The amino acid metabolism pathway of peripheral T lymphocytes and ketamine-induced schizophrenia-like phenotype
Bibliographic record
Abstract
BACKGROUND: The intricate interplay between peripheral adaptive immune cells and the central nervous system (CNS) has garnered increasing recognition. Given that alterations in cell quantities often translate into modifications in metabolite profiles and that these metabolic changes can potentially traverse the bloodstream and enter the CNS, thereby modulating the progression of mental illnesses, we sought to explore the metabolic profiles of peripheral immune cells in a ketamine-treated mouse model of schizophrenia. METHODS: We used flow cytometry to scrutinize the alterations in peripheral adaptive immune cells in a ketamine-induced schizophrenia mouse model. Subsequently, we implemented an untargeted metabolomic approach with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) to detect the metabolite profiles of peripheral abnormal lymphocytes and identify differential metabolites present in plasma. We then employed targeted metabolomics using UPLC-MS/MS to quantify the common differential metabolites detected in mouse plasma. RESULTS: T cells in a ketamine-induced schizophrenia mouse model. Subsequent untargeted metabolomics analysis revealed that the amino acid metabolism pathway underwent substantial alterations. A detailed quantification of 22 amino acid profiles in the peripheral plasma indicated significant elevation in the levels of glycine, alanine, asparagine, and aspartic acid. LIMITATIONS: Our ongoing research has yet to conclusively identify the precise amino acid metabolism pathway that serves as the pivotal factor in the manifestation of the schizophrenia-like phenotype induced by ketamine. CONCLUSION: The peripheral amino acid metabolism pathway is involved in the ketamine-induced schizophrenia-like phenotype. The metabolic profile of peripheral immune cells could provide accurate biomarkers for the diagnosis and treatment of psychiatric diseases.
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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 | 0.000 | 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".