Direct binding and internalization of diverse extracellular nucleic acid species through the collagenous domain of class A scavenger receptors
Bibliographic record
Abstract
Nucleic acids are potential pathogen-associated or danger-associated molecular patterns that modulate immune responses and the development of autoimmune disorders. Class A scavenger receptors (SR-As) are a diverse group of pattern recognition receptors that recognize a variety of polyanionic ligands including nucleic acids. While SR-As are important for the recognition and internalization of extracellular dsRNA, little is known about extracellular DNA, despite its association with chronic infections and autoimmune disorders. In this study, we investigated the specificity of and requirement for SR-As in binding and internalizing different species, sequences and lengths of nucleic acids. We purified recombinant coiled-coil/collagenous and scavenger receptor cysteine-rich (SRCR) domains that have been implicated as potential ligand-binding domains. We detected a direct interaction of RNA and DNA species with the coiled-coil/collagenous domain, but not the SRCR domain. Despite the presence of additional surface receptors that bind nucleic acids, SR-As were found to be sufficient for nucleic acid binding and uptake in A549 human lung epithelial cells. Moreover, these findings suggest that the coiled-coil/collagenous domain of SR-As is sufficient to bind nucleic acids independent of species, sequence or length.
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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 | 0.000 | 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".