Resolving Hexose-Phosphates by LC-MS Leads to New Insights in PGM1-CDG Pathophysiology
Bibliographic record
Abstract
Hexose-phosphates play a role in many metabolic pathways, such as glycolysis and glycosylation. To understand the molecular basis of diseases such as congenital disorders of glycosylation (CDG), information about the source and abundance of hexose-phosphates is imperative. Mass spectrometry (MS)-based tracer metabolomics can provide this information, but hexose-phosphates are structural isomers with similar physicochemical properties, which makes them difficult to differentiate using MS. Here, we present and compare two optimized liquid-chromatography-based MS methods for the identification of relevant hexose-phosphates, compatible with tracer metabolomics. A combination of these two methods led to the analysis of eight hexose-monophosphates and two hexose-bisphosphates that can occur in humans. Both methods displayed linearity in 3 to 4 orders of magnitude, with limits of quantification between 0.5 and 50 nM, which is well within the cellular concentration range. The applicability of these methods to biological models was then proven in a study of the effect of galactose treatment in phosphoglucomutase 1 (PGM1)-CDG fibroblasts. Here, we show, for the first time, the hexose-phosphate profiles in CDG and how these change upon treatment.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".