Quantification of seminolipid by LC-ESI-MS/MS-multiple reaction monitoring: compensatory levels in Cgt mice
Bibliographic record
Abstract
Seminolipid, also known as sulfogalactosylglycerolipid (SGG), plays important roles in male reproduction. Therefore, an accurate and sensitive method for SGG quantification in testes and sperm is needed. Here we compare SGG quantitation by the traditional colorimetric Azure A assay with LC-ESI-MS/MS using multiple reaction monitoring (MRM). Inclusion of deuterated SGG as the internal standard endowed accuracy to the MRM method. The results showed reasonable agreement between the two procedures for purified samples, but for crude lipid extracts, the colorimetric assay significantly overestimated the SGG content. Using ESI-MS/MS MRM, C16:0-alkyl/C16:0-acyl SGG of Cgt(+/⁻) mice was quantified to be 406.06 ± 23.63 μg/g testis and 0.13 ± 0.02 μg/million sperm, corresponding to 78% and 87% of the wild-type values, respectively. CGT (ceramide galactosyltransferase) is a critical enzyme in the SGG biosynthesis pathway. Cgt⁻/⁻ males depleted of SGG are infertile due to spermatogenesis arrest. However, Cgt(+/⁻) males sire offspring. The higher than 50% expression level of SGG in Cgt(+/⁻) animals, compared with the wild-type expression, might be partly due to compensatory translation of the active CGT enzyme. The results also indicated that 78% of SGG levels in Cgt(+/⁻) mice were sufficient for normal spermatogenesis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".