Popis: |
IntroductionThe critical early stages of infection and innate immune responses to porcine epidemic diarrhea virus (PEDV) at the intestinal epithelium remain underexplored due to the limitations of traditional cell culture and animal models. This study aims to establish a porcine enteroid culture model to investigate potential differences in susceptibility to infection across segments of the porcine small intestine (duodenum, jejunum, and ileum).MethodsIntestinal crypt cells from nursery pigs were cultured in Matrigel to differentiate into porcine enteroid monolayer cultures (PEMCs). Following characterization, PEMCs were enzymatically dissociated and subcultured on transwell inserts (PETCs) for apical surface exposure and infection studies. Characterization of region-specific PEMCs and PETCs included assessment of morphology, proliferation, viability, and cellular phenotyping via immunohistochemistry/immunocytochemistry and gene expression analysis. Subsequently, PETCs were inoculated with 105 TCID50 (50% tissue culture infectious dose)/mL of a high pathogenic PEDV non-S INDEL strain and incubated for 24 h. Infection outcomes were assessed by cytopathic effect, PEDV N protein expression (immunofluorescence assay, IFA), and PEDV N-gene detection (quantitative reverse transcription polymerase chain reaction, RT-qPCR).ResultsNo significant morphological and phenotypical differences were observed among PEMCs and PETCs across intestinal regions, resembling the porcine intestinal epithelium. Although PETCs established from different segments of the small intestine were susceptible to PEDV infection, jejunum-derived PETCs exhibited higher PEDV replication, confirmed by IFA and RT-qPCR.DiscussionThis segment-specific enteroid culture model provides a reliable platform for virological studies, offering a controlled environment that overcomes the limitations of in vivo and traditional cell culture methods. Standardizing culture conditions and characterizing the model are essential for advancing enteroid-based infection models. |