Knowledge graph-derived feed efficiency analysis via pig intestinal microbiota

Abstract

Feed efficiency (FE) is essential for pig production, has been reported to be partially explained by gut microbiota. Despite an extensive body of research literature to this topic, studies regarding the regulation of feed efficiency by intestinal microbiota remain fragmented and mostly confined to disorganized or semi-structured unrestricted texts. Meanwhile, structured databases for microbiota analysis are available, yet they often lack a comprehensive understanding of the associated biological processes. Therefore, we have devised an approach to construct a comprehensive knowledge graph by combining unstructured textual intelligence with structured database information and applied it to investigate the relationship between pig intestinal microbes and FE. Firstly, we created the pimReading knowledge base and the domain ontology of pig gut microbiota by annotating, extracting, and integrating semantic information from 157 scientific publications. Secondly, we created the pimPubtator by utilizing PubTator to expand the semantic information related to microbiota. Thirdly, we created the pimDatabase by mapping and combining the ADDAGMA, gutMGene, and KEGG databases based on the ontology. These three knowledge bases were integrated to form the Pig Intestinal Microbial Knowledge Graph (PIMKG). Additionally, we created three biological query cases to validate the performance of PIMKG. These cases not only allow us to identify microbes with the most significant impact on FE but also provide insights into the metabolites produced by these microbes and the associated metabolic pathways. This study introduces PIMKG, mapping key microbes in pig feed efficiency and guiding microbiota-targeted optimization.