Immunopathological characterization of porcine circovirus type 2 infection-associated follicular changes in inguinal lymph nodes using high-throughput tissue microarray

Abstract

The immunopathogenesis of porcine circovirus type 2 (PCV2) infection in conventional pigs is complicated by various environmental factors and individual variation and is difficult to be completely reproduced experimentally. In the present field-based study, a tissue microarray (TMA) consisting of a series of lymphoid follicles having different PCV2-loads was constructed using formalin-fixed and paraffin-embedded superficial inguinal lymph nodes (LNs) from 102 pigs. Using the TMA, a wide range of parameters, including co-infected viral pathogens, immune cell subsets, and cell apoptosis/proliferation activity by immunohistochemical (IHC) staining or in situ hybridization (ISH) were measured, characterized, and compared. The signal location and area extent of each parameter were interpreted by pathologists, semi-quantified by automated image analysis software, and analyzed statistically. The results herein demonstrated a significant negative correlation between PCV2 and CD79a (p<0.001) and a significant positive correlation between PCV2 and lysozyme (p<0.001) or TUNEL (p<0.001) using Pearson correlation analysis. The amount of porcine respiratory and reproductive syndrome virus (PRRSV) and porcine parvovirus antigens did not correlate with the tissue loads of PCV2 nucleic acid. Multiple regression analysis further predicted that PCV2 contributed major effects on CD79a, lysozyme, and TUNEL but PRRSV showed relatively less effects on these parameters. In addition, the total signal intensity of Ki67 (index of cell proliferation activity) did not change significantly among cases with different PCV2 loads; however, as the loading of PCV2 nucleic acid increased, the main contribution of Ki67 signal gradually shifted from B cells in the germinal center to T cells and macrophages in the interfollicular regions. In the present study, the use of TMA to establish a mathematical model with a wider range of statistical analysis can bring us a step forward to understand the immunopathogenesis of PCV2 infection-associated follicular changes in LNs.