Accurate identification and quantification of commensal microbiota bound by host immunoglobulins

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.

The asthma gut microbiota influences lung inflammation in gnotobiotic mice

“…Input, IgA- and IgA+ positive fractions were acquired and sequenced as previously described (83). To analyze these data we used either the IgA index (77) or a Bayesian method to predict the probability of a bacteria being found in either the IgA+ or IgA- fractions (84).…”

Targeting of the human nasal microbiota by secretory IgA antibodies

“…The alpha diversity of the sIgA-positive and sIgA-negative fractions was significantly reduced compared to the non-sorted samples ( Figure 3A ). IgA probability ratios (‘IgA scores’ from here on) were calculated for all genus-level and species-level taxa (13) ( Supplementary file ). These scores are a measure for sIgA targeting of specific taxa within an individual, and range from −1 (all bacteria of a given taxon are in the sIgA-negative fraction) to +1 (all bacteria of a given taxon are in the sIgA-positive fraction).…”

“…Then, using a sequence depth of 1100 reads (for analysis of the non-sorted samples only) or 800 reads (for all other analyses), matrices were computed using the Phyloseq (v1.38) (43) and Microbial (v0.0.20) (44) packages in R. Beta diversity metrics were demonstrated using weighted Unifrac distance and Principal Coordinate Analysis (PCoA) in QIIME2. IgA probability ratios were calculated using the IgAScores package in R (13). CST classification was performed using a custom Python script (deposited on GitHub: https://github.com/AhmedElsherbini/CST_picker), according to the original CST definitions (4).…”

“…These important insights into the role of sIgA in the control of the microbiota were gained particularly through studies on sIgA in the gastrointestinal tract. Especially the development of the IgA-seq technology, in which microbiota are sorted based on being coated by sIgA or not and subsequently analyzed by metagenomic sequencing (9,12,13), has significantly contributed to our understanding of the role of sIgA in the homeostasis of the microbiome and defense against pathogens in the gastrointestinal tract (9,10,12,14). However, we currently still lack a comprehensive understanding of how sIgA affects microbiota in other mucosal niches, in particular in the human nose.…”

Junctional adhesion molecule-A deletion increases phagocytosis and improves survival in a murine model of sepsis

“…A similar increase in mucosal IgA was identified using flow cytometry in intraepithelial lymphocytes and when examining ELISA of gut homogenate tissue. IgA has been shown to play an integral role in maintaining homeostasis in the gut microbiota, with low affinity IgA contributing to microbiota maintenance and high-affinity IgA acting on pathogens to promote clearance and inhibit virulence (56,57). There is an important relationship between JAM-A and IgA in terms of susceptibility to colitis.…”