miR-107-enriched exosomes promote ROS/wnt/autophagy, inhibit intracellular mycobacterial growth and attenuate lung infection
Bibliographic record
Abstract
) infection remain poorly characterized. Here, we found that miR-107 highly expressed in exosomes from plasma of TB patients but not healthy control (HC) subjects. Consistently, such miR-107-high exosomes were also detected in both the extracellular fluid released by mycobacterial-infected macrophages and the plasma of mycobacterial-infected mice. Interestingly, adding the miR-107-high plasma exosomes or the miR-107 mimics to infected THP-1 macrophages inhibited intracellular mycobacterial growth. Consistently, while nanoscale and fluorescence imaging revealed that miR-107 could be transferred inter-cellularly via exosomes, miR-107-enriched exosomes from miR-107 overexpressing cells also inhibited mycobacterial growth in THP-1 macrophages and primary monocytes/peripheral blood mononuclear cells (PBMC). Mechanistically, miR-107-high exosomes increased ROS production; miR-107 regulated Wnt pathway by targeting Wnt16 and promoted autophagy in THP-1 macrophages. Furthermore, treatment of infected mice with miR-107-enriched exosomes reduced mycobacterial infection in lung tissues. Our results raise a possibility to explore miR-107-high plasma exosomes for a potential surrogate marker for TB. Findings suggest that exosomes enriched with miR-107 or other bio-active molecules may potentially serve as an attractive approach for treatment of infection.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".