The Establishment of a Novel γ-Interferon In Vitro Release Assay for the Differentiation of Mycobacterial Bovis-Infected and BCG-Vaccinated Cattle.

BCG vaccination is increasingly reconsidered in the effective prevention of bovine tuberculosis (bTB). However, the primary challenge in BCG vaccination for cattle is the lack of a technique for differentiating between infected and vaccinated animals (DIVA). This study aimed to establish a novel DIVA diagnostic test based on an interferon-gamma in vitro release assay (IGRA). The plasmid encoding three differential antigens (Rv3872, CFP-10, and ESAT-6) absent in BCG genes but present in virulent M. bovis was previously constructed. Thus, a recombinant protein called RCE (Rv3872, CFP-10, and ESAT-6) was expressed, and an RCE-based DIVA IGRA (RCE-IGRA) was established. The RCE concentration was optimized at 4 µg/mL by evaluating 97 cattle (74 of which were bTB-positive, and 23 were negative) using a commercial IGRA bTB diagnostic kit. Further, 84 cattle were tested in parallel with the RCE-IGRA and commercial PPD-based IGRA (PPD-IGRA), and the results showed a high correlation with a kappa value of 0.83. The study included BCG-vaccinated calves (n = 6), bTB-positive cattle (n = 6), and bTB-negative non-vaccinated calves (n = 6). After 3 months post-vaccination, PPD-IGRA generated positive results in both vaccinated and infected calves. However, RCE-IGRA developed positive results in infected calves but negative results in vaccinated calves. In conclusion, this DIVA method has broad prospects in differentiating BCG vaccination from natural infection to prevent bTB.

Investigation of the safety and protective efficacy of an attenuated and marker M. bovis-BoHV-1 combined vaccine in bovines.

Bovine respiratory disease (BRD) is one of the most common diseases in the cattle industry worldwide; it is caused by multiple bacterial or viral coinfections, of which Mycoplasma bovis (M. bovis) and bovine herpesvirus type 1 (BoHV-1) are the most notable pathogens. Although live vaccines have demonstrated better efficacy against BRD induced by both pathogens, there are no combined live and marker vaccines. Therefore, we developed an attenuated and marker M. bovis-BoHV-1 combined vaccine based on the M. bovis HB150 and BoHV-1 gG-/tk- strain previously constructed in our lab and evaluated in rabbits. This study aimed to further evaluate its safety and protective efficacy in cattle using different antigen ratios. After immunization, all vaccinated cattle had a normal rectal temperature and mental status without respiratory symptoms. CD4+, CD8+, and CD19+ cells significantly increased in immunized cattle and induced higher humoral and cellular immune responses, and the expression of key cytokines such as IL-4, IL-12, TNF-α, and IFN-γ can be promoted after vaccination. The 1.0 × 108 CFU of M. bovis HB150 and 1.0 × 106 TCID50 BoHV-1 gG-/tk- combined strain elicited the most antibodies while significantly increasing IgG and cellular immunity after challenge. In conclusion, the M. bovis HB150 and BoHV-1 gG-/tk- combined strain was clinically safe and protective in calves; the mix of 1.0 × 108 CFU of M. bovis HB150 and 1.0 × 106 TCID50 BoHV-1 gG-/tk- strain was most promising due to its low amount of shedding and highest humoral and cellular immune responses compared with others. This study introduces an M. bovis-BoHV-1 combined vaccine for application in the cattle industry.

Prevalence, Molecular Characteristics and Virulence Identification of Bovine Parainfluenza Virus Type 3 in China.

Bovine parainfluenza virus type 3 (BPIV-3) is one of the major pathogens of the bovine respiratory disease complex (BRDC). BPIV-3 surveillance in China has been quite limited. In this study, we used PCR to test 302 cattle in China, and found that the positive rate was 4.64% and the herd-level positive rate was 13.16%. Six BPIV-3C strains were isolated and confirmed by electron microscopy, and their titers were determined. Three were sequenced by next-generation sequencing (NGS). Phylogenetic analyses showed that all isolates were most closely related to strain NX49 from Ningxia; the genetic diversity of genotype C strains was lower than strains of genotypes A and B; the HN, P, and N genes were more suitable for genotyping and evolutionary analyses of BPIV-3. Protein variation analyses showed that all isolates had mutations at amino acid sites in the proteins HN, M, F, and L. Genetic recombination analyses provided evidence for homologous recombination of BPIV-3 of bovine origin. The virulence experiment indicated that strain Hubei-03 had the highest pathogenicity and could be used as a vaccine candidate. These findings apply an important basis for the precise control of BPIV-3 in China.

Predicting Protein-Protein Interactions via Gated Graph Attention Signed Network.

Protein-protein interactions (PPIs) play a key role in signal transduction and pharmacogenomics, and hence, accurate PPI prediction is crucial. Graph structures have received increasing attention owing to their outstanding performance in machine learning. In practice, PPIs can be expressed as a signed network (i.e., graph structure), wherein the nodes in the network represent proteins, and edges represent the interactions (positive or negative effects) of protein nodes. PPI predictions can be realized by predicting the links of the signed network; therefore, the use of gated graph attention for signed networks (SN-GGAT) is proposed herein. First, the concept of graph attention network (GAT) is applied to signed networks, in which "attention" represents the weight of neighbor nodes, and GAT updates the node features through the weighted aggregation of neighbor nodes. Then, the gating mechanism is defined and combined with the balance theory to obtain the high-order relations of protein nodes to improve the attention effect, making the attention mechanism follow the principle of "low-order high attention, high-order low attention, different signs opposite". PPIs are subsequently predicted on the Saccharomyces cerevisiae core dataset and the Human dataset. The test results demonstrate that the proposed method exhibits strong competitiveness.