Fungal lysozyme leverages the gut microbiota to curb DSS-induced colitis
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Bibliographic record
Abstract
Colitis is characterized by colonic inflammation and impaired gut health. Both features aggravate obesity and insulin resistance. Host defense peptides (HDPs) are key regulators of gut homeostasis and generally malfunctioning in above-mentioned conditions. We aimed here to improve bowel function in diet-induced obesity and chemically induced colitis through daily oral administration of lysozyme, a well-characterized HDP, derived from Acremonium alcalophilum.C57BL6/J mice were fed either low-fat reference diet or HFD ± daily gavage of lysozyme for 12 weeks, followed by metabolic assessment and evaluation of colonic microbiota encroachment. To further evaluate the efficacy of intestinal inflammation, we next supplemented chow-fed BALB/c mice with lysozyme during Dextran Sulfate Sodium (DSS)-induced colitis in either conventional or microbiota-depleted mice. We assessed longitudinal microbiome alterations by 16S amplicon sequencing in both models.Lysozyme dose-dependently alleviated intestinal inflammation in DSS-challenged mice and further protected against HFD-induced microbiota encroachment and fasting hyperinsulinemia. Observed improvements of intestinal health relied on a complex gut flora, with the observation that microbiota depletion abrogated lysozyme’s capacity to mitigate DSS-induced colitis.Akkermansia muciniphila associated with impaired gut health in both models, a trajectory that was mitigated by lysozyme administration. In agreement with this notion, PICRUSt2 analysis revealed specific pathways consistently affected by lysozyme administration, independent of vivarium, disease model and mouse strain.Taking together, lysozyme leveraged the gut microbiota to curb DSS-induced inflammation, alleviated HFD-induced gastrointestinal disturbances and lowered fasting insulin levels in obese mice. Collectively, these data present A. alcalophilum-derived lysozyme as a promising candidate to enhance gut health.
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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 it