Determination of Methionine and Selenomethionine in Yeast by Species-Specific Isotope Dilution GC/MS
Why this work is in the frame
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Bibliographic record
Abstract
A method for the simultaneous determination of methionine (Met) and selenomethionine (SeMet) in yeast using species-specific isotope dilution (ID) gas chromatography/mass spectrometry (GC/MS) is described. Samples were digested by refluxing for 16 h with 4 M methanesulfonic acid. Analytes were derivatized with methyl chloroformate and extracted into chloroform for GC/MS analysis. In addition to use of commercially available 13C-enriched Met and SeMet spikes for species specific ID analysis, a 74Se-enriched SeMet spike was also available for comparison of results. In selective ion monitoring mode, the intensities of ions at m/z 221, 222, 269, 270, and 263 were used to calculate the 221/222, 269/270, and 269/263 ion ratios for quantification of Met and SeMet. Concentrations of 5959 +/- 33 and 3404 +/- 12 microg g(-1) (one standard deviation, n = 6) with relative standard deviations of 0.55 and 0.36% for Met and SeMet, respectively, were obtained using 13C-enriched spikes. A concentration of 3417 +/- 8 microg g(-1) (one standard deviation, n = 6) was obtained using the 74Se-enriched SeMet spike. The concentration of SeMet measured in the yeast is equivalent to 66.43 +/- 0.24% of total Se and 30.31 +/- 0.11% of total Met is in the form of SeMet. Method detection limits (three times the standard deviation) of 3.4 and 1.0 microg g(-1) were estimated for Met and SeMet, respectively, based on a 0.25-g subsample of yeast with 1 mL of extract used for derivatization. A similar concentration of 5930 +/- 29 microg g(-1) (one standard deviation, n = 4) for Met and a lower concentration of 2787 +/- 49 microg g(-1) (one standard deviation, n = 4) for SeMet were obtained for this yeast sample using species-specific ID analysis based on GC/MS with 13C-enriched Met and SeMet spikes when a 2-h open microwave digestion approach using 8 M methanesulfonic acid was used.
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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 it