RT Journal Article
SR Electronic
T1 Accurate identification and quantification of commensal microbiota bound by host immunoglobulins
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.08.19.257501
DO 10.1101/2020.08.19.257501
A1 Matthew A. Jackson
A1 Claire Pearson
A1 Nicholas E. Ilott
A1 Kelsey E. Huus
A1 Ahmed N. Hegazy
A1 Jonathan Webber
A1 B. Brett Finlay
A1 Andrew J. Macpherson
A1 Fiona Powrie
A1 Lilian H. Lam
YR 2020
UL http://biorxiv.org/content/early/2020/08/20/2020.08.19.257501.abstract
AB 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 StatementThe authors have declared no competing interest.