Synthetic methylated CpG ODNs are potent in vivo adjuvants when delivered in liposomal nanoparticles
Bibliographic record
Abstract
Although it is well documented that the immunological activity of cytosine-guanine (CpG) motifs is abrogated by 5' methylation of the cytosine residue, encapsulation within stabilized lipid nanoparticles endows these methylated cytosine-guanine- (mCpG-) containing oligonucleotides (ODNs) with potent immunostimulatory activity in murine animal models. Surprisingly, not only do liposomal nanoparticulate (LN) mCpG ODN possess immunostimulatory activity, their potency is found to be equivalent and often greater than the equivalent unmethylated form, as judged by a number of ex vivo innate and adaptive immune parameters and anti-tumor efficacy in murine models. Preliminary data indicate that both methylated and unmethylated CpG ODN act through a common receptor signaling pathway, specifically via toll-like receptor (TLR) 9, based on observations of up-regulated TLR9 expression, induction of nitric oxide and dependence on endosomal maturation. This is confirmed in TLR9 knockout animals which show no immunostimulatory activity following treatment with LN-mCpG ODN. These data, therefore, indicate that the mCpG DNA is fully competent to interact with TLR9 to initiate potent immune responses. Furthermore, this work implicates an as yet unidentified mechanism upstream of TLR9 which regulates the relative activities of free methylated versus unmethylated CpG ODN that is effectively bypassed by particulate delivery of CpG ODN.
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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".