Proteomic Profiling of Growth Hormone-Responsive Proteins in Human Peripheral Blood Leukocytes
Bibliographic record
Abstract
CONTEXT: GH is a known modulator of the immune system, but the effect of exogenous GH administration on white blood cell proteins has not been investigated. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a powerful platform for the study of GH effects on immune system proteins. OBJECTIVE: Our objective was to explore a novel approach for the detection of GH-responsive proteins in human leukocytes by proteomic analysis using SELDI-TOF MS. DESIGN: We conducted a randomized double-blind, placebo-controlled GH administration study of 8 wk treatment followed by 6 wk washout. Pre- and posttreatment samples from 30 subjects were used for biomarker discovery. SETTING: The study was performed at a clinical research facility. PARTICIPANTS: We studied 30 recreationally trained healthy athletes. INTERVENTION: Subjects received either recombinant human GH (2 mg/d sc; n = 22) or placebo (n = 8) for 8 wk. MAIN OUTCOME MEASURES: Proteomic profiles were determined using CM10 weak cation-exchange protein chips, and some GH-regulated proteins were purified and identified by mass spectrometry and/or immunoblotting. RESULTS: SELDI-TOF analysis revealed a number of GH-regulated peptides/proteins in the 3- to 22-kDa range that are either up- or down-regulated by GH. Several of these may be useful as biomarkers of GH action. The calcium-binding, proinflammatory calgranulins S100A8, S100A9, and S100A12 were all significantly down-regulated in response to GH treatment. CONCLUSION: This study illustrates the novel use of human leukocyte proteomic profiling by SELDI-TOF MS and reveals the negative regulation of proinflammatory S100 proteins by GH in human white blood cells.
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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) | 0.001 | 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".