Dexamethasone-Induced Perturbations in Tissue Metabolomics Revealed by Chemical Isotope Labeling LC-MS Analysis
Bibliographic record
Abstract
Dexamethasone (Dex) is a synthetic glucocorticoid (GC) drug commonly used clinically for the treatment of several inflammatory and immune-mediated diseases. Despite its broad range of indications, the long-term use of Dex is known to be associated with specific abnormalities in several tissues and organs. In this study, the metabolomic effects on five different organs induced by the chronic administration of Dex in the Sprague-Dawley rat model were investigated using the chemical isotope labeling liquid chromatography-mass spectrometry (CIL LC-MS) platform, which targets the amine/phenol submetabolomes. Compared to controls, a prolonged intake of Dex resulted in significant perturbations in the levels of 492, 442, 300, 186, and 105 metabolites in the brain, skeletal muscle, liver, kidney, and heart tissues, respectively. The positively identified metabolites were mapped to diverse molecular pathways in different organs. In the brain, perturbations in protein biosynthesis, amino acid metabolism, and monoamine neurotransmitter synthesis were identified, while in the heart, pyrimidine metabolism and branched amino acid biosynthesis were the most significantly impaired pathways. In the kidney, several amino acid pathways were dysregulated, which reflected impairments in several biological functions, including gluconeogenesis and ureagenesis. Beta-alanine metabolism and uridine homeostasis were profoundly affected in liver tissues, whereas alterations of glutathione, arginine, glutamine, and nitrogen metabolism pointed to the modulation of muscle metabolism and disturbances in energy production and muscle mass in skeletal muscle. The differential expression of multiple dipeptides was most significant in the liver (down-regulated), brain (up-regulation), and kidney tissues, but not in the heart or skeletal muscle tissues. The identification of clinically relevant pathways provides holistic insights into the tissue molecular responses induced by Dex and understanding of the underlying mechanisms associated with their side effects. Our data suggest a potential role for glutathione supplementation and dipeptide modulators as novel therapeutic interventions to mitigate the side effects induced by Dex therapy.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".