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Organotypic Tissue Model Systems for Investigating Host-Pathogen Interactions In Vitro

Organotypic Tissue Model Systems for Investigating Host-Pathogen Interactions In Vitro

Published on: March 28, 2025

Predicting inter-microbial host specificity in oral biofilms using a lightweight relation-aware knowledge graph

Prabhu Manickam Natarajan^1,2, Sudhir Rama Varma^1,2, Jayaraj Kodangattil Narayanan^2,3

¹Department of Clinical sciences, College of Dentistry, Ajman University, Ajman, United Arab Emirates.

Frontiers in Cellular and Infection Microbiology

|March 9, 2026

Summary

This study introduces a novel graph-based model to predict microbial interactions in the oral cavity, improving the detection of disease-associated viruses and understanding oral microbiome dynamics.

Keywords:

bacteriophages hostspecificity knowledge graph oral biofilms oral microbiome periodontal disease phage–host interactions virome

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Published on: October 20, 2011

Area of Science:

Microbiology and Bioinformatics
Computational Biology
Systems Biology

Background:

The human oral cavity harbors a complex microbial ecosystem, including bacteria and viruses, forming biofilms.
Phage-bacteria specificity is vital for microbial community stability and preventing dysbiosis, but mapping it is experimentally challenging.
Traditional methods struggle to capture the ecological complexity of oral microbiome interactions.

Purpose of the Study:

To develop a graph-based model for predicting inter-microbial host specificity in oral biofilms.
To integrate diverse data (taxonomic, ecological, infection) into a knowledge graph for relational learning.
To improve predictions of phage-bacteria interactions and identify microbial hubs linked to periodontal disease.

Main Methods:

Constructed a heterogeneous, relation-aware knowledge graph of the oral microbiome, including taxa, niches, and infection relationships.
Integrated microbial features with graph embeddings and developed a relation-aware graph neural network (IK-BRNet).
Employed stratified cross-validation with class imbalance correction for model evaluation against a conventional Graph Attention Network (GAT).

Main Results:

IK-BRNet demonstrated faster convergence and superior discrimination, achieving a higher AUC-ROC (0.929 vs. 0.904) compared to GAT.
IK-BRNet significantly improved sensitivity for disease-associated viral taxa (93.8% vs. 56.3%), reducing false negatives.
Site-specific predictions aligned with biological validity, identifying higher disease scores for dental plaque-associated viruses.

Conclusions:

Relation-aware graph learning provides an effective framework for modeling inter-microbial host specificity in oral biofilms.
The developed model enhances oral microbiome network inference, aiding disease screening and ecological analysis.
This approach supports advancements in microbiome-based dentistry and understanding oral health.