A new strategy to uncover fragile X proteomic biomarkers using the nascent proteome of peripheral blood mononuclear cells (PBMCs)
Bibliographic record
Abstract
Fragile X syndrome (FXS) is the most prevalent inherited cause of intellectual disabilities and autism spectrum disorders. FXS result from the loss of expression of the FMRP protein, an RNA-binding protein that regulates the expression of key synaptic effectors. FXS is also characterized by a wide array of behavioural, cognitive and metabolic impairments. The severity and penetrance of those comorbidities are extremely variable, meaning that a considerable phenotypic heterogeneity is found among fragile X individuals. Unfortunately, clinicians currently have no tools at their disposal to assay a patient prognosis upon diagnosis. Since the absence of FMRP was repeatedly associated with an aberrant protein synthesis, we decided to study the nascent proteome in order to screen for potential proteomic biomarkers of FXS. We used a BONCAT (Biorthogonal Non-canonical Amino Acids Tagging) method coupled to label-free mass spectrometry to purify and quantify nascent proteins of peripheral blood mononuclear cells (PBMCs) from 7 fragile X male patients and 7 age-matched controls. The proteomic analysis identified several proteins which were either up or downregulated in PBMCs from FXS individuals. Eleven of those proteins were considered as potential biomarkers, of which 5 were further validated by Western blot. The gene ontology enrichment analysis highlighted molecular pathways that may contribute to FXS physiopathology. Our results suggest that the nascent proteome of PBMCs is well suited for the discovery of FXS biomarkers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| 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".