Active and Secretory IgA-Coated Bacterial Fractions Elucidate Dysbiosis in Clostridium difficile Infection

Abstract

The onset of Clostridium difficile infection (CDI) has been associated with treatment with wide-spectrum antibiotics. Antibiotic treatment alters the activity of gut commensals and may result in modified patterns of immune responses to pathogens. To study these mechanisms during CDI, we separated bacteria with high cellular RNA content (the active bacteria) and their inactive counterparts by fluorescence-activated cell sorting (FACS) of the fecal bacterial suspension. The gut dysbiosis due to the antibiotic treatment may result in modification of immune recognition of intestinal bacteria. The immune recognition patterns were assessed by FACS of bacterial fractions either coated or not with intestinal secretory immunoglobulin A (SIgA). We described the taxonomic distributions of these four bacterial fractions (active versus inactive and SIgA coated versus non-SIgA coated) by massive 16S rRNA gene amplicon sequencing and quantified the proportion of C. difficile toxin genes in the samples. The overall gut microbiome composition was more robustly influenced by antibiotics than by the C. difficile toxins. Bayesian networks revealed that the C. difficile cluster was preferentially SIgA coated during CDI. In contrast, in the CDI-negative group Fusobacterium was the characteristic genus of the SIgA-opsonized fraction. Lactobacillales and Clostridium cluster IV were mostly inactive in CDI-positive patients. In conclusion, although the proportion of C. difficile in the gut is very low, it is able to initiate infection during the gut dysbiosis caused by environmental stress (antibiotic treatment) as a consequence of decreased activity of the protective bacteria. IMPORTANCE C. difficile is a major enteric pathogen with worldwide distribution. Its expansion is associated with broad-spectrum antibiotics which disturb the normal gut microbiome. In this study, the DNA sequencing of highly active bacteria and bacteria opsonized by intestinal secretory immunoglobulin A (SIgA) separated from the whole bacterial community by FACS elucidated how the gut dysbiosis promotes C. difficile infection (CDI). Bacterial groups with inhibitory effects on C. difficile growth, such as Lactobacillales, were mostly inactive in the CDI patients. C. difficile was typical for the bacterial fraction opsonized by SIgA in patients with CDI, while Fusobacterium was characteristic for the SIgA-opsonized fraction of the controls. The study demonstrates that sequencing of specific bacterial fractions provides additional information about dysbiotic processes in the gut. The detected patterns have been confirmed with the whole patient cohort independently of the taxonomic differences detected in the nonfractionated microbiomes.

Keywords: 16S rRNA gene sequencing; Bayesian networks; Clostridium difficile infection; antibiotics; dysbiosis; fluorescence-activated cell sorting; human gut microbiome; secretory immunoglobulin A.

FIG 1

Cell sorting scheme and its impact on bacterial diversity of the samples. (A) FACS biplots showing setup of sorting gates in comparison with negative controls. Each sample was used in two separated sorting rounds: (i) active-bacterium sorting and (ii) IgA-coated-bacterium sorting. For each fraction, 540,000 cells were separated and 16S rRNA genes were amplified and sequenced. FSC, forward scatter. (B) Canonical correspondence analysis of ordination of fractionated fecal samples by fitting their overall bacterial composition into variables of active-F, inactive-F, IgA-pos-F, and IgA-neg-F. The fractionated samples are numbered 1 to 24 and are colored in accordance with the fraction colors. Analysis showed that the fractions have a significant influence on the bacterial composition, meaning that the differences among samples belonging to different fractions may be detected in the subsequent analysis.

FIG 2

Impact of medical data on bacterial diversity of fractionated samples. A canonical correspondence analysis was undertaken, whereby the bacterial composition was tested to assess the fit with medical data. The analysis was performed separately for each fraction (active-F, inactive-F, IgA-pos-F, and IgA-neg-F). The numerical factors taken into account were the total dose of antibiotic treatment and the loads of toxin A and toxin B genes quantified by qPCR. The categorical medical data taken into account were the diagnosis of CDI and the type of antibiotic treatment ([i] antibiotic against CDI, [ii] antibiotics promoting CDI, and [iii] antibiotics with neutral effect on CDI onset—not associated with CDI). The bacterial species (black) and numerical variables (green boldface) with a significant influence on the ordination of samples are shown (P < 0.01). The categorical medical data (olive green boldface) are shown with asterisks corresponding to the P value (**, P < 0.01; ***, P < 0.001). The diagnosis of CDI as a categorical factor did not have a significant influence on the ordination of samples (P > 0.05); its ordination effect is shown in light gray for illustration purposes only. Samples are marked by numbers in red or blue corresponding to CDI-positive or CDI-negative patients, respectively. ud., undetermined taxon.

FIG 3

Fold change differences for each bacterial genus in the two fraction pairs. (A) Heat map showing comparison of proportion of each genus in the active-F fraction with its proportion in the inactive-F fraction, as well as its proportion in the IgA-pos-F fraction compared with its proportion in the IgA-neg-F fraction. Genera significantly increased (P < 0.01) in active-F or IgA-pos-F fractions are marked in red, while the genera significantly increased in inactive-F or IgA-neg-F fractions are marked in blue. The intensity of red or blue depends on the fold increase. The white fields show that there was no significant increase in any of the compared fractions. The gray fields show that a genus was not detected in the sample at all. The samples and the bacterial genera are ordered alphabetically. ud., undetermined taxon. (B) The results shown in panel A have been tested for their cooccurrence with medical data in a Bayesian network. Summary of the strongest associations obtained by network modeling. The Venn diagrams show which bacterial genera were found to have significantly increased proportions in one of the four separated fractions compared with their fraction counterparts. CDI-positive and CDI-negative groups had different patterns of bacterial activity and SIgA coating.