Single-stranded DNA oligonucleotides containing CpG motifs are non-stimulatory in vitro but offer protection in vivo against Burkholderia pseudomallei
Bibliographic record
Abstract
Therapies that modulate and appropriately direct the immune response are promising candidates for the treatment of infectious diseases. One such candidate therapeutic is DZ13, a short, synthetic, single-stranded DNA molecule. This molecule has enzymatic activity and can modulate the immune response by binding to and degrading the mRNA encoding a key immuno-regulatory molecule. Originally developed and entering clinical trials as an anti-cancer agent, DZ13 has also been evaluated as a treatment for viral infections, and has been shown to provide protection against infection with influenza virus in a mouse model of infection. In this work, we evaluated whether the immuno-modulatory properties of DZ13 could provide protection against the potential biothreat pathogen Burkholderia pseudomallei which causes the neglected tropical disease melioidosis. Treatment of mice infected with B. pseudomallei demonstrated that DZ13 did indeed provide excellent protection after only two post-exposure treatments. However, our data indicated that the enzymatic activity contained in DZ13 was not required for protection, with control oligonucleotide treatments lacking activity against the target mRNA equally as protective against B. pseudomallei. We have designed new sequences to study the mechanism of protection further. These novel sequences offer enhanced protection against infection, but are not directly anti-microbial and do not appear to be stimulating the immune system via TLR9 or other key innate immune sensors, despite containing CpG motifs. The molecular mechanism of these novel sequences remains to be elucidated, but the data highlights that these oligonucleotide-sensing pathways are attractive and relevant targets to modulate during bacterial and viral infections.
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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 | 0.000 |
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".