Early-life gut mycobiome core species modulate metabolic health in mice
Bibliographic record
Abstract
The gut microbiome causally contributes to obesity; however, the role of fungi remains understudied. We previously identified three core species of the infant gut mycobiome (Rhodotorula mucilaginosa, Malassezia restricta and Candida albicans) that correlated with body mass index, however their causal contributions to obesity development are unknown. Here we show the effects of early-life colonization by these fungal species on metabolic health in gnotobiotic mice fed standard (SD) or high-fat-high-sucrose (HFHS) diets. Each species resulted in bacterial microbiome compositional and functional differences. R. mucilaginosa and M. restricta increased adiposity in mice fed SD, while only R. mucilaginosa exacerbated metabolic disease. In contrast, C. albicans resulted in leanness and resistance to diet-induced obesity. Intestinal nutrient transporter expression was unaffected by the presence of fungi in jejunal enteroids, yet the immune landscape in white adipose tissue was distinctly impacted by each fungal species, suggesting that these phenotypes may be a result of fungal immune regulation. This work revealed that three common fungal colonizers have distinct causal influences on obesity and metabolic inflammation and justifies the consideration of fungi in microbiome research on host metabolism. Here, the authors use gnotobiotic mice to show that three core species of the human infant gut mycobiome (Rhodotorula mucilaginosa, Malassezia restricta and Candida albicans) exert distinct modulatory effects on the gut microbiome and immune landscape in white adipose tissue, with R. mucilaginosa and M. restricta increasing adiposity and exacerbating metabolic disease, while C. albicans resulted in leanness and resistance to diet-induced obesity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".