Diurnal protein expression in blood revealed by high throughput mass spectrometry proteomics and implications for translational medicine and body time of day
Bibliographic record
Abstract
Molecular gene cycling is useful for determining body time of day (BTOD) with important applications in personalized medicine, including cardiovascular disease and cancer, our leading causes of death. However, it impractically requires repetitive invasive tissue sampling that is obviously not applicable for humans. Here we characterize diurnal protein cycling in blood using high-throughput proteomics; blood proteins are easily accessible, minimally invasive, and can importantly serve as surrogates for what is happening elsewhere in the body in health and disease. As proof of the concept, we used normal C57BL/6 mice maintained under regular 24-h light and dark cycles. First, we demonstrated fingerprint patterns in 24-h plasma, revealed using surface-enhanced laser desorption and ionization (SELDI). Second, we characterized diurnal cycling proteins in blood using chromatography and tandem electrospray ionization mass spectrometry. Importantly, we noted little association between the cycling blood proteome and tissue transcriptome, delineating the necessity to identify de novo cycling proteins in blood for measuring BTOD. Furthermore, we explored known interaction networks to identify putative functional pathways regulating protein expression patterns in blood, thus shedding new light on our understanding of integrative physiology. These studies have profound clinical significance in translating the concept of BTOD to the practical realm for molecular diagnostics and open new opportunities for clinically relevant discoveries when applied to ELISA-based molecular testing and/or point-of-care devices.
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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) | 0.000 | 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) | 0.000 | 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".