Long-range hypothalamic neuronal projections to the subventricular zone neurogenic niche regulates neural stem cell function through endocannabinoid signaling
Bibliographic record
Abstract
The subventricular zone neurogenic niche receives neuronal inputs from many areas of the brain. Although these inputs are known to regulate neurogenesis through neurotransmitter release at synapse-like connections between neuronal axon terminals and neural stem and progenitor cells (NPCs), few studies have examined how neuronal modulators at these synapse-like connections affect SVZ NPC function. Our recent work identified monoacylglycerol lipase (Mgll), a hydrolase that breaks down endocannabinoid 2-AG, as an extrinsic factor to regulate SVZ NPC function in culture. 2-AG is a well-known retrograde modulator that controls neurotransmitter release. To date, it remains unknown how Mgll-modulated 2-AG signaling in the SVZ niche regulate SVZ NPC behavior in vivo. In this regard, we propose to examine the role of neuronal Mgll activity from distinct brain region in regulating SVZ NPC function. Using retrograde monosynaptic rabies virus tracing and anterograde tracing, we identified direct synapse-like connections between hypothalamic arcuate nucleus (ARC) neuron axon terminals and a subpopulation of NPCs residing in the ventral SVZ microdomain as previously reported. Intriguingly, specific removal of Mgll expression in ARC neurons using virally transfected Cre recombinase in Mgll-floxed mice resulted in a decrease of SVZ NPCs in this microdomain and a subsequent reduction of proliferating cells in this microdomain. These findings provide an understanding of how long-range neuronal Mgll activity regulates adult SVZ NPC function in a microdomain-specific fashion.
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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".