Nucleic Acids Exert a Sequence-independent Cooperative Effect on Sequence-dependent Activation of Toll-like Receptor 9
Bibliographic record
Abstract
Toll-like receptor 9 (TLR9) activates the innate immune system in response to microbial DNA or mimicking oligodeoxynucleotides. Although cell stimulation experiments demonstrate the preferential activation of TLR9 by CpG-containing nucleic acids, direct binding investigations have reached contradictory conclusions with respect to the ability of this receptor to bind nucleic acids in a sequence-specific manner. To address this apparent discrepancy, we report the purification of the soluble ectodomain of human TLR9 with characterization of its ligand binding properties. We observe that TLR9 has a high degree of specificity in its ability to bind nucleic acids that contain CpG dinucleotides as well as higher order motifs that mediate species-specific activation. However, TLR9 is also functionally influenced by nucleic acids in a sequence-independent fashion as both stimulatory and nonstimulatory nucleic acids sensitize TLR9 for in vitro ligand binding as well as in vivo activation. We propose a model in which receptor activation is achieved in a sequence-dependent manner, and sensitivity is modulated by the absolute concentration of nucleic acids in a sequence-independent fashion. This model bears resemblance to that recently proposed for Toll in that activation is a two-step process in which formation of a ligand-bound monomer precedes formation of the activated dimer. In each model receptor sensitivity is determined within the second step with the crucial distinction that Toll undergoes negative cooperativity, whereas TLR9 is sensitized through a positive cooperative effect.
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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) | 0.002 | 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".