Tight junction protein gene expression patterns and changes in transcript abundance during development of model fish gill epithelia
Bibliographic record
Abstract
In vertebrates, tight junction (TJ) proteins play an important role in epithelium formation and development, the maintenance of tissue integrity and regulation of TJ permeability. In this study, primary cultured model gill epithelia composed of pavement cells (PVCs) were used to examine TJ protein transcript abundance during the development of epithelium confluence and epithelium resistive properties. Differences in TJ protein expression patterns and transcript abundance between gill models composed of PVCs and models composed of PVCs and mitochondrion-rich cells (MRCs) were also examined. Marked alterations in TJ protein transcript abundance were observed as cells developed to confluence in flask-cultured model gill epithelia. In contrast, during the formation of tissue resistance in insert-cultured epithelia (i.e. epithelia cultured on a permeable substrate), changes in TJ protein mRNA abundance were conservative, despite paracellular marker flux decreasing by orders of magnitude. In both cases significant changes in claudin-8b, -8d, -27b, -28b and -32a transcript abundance were observed, suggesting that temporal alterations in the abundance of these genes are important end points of model gill epithelium integrity. When MRCs were present in cultured gill models, the mRNA abundance of several TJ proteins significantly altered and claudin-10c, -10d and -33b were only detected in preparations that included MRCs. These data provide insight into the role of select TJ proteins in the formation and development of gill epithelia and the maintenance of gill barrier properties. In addition, observations reveal a heterogeneous distribution of claudin TJ proteins in the gill epithelial cells of rainbow trout.
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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 | 0.000 |
| 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".