Targeted metabolomic analyses of cellular models of pelizaeus-merzbacher disease reveal plasmalogen and myo-inositol solute carrier dysfunction
Bibliographic record
Abstract
BACKGROUND: Leukodystrophies are devastating diseases characterized by dys- and hypo-myelination. While there are a number of histological and imaging studies of these disorders, there are limited biochemical data available. We undertook targeted lipidomic analyses of Pelizaeus-Merzbacher disease (PMD) fibroblasts, PMD lymphocytes, and 158JP oligodendrocytes, a murine model of PMD, to define the lipid changes in these cell models. Further targeted metabolomics analyses were conducted to obtain a preliminary evaluation of the metabolic consequences of lipid changes and gene mutations in these cell models. RESULTS: In both PMD fibroblasts and lymphocytes, and 158JP oligodendrocytes, ethanolamine plasmalogens were significantly decreased. Labeling studies with 158JP oligodendrocytes further demonstrated a decreased rate of lipid remodeling at sn-2. Targeted metabolomics analyses of these cells revealed dramatic increases in cellular levels of myo-inositol. Further uptake studies demonstrated increased rates of myo-inositol uptake by PMD lymphocytes. CONCLUSIONS: Our data demonstrating PlsEtn decrements, support previous studies indicating leukodystrophy cells possess significant peroxisomal deficits. Our data for the first time also demonstrate that decrements in peroxisomal function coupled with the PLP1 gene defects of PMD, result in changes in the function of membrane myo-inositol solute carriers resulting in dramatic increases in cellular myo-inositol levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".