Validation and Optimization of a Stable Isotope-Labeled Substrate Assay for Measuring AGAT Activity
Bibliographic record
Abstract
L-arginine: glycine amidinotransferase (AGAT) gained academic interest as the rate-limiting enzyme in creatine biosynthesis and its role in the regulation of creatine homeostasis. Of clinical relevance is the diagnosis of patients with AGAT deficiency but also the potential role of AGAT as therapeutic target for the treatment of another creatine deficiency syndrome, guanidinoacetate N-methyltransferase (GAMT) deficiency. Applying a stable isotope-labeled substrate method, we utilized ARG 15N2 (ARG-δ2) and GLY 13C215N (GLY-δ3) to determine the rate of 1,2-13C2,15N3 guanidinoacetate (GAA-δ5) formation to assess AGAT activity in various mouse tissue samples and human-derived cells. Following modification and optimization of the assay, we analyzed AGAT activity in several mouse organs. The Km and Vmax of AGAT in mouse kidney for GLY-δ3 were 2.06 mM and 6.48 ± 0.26 pmol/min/mg kidney, and those for ARG-δ2, they were 2.67 mM and 2.17 ± 0.49 pmol/min/mg kidney, respectively. Our results showed that mouse kidneys had the highest levels of enzymatic activity, followed by brain and liver, with 4.6, 1.8, and 0.4 pmol/min/mg tissue, respectively. Both the heart and muscle had no detectable levels of AGAT activity. We noted that due to interference with arginase in the liver, performing the enzyme assay in liver homogenates required the addition of Nor-NOHA, an arginase inhibitor. In immortalized human cell lines, we found the highest levels of AGAT activity in RH30 cells, followed by HepaRG, HAP1, and HeLa cells. AGAT activity was readily detectable in lymphoblasts and leukocytes from healthy controls. In our assay, AGAT activity was not detectable in HEK293 cells, in human fibroblasts, and in the lymphoblasts of a patient with AGAT deficiency. Our results demonstrate that this enzyme assay is capable of accurately quantifying AGAT activity from both tissues and cells for diagnostic purposes and research.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".