INFLUENCE OF TYPE 2 BOVINE VIRAL DIARRHEA VIRUS NPRO ON ENHANCEMENT OF BOVINE RESPIRATORY SYNCYTIAL VIRUS REPLICATION MEDIATED BY ANTAGONISM OF HOST CELL INTERFERON TYPE I RESPONSES

Bovine viral diarrhea virus (BVDV) is a member of the genus Pestivirus, Family Flaviviridae. The virus can infect many species of animals of the order Artiodactyla. The BVDV genome encodes an auto protease, Npro, that degrades interferon regulatory factor-3 (IRF-3) reducing type I interferon (IFN-I) production from host cells. Bovine respiratory syncytial virus (BRSV) is a member of the genus Pneumovirus, Family Paramyxoviridae. Concurrent infection with BVDV and BRSV causes more severe respiratory and enteric disease than infection with either virus alone. Our hypothesis was that Npro modulates the innate immune responses to BVDV infection and enhances replication of BVDV or BRSV co-infection. The noncytopathic BVDV2 viruses NY93/c N- Npro 18 EGFP (a mutant with modified Npro fused with enhanced green fluorescent protein), NY93 infectious clone (NY93/c), wild-type NY93-BVDV2 (NY93-wt), and BRSV were evaluated in this study. The objectives of this study were: (1) to characterize the replication kinetics and IFN-I induction in Madin-Darby bovine kidney (MDBK) cells following infection with each of the BVDV isolates, and (2) to characterize the influence of BVDV-mediated IFN-I antagonism on enhancement of BRSV replication in bovine turbinate (BT) cells. NY93/c N- Npro 18 EGFP replicated 0.4 – 1.6 TCID50 logs lower than NY93-wt in MDBK cells. NY93/c N- Npro 18 EGFP-infected MDBK cells synthesized IFN-I significantly higher than NY93/c- and NY93-wt-infected MDBK cells. BT cells co-infected with NY93/c N- Npro 18 EGFP/BRSV or NY93-wt/BRSV were evaluated to determine the effects of co-infection on BRSV replication and IFN-I induction in BT cells. BRSV RNA levels in NY93-wt/BRSV co-infected BT cells were 2.49, 2.79, and 2.89 copy number logs significantly greater than in NY93/c N- Npro 18 EGFP/BRSV co-infected BT cells on days 5, 7, and 9 post-infection, respectively. BVDV RNA levels in NY93/c N- Npro 18 EGFP-infected BT cells were 1.64 – 4.38 copy number logs lower than in NY93-wt-infected BT cells. NY93/c N- Npro 18 EGFP single and co-infected BT cells synthesized IFN-I significantly higher than NY93-wt single and co-infected BT cells. In summary, these findings suggest: (1) NY93/c N- Npro 18 EGFP BVDV2 induced higher levels of IFN-I than BVDV2-wt and may be useful as a safer, replicating BVDV vaccine, and (2) Enhancement of BRSV infection by BVDV co-infection is mediated by antagonism of IFN-I.

Evaluating Measurement Invariance with Censored Ordinal Data: A Monte Carlo Comparison of Alternative Model Estimators and Scales of Measurement

Evaluations of measurement invariance provide essential construct validity evidence. However, the quality of such evidence is partly dependent upon the validity of the resulting statistical conclusions. The presence of Type I or Type II errors can render measurement invariance conclusions meaningless. The purpose of this study was to determine the effects of categorization and censoring on the behavior of the chi-square/likelihood ratio test statistic and two alternative fit indices (CFI and RMSEA) under the context of evaluating measurement invariance. Monte Carlo simulation was used to examine Type I error and power rates for the (a) overall test statistic/fit indices, and (b) change in test statistic/fit indices. Data were generated according to a multiple-group single-factor CFA model across 40 conditions that varied by sample size, strength of item factor loadings, and categorization thresholds. Seven different combinations of model estimators (ML, Yuan-Bentler scaled ML, and WLSMV) and specified measurement scales (continuous, censored, and categorical) were used to analyze each of the simulation conditions. As hypothesized, non-normality increased Type I error rates for the continuous scale of measurement and did not affect error rates for the categorical scale of measurement. Maximum likelihood estimation combined with a categorical scale of measurement resulted in more correct statistical conclusions than the other analysis combinations. For the continuous and censored scales of measurement, the Yuan-Bentler scaled ML resulted in more correct conclusions than normal-theory ML. The censored measurement scale did not offer any advantages over the continuous measurement scale. Comparing across fit statistics and indices, the chi-square-based test statistics were preferred over the alternative fit indices, and ΔRMSEA was preferred over ΔCFI. Results from this study should be used to inform the modeling decisions of applied researchers. However, no single analysis combination can be recommended for all situations. Therefore, it is essential that researchers consider the context and purpose of their analyses.