Enzymes Drive Glutathione Shunt to Explain Oxidative State Using an In-Parallel Multi-Omic Method
Bibliographic record
Abstract
The glutathione shunt is one of the most important contributors to the cellular redox state, with implications across cancer, chronic diseases, diseases of ageing, and autoimmune diseases, including inflammatory bowel disease (IBD). Traditionally, the redox state is gauged by the ratio of the surrogate metabolites GSH and GSSG. However, this presents methodological challenges and offers a constrained illustration of metabolites without a systems-level understanding of redox dynamics, failing to elucidate variations across an entire biochemical network. Targeted proteomics can fill this void. Here, we describe an in-parallel metabolomic and proteomic targeted method to encompass measurements directly related to the shunt. Samples are simultaneously prepared to extract the substrate building blocks, cysteine, cystine, methionine, glutamic acid, and kynurenine; and the proteins, SLC7A11 (xCT), Glutamate Cysteine Ligase (GSH1), Glutathione Synthetase (GSH2), Glutathione Peroxidase (GPx), and Glutathione Reductase (GSHR) for targeted mass spectrometry. We demonstrate the method by targeted analysis of proteins in plasma, serum, nasal swab, and saliva and apply the multi-omic method to assess changes in the glutathione shunt in the serum of patients diagnosed with IBD. This allows for a broader narrative to establish context at which the glutathione shunt is operating.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".