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.

Listeria monocytogenes isolates from Cornu aspersum snails: Whole genome-based characterization and host-pathogen interactions in a snail infection model.

Even though Listeria monocytogenes is an extensive-studied foodborne pathogen, genome analysis of isolates from snails that may represent a reservoir of L. monocytogenes are still scarce. Here, we use whole-genome sequencing (WGS) to assess the genomic diversity of hypervirulent, virulent and non-virulent phenotypes of 15 L. monocytogenes isolated from snails to unveil their survival, virulence, and host-pathogen mechanisms of interactions in a snail infection model. Most of isolates (66.7%) were characterized as multidrug resistant (MDR) and belonged to clonal complexes (CCs) which are strongly associated with cases of human infection. All isolates contained intact genes associated with invasion and infection while hypervirulent isolates are adapted to host environment, possessing genes which are involved in teichoic acid biosynthesis, peptidoglycan modification and biofilm formation, correlating with their tolerance to haemolymph plasma phenotype and biofilm formation ability. A snail infection model showed that hypervirulent isolates triggered programmed host cell death pathway by increasing up to 30% the circulating apoptotic hemocytes in combination with induced nitrate production and reactive oxygen species (ROS) generation in snails' haemolymph. In contrast, the administration of the non-virulent strain which possesses a truncated mogR gene that regulates flagellar motility gene expression led only to an increase of necrotic non-apoptotic cells. Overall, this study provides significant insights into the genetic diversity of L. monocytogenes from snails, the genomic features of them linked to their hypervirulent/non-virulent phenotype, and the mechanisms of host-pathogen interactions.