Mincle-binding DNA aptamer demonstrates therapeutic potential in a model of inflammatory bowel disease
Bibliographic record
Abstract
Pattern recognition receptors such as Mincle (Clec4e) play a significant role in the regulation of inflammation. Enhanced signaling of Mincle through the release of damage-associated molecular patterns during sterile inflammation has been shown to be important in the progression and manifestation of several diseases. A limitation to Mincle-targeted therapeutics is the feasibility of human-scale antibody therapy and the lack of alternative small-molecule inhibitors. Herein, we describe a highly specific neutralizing DNA aptamer targeting Mincle and demonstrate its therapeutic potential. Our data demonstrate that AptMincle selectively binds to both human and mouse Mincle with high affinity and is able to directly target and reduce Mincle activation. AptMincle can specifically reduce trehalose-6,6-dibehenate (TDB)-induced Syk and P65 phosphorylation in vitro in a manner comparable to that of the commercially available neutralizing antibody in vitro . Moreover, a bio-stable modified aptamer, AptMincle DRBL , was successful in reducing disease activity in a dextran sodium sulfate (DSS)-induced model of ulcerative colitis in a dose- and sequence-dependent manner. The results present an alternative, highly specific DNA aptamer with antagonistic function for use in the investigation of Mincle-associated diseases. The data also show the translational potential of Mincle-targeting aptamers as a new category of biologic therapy in the treatment of inflammatory bowel disease (IBD).
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".