Tight Junction Elaboration Aids Hypersaline Secretion in Euryhaline Teleost Fish
Bibliographic record
Abstract
We examined how the Na + conductive paracellular pathway between ionocytes and accessory cells is modified in hypersaline conditions to allow Na + secretion in extreme environments. Mummichogs ( Fundulus heteroclitus ) were acclimated to seawater (SW) or hypersaline conditions (2SW, 64 ‰) for 30 days. In 2SW fish, invasive cellular projections of accessory cells with increased area of simple tight junctions were detected by the punctate distribution of CFTR immunofluorescence and by TEM ultrastructure of the opercular epithelium (OE), a gill‐like tissue rich in mitochondrion‐rich ionocytes. Punctate CFTR distribution was not explained by membrane raft organization, as chlorpromazine (50 μM) and filipin (1.5 μM) were ineffective in changing electrophysiology of OE. Isolated OE from SW fish with SW on the mucosal side had a transepithelial potential (V t ) of +40.1 + 0.9 mV (n = 24), sufficient to allow passive Na + secretion (Nernst equilibrium potential ≡ E Na = +24.11 mV). OE from fish acclimated to 2SW and bathed in 2SW (mucosal side) had significantly higher V t of +45.1 + 1.2 mV (n = 24; P < 0.001) and plasma Na + was slightly elevated, sufficient for passive Na + secretion (E Na = +40.74 mV), but with a severely diminished net driving force. Paradoxically, estimates of shunt conductance from epithelial conductance (G t ) vs. short‐circuit current (I sc ) plots (extrapolation to zero I sc ) revealed significant reduction in total epithelial shunt conductance in 2SW acclimated fish. We conclude that whereas most epithelial tight junctions become less conductive in hypersaline conditions, those localized to the paracellular Na + exit pathway become more conductive, compensating for lower driving force. Funded by NSERC and CFI.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".