Functional archetypes in the human gut microbiome reveal metabolic diversity, stability, and confound disease-associated signatures
Bibliographic record
Abstract
Understanding the functional diversity of the gut microbiome is essential for decoding its roles in health and disease. Using a deep-learning framework, we identified three functional archetypes defining healthy adult gut microbiomes, each characterized by specific metabolic potentials: sugar metabolism with branched-chain amino acid and cell wall synthesis (Archetype 1), fatty acid and TCA cycle metabolism (Archetype 2), and amino acid and nitrogen metabolism (Archetype 3). Archetype proximity is linked to stability, with Archetype 2 representing the most resilient state, likely due to its metabolic flexibility. Functional diversity emerged as a confounder in disease-associated microbial signatures. In inflammatory bowel disease, we observed archetype-specific shifts, including increased carbohydrate metabolism in Archetype 1-dominant samples and inflammatory pathways in Archetype 3-dominant samples, suggesting distinct opportunities for microbiome-targeted interventions. This framework addresses key challenges in microbiome research, including inter-individual variability and confounding, while providing robust insights into disease-associated functional shifts and microbial ecosystem dynamics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".