Elucidating the Motif for CpG Oligonucleotide Binding to the Dendritic Cell Receptor DEC-205 Leads to Improved Adjuvants for Liver-Resident Memory
Bibliographic record
Abstract
DEC-205 is a cell-surface receptor that transports bound ligands into the endocytic pathway for degradation or release within lysosomal endosomes. This receptor has been reported to bind a number of ligands, including keratin, and some classes of CpG oligodeoxynucleotides (ODN). In this study, we explore in detail the requirements for binding ODNs, revealing that DEC-205 efficiently binds single-stranded, phosphorothioated ODN of ≥14 bases, with preference for the DNA base thymidine, but with no requirement for a CpG motif. DEC-205 fails to bind double-stranded phosphodiester ODN, and thus does not bind the natural type of DNA found in mammals. The ODN binding preferences of DEC-205 result in strong binding of B class ODN, moderate binding to C class ODN, minimal binding to P class ODN, and no binding to A class ODN. Consistent with DEC-205 binding capacity, induction of serum IL-12p70 or activation of B cells by each class of ODN correlated with DEC-205 dependence in mice. Thus, the greater the DEC-205 binding capacity, the greater the dependence on DEC-205 for optimal responses. Finally, by covalently linking a B class ODN that efficiently binds DEC-205, to a P class ODN that shows poor binding, we improved DEC-205 binding and increased adjuvancy of the hybrid ODN. The hybrid ODN efficiently enhanced induction of effector CD8 T cells in a DEC-205-dependent manner. Furthermore, the hybrid ODN induced robust memory responses, and was particularly effective at promoting the development of liver tissue-resident memory T cells.
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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 | 0.001 |
| 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".