Construction of pCMV- myd88 and pCMV- traf6 Eukaryotic Expression Vectors in Zebrafish ( Danio rerio )
Bibliographic record
Abstract
Both myeloid differentiation primary response gene 88 (MyD88) and TNF receptor associated factor 6 (TRAF6) are key adaptor molecules in the Toll-like receptors family (TLR). Zebrafish is a unique model organism for studying the innate immune response. In order to construct eukaryotic expression vectors of MyD88 and TRAF6, and use them to study the immune response mechanism, we cloned the full-length CDS sequence of the coding region of zebrafish myd88 and traf6 genes in this study. The full length of zebrafish myd88 and traf6 gene CDS is 855bp and 1629bp, respectively. Structural analysis showed that there are two conserved domains of zebrafish MyD88 including DD domain and TIR domain; four conserved domains of TRAF6 including RING domain, two zf domains, coiled-coil and MATH. And they have high amino acid sequence identity with other species. Phylogenetic tree analysis found that the zebrafish myd88 or traf6 gene has high structural homology and closely related to teleost fish, indicating that they are consistent with their evolutionary status. Then the CDS sequence of myd88 and traf6 genes were constructed to the expression vector of pCMV-Tag2B. Zebrafish pCMV-myd88 and pCMV-traf6 eukaryotic expression vectors were successfully cloned by double enzyme digestion. In order to verify the biological function of these two eukaryotic expression vectors, we performed NF-κB reporter gene verification in HEK293T cell. After overexpression of myd88 and traf6 gene, the transcriptional activity of nfκb1 in zebrafish NF-κB family is significantly increased, about 2.5 and 8 times, respectively, contract to that of the control group. In view of the important role of MyD88 and TRAF6 in innate immune function, our results provide a powerful research tool for the future study on the innate immune signal transduction process of zebrafish.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".