Combined plasma metabolomic and transcriptomic analysis identify histidine as a biomarker and potential contributor in SLE pathogenesis
Bibliographic record
Abstract
OBJECTIVES: To investigate metabolite alterations in the plasma of SLE patients to identify novel biomarkers and provide insight into SLE pathogenesis. METHODS: Patients with SLE (n = 41, discovery cohort and n = 37, replication cohort), healthy controls (n = 30 and n = 29) and patients with RA (n = 19, disease control) were recruited. Metabolic profiles of the plasma samples were analysed using liquid chromatography-time-of-flight mass spectrometry and capillary electrophoresis-time-of-flight mass spectrometry. Transcriptome data was analysed using RNA-sequencing for 18 immune cell subsets. The importance of histidine (His) in plasmablast differentiation was investigated by using mouse splenic B cells. RESULTS: We demonstrate that a specific amino acid combination including His can effectively distinguish between SLE patients and healthy controls. Random forest and partial least squares-discriminant analysis identified His as an effective classifier for SLE patients. A decrease in His plasma levels correlated with damage accrual independent of prednisolone dosage and type I IFN signature. The oxidative phosphorylation signature in plasmablasts negatively correlated with His levels. We also showed that plasmablast differentiation induced by innate immune signals was dependent on His. CONCLUSIONS: Plasma His levels are a potential biomarker for SLE patients and are associated with damage accrual. Our data suggest the importance of His as a pathogenic metabolite in SLE pathogenesis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".