Sphingolipid Profiling Reveals Different Extent of Ceramide Accumulation in Bovine Retroperitoneal and Subcutaneous Adipose Tissues
Bibliographic record
Abstract
Sphingolipids are bioactive lipids that can modulate insulin sensitivity, cellular differentiation, and apoptosis in a tissue-specific manner. However, their comparative profiles in bovine retroperitoneal (RPAT) and subcutaneous adipose tissue (SCAT) are currently unknown. We aimed to characterize the sphingolipid profiles using a targeted lipidomics approach and to assess whether potentially related sphingolipid pathways are different between SCAT and RPAT. Holstein bulls (n = 6) were slaughtered, and SCAT and RPAT samples were collected for sphingolipid profiling. A total of 70 sphingolipid species were detected and quantified by UPLC-MS/MS in multiple reaction monitoring (MRM) mode, including ceramide (Cer), dihydroceramide (DHCer), sphingomyelin (SM), dihydrosphingomyelin (DHSM), ceramide-1-phosphate (C1P), sphingosine-1-phosphate (S1P), galactosylceramide (GalCer), glucosylceramide (GluCer), lactosylceramide (LacCer), sphinganine (DHSph), and sphingosine (Sph). Our results showed that sphingolipids of the de novo synthesis pathway, such as DHSph, DHCer, and Cer, were more concentrated in RPAT than in SCAT. Sphingolipids of the salvage pathway and the sphingomyelinase pathway, such as Sph, S1P, C1P, glycosphingolipid, and SM, were more concentrated in SCAT. Our results indicate that RPAT had a greater extent of ceramide accumulation, thereby increasing the concentration of further sphingolipid intermediates in the de novo synthesis pathway. This distinctive sphingolipid distribution pattern in RPAT and SCAT can potentially explain the tissue-specific activity in insulin sensitivity, proinflammation, and oxidative stress in RPAT and SCAT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".