Platelet proteo-transcriptomic profiling validates mediators of thrombosis and proteostasis in patients with myeloproliferative neoplasms
Bibliographic record
Abstract
Abstract Patients with chronic Myeloproliferative Neoplasms (MPN) including polycythemia vera (PV) and essential thrombocythemia (ET) exhibit unique clinical features, such as a tendency toward thrombosis and hemorrhage, and risk of disease progression to secondary bone marrow fibrosis and/or acute leukemia. Although an increase in blood cell lineage counts (quantitative features) contribute to these morbid sequelae, the significant qualitative abnormalities of myeloid cells that contribute to vascular risk are not well understood. Here, we address this critical knowledge gap via a comprehensive and untargeted profiling of the platelet proteome in a large (n= 140) cohort of patients (from two independent sites) with an established diagnosis of PV and ET (and complement prior work on the MPN platelet transcriptome from a third site). We discover distinct MPN platelet protein expression and confirm key molecular impairments associated with proteostasis and thrombosis mechanisms of potential relevance to MPN pathology. Specifically, we validate expression of high-priority candidate markers from the platelet transcriptome at the platelet proteome ( e.g., calreticulin (CALR), Fc gamma receptor (FcγRIIA ) and galectin-1 (LGALS1 ) pointing to their likely significance in the proinflammatory, prothrombotic and profibrotic phenotypes in patients with MPN. Together, our proteo-transcriptomic study identifies the peripherally-derived platelet molecular profile as a potential window into MPN pathophysiology and demonstrates the value of integrative multi-omic approaches in gaining a better understanding of the complex molecular dynamics of disease. Highlights MPN patient platelet proteome identifies key pathobiological mediators of thrombosis and proteostasis. The MPN platelet proteomic profile validates our prior findings from the platelet transcriptome.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".