Introduction: Tunnel composting technology for preparing Agaricus subrufescens cultivation media can achieve a higher biological efficiency (BE) and a lower contamination rate (CR). However, this technology lacks in-depth and systematic study.Methods: In the present study, the changes in the microbiome and microbial metabolic functions were surveyed using metagenomic analysis. The physicochemical parameters, agronomic properties and nutritional qualities were also evaluated.Results and discussion: Results showed that the contents of cellulose, hemicellulose and lignin dropped to 10.18, 11.58, 27.53%, respectively at the end of composting. The tunnel composting technology led to significant increases in crude protein content (32.56%) and crude fiber content (13.68%). Variations of physicochemical characteristics led to different successions of microbial communities. Bacteria manifested significantly higher abundance than fungi. Firmicutes, Actinobacteriota, Chloroffexi and Deinococcota were the predominant bacterial phyla. Ascomycota and Basidiomycota were the dominant fungal phyla in the thermophilic phase. Pseudonocardia, Truepera, and Thermopolyspora were positively correlated with the yield of A. subrufescens. In addition to TN, most of the physicochemical properties were significantly correlated with fungal communities in the thermophilic phase. The metabolisms of carbohydrate, amino acid and energy were the primary enrichment pathways. These findings deepen the understanding of microbial communities composition during the composting of A. subrufescens substrates. Moreover, this study provides a basis for improving tunnel composting technology.

Study of interface residue pairs is important for understanding the interactions between monomers inside a trimer protein–protein complex. We developed a two-layer support vector machine (SVM) ensemble-classifier that considers physicochemical and geometric properties of amino acids and the influence of surrounding amino acids. Different descriptors and different combinations may give different prediction results. We propose feature combination engineering based on correlation coefficients and F-values. The accuracy of our method is 65.38% in independent test set, indicating biological significance. Our predictions are consistent with the experimental results. It shows the effectiveness and reliability of our method to predict interface residue pairs of protein trimers.

Background: Infectious bronchitis virus (IBV) is a gammacoronavirus that causes a highly contagious disease in chickens and seriously endangers the poultry industry. The GI-19 is a predominant lineage. However, no effective commercially available vaccines against this virus are available. Methods: In this present study, the CHO eukaryotic and the E.coli prokaryotic expression system were used to express S1-SpyTag and AP205-SpyCatcher, respectively. Subsequently, the purified S1-SpyTag and AP205-SpyCatcher were coupled to form the nanoparticles AP205-S1 (nAP205-S1) in PBS buffer at 4 °C for 48 h. S1-SpyTag and nAP205-S1 were formulated into vaccines with white oil adjuvant and employed to immunize 1-day-old SPF chickens for the comparative evaluation of their immune efficacy. Results: The nAP205-S1 vaccine in chickens induced robust IBV-specific humoral and cellular immune responses in vivo. Importantly, the humoral and cellular immune responses elicited by the nAP205-S1 vaccine were more robust than those induced by the IBV S1-SpyTag vaccine at both the same dose and double the dose, with a notably significant difference observed in the cellular immune response. Furthermore, experimental data revealed that chicken flocks vaccinated with nAP205-S1 achieved 100% group protection following a challenge, exhibiting a potent protective immune response and effectively inhibiting viral shedding. Conclusions: These results reveal the potential of developing a novel nanoparticle vaccine with broadly protective immunity against GI-19 IBV.