Identification of microbial metabolic functional guilds from large genomic datasets

Area of Science:

• Microbial Ecology
• Biogeochemistry
• Genomics

Background:

• Heterotrophic microbes are crucial for Earth's biogeochemical cycles, influencing organic matter consumption.
• Modeling microbial roles requires simplifying complex communities and linking them to biogeochemical functions.
• Microbial metabolic functional guilds offer a way to reduce complexity but identifying them is challenging.

Purpose of the Study:

• To present a novel method for defining microbial metabolic functional guilds from annotated genomes.
• To enable the identification of functional guilds from both cultured and uncultured organisms.
• To facilitate the integration of microbial functions into Earth System models.

Main Methods:

• Utilized an Aspect Bernoulli (AB) model for guild identification.
• Applied the method to three large genomic datasets (1,733-3,840 genomes each).
• Defined guilds based on metabolic functions present in annotated genomes.

Main Results:

• Successfully identified ecologically relevant microbial metabolic functional guilds.
• Discovered guilds associated with DMSP degradation, dissimilatory nitrate reduction to ammonia, and motile copiotrophy.
• Demonstrated a way to hypothesize co-occurring functions within microbes without cultured isolates.

Conclusions:

• The presented method allows for the definition of microbial metabolic functional guilds from genomic data.
• This approach aids in generating functional hypotheses for microbes, including uncultured ones.
• Applying metabolic functional guilds to environmental samples can enhance understanding of microbial roles in carbon cycling.