Background: Metagenomics offers a culture-independent framework for comprehensively characterizing microbial communities by directly extracting and sequencing DNA from environmental samples. In this study, we employed high-throughput metagenomic sequencing to explore microbial communities inhabiting fungal-rich environments, emphasizing taxonomic composition, functional potential, and antibiotic resistance gene (ARG) dynamics.
Methods: Six samples from two distinct groups (HFJ and QFJ) were subjected to Illumina-based shotgun sequencing, followed by rigorous quality control, taxonomic classification, KEGG-based functional annotation, and ARG identification via the CARD database. Comparative analysis revealed stark contrasts between the two groups.
Results: HFJ samples were dominated by eukaryotic taxa, particularly Saccharomyces cerevisiae, and exhibited elevated carbohydrate metabolism, aligning with the ecological role of fermentative fungi. Conversely, QFJ samples displayed higher bacterial diversity, particularly Firmicutes and Proteobacteria, and were enriched in lipid and amino acid metabolism pathways. Striking differences were also observed in ARG profiles. QFJ samples harbored greater ARG abundance, particularly genes conferring resistance to beta-lactams, aminoglycosides, and tetracyclines, indicating higher resistance potential and possible horizontal gene transfer activity.
Conclusion: Our results reveal distinct microbial, functional and resistome profiles in fungal-rich versus bacterial-rich fermentation environments. Fungal dominance correlated with lower bacterial diversity and a reduced abundance of certain ARGs, whereas bacterial-rich samples exhibited higher diversity and ARG prevalence. These correlations generate the hypothesis that fungal dominance may suppress bacterial growth or ARG dissemination; however, causal relationships cannot be inferred from our cross-sectional data. The study highlights the potential of metagenomic surveillance to elucidate ecological niches that influence bacterial diversity and resistance dynamics.
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| Evidence ID | Analyze ID | Gene/Complex | Systematic Name/Complex Accession | Qualifier | Gene Ontology Term ID | Gene Ontology Term | Aspect | Annotation Extension | Evidence | Method | Source | Assigned On | Reference |
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| Evidence ID | Analyze ID | Gene | Gene Systematic Name | Phenotype | Experiment Type | Experiment Type Category | Mutant Information | Strain Background | Chemical | Details | Reference |
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| Evidence ID | Analyze ID | Gene | Gene Systematic Name | Disease Ontology Term | Disease Ontology Term ID | Qualifier | Evidence | Method | Source | Assigned On | Reference |
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| Evidence ID | Analyze ID | Regulator | Regulator Systematic Name | Target | Target Systematic Name | Direction | Regulation of | Happens During | Regulator Type | Direction | Regulation Of | Happens During | Method | Evidence | Strain Background | Reference |
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| Site | Modification | Modifier | Source | Reference |
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| Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Allele | Assay | Annotation | Action | Phenotype | SGA score | P-value | Source | Reference | Note |
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| Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Assay | Annotation | Action | Modification | Source | Reference | Note |
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| Complement ID | Locus ID | Gene | Species | Gene ID | Strain background | Direction | Details | Source | Reference |
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| Evidence ID | Analyze ID | Dataset | Description | Keywords | Number of Conditions | Reference |
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| Evidence ID | Analyze ID | File | Description |
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