody as a positive control, we determine optimal reagents and fluorescence activated cell sorting (FACS) parameters for IgA-Seq. Using simulated IgA-Seq data, we show that existing IgA-Seq scoring methods are influenced by pre-sort relative abundances. This has consequences for the interpretation of case-control studies where there are inherent differences in microbiota composition between groups. We show that these effects can be addressed using a novel scoring approach based on posterior probabilities. Finally, we demonstrate the utility of both the IgA-Seq protocol and probability-based scores by examining both novel and published data from in vivo disease models.

Conclusions We provide a detailed IgA-Seq protocol to accurately isolate IgA-bound taxa from intestinal samples. Using simulated and experimental data, we demonstrate novel probability-based scores that adjust for the compositional nature of relative abundance data to accurately quantify taxon-level IgA binding. All scoring approaches are made available in the IgAScores R package. These methods should improve the generation and interpretation of IgA-Seq datasets and could be applied to study other immunoglobulins and sample types.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://www.ebi.ac.uk/ena/browser/view/PRJEB39814

  • https://github.com/microbialman/IgAScores

  • https://github.com/microbialman/IgAScoresAnalyses

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 20, 2020.
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Accurate identification and quantification of commensal microbiota bound by host immunoglobulins
Matthew A. Jackson, Claire Pearson, Nicholas E. Ilott, Kelsey E. Huus, Ahmed N. Hegazy, Jonathan Webber, B. Brett Finlay, Andrew J. Macpherson, Fiona Powrie, Lilian H. Lam
bioRxiv 2020.08.19.257501; doi: https://doi.org/10.1101/2020.08.19.257501
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Accurate identification and quantification of commensal microbiota bound by host immunoglobulins
Matthew A. Jackson, Claire Pearson, Nicholas E. Ilott, Kelsey E. Huus, Ahmed N. Hegazy, Jonathan Webber, B. Brett Finlay, Andrew J. Macpherson, Fiona Powrie, Lilian H. Lam
bioRxiv 2020.08.19.257501; doi: https://doi.org/10.1101/2020.08.19.257501

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