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.

IgA deficiency destabilizes immunological homeostasis towards intestinal microbiota and increases the risk of systemic immune dysregulation

“…In comparison, previous studies used the ratio of the relative abundance of IgA+ and IgAbacteria (Palm et al, 2014) or an IgA binding index (Kau et al, 2015) to define which microbes are targeted by IgA, each of which has drawbacks. The Bayesian approach provides several important advantages compared to other methods including avoiding artifactual associations based on different relative abundances of ASVs, increased power to detect differences in antibody binding to specific ASVs between experimental groups and lower coefficients of variation (Jackson et al, 2021). This study reveals clear connections at the level of common microbial targets across the mucosal (IgA and IgM) and systemic (IgG) antibody networks.…”

“…In this study, we developed an integrated approach (mFLOW-Seq) to simultaneously define the specific microbial targets of local secretory IgA and IgM and systemic IgG in children with IgA deficiency and their unaffected siblings, in addition to the host immunophenotype, investigating the impact of IgA deficiency on both cytokine milieu and lymphocyte subset presence and activation state. In our analysis of antibody targets, we adapted a Bayesian analytic approach (Jackson et al, 2021) to determine the probability of binding by each antibody isotype (i.e., IgA, IgM and IgG) to provide an intuitive and specific measure of antibody targeting across multiple isotypes. In comparison, previous studies used the ratio of the relative abundance of IgA+ and IgAbacteria (Palm et al, 2014) or an IgA binding index (Kau et al, 2015) to define which microbes are targeted by IgA, each of which has drawbacks.…”

“…To explore the different models ("discrete" vs. "continuous") of antibody binding to commensals in the gut, we employed Bayesian analysis to define the absolute probability on a scale of 0-100% that a microbe from a given ASV is bound by sIgA, sIgM or both (Jackson et al, 2021). The Bayesian approach provides several important advantages compared to other methods including avoiding artifactual associations based on different relative abundances of ASVs, increased power to detect differences in antibody binding to specific ASVs between experimental groups and lower coefficients of variation (Jackson et al, 2021). Linear mixed effect (LME) modeling was then applied to binding probabilities to define which ASVs are preferentially coated by sIgM or sIgA.…”

“…Difference in centroids between groups were calculated using PERMANOVA test. The binding probability ratios of each ASV was calculated for the IgA, IgM and IgG populations using the method described before (Jackson et al, 2021) with the following modifications. Differences between binding probability ratios were estimated using linear mixed effects models.…”

Hierarchical modelling of immunoglobulin coated bacteria in dogs with chronic enteropathy shows reduction in coating with disease remission but marked inter-individual and treatment-response variability

“…A major challenge to accurately characterise low biomass communities is contamination from external sources. A recent study using flow cytometry has also reported the enrichment of low abundance contaminant reads in their sorted samples [ 68 ]. Contamination could potentially occur during sorting and/or library preparation [ 69 ].…”