PFN2 and GAMT as common molecular determinants of axonal Charcot-Marie-Tooth disease
Bibliographic record
Abstract
BACKGROUND: Charcot-Marie-Tooth type 2 (CMT2) neuropathy is characterised by a vast clinical and genetic heterogeneity complicating its diagnosis and therapeutic intervention. Identification of molecular signatures that are common to multiple CMT2 subtypes can aid in developing therapeutic strategies and measuring disease outcomes. METHODS: A proteomics-based approach was performed on lymphoblasts from CMT2 patients genetically diagnosed with different gene mutations to identify differentially regulated proteins. The candidate proteins were validated through real-time quantitative PCR and western blotting on lymphoblast samples of patients and controls, motor neurons differentiated from patient-derived induced pluripotent stem cells (iPSCs) and sciatic nerves of CMT2 mouse models. RESULTS: Proteomic profiling of patient lymphoblasts resulted in the identification of profilin 2 (PFN2) and guanidinoacetate methyltransferase (GAMT) as commonly downregulated proteins in different genotypes compared with healthy controls. This decrease was also observed at the transcriptional level on screening 43 CMT2 patients and 22 controls, respectively. A progressive decrease in PFN2 expression with age was observed in patients, while in healthy controls its expression increased with age. Reduced PFN2 expression was also observed in motor neurons differentiated from CMT2 patient-derived iPSCs and sciatic nerves of CMT2 mice when compared with controls. However, no change in GAMT levels was observed in motor neurons and CMT2 mouse-derived sciatic nerves. CONCLUSIONS: We unveil PFN2 and GAMT as molecular determinants of CMT2 with possible indications of the role of PFN2 in the pathogenesis and disease progression. This is the first study describing biomarkers that can boost the development of therapeutic strategies targeting a wider spectrum of CMT2 patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".