Evolution of a bispecific G-quadruplex-forming circular aptamer to block IL-6/sIL-6R interaction for inflammation inhibition
Bibliographic record
Abstract
IL-6 (interleukin-6) is an essential cytokine that participates in many inflammatory and immune responses, and disrupting the interaction between IL-6 and its receptor sIL-6R (soluble form of IL-6 receptor) represents a promising treatment strategy for inflammation and related diseases. Herein we report the first-ever effort of evolving a bispecific circular aptamer, named CIL-6A6-1, that is capable of binding both IL-6 and sIL-6R with nanomolar affinities and is stable in serum for more than 48 hours. CIL-6A6-1 can effectively block the IL-6/sIL-6R interaction and significantly inhibit cell inflammation. Most importantly, this bispecific aptamer is much more effective than aptamers that bind IL-6 and sIL-6R alone as well as tocilizumab, a commercially available humanized monoclonal antibody against sIL-6R, highlighting the advantage of selecting bispecific circular aptamers as molecular tools for anti-inflammation therapy. Interestingly, CIL-6A6-1 is predicted to adopt a unique structural fold with two G-quadruplex motifs capped by a long single-stranded region, which differs from all known DNA aptamers. This unique structural fold may also contribute to its excellent functionality and high stability in biological complex media. We anticipate that our study will represent a significant step forward towards demonstrating the practical utility of bispecific DNA aptamers for therapeutic applications.
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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".