Diacylglycerol-Containing Docosahexaenoic Acid in Acyl Chain Modulates Airway Smooth Muscle Tone
Bibliographic record
Abstract
We synthesized and assessed the role of a diacylglycerol (DAG)-containing docosahexaenoic acid (DHA), that is, 1-stearoyl-2-docosahexaenoyl-sn-glycerol (SDHG), in the contraction of guinea pig airway smooth muscle (ASM). We compared its action with 1-stearoyl-2-arachidonoyl-sn-glycerol (SAG) and 1,2-dioctanoyl-sn-glycerol (1,2-DiC8), a stable DAG analog. The three DAGs (SAG, SDHG, and 1,2-DiC8) induced reversible concentration-dependent contraction of ASM. SDHG induced higher guinea pig ASM contraction than did SAG and 1,2-DiC8. The effects of SDHG were blocked, to different extents, by nifedipine (L-type Ca2+ channel blocker). By employing GF-109203X (protein kinase C [PKC] inhibitor) and lanthanum (La3+), a nonselective cation channel blocker, we observed that SDHG evoked ASM contractile response via PKC-dependent and PKC-independent (but Ca2+-dependent) pathways. Interestingly, SAG exerted its action only by increasing [Ca2+]i and did not require PKC activation. To probe the implication of calcium mobilization, we employed thapsigargin (TG), which also induced ASM contraction in a calcium-dependent manner. SDHG and 1,2-DiC8, in a PKC-dependent manner, induced the phosphorylation of CPI-17 (myosin light chain phosphatase inhibitor of 17 kD). Furthermore, SAG and TG failed to phosphorylate CPI-17 in ASM cells. Our results suggest that different DAG species, produced during a dietary supplementation with fatty acids, could modulate the reactivity of airway smooth muscles in a PKC-dependent and -independent manner, and hence, may play a critical role in health and disease.
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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) | 0.001 | 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".