Enhanced metagenomic strategies for elucidating the complexities of gut microbiota: a review
The human gastrointestinal tract (GIT) is inhabited by a heterogeneous and dynamic microbial community that influences host health at multiple levels both metabolically, immunologically and via neurological pathways. Though the gut microbiota—overwhelmingly Bacteroidetes and Firmicutes—has essential...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Microbiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2025.1626002/full |
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| Summary: | The human gastrointestinal tract (GIT) is inhabited by a heterogeneous and dynamic microbial community that influences host health at multiple levels both metabolically, immunologically and via neurological pathways. Though the gut microbiota—overwhelmingly Bacteroidetes and Firmicutes—has essential functions in nutrient metabolism, immune regulation, and resistance to pathogens, its dysbiosis is likewise associated with pathologies, such as inflammatory bowel disease (IBD), obesity, type 2 diabetes (T2D), and neurodegenerative diseases. While conventional metagenomic techniques laid the groundwork for understanding microbial composition, next-generation enhanced metagenomic techniques permit an unprecedented resolution in exploring the functional and spatial complexity of gut communities. Advanced frameworks such as high-throughput sequencing, bioinformatic and multi-omics technologies are expanding the understanding of microbial gene regulation, metagenomic pathways, and host-microbe communication. Beyond taxonomic profiling, they map niche-specific activities of gut microbiota along a dichotomy of facultative mutualism, evidenced by relations of beneficial symbionts, represented here by Enterobacteriaceae. In this review, we critically consider the latest approaches (e.g., long-read sequencing, single-cell metagenomics and AI-guided annotation) that mitigate biases stemming from DNA extraction, sequencing depth and functional inference. |
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| ISSN: | 1664-302X |