Ecobiotherapy Rich in Firmicutes Decreases Susceptibility to Colitis in a Humanized Gnotobiotic Mouse Model
Bibliographic record
Abstract
BACKGROUND: Alterations in the intestinal microbiota, characterized by depletion of anti-inflammatory bacteria, such as Firmicutes, in patients with ulcerative colitis (UC) have prompted interest in microbiota-modulating strategies for this condition. The aim of this study was to evaluate the role of fecal and synthetic human microbial ecosystems, low or enriched in Firmicutes, on colitis susceptibility and host immune responses. METHODS: The microbiota of selected healthy and UC human donors was characterized by culture method and 16S rRNA-based sequencing. Germ-free mice were colonized with fecal or a synthetic ecosystem enriched (healthy donors) or low (UC donors) in Firmicutes. Experimental colitis was induced using dextran sodium sulfate. Colon transcriptome and colon lamina propria cells were evaluated in mice postcolonization by RNA-seq and flow cytometry, respectively, and T helper (TH) 17 differentiation was assessed in vitro. RESULTS: Mice colonized with microbiota from patients with UC low in Firmicutes had increased sensitivity to colitis compared with mice colonized with fecal or synthetic ecosystems rich in Firmicutes. Microbiota low in Firmicutes increased expression of TH17-related genes and expansion of interleukin-17A-expressing CD4 cells in vivo. Supplementation with bacterial isolates belonging to the Firmicutes phylum abrogated the heightened TH17 responses in vitro. CONCLUSIONS: A microbiota rich in Firmicutes derived from fecal samples of a healthy human donor, or assembled synthetically, downregulated colonic inflammation and TH17 pathways in mice. The results support the use of ecobiotherapy strategies, enriched in Firmicutes, for the prevention or treatment of UC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".