OHMI: the ontology of host-microbiome interactions

Yongqun He, Haihe Wang, Jie Zheng, Daniel P Beiting, Anna Maria Masci, Hong Yu, Kaiyong Liu, Jianmin Wu, Jeffrey L Curtis, Barry Smith, Alexander V Alekseyenko, Jihad S Obeid, Yongqun He, Haihe Wang, Jie Zheng, Daniel P Beiting, Anna Maria Masci, Hong Yu, Kaiyong Liu, Jianmin Wu, Jeffrey L Curtis, Barry Smith, Alexander V Alekseyenko, Jihad S Obeid

Abstract

Background: Host-microbiome interactions (HMIs) are critical for the modulation of biological processes and are associated with several diseases. Extensive HMI studies have generated large amounts of data. We propose that the logical representation of the knowledge derived from these data and the standardized representation of experimental variables and processes can foster integration of data and reproducibility of experiments and thereby further HMI knowledge discovery.

Methods: Through a multi-institutional collaboration, a community-based Ontology of Host-Microbiome Interactions (OHMI) was developed following the Open Biological/Biomedical Ontologies (OBO) Foundry principles. As an OBO library ontology, OHMI leverages established ontologies to create logically structured representations of (1) microbiomes, microbial taxonomy, host species, host anatomical entities, and HMIs under different conditions and (2) associated study protocols and types of data analysis and experimental results.

Results: Aligned with the Basic Formal Ontology, OHMI comprises over 1000 terms, including terms imported from more than 10 existing ontologies together with some 500 OHMI-specific terms. A specific OHMI design pattern was generated to represent typical host-microbiome interaction studies. As one major OHMI use case, drawing on data from over 50 peer-reviewed publications, we identified over 100 bacteria and fungi from the gut, oral cavity, skin, and airway that are associated with six rheumatic diseases including rheumatoid arthritis. Our ontological study identified new high-level microbiota taxonomical structures. Two microbiome-related competency questions were also designed and addressed. We were also able to use OHMI to represent statistically significant results identified from a large existing microbiome database data analysis.

Conclusion: OHMI represents entities and relations in the domain of HMIs. It supports shared knowledge representation, data and metadata standardization and integration, and can be used in formulation of advanced queries for purposes of data analysis.

Keywords: Host-microbiome interaction; Metadata; Microbiome; OBO Foundry; OHMI; Ontology; Ontology of host-microbiome interactions; Rheumatic disease; Rheumatoid arthritis.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Selected upper level terms and hierarchy of OHMI. OHMI terms are marked by red labels. The full names of listed ontologies are provided in the list of abbreviations at the end of this paper
Fig. 2
Fig. 2
Illustration of OHMI ontology design pattern for representing host-microbiome interactions. The red box represents different levels of host-microbiome interactions. A specific example is the OHMI representation of a human-microbiome interaction in which the human host has the disease ankylosing spondylitis (AS). The human and microbiome classes are duplicated in this figure for clarity. Note that not every organism has the ‘host role’, and the role is here assigned to a host organism only in the case of host-microbiome interactions
Fig. 3
Fig. 3
Ontological representation of the bacteria populations increased in the guts of patients with at least two different rheumatic diseases as compared with healthy controls. (a) Bacterial population increased in patient guts. (b) Bacterial population decreased in patient guts. Many increased and decreased bacterial populations are within the same genus. The red and blue circles represent increased and decreased profiles, respectively. Taxonomy terms without circle and label are used to generate ontological hierarchies
Fig. 4
Fig. 4
OHMI design pattern of key entities important for HMI investigation. Note that not every organism has the ‘host role’, and the role is here assigned to a host organism only in the case of host-microbiome interaction
Fig. 5
Fig. 5
Query of diseases associated with increased E. coli in human gut. (a) DL query based on the host-pathogen interaction classifications; (b) SPARQL query based on the linkage from organism to disease. The SPARQL query was conducted using the Ontobee SPARQL endpoint (http://www.ontobee.org/sparql)
Fig. 6
Fig. 6
The hierarchy of microbes associated with RA and their profiles. The red and blue circles represent the increased and decreased profiles, respectively. Labeled letters represent locations as follows: G – human gut, O – human oral cavity; R – human respiratory airway. Those taxonomy terms without circle and label are used only to generate the hierarchy
Fig. 7
Fig. 7
Data mining and ontology representation of microbiome profiles at different species level between diarrhea and health controls. (a) MicrobiomeDB data mining. (b) OHMI representation of the results

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