Detection of follistatin‐based inhibitors of the TGF‐β signaling pathways in serum/plasma by means of LC‐HRMS/MS and Western blotting
Bibliographic record
Abstract
Cytokines of the transforming growth factor beta (TGF-β) superfamily such as myostatin and activin A are considered as key regulators of skeletal muscle mass. In vivo, their activity is controlled by different binding proteins such as follistatin (FST), whose interaction with the circulating growth factors prevents activation of the activin type II receptors. FST-based protein therapeutics are therefore not only promising drug candidates for the treatment of muscular diseases but also potential performance-enhancing agents in sports. Within this study, two complementary detection assays for FST-based inhibitors of the TGF-β signaling pathways in doping control serum and plasma samples were developed by using both monomeric FST and dimeric FST-Fc fusion proteins as model compounds. The initial testing procedure is based on immunoaffinity purification, tryptic digestion, and LC-HRMS/MS, offering high specificity by targeting tryptic signature peptides of FST. As the glycoprotein is also produced endogenously, the confirmation method employs immunoaffinity purification, sodium dodecyl sulfate polyacrylamide gel electrophoresis, and Western blotting in order to detect the intact proteins and differentiate synthetic FST-Fc constructs from naturally occurring FST isoforms. Both assays were found to be highly specific with an estimated detection limit of 10 ng/ml. Moreover, a commercial sandwich enzyme-linked immunosorbent assay was used to determine endogenous FST values. The detected FST serum levels of healthy volunteers were found below 5 ng/ml, which is in accordance with reference values from the literature and below the doping control detection methods' limit of detection (LOD). The presented assays expand the range of available tests for emerging doping agents, and the initial testing procedure can readily be modified to include further protein drugs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".