Shandong Agricultural University

Gyrovirus homsa 1 (GyH1) is an established causative agent of transmissible viral proventriculitis (TVP) in chickens. This study developed a colloidal gold immunochromatography assay (GICA) for GyH1 detection. Polyclonal and monoclonal antibodies against recombinant VP1 protein were generated. Colloidal gold nanoparticles were synthesized using the trisodium citrate reduction method, yielding uniform particle. Monoclonal antibodies were conjugated to colloidal gold particles, while polyclonal antibodies served as capture reagents immobilized on the membrane. Optimal labeling conditions were determined as pH 8.8 with a monoclonal concentration of 7.2 μg/mL. The assay demonstrated specific recognition of GyH1with a detection limit of 10³ copies/μL. Batch-to-batch reproducibility was confirmed across all test strip. In summary, the GyH1 GICA exhibits high specificity, sensitivity, and reproducibility, providing an effective and low-cost tool for monitoring GyH1 early-stage infection status and technical support for field detection applications.

Deep learning is revolutionizing enzyme engineering through efficient residue redesign. Leveraging deep learning for enzyme engineering, we redesigned a pectinase using ProteinMPNN guided by multiple sequence alignment. Our top-performing variant, DS-5, incorporated 72 mutations and achieved an 8.9-fold increase in catalytic activity compared to the wild-type. DS-5 also displayed significantly improved thermostability, with an optimal temperature increasing by 10°C, and robust performance over a wide pH range (7.0-11.0). Structural and molecular dynamics analyses revealed the source of this enhancement: a remodeled surface electrostatic potential due to the increase of five positively charged residues, forming an extended positive groove that potentially improves substrate binding affinity. This rationally designed enzyme demonstrated superior performance in applied settings, including apple juice clarification and tobacco degradation. Furthermore, treating tobacco leaves with DS-5 substantially improved their sensory profile by elevating the concentration of desirable flavor compounds like sucrose and lactones. Our study provides a framework for deep learning-guided engineering of highly efficient enzymes, directly linking catalytic improvements to enhanced end-product quality for industrial applications.

Decades of artificial selection have left distinct genomic signatures in Pekin duck. Here, we used whole-genome resequencing data from six Pekin duck populations, including two conservation populations and four breeding populations, to explore the genomic footprints left by decades of artificial selection in Pekin ducks. Through allele frequency analyses, we identified specific selective regions among the breeding populations. Compared with the conservation populations, the strongest selection signals in Maple Leaf ducks were found in genomic regions encompassing FAM184B and LAP3, with two missense mutations in FAM184B showing marked allele frequency differences between Maple Leaf and conservation populations, while in Cherry Valley ducks, the top signals were located within or near XRCC3, MNAT1, and SLC34A2. In comparisons between the Nankou Pekin duck breeding populations and the conservation population, NUDT6 and INPP4A were identified as the candidate genes under selection in PK4 and PK3, respectively. Analysis of the shared selective regions revealed 214 candidate genes, which were mainly enriched in pathways related to lipid metabolism and organ development. These results suggest that different breeding programs have imposed distinct selection pressures on the Pekin duck genome. Overall, this study provides valuable insights for the conservation and genetic improvement of Pekin ducks.