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Analysis of Interactions between Endobiotics and Human Gut Microbiota Using In Vitro Bath Fermentation Systems

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Knowledge graph-derived feed efficiency analysis via pig gut microbiota.

Junmei Zhang¹, Qin Jiang^1,2, Zhihong Du¹

¹National Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.

Scientific Reports

|June 17, 2024

Summary

This study introduces the Pig Gut Microbial Knowledge Graph (PGMKG) to link pig gut microbes with feed efficiency (FE). PGMKG integrates data to identify key microbes and pathways influencing FE for targeted optimization.

Keywords:

Feed efficiency Gut microbiota Knowledge graph Pig

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Area of Science:

Animal Science
Microbiology
Bioinformatics

Background:

Feed efficiency (FE) in pigs is crucial for production, with gut microbiota playing a significant role.
Existing research on gut microbiota and FE is fragmented, lacking integrated biological context.
Current structured databases for microbiota analysis often miss comprehensive biological process information.

Purpose of the Study:

To develop a comprehensive knowledge graph (PGMKG) integrating textual and structured data on pig gut microbes and FE.
To investigate the intricate relationship between pig gut microbiota and feed efficiency.
To identify key microbes, metabolites, and pathways impacting FE for targeted optimization.

Main Methods:

Constructed the pgmReading knowledge base and domain ontology from 157 publications.
Expanded semantic information using PubTator to create pgmPubtator.
Integrated ADDAGMA, gutMGene, and KEGG databases to form pgmDatabase.
Combined these resources into the Pig Gut Microbial Knowledge Graph (PGMKG).
Validated PGMKG performance using five biological query cases.

Main Results:

Successfully created the Pig Gut Microbial Knowledge Graph (PGMKG).
Identified microbes significantly impacting pig feed efficiency.
Provided insights into microbial metabolites and associated metabolic pathways influencing FE.
Demonstrated PGMKG's capability to map key microbes and guide microbiota-targeted optimization.

Conclusions:

PGMKG offers a novel, integrated approach to understanding pig gut microbiota and FE.
The knowledge graph facilitates the identification of microbial targets for improving FE.
This study provides a foundation for microbiota-targeted strategies in pig production.