Predicting the relationships between gut microbiota and mental disorders with knowledge graphs

Ting Liu,Xu Wang,K. Anton Feenstra,Jaap Heringa,Zhisheng Huang,Xueli Pan

doi:10.1007/s13755-020-00128-2

Ting Liu, Xu Wang + Show 4 more

Open Access

https://doi.org/10.1007/s13755-020-00128-2

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Abstract

Gut microbiota produce and modulate the production of neurotransmitters which have been implicated in mental disorders. Neurotransmitters may act as ‘matchmaker’ between gut microbiota imbalance and mental disorders. Most of the relevant research effort goes into the relationship between gut microbiota and neurotransmitters and the other between neurotransmitters and mental disorders, while few studies collect and analyze the dispersed research results in systematic ways. We therefore gather the dispersed results that in the existing studies into a structured knowledge base for identifying and predicting the potential relationships between gut microbiota and mental disorders. In this study, we propose to construct a gut microbiota knowledge graph for mental disorder, which named as MiKG4MD. It is extendable by linking to future ontologies by just adding new relationships between existing information and new entities. This extendibility is emphasized for the integration with existing popular ontologies/terminologies, e.g. UMLS, MeSH, and KEGG. We demonstrate the performance of MiKG4MD with three SPARQL query test cases. Results show that the MiKG4MD knowledge graph is an effective method to predict the relationships between gut microbiota and mental disorders.

Highlights

The microbiota-gut–brain axis used to describe the complex networks and relationships between gut microbiota and the host, which reflects the inextricable association between gut microbiota and the mental health of the host [1]
We extended the knowledge base by integrating with existing popular biomedical ontologies, e.g. Unified Medical Language System (UMLS), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Medical Subject Headings (MeSH)
In the process of constructing such a domain knowledge graph, relationships can be divided into explicit relationships and implicit relationships

Summary

Introduction

The microbiota-gut–brain axis used to describe the complex networks and relationships between gut microbiota and the host, which reflects the inextricable association between gut microbiota and the mental health of the host [1]. Interruptions of serotonin and norepinephrine movement lead to depression and anxiety disorders [13] Dopamine is another neurotransmitter linked to mental disorders, such as schizophrenia and autism spectrum disorder [14, 15]. We proposed a novel knowledge graph which we named as MiKG4MD to identify and predict the relationships between gut microbiota and mental disorders. Results show that the MiKG4MD knowledge graph is an effective method to predict the relationship between gut microbiota and mental disorders. Constructing such a knowledge graph that gathers existing knowledge resources enables users to achieve semantic queries and question answering but may be supporting medical researchers to make better decisions to implement novel therapies for various mental diseases

Methodology

Discussion and conclusion

Limitations and outlook

Compliance with ethical standards