Systemic Treatment with a miR‐146a Mimic Suppresses Endotoxin Sensitivity and Partially Protects Mice from the Progression of Acute Graft‐versus‐Host Disease
Bibliographic record
Abstract
Abstract Acute GVHD (aGVHD) is driven by interactions between the allogenic T cell response, inflammation, tissue injury and microbial products that enter the circulation when protective barriers such as the intestinal epithelium become compromised. Mice with aGVHD become hypersensitive to LPS, secreting large quantities of inflammatory mediators that exacerbate tissue injury. We hypothesized that microRNA (miR) modulators could be used in vivo to mitigate LPS hypersensitivity, altering the course of aGVHD. Using the C57BL/6 → (C57BL/6 × DBA/2)F1‐hybrid model of aGVHD, we measured intestinal permeability over time and used a qPCR array to detect concomitant changes in the expression levels of certain microRNAs (miRs) in the intestine. Large increases in permeability were seen on day 15, when endotoxemia becomes detectable and GVHD‐associated histopathological lesions develop. Amongst the miRs with altered expression levels were some that regulate sensitivity to endotoxin. We chose to focus on miR‐146a and treated recipient mice systemically with a miR‐146a mimic early in the GVH reaction. This led to a reduction in the burst of IFNγ that likely plays a priming role in the mechanism underlying heightened sensitivity to endotoxin. LPS‐induced TNFα release and GVHD‐associated weight loss were also diminished and survival was prolonged. In summary, systemic treatment with a miR‐146a mimic dampens the heightened sensitivity to LPS that occurs concomitantly with increased intestinal permeability and provides partial protection from the progression of acute GVHD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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".