Using optical tools to probe chloride (dys)regulation in stress circuits
Bibliographic record
Abstract
Authors: Jaideep Bains¹, Aaron Lanz¹ ¹University of Calgary Abstract: In mammals, an immediate threat activates multiple, interconnected neural networks to launch an innate behavioral program that maximizes the probability of survival. These networks also drive corticotropin-releasing hormone (CRH) neurons in the paraventricular nucleus of the hypothalamus (PVN) to release hormones that allow the animal to cope in the face of challenge and restore homeostasis. CRH neurons are tightly regulated by inhibitory GABA synapses. The initiation of the endocrine cascade requires a dephosphorylation of the K-Cl co-transporter, KCC2, which compromises Cl buffering and depolarizes the ECl. Although the downregulation of KCC2 has been linked to alterations in cell output, the intricacies of chloride dysregulation on circuit function have not been fully explored. Fluctuations in chloride could alter spike pattern generation to promote bursting or alter population encoding. Additionally, GABA-mediated calcium spikes could change plasticity rules at inhibitory and excitatory synapses. To study chloride homeostasis in hypothalamic CRH neurons, we exploited the light-sensitive inward chloride pump, halorhodopsin, to manipulate chloride gradients with high temporal precision. We show that in cells expressing this pump, photostimulation is sufficient to collapse the Cl gradient at GABA synapses; the recovery from this collapse is rapid (approximately 5 s). Pharmacological inhibition of KCC2 or acute stress prolonged the recovery following Cl loading and also revealed an increase in bursting activity in CRH neurons. Furthermore, inhibition of KCC2 was also sufficient to gate an activity-dependent form of metaplasticity at glutamate synapses onto CRH neurons. We propose that local chloride gradients in CRH neurons control membrane excitability to regulate dendritic peptide release and plasticity at neighbouring synapses.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".