MetaCompare: a computational pipeline for prioritizing environmental resistome risk

Area of Science:

• Environmental microbiology
• Genomics
• Public health

Background:

• Antibiotic resistance gene (ARG) spread is a major public health issue.
• Environmental compartments are significant reservoirs and pathways for ARG dissemination.
• A systematic method for comparing and prioritizing environmental risks of ARG spread is needed.

Purpose of the Study:

• To introduce MetaCompare, a tool for ranking 'resistome risk'.
• To define and quantify the potential for ARGs to associate with mobile genetic elements (MGEs) and transfer to pathogens using metagenomic data.

Main Methods:

• Developed a computational pipeline to evaluate ARGs based on relative abundance, mobility, and pathogen association.
• Utilized shotgun sequencing data assembly and contig analysis to identify ARG-containing sequences with similarity to MGEs or human pathogens.
• Projected assembled metagenomes into a 3D hazard space to assign resistome risk scores.

Main Results:

• MetaCompare successfully ranked resistome risk in distinct aquatic environments.
• Unsupervised machine learning clustered samples in the hazard space consistent with their environmental origin.
• A clear ascending resistome risk ranking was observed: wastewater treatment plant effluent < dairy lagoon < hospital sewage.

Conclusions:

• MetaCompare provides a systematic approach to assess and prioritize environmental resistome risks.
• The tool aids in understanding ARG mobilization potential and public health implications.
• Environmental compartments pose varying levels of risk for antibiotic resistance gene spread.