Human Beta Defensin 2 Ameliorated Alcohol-Associated Liver Disease in Mice
Bibliographic record
Abstract
Alcohol-associated liver disease (ALD) is a prevalent liver disorder and significant global healthcare burden with limited effective therapeutic options. The gut-liver axis is a critical factor contributing to susceptibility to liver injury due to alcohol consumption. In the current study, we tested whether human beta defensin-2 (hBD-2), a small anti-microbial peptide, attenuates experimental chronic ALD. Male C57Bl/6J mice were fed an ethanol (EtOH)-containing diet for 6 weeks with daily administration of hBD-2 (1.2 mg/kg) by oral gavage during the final week. Two independent cohorts of mice with distinct baseline gut microbiota were used. Oral hBD-2 administration attenuated liver injury in both cohorts as determined by decreased plasma ALT activity. Notably, the degree of hBD-2-mediated reduction of EtOH-associated liver steatosis, hepatocellular death, and inflammation was different between cohorts, suggesting microbiota-specific mechanisms underlying the beneficial effects of hBD-2. Indeed, we observed differential mechanisms of hBD-2 between cohorts, which included an induction of hepatic and small intestinal IL-17A and IL-22, as well as an increase in T regulatory cell abundance in the gut and mesenteric lymph nodes. Lastly, hBD-2 modulated the gut microbiota composition in EtOH-fed mice in both cohorts, with significant decreases in multiple genera including Barnesiella, Parabacteroides, Akkermansia, and Alistipes, as well as altered abundance of several bacteria within the family Ruminococcaceae. Collectively, our results demonstrated a protective effect of hBD-2 in experimental ALD associated with immunomodulation and microbiota alteration. These data suggest that while the beneficial effects of hBD-2 on liver injury are uniform, the specific mechanisms of action are associated with baseline microbiota.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".