Changes to the Human Serum Proteome in Response to High Intensity Interval Exercise: A Sequential Top-Down Proteomic Analysis
Bibliographic record
Abstract
Exercise has been shown to improve health status and prevent chronic diseases. In contrast, overtraining can lead to maladaptation and detrimental health outcomes. These outcomes appear to be mediated in part by released peptides and, potentially, alterations in protein abundances and their modified forms, termed proteoforms. Proteoform biomarkers that either predict the beneficial effects of exercise or indicate (mal)adaptation are yet to be elucidated. Thus, we assessed the influence of high-intensity interval exercise (HIIE) on the human serum proteome to identify novel exercise-regulated proteoforms. To this end, a top-down proteomics approach was used, whereby two-dimensional gel electrophoresis was used to resolve and differentially profile intact proteoforms, followed by protein identification via liquid chromatography-tandem mass spectrometry. Blood was collected from six young-adult healthy males, pre-exercise and 5 min and 1h post-exercise. Exercise consisted of a maximal cycle ergometer test followed by 8x1 min high-intensity intervals at 90% Wmax, with 1 min non-active recovery between intervals. Twenty resolved serum proteoforms changed significantly in abundance at 5 min and/or 1h post-HIIE, including apolipoproteins, serpins (protease inhibitors), and immune system proteins, known to have broad anti-inflammatory and antioxidant effects, involvement in lipid clearance, and cardio-/neuro-protective effects. This initial screening for potential biomarkers indicates that a top-down analytical proteomic approach may prove useful in further characterizing the response to exercise and in understanding the molecular mechanisms that lead to health benefits, as well as identifying novel biomarkers for exercise (mal)adaptation.
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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".