Presence of Arylsulfatase A and Sulfogalactosylglycerolipid in Mouse Ovaries: Localization to the Corpus Luteum
Bibliographic record
Abstract
Arylsulfatase A (AS-A) is a lysosomal enzyme, which catalyzes the desulfation of certain sulfogalactolipids, including sulfogalactosylglycerolipid (SGG), a molecule implicated in cell adhesion. In this report, immunocytochemistry revealed the selective presence of AS-A in the corpus luteum of mouse ovaries. Immunoblotting indicated that mouse corpus luteum AS-A had a molecular mass of 66 kDa, similar to AS-A of other tissues. Corpus luteum AS-A was active, capable of desulfating the artificial substrate, p-nitrocatechol sulfate, at the optimum pH of five. To understand further the role of AS-A in female reproduction, levels of AS-A were determined during corpus luteum development in pseudopregnant mice and during luteolysis after cessation of pseudopregnancy. Immunocytochemistry, immunoblotting and desulfation activity showed that AS-A expression was evident at the onset of pseudopregnancy in the newly formed corpora lutea, and its level increased steadily during gland development. The increase in the expression and activity of AS-A continued throughout luteolysis after the decrease in serum progesterone levels. We also observed the selective presence of SGG on the luteal cell surface in developed corpora lutea, as shown by immunofluorescence of mouse ovary sections as well as high-performance thin-layer chromatography of lipids isolated from mouse and pig corpora lutea. The identity of the "SGG" band on the thin layer silica plate was further validated by electrospray ionization mass spectrometry. Significantly, SGG disappeared in regressing corpora lutea. Therefore, lysosomal AS-A may be involved in cell-surface remodeling during luteolysis by desulfating SGG after its endocytosis and targeting to the lysosome.
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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 | 0.001 | 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".