Nitrogen isotopic discrimination in dietary amino acids: The threonine anomaly
Bibliographic record
Abstract
Rationale The “Threonine Anomaly” relates to an observation made 25 years ago on the change in Thr nitrogen isotopic ratio in mammalian metabolism. Unlike all other amino acids, Thr in body protein is found to be depleted (rather than enriched) in 15 N relative to dietary Thr. Interpreting isotopic discrimination has become a useful source of ecological and palaeodietary information and it is desirable that the underlying processes are understood. Methods The principal enzyme of threonine catabolism, suggested to be responsible for the anomaly, threonine dehydratase, was prepared from rat liver. A time course of incubation of the enzyme with pure threonine was followed, and samples of residual threonine prepared for isotopic analysis by combustion in an automated carbon and nitrogen analyser coupled to a continuous flow isotope ratio mass spectrometer. Results We show experimentally, in vitro , that the enzymic reaction catabolising Thr cannot be responsible for its 15 N depletion. Plots of delta 15 N against both reaction time course and percentage completion show in fact an accelerating enrichment. Conclusions A previously advanced suggestion that the unique catabolic mechanism for threonine was responsible for the anomalous depletion in 15 N is clearly not the case. We therefore offer alternative explanations, based on threonine's role at an organismal rather than cellular level. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".