Predicting the immunomodulatory activity of probiotic lactic acid bacteria using supervised Machine Learning in a Cornu aspersum snail model.

In the process of screening for probiotic strains, there are no clearly established bacterial phenotypic markers which could be used for the prediction of their in vivo mechanism of action. In this work, we demonstrate for the first time that Machine Learning (ML) methods can be used for accurately predicting the in vivo immunomodulatory activity of probiotic strains based on their cell surface phenotypic features using a snail host-microbe interaction model. A broad range of snail gut presumptive probiotics, including 240 new lactic acid bacterial strains (Lactobacillus, Leuconostoc, Lactococcus, and Enterococcus), were isolated and characterized based on their capacity to withstand snails' gastrointestinal defense barriers, such as the pedal mucus, gastric mucus, gastric juices, and acidic pH, in association with their cell surface hydrophobicity, autoaggregation, and biofilm formation ability. The implemented ML pipeline predicted with high accuracy (88%) strains with a strong capacity to enhance chemotaxis and phagocytic activity of snails' hemolymph cells, while also revealed bacterial autoaggregation and cell surface hydrophobicity as the most important parameters that significantly affect host immune responses. The results show that ML approaches may be useful to derive a predictive understanding of host-probiotic interactions, while also highlighted the use of snails as an efficient animal model for screening presumptive probiotic strains in the light of their interaction with cellular innate immune responses.

The gut microbiota posttranslationally modifies IgA1 in autoimmune glomerulonephritis.

Mechanisms underlying the disruption of self-tolerance in acquired autoimmunity remain unclear. Immunoglobulin A (IgA) nephropathy is an acquired autoimmune disease where deglycosylated IgA1 (IgA subclass 1) auto-antigens are recognized by IgG auto-antibodies, forming immune complexes that are deposited in the kidneys, leading to glomerulonephritis. In the intestinal microbiota of patients with IgA nephropathy, there was increased relative abundance of mucin-degrading bacteria, including Akkermansia muciniphila. IgA1 was deglycosylated by A. muciniphila both in vitro and in the intestinal lumen of mice. This generated neo-epitopes that were recognized by autoreactive IgG from the sera of patients with IgA nephropathy. Mice expressing human IgA1 and the human Fc α receptor I (α1KI-CD89tg) that underwent intestinal colonization by A. muciniphila developed an aggravated IgA nephropathy phenotype. After deglycosylation of IgA1 by A. muciniphila in the mouse gut lumen, IgA1 crossed the intestinal epithelium into the circulation by retrotranscytosis and became deposited in the glomeruli of mouse kidneys. Human α-defensins-a risk locus for IgA nephropathy-inhibited growth of A. muciniphila in vitro. A negative correlation observed between stool concentration of α-defensin 6 and quantity of A. muciniphila in the guts of control participants was lost in patients with IgA nephropathy. This study demonstrates that gut microbiota dysbiosis contributes to generation of auto-antigens in patients with IgA nephropathy and in a mouse model of this disease.