Mapping of Selenium Metabolic Pathway in Yeast by Liquid Chromatography−Orbitrap Mass Spectrometry
Bibliographic record
Abstract
A high-resolution mass spectrometric detection method is described for the identification of key metabolites in the selenium pathway in selenium enriched yeast. Iodoacetic acid (IAA) was used as the derivatizing reagent to stabilize the selenols. Oxidized forms of selenocysteine (Se-Cys), selenohomocystine (Se-HCys), selenoglutathione (Se-GSH), seleno-γ-glutamyl-cysteine (Se-Glu-Cys), N-(2,3-dihydroxy-1-oxopropyl)-selenocysteine (Se-DOP-Cys), N-(2,3-dihydroxy-1-oxopropyl)-selenohomocysteine (Se-DOP-HCys), selenomethionine (SeMet), seleno-S-adenosyl-homocysteine (Se-AdoHcy), the conjugate of glutathione and N-(2,3-dihydroxy-1-oxopropyl)-selenocysteine (GSH-Se-DOP-Cys), and the conjugate of glutathione and N-(2,3-dihydroxy-1-oxopropyl)-selenohomocysteine (GSH-Se-DOP-HCys) were found in the selenium enriched yeast certified reference material (SELM-1). Selenols were also derivatized with a mercury tag, p-hydroxymercurybenzoate (PHMB). The selenol-PHMB complexes showed the overlapped isotopic patterns of selenium and mercury, which provided supporting information for the identification of selenols. Both methods showed good agreement (<4 ppm difference) between the theoretical masses of the target compounds and the measured masses in the yeast matrix. The method using IAA as the derivatizing reagent was used to study the response of Saccharomyces cerevisiae to three forms of selenium, Se-Met, Na(2)SeO(3) (Se(IV)), and Na(2)SeO(4)·10H(2)O (Se(VI)) (concentration of Se: 100 mg/L). The production of selenocompounds observed over a 6 h period was high in the Se-Met treated group compared to the groups treated with Se(IV) and Se(VI).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".