Abstract

Previously, we evaluated the effect of the immunobiotic strain Lactobacillus rhamnosus CRL1505 on the transcriptomic response of porcine intestinal epithelial (PIE) cells triggered by the challenge with the Toll-like receptor 3 (TLR-3) agonist poly(I:C) and successfully identified a group of genes that can be used as prospective biomarkers for the screening of new antiviral immunobiotics. In this work, several strains of lactobacilli were evaluated according to their ability to modulate the expression of IFN alpha, IFN beta, RIG1, TLR3, OAS1, RNASEL, MX2, A20, CXCL5, CCL4, IL-15, SELL, SELE, EPCAM, PTGS2, PTEGES, and PTGER4 in PIE cells after the stimulation with poly(I:C). Comparative analysis of transcripts variations revealed that one of the studied bacteria, Lactobacillus plantarum MPL16, clustered together with the CRL1505 strain, indicating a similar immunomodulatory potential. Two sets of in vivo experiments in Balb/c mice were performed to evaluate L. plantarum MPL16 immunomodulatory activities. Orally administered MPL16 prior intraperitoneal injection of poly(I:C) significantly reduced the levels of the proinflammatory mediators tumor necrosis factor alpha (TNF-alpha), interleukin 6 (IL-6), and IL-15 in the intestinal mucosa. In addition, orally administered L. plantarum MPL16 prior nasal stimulation with poly(I:C) or respiratory syncytial virus infection significantly decreased the levels of the biochemical markers of lung tissue damage. In addition, reduced levels of the proinflammatory mediators TNF-alpha, IL-6, and IL-8 were found in MPL16-treated mice. Improved levels of IFN-beta and IFN-gamma in the respiratory mucosa were observed in mice treated with L. plantarum MPL16 when compared to control mice. The immunological changes induced by L. plantarum MPL16 were not different from those previously reported for the CRL1505 strain in in vitro and in vivo studies. The results of this work confirm that new immunobiotic strains with the ability of stimulating both local and distal antiviral immune responses can be efficiently selected by evaluating the expression of biomarkers in PIE cells.