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Ecological Trait-Based Digital Categorization of Microbial Genomes for Denitrification Potential
Raphael D Isokpehi1, Yungkul Kim1, Sarah E Krejci1
1Oyster Microbiome Project, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL 32114, USA.
Microorganisms
|April 27, 2024
Summary
This study categorizes microbial genomes by their denitrification potential, identifying 3,280 bacterial strains with complete denitrification capabilities. This research provides valuable data for understanding microbial roles in the nitrogen cycle.
Area of Science:
- Microbiology
- Environmental Science
- Genomics
Background:
- Microorganisms play a crucial role in the global nitrogen cycle through various transformations.
- Denitrification, a key process converting nitrate to nitrogen gas, is a microbial trait influenced by specific enzyme genes.
- Existing genomic data lacks comprehensive datasets and tools for investigating denitrification potential.
Purpose of the Study:
- To categorize archaeal and bacterial genomes based on their denitrification potential.
- To establish denitrification traits using rules for enzyme involvement in the nitrate reduction pathway.
- To create an integrated dataset for investigating microbial denitrification capabilities.
Main Methods:
- Integrated datasets on microbial genomes, taxonomic lineage, ecosystem, and denitrifying enzymes.
- Developed a four-digit binary-coding scheme to classify genomes by denitrification traits.
- Analyzed 62,624 microbial genomes for the presence of twelve denitrifying enzymes.
Main Results:
- Created a dataset of 62,624 microbial genomes annotated for denitrification potential.
- Identified 3,280 bacterial genomes from 260 genera exhibiting complete denitrification traits.
- Found complete denitrification potential in strains like Arcobacteraceae across diverse ecosystems.
Conclusions:
- The developed dataset and tools facilitate research into microbial denitrification.
- This resource aids in understanding biochemical, molecular, and physiological aspects of denitrification.
- The data can be used to identify microbial strains for synthetic denitrifying communities.
Keywords:
ArcobacteraceaeKEGG Orthologyarchaeabacteriabioinformaticsdata investigationsdenitrificationecological traitgenomesmicrobial ecologysynthetic denitrifying communitiesvisual analyticsMore Related Videos
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