Determining the Role of Wnt Signaling in Zebrafish Oocyte Maturation Through Examination of β-catenin and Dishevelled mRNA Concentrations
Bibliographic record
Abstract
During oocyte maturation, the oocyte progresses from prophase I to metaphase II of meiosis, and a multitude of other cellular changes occur1. Wnt singaling pathways are known to regulate gene expression, cell behavior, cell adhesion, and cell polarity, as well as play an essential role in embryonic development. Because of this, I am examining the role of Wnt signaling pathways in the earlier process of oocyte maturation, specifically by looking at two Wnt signaling pathway components: β-catenin (ctnnb1) and Dishevelled (dvl2). β-catenin is a an interesting protein to study because it plays a dual role as both a cell adhesion protein when attached to membrane-bound complexes, and a coactivator for transcription by the Wnt pathway when free in the cytoplasm. Dishevelled is the “hub” of Wnt signaling and plays a key role in relaying external signals to internal pathway components. Preliminary research has suggested that β-catenin increases in relative cytoplasmic concentration after maturation, and my findings from last summer showed that this change is not the result of migration from cytoskeleton associated membrane-bound complexes. The first step in my research is to determine a reference gene for zebrafish oocyte maturation, as none are well classified for this specific scenario. I examined β –actin, GAPDH and ef1-α, as these were found to have constant expression during zebrafish embryo development or bovine oocyte development. This will be followed by examining the changes in mRNA concentrations for β-catenin and Dishevelled over the course of oocyte maturation in order to determine the role and importance of the Wnt signaling pathway in this process. Changes in mRNA concentrations are determined through real-time RTPCR analysis. The results of my research will contribute to our understanding of the cellular processes which occur during oocyte maturation, and the importance of signaling pathways such as the Wnt pathway in these processes.
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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 | 0.001 | 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) | 0.002 | 0.001 |
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".