Construction and characterization of a reverse genetics system of bovine parainfluenza virus type 3c as a tool for rapid screening of antivirals in vitro.

Bovine parainfluenza virus type 3 (BPIV3) is a key pathogen associated with bovine respiratory disease complex (BRDC). However, its specific pathogenesis mechanisms have not been fully elucidated. Reverse genetics provides a useful method for understanding the pathogenic mechanism of BPIV3. To ensure the functionality of the rescue platforms, we first constructed a minigenome (MG) system of BPIV3 utilizing a 5-plasmid system in this investigation. Then, a full-length infection clone of BPIV3 was obtained from the SX-2021 strain, and different methods were employed to identify the rescued virus. Additionally, we recovered a recombinant BPIV3 using the reverse genetics system that could express enhanced green fluorescence protein (eGFP). Through the growth curve assays, the replicate capability of rBPIV3-SX-EGFP was found to be similar to that of the parental virus. Subsequently, the rBPIV3-SX-EGFP was used to determine the antiviral activity of ribavirin. The results showed that ribavirin had an anti-BPIV3 effect in MDBK cells. In conclusion, the successful development of a reverse genetic system for the SX-2021 strain establishes a foundation for future studies on BPIV3, including investigations into its pathogenic mechanism, gene function, and antiviral screening properties.

Antigenic variation in hemagglutinin-neuraminidase of Newcastle disease virus isolated from Tibet, China.

Vaccines are widely used to prevent Newcastle disease virus (NDV). Under the pressure of immunization, NDVs with mutations among epitopes of F and HN protein were isolated, which indicates that the efficiency of vaccine may decrease in terms of preventing emerged NDV. However, the lack of evidences to support whether these mutations contribute to antigenic mutation and immune escape in NDV leading to the controversy that the matched vaccine is more effective than the mismatched vaccine. In this study, a genotype VII velogenic NDV strain (C22) was isolated from a vaccinated farm in Tibet, China. We found that this strain was close to NDV from east China, but it had a specific mutation (K138R) in one epitope (131DYIGGIGKE139) of HN protein. This mutation might change the interaction between amino acids in stalk-head link region of HN protein and then induce the specific antibody to worse recognize the C22 strain, but it did not alter viral virulence and growth ability. Then, the C22 strain was attenuated via modification of the F protein cleavage site to generate a matched vaccine. Comparing to a mismatched vaccine (LaSota), this matched vaccine showed advantages in inhibiting viral shedding and tissue damage. However, both vaccines induced chicken to generate similar level of neutralizing antibodies against C22, C22mut (R138K) and LaSota. These results suggest that the epitope mutation is insufficient to help NDV escaping neutralizing antibodies of vaccinated chicken, supporting that the merits of NDV matched vaccine are not totally related to humoral immunity.

Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss.

Aim: To explore the role of smell and taste changes in preventing and controlling the COVID-19 pandemic, we aimed to build a forecast model for trends in COVID-19 prediction based on Google Trends data for smell and taste loss. Methods: Data on confirmed COVID-19 cases from 6 January 2020 to 26 December 2021 were collected from the World Health Organization (WHO) website. The keywords "loss of smell" and "loss of taste" were used to search the Google Trends platform. We constructed a transfer function model for multivariate time-series analysis and to forecast confirmed cases. Results: From 6 January 2020 to 28 November 2021, a total of 99 weeks of data were analyzed. When the delay period was set from 1 to 3 weeks, the input sequence (Google Trends of loss of smell and taste data) and response sequence (number of new confirmed COVID-19 cases per week) were significantly correlated (P < 0.01). The transfer function model showed that worldwide and in India, the absolute error of the model in predicting the number of newly diagnosed COVID-19 cases in the following 3 weeks ranged from 0.08 to 3.10 (maximum value 100; the same below). In the United States, the absolute error of forecasts for the following 3 weeks ranged from 9.19 to 16.99, and the forecast effect was relatively accurate. For global data, the results showed that when the last point of the response sequence was at the midpoint of the uptrend or downtrend (25 July 2021; 21 November 2021; 23 May 2021; and 12 September 2021), the absolute error of the model forecast value for the following 4 weeks ranged from 0.15 to 5.77. When the last point of the response sequence was at the extreme point (2 May 2021; 29 August 2021; 20 June 2021; and 17 October 2021), the model could accurately forecast the trend in the number of confirmed cases after the extreme points. Our developed model could successfully predict the development trends of COVID-19. Conclusion: Google Trends for loss of smell and taste could be used to accurately forecast the development trend of COVID-19 cases 1-3 weeks in advance.