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Record W4385335105 · doi:10.1101/2023.07.26.23293222

Integrative Analysis of the Blood Proteome by Mendelian Randomization Reveals Regulatory Networks in Calcific Aortic Valve Disease

2023· preprint· en· W4385335105 on OpenAlexafffund
Mewen Briend, Louis-Hippolyte Minvielle Moncla, Valentine Duclos, Samuel Mathieu, Anne Rufiange, Sébastien Thériault, Benoît J. Arsenault, Yohan Bossé, Patrick Mathieu

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health ResearchInstitut universitaire de cardiologie et de pneumologie de Québec, Université Laval
KeywordsMendelian randomizationMedicineProteomeBiologyBioinformaticsInternal medicineGeneGenetics

Abstract

fetched live from OpenAlex

Abstract Background Calcific aortic valve disease (CAVD) is a disorder characterized by fibrocalcific remodeling of the aortic valve (AV). The blood molecular phenome involved in CAVD is presently unknown. Methods We carried out a proteome-wide two-sample Mendelian randomization (MR) study to identify circulating molecules causally associated with CAVD. We queried as the exposition a large cohort of 35,559 subjects in whom 4,719 blood proteins were measured. For the outcome, we leveraged a recent GWAS for CAVD including 13,765 cases and 640,102 controls. Single-cell RNA-seq was analyzed to highlight potential pathways affected by the blood proteome. Results In MR, we identified 49 blood proteins robustly associated with the risk of CAVD. The blood proteins formed a network enriched in the immune response and ligand-receptor interactions. PCSK9, APOC3, ACE and IL6 were identified as actionable targets suitable for drug repurposing. Modulators of innate (IL6R, CNTFR, KIR2DL3-4) and adaptive (IL15RA, IGLL1, LILRA6) immune responses were associated with CAVD. Different regulators of platelets activity such as soluble GP1BA, COMP and VTN were also related to the risk of CAVD. Circulating modulators of the transforming growth factor-beta (TGF-beta) family such as ASPN, LEFTY2 and FSTL3 were associated with the risk of CAVD and their directional effects were consistent with the role of this pathway in the pathogenesis. Analysis of ligand-receptor interactions in the AV, which was inferred from single cell RNA-seq, provided further evidence that the IL6 and TGF-beta pathways are activated in CAVD. Conclusions We identified 49 blood proteins robustly and causally associated with CAVD, which were involved in the metabolism of lipids, immunity, regulation of blood pressure, platelet activation and modulation of growth factors activity. The present MR scan of the blood proteome provides a roadmap for follow-up studies and drug repurposing in CAVD. Clinical Perspective What is new? The causal blood molecular phenome is presently unknown in CAVD; herein we investigated by Mendelian randomization the causal associations between the blood proteome and the risk of CAVD. In total, 49 blood proteins were found causally associated with the risk of CAVD and were involved in the metabolism of lipids, control of the immune response, regulation of blood pressure, platelet activity and the modulation of growth factors activity. Single cell RNA-seq analysis of calcific aortic valves revealed several ligand-receptor interactions potentially affected by the blood phenome. What are the clinical implications? There is no drug therapy available to treat CAVD. Analysis of the blood proteome by Mendelian randomisation showed that in-development, approved drugs or biologics targeting PCSK9, APOC3 and ACE could be repositioned and investigated in order to treat CAVD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.015
GPT teacher head0.314
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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