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 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 .