Dysbiosis of the Female Murine Gut Microbiome Exacerbates Neutrophil-mediated Vascular Allograft Injury by Affecting Immunoregulation by Acetate
Bibliographic record
Abstract
BACKGROUND: The gut microbiota affects immune responses that cause organ transplant rejection, but the mechanisms by which this occurs remain poorly understood. METHODS: We have examined, in a murine model, how disruption of the gut microbiota with antibiotics early in life alters this microbial community later in life to affect immune responses that injure vascular allografts. RESULTS: Analysis of 16S rRNA and whole genome sequencing of the gut microbiota demonstrated that early life disruption of this microbial community with antibiotics caused a reduction in taxa and enzymatic genes involved in the synthesis of acetate, an immunoregulatory metabolite in mice and humans. When allograft vascular injury was examined, early life disruption of the gut microbiota increased neutrophil accumulation and related medial injury of transplanted arteries. Normalizing the gut microbiota by co-housing and oral administration of acetate prevented neutrophil-mediated vascular allograft injury. CONCLUSIONS: Dysbiosis of the gut microbiome that reduces its production of the immunoregulatory metabolite acetate exacerbates neutrophil-mediated allograft vascular injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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".