Identification of Selenium-Containing Glutathione S-Conjugates in a Yeast Extract by Two-Dimensional Liquid Chromatography with Inductively Coupled Plasma MS and Nanoelectrospray MS/MS Detection
Bibliographic record
Abstract
An approach for the identification of unknown selenium-containing biomolecules was developed, enabling the identification of selenodiglutathione (GS-Se-SG) and the mixed selenotrisulfide of glutathione and cysteinylglycine (GS-Se-SCG) in aqueous yeast extracts. The method consists of two-dimensional liquid chromatography, inductively coupled plasma mass spectrometry (ICPMS) and nanoelectrospray tandem mass spectrometry. Analytes were separated by size-exclusion chromatography followed by preconcentration and separation on a porous graphitic carbon HPLC column. The HPLC effluent was monitored for selenium by ICPMS, and two selenium-containing fractions were isolated and analyzed by nanoelectrospray MS. The nanoelectrospray technique has a low sample consumption of approximately 80 nL/min, enabling a preconcentration of the sample to a few microliters. Mass spectra of the two fractions showed the characteristic Se isotopic pattern centered at m/z 693.1 and 564.0 for the [M + H]+ 80Se ions. MS/MS spectra of adjacent parent ions confirmed the presence of Se. The two selenium species were identified as GS-Se-SG and GS-Se-SCG by collision induced dissociation (CID). The accurately measured masses of the most abundant 691 and 693 u parent ions are in good agreement (differences = 3 ppm) with the theoretical masses. To our knowledge, this is the first identification of GS-Se-SG and GS-Se-SCG in biological matrixes by MS/MS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".