microRNA-184 distribution and consequences on glial septate junctions and the blood-brain barrier
Bibliographic record
Abstract
Cellular permeability barriers restrict the diffusion of solutes, pathogens, and cells across tissues. In Drosophila melanogaster, septate and tricellular junctions create permeability barriers in epithelia and the blood-brain barrier in glia. In vitro, and in vivo studies in the epithelia of the Drosophila wing imaginal discs identified microRNA-184 (miR-184) as a potential regulator of a subset of pleated septate and tricellular junction proteins. However, which tissues express miR-184, and the consequences of miR-184 expression on the blood-brain barrier has not been examined. Using a miR-184 sensor, we found that miR-184 is absent in tissues with pleated septate junctions but is present in tissues with smooth septate junctions. When expressed in the subperineurial glia that form the blood-brain barrier, miR-184 resulted in the loss of targeted septate junction proteins, a compromised blood-brain barrier, decreased locomotion, and lethality. Interestingly, qRT-PCR analysis revealed that miR-184 expression did not alter mRNA levels of targeted genes. Conversely, expression of miR-184 led to an increase in the mRNA and expression of the non-target Nervana2 protein. Thus, mRNA-184 can regulate multiple pleated septate junction proteins either directly through loss of translation or indirectly by disruption of the septate junction domain.
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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".