Biomedical knowledge graph construction of Sus scrofa and its application in anti-PRRSV traditional Chinese medicine discovery

As a new data management paradigm, knowledge graphs can integrate multiple data sources and achieve quick responses, reasoning and better predictions in drug discovery. Characterized by powerful contagion and a high rate of morbidity and mortality, porcine reproductive and respiratory syndrome (PRRS) is a common infectious disease in the global swine industry that causes economically great losses. Traditional Chinese medicine (TCM) has advantages in low adverse effects and a relatively affordable cost of application, and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective approaches. Here, we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of TCMs. Subsequently, we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model (i.e., transR) from six typical models, namely, transE, transR, DistMult, ComplEx, RESCAL and RotatE, according to five indicators, namely, MRR, MR, HITS@1, HITS@3 and HITS@10. Based on embedding vectors trained by the optimal model, anti-PRRSV TCMs were predicted by two paths, namely, VHC-Herb and VHPC-Herb, and potential anti-PRRSV TCMs were identified by retrieving the HERB database according to the pharmacological properties corresponding to symptoms of PRRS. Ultimately, Dan Shen's (Salvia miltiorrhiza Bunge) capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded 90% when the concentrations of Dan Shen extract were 0.004, 0.008, 0.016 and 0.032 mg/mL. In summary, this is the first report on the Sus Scrofa knowledge graph including TCM information, and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.