Short communicationAre the decrease in circulating anti-α1,3-Gal IgG and the lower content of galactosyl transferase A1 in the microbiota of patients with multiple sclerosis a novel environmental risk factor for the disease?
Introduction
The amount and taxonomic composition of the intestinal microbiota affect the host immune response in experimental models (Berer and Krishnamoorthy, 2014). Gut microbiota patterns from patients suffering from multiple sclerosis (MS) differ from those of healthy individuals. MS patients have reduced relative abundances of Bacteroidaceae, Bacteroides, Parabacteroides, Faecalibacterium, Prevotella, Anaerostipes, Collinsella and Slackia (Miyake et al., 2015, Cantarel et al., 2015, Chen et al., 2016a, Jangi et al., 2016), which are believed to affect host immunity (Kamada et al., 2013).
Recently, we reported that MS patients exhibit a decrease in anti-α1,3-Gal IgG blood levels compared with those of age/gender-matched healthy individuals (Le Berre et al., 2017). The α1,3-Gal epitope is lacking in human glycans following the loss-mutation of the glycosyltransferase A1 (GGTA1) gene that controls its synthesis (Padler-Karavani et al., 2008). Anti-α1,3-Gal antibodies, which appear during the first year of life, supposedly due to the immunization against gut microbiota (Cooper et al., 1994), represent a substantial fraction of the total IgG and IgM pool in humans (up to 1 percent of B cells display a B cell receptor committed to α1,3-Gal) (Galili, 2013). The levels of anti-α1,3-Gal antibodies may affect the immune response against GGTA1-positive infectious agents by several mechanisms (Galili, 2016, Soares, 2015) and may also be involved in autoimmune processes (Galili, 2013).
In this Viewpoint, we suggest that the abnormal level of anti-α1,3-Gal antibodies could be related to abnormal amounts of GGTA1 gene-positive microorganisms in the MS patient microbiota, providing a new link to novel alterations in environmental factors in the disease (working hypothesis in Fig. 1A).
Section snippets
Methods
To test our hypothesis, we obtained the publicly accessible raw 16S ribosomal (r) RNA data from 2 recently published studies with publicly available data sets. 1) Chen et al. investigated the gut microbiota in relapsing remitting MS (RRMS) (n = 31) patients and in age- and gender-matched healthy controls (n = 36). Taxonomic profiles were generated by sequencing the V3–V5 region of the 16S rRNA on the MiSeq platform (Illumina) (Chen et al., 2016a). 2) Jangi et al. investigated the gut microbiota in
Results
We first analyzed the 16 S rRNA raw data from Chen et al., who investigated intestinal microbiota alterations in MS patients compared to age/gender-matched healthy controls (Chen et al., 2016a). Using PICRUSt, we produced a metagenome prediction table with enzyme entry annotations based on the closed-reference OTU table. There were no outliers in the two groups of subjects in terms of the EC 2.4.1.87 level. The relative abundance of EC 2.4.1.87 was very significantly decreased in MS patients
Discussion
The etiology of MS is still mysterious despite evidence of two major avenues: genetic susceptibility (Parnell and Booth, 2017) and environmental factors such as EBV acute primoinfection (Soulillou, 2013, Ascherio et al., 2001) or geographically linked variables (Kurtzke and Delasnerie-Laupretre, 1996). Studies in twins have emphasized the importance of environment in disease.
In this Viewpoint, we suggest for the first time a defect in the relative abundance of the GGTA1 gene, which controls the
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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2022, Clinical ImmunologyCitation Excerpt :As microbiota is modified in MS patients, we hypothesized that the decrease of anti-Gal antibodies in MS could be linked to changes in bacterium abundances. To test this hypothesis, we performed bioinformatic analysis on publicly available 16S rRNA data using PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) in order to compare α1,3GT gene prediction levels in MS and in healthy individuals [89]. Briefly, PICRUSt uses 16S rRNA data to predict microbial community functional profiles (e.g. predicted metagenome).
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