Mankel, D. | Search bioRxiv

bioRxiv2025

Improved detection of fungi and uncultivated microorganisms in soil metagenomes using a comprehensive genome database

Soils harbor diverse microbial communities crucial for ecosystem functioning, but poor genomic representation of many uncultured soil microorganisms limits the utility of existing databases to address some of the most pressing questions in environmental microbiology. To address this, we developed the SoilMicrobeDB, a comprehensive, genome-based reference database to enhance metagenomic classification for soil ecosystems, with a focus on previously underrepresented fungal taxa and uncultured organisms. We evaluated the database using a large soil metagenome dataset, comparing classification rates, analyzing fungal-bacterial ratios against phospholipid fatty acid (PLFA) estimates, and validating lineage abundances with rRNA amplicon sequencing data. Mock community analysis was also conducted to test the precision of community classification and the prevalence of false positives. The SoilMicrobeDB workflow improved metagenomic read classification by over 20% and provided more accurate fungal abundance estimates, particularly for nutrient cycling groups such as ectomycorrhizal fungi. Metagenomic-derived fungal-bacterial ratios were correlated with PLFA and qPCR estimates, and lineage proportions were aligned with relative abundances estimates from rRNA amplicon sequencing. Uncultured taxa represented up to 50% of classifiable soil microbial communities in certain biomes. SoilMicrobeDB offers robust taxonomic and functional profiling of soil communities and provides a scalable and updatable tool for soil microbial ecology research. SoilMicrobeDB is accessible through an interactive platform linking genomes to environmental factors, enabling researchers to explore microbial distributions across soil conditions and potentially leading to new insights into soil ecology and management practices.

bioinformatics↗

bioRxiv2024

Dual-BONCAT Reveals Distinct Day- and Night-Active Microbial Populations in Salt Marsh Sediments

Bio-Orthogonal Non-Canonical Amino Acid Tagging (BONCAT) has emerged as a prominent molecular technique that enables microbial ecologists to visualize and identify metabolically active cells in cultures and complex microbial communities. To date, researchers have used just one non-canonical amino acid (ncAA) in a given experiment; here, we validate a novel approach using two different ncAAs in a single experiment. This advancement facilitates the detection of differentially active subpopulations within the same experimental context, thereby reducing the uncertainty and variability associated with parallel treatments, and providing precise spatial information about organisms that are active under distinct conditions or at different times. We show that both ncAAs can be taken up by E. coli cultures and by constituents of the Little Sippewissett Salt Marsh microbiome, resulting in fluorescence signals that are significantly higher than background and ncAA-free control experiments, as well as differential labeling patterns reflective of distinct subpopulations. As a proof of concept, we implemented this "dual-BONCAT" approach in salt marsh sediments, adding one ncAA during daytime hours and the other at night. Subpopulations of cells that were anabolically active during the day and/or night were distinguishable by both fluorescence microscopy, and by fluorescence-activated cell sorting. Subsequent high-throughput 16S rRNA gene amplicon sequencing of active subpopulations revealed that Methylobacterium, potentially feeding on plant exudate carbon, was preferentially active during the day, while sulfur-cycling taxa dominated the night-active population. Dual-BONCAT offers an important advancement in multiplexing substrate-analog probing techniques, providing a more realistic understanding of metabolic activity under distinct environmental conditions. ImportanceMicrobial communities are complex and dynamic, with different groups of microbes active under distinct conditions. Bio-Orthogonal Non-Canonical Amino Acid Tagging (BONCAT) uses synthetic amino acids to tag newly made proteins, allowing researchers to see and identify the active subset of a community. While BONCAT studies to date have used a single synthetic amino acid to evaluate cell activity in a single experimental context, here we introduce a new approach, "dual-BONCAT," using two synthetic amino acids to track differential responses to changing conditions. After validating the approach with E. coli, we deployed it in a salt marsh sediment community, finding that organisms potentially feeding on plant root sugars were more active during the day, and microbes likely metabolizing sulfur were more active at night. We believe dual-BONCAT will prove useful in many studies, as it illuminates microbial community responses to changing conditions, which has important implications for ecosystem dynamics.

microbiology↗