Integrating biological knowledge for mechanistic inference in the host-associated microbiome

Area of Science:

• Microbiome research
• Computational biology
• Host-microbe interactions

Background:

• High-throughput technologies have advanced the study of microbial communities in relation to human health and disease.
• Numerous resources now synthesize microbial traits, functions, and metabolic potential with host pathways impacting disease phenotypes.
• These integrated knowledge bases can facilitate mechanistic explanations for observed correlations between microbial communities and disease.

Purpose of the Study:

• To survey existing computational resources and methodologies for integrating microbial and host knowledge.
• To evaluate these knowledge bases based on access methods, content, and source characteristics.
• To discuss challenges in creating and utilizing these knowledge bases.

Main Methods:

• Review of existing literature and databases.
• Evaluation of knowledge base accessibility, content, and source characteristics.
• Identification of challenges in nomenclature, ontology/taxonomy grounding, and data structure.

Main Results:

• A survey of current resources for integrating microbial and host knowledge was conducted.
• Key challenges identified include inconsistent nomenclature, lack of standardized ontologies, and limited accessibility.
• A code and data repository is provided for knowledge source mappings.

Conclusions:

• Addressing challenges in knowledge base creation and utilization is crucial for developing effective tools.
• Systematic and unbiased exploration of existing information can yield new insights into microbial mechanisms in disease.
• Improved integration of microbial and host knowledge will advance our understanding of the microbiome's role in health and disease.