2-B-158 - Impact of ependymal cell metabolic perturbation on brain function
Bibliographic record
Abstract
Authors: Nilesh Sharma¹, Jeff Biernaskie¹ ¹University of Calgary Abstract: Metabolic regulation is thought to be an important feature within stem cell niches. Recent work suggests that ependymal cells (ECs), that line the ventricular system of the brain, could be critical players in regulating the ventricular-subventricular zone (V-SVZ). ECs are multi-ciliated glial cells that are responsible for regulating the neural stem cell niche and propelling the cerebrospinal fluid. Transcriptional profiling of the adult SVZ niche showed that ECs are highly enriched in glucose transporter 1 (GLUT1), leading us to hypothesize that ECs may regulate NSC behavior by modulating metabolism within the V-SVZ. To test this, I performed a conditional deletion of GLUT1 in adult ECs in vivo using aSMACreERT2:ROSATdTomato:GLUT1flox/flox mice to delete GLUT1 in aSMA+ ECs. At 1-month post-GLUT1 deletion, an increase in overall proliferation (marked with Ki67) was observed within the V-SVZ niche. A sex dimorphic effect was observed on neurogenesis; with females displaying a reduction in the number of DCX+ neuroblasts, while males exhibited no change. Interestingly, this reduction was more pronounced in the anterior V-SVZ compared to posterior V-SVZ, suggesting sensitivity to GLUT1KO might be spatially dependent. There was also a marked increase in GFAP staining and an accumulation of lipid droplets within the V-SVZ post-GLUT1 deletion suggesting that disruption in glucose metabolism may perturb local lipid metabolism. Altogether, these results indicate that EC metabolism regulates the NSC niche and may play a broader role in maintaining brain homeostasis.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".