Cytosine-Phosphate-Guanine Oligodeoxynucleotides Containing GACGTT Motifs Enhance the Immune Responses Elicited by Keyhole Limpet Hemocyanin Antigen in Dairy Cattle
Bibliographic record
Abstract
Adjuvants are important components of vaccine formulations. Effective adjuvants line innate and adaptive immunity by signaling through pathogen recognition receptors. Synthetic cytosine-phosphate-guanine (CpG) oligodeoxynucleotides (ODNs) have been shown to have potentials as adjuvants for vaccines. However, the immunostimulatory effect of CpG is species-specific and depends on the sequence of CpG motifs. A CpG ODN (2135), containing 3 identical copies of GTCGTT motif, was previously reported to have the strongest effects on bovine peripheral blood mononuclear cells (PBMC). Based on the sequence of 2135, we replaced the GTCGTT motif with 11 other sequences containing CG and investigated their effects on bovine lymphocyte proliferation. Results showed that the CpG ODNs containing 3 copies of GACGTT motif had the highest lymphocyte stimulation index (7.91±1.18), which was significantly (P<0.05) higher than that of 2135 (4.25±0.56). The CpG ODNs containing 3 copies of GACGTT motif also significantly increased the mRNA expression of interferon (IFN)-α, interleukin (IL)-12, and IL-21 in bovine PBMC. When dairy cows were immunized with the keyhole limpet hemocyanin (KLH) antigen formulated with CpG ODNs containing 3 copies of GACGTT, production of KLH-specific antibodies in serum and in milk whey was significantly (P<0.05) enhanced. IFN-γ in whole blood stimulated by KLH was also significantly (P<0.05) increased in cows immunized with KLH plus CpG ODNs. Our results indicate that CpG ODNs containing 3 copies of the GACGTT motifs is a potential adjuvant for bovine vaccines.
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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".