Toll-Like Receptor 4 Activation Reduces Adrenal Chromaffin Cell Excitability Through a Nuclear Factor-κB-Dependent Pathway
Bibliographic record
Abstract
The adrenal medulla contains fenestrated capillaries that allow catecholamines and neuropeptides secreted by adrenal chromaffin cells (ACCs) to readily access the circulation. These capillaries may also allow bacterial products to enter the adrenal medulla and interact with ACCs during infection. One potential mediator of this interaction is toll-like receptor 4 (TLR-4), a pattern-recognition receptor that detects lipopolysaccharide (LPS) from Gram-negative bacteria. Evidence suggests that excitable cells can express TLR-4 and that LPS can modulate important neuronal and endocrine functions. The present study was therefore performed to test the hypothesis that TLR-4 activation by LPS affects ACC excitability and secretory output. RT-PCR revealed that TLR-4, cluster of differentiation 14, myeloid differentiation protein-2, and myeloid-derived factor 88 are expressed within mouse adrenal medullae. TLR-4 immunoreactivity was observed within all tyrosine hydroxylase immunoreactive ACCs. Incubation of isolated ACCs in LPS dose dependently hyperpolarized the resting membrane potential and enhanced large conductance (BK) Ca(2+)-activated K(+) currents. LPS (10 μg/ml) also increased rheobase, decreased the number of action potentials fired at rheobase, and reduced the percentage of ACCs exhibiting spontaneous and anodal break action potentials. Although catecholamine release was unaltered, LPS significantly reduced high-K(+)-stimulated neuropeptide Y release from isolated ACCs. LPS did not alter the excitability of ACCs from TLR-4(-/-) mice. Inhibition of nuclear factor-κB signaling with SC-514 (20 μm) abolished the effects of LPS on ACC excitability. Our findings suggest that LPS acts at TLR-4 to reduce ACC excitability and neuropeptide Y release through an nuclear factor-κB-dependent pathway.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".