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<i>SOX17</i> Enhancer Variants Disrupt Transcription Factor Binding And Enhancer Inactivity Drives Pulmonary Hypertension

2023· article· en· W4366082881 on OpenAlexaff
Rachel Walters, Eleni Vasilaki, Jurjan Aman, Chien-Nien Chen, Yukyee Wu, Olin D. Liang, Ali Ashek, Olivier Dubois, Lin Zhao, Farah Sabrin, Inês Cebola, Jorge Ferrer, Nicholas W. Morrell, James R. Klinger, Martin R. Wilkins, Lan Zhao, Christopher J. Rhodes

Bibliographic record

VenueCirculation · 2023
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsNutrasource
FundersNational Heart, Lung, and Blood InstituteNIHR Sheffield Biomedical Research CentreMedical Research CouncilEuropean Respiratory SocietyAcademy of Medical SciencesNIHR BioResourceNational Institutes of HealthDepartment of Health and Social CareNational Institute of General Medical SciencesNational Institute for Health and Care ResearchWellcome TrustNederlandse Organisatie voor Wetenschappelijk OnderzoekBritish Heart FoundationNHS Blood and TransplantAmerican Heart Association
KeywordsEnhancerTranscription factorIn silicoMedicineGeneticsBioinformaticsBiologyGene

Abstract

fetched live from OpenAlex

Background: Pulmonary arterial hypertension (PAH) is a rare disease characterized by remodeling of the pulmonary arteries, increased vascular resistance, and right-sided heart failure. Genome-wide association studies of idiopathic/heritable PAH established novel genetic risk variants, including conserved enhancers upstream of transcription factor (TF) SOX17 containing 2 independent signals. SOX17 is an important TF in embryonic development and in the homeostasis of pulmonary artery endothelial cells (hPAEC) in the adult. Rare pathogenic mutations in SOX17 cause heritable PAH. We hypothesized that PAH risk alleles in an enhancer region impair TF-binding upstream of SOX17 , which in turn reduces SOX17 expression and contributes to disturbed endothelial cell function and PAH development. Methods: CRISPR manipulation and siRNA were used to modulate SOX17 expression. Electromobility shift assays were used to confirm in silico – predicted TF differential binding to the SOX17 variants. Functional assays in hPAECs were used to establish the biological consequences of SOX17 loss. In silico analysis with the connectivity map was used to predict compounds that rescue disturbed SOX17 signaling. Mice with deletion of the SOX17 -signal 1 enhancer region ( SOX17 -4593/enhKO) were phenotyped in response to chronic hypoxia and SU5416/hypoxia. Results: CRISPR inhibition of SOX17 -signal 2 and deletion of SOX17 -signal 1 specifically decreased SOX17 expression. Electromobility shift assays demonstrated differential binding of hPAEC nuclear proteins to the risk and nonrisk alleles from both SOX17 signals. Candidate TFs HOXA5 and ROR-α were identified through in silico analysis and antibody electromobility shift assays. Analysis of the hPAEC transcriptomes revealed alteration of PAH-relevant pathways on SOX17 silencing, including extracellular matrix regulation. SOX17 silencing in hPAECs resulted in increased apoptosis, proliferation, and disturbance of barrier function. With the use of the connectivity map, compounds were identified that reversed the SOX17-dysfunction transcriptomic signatures in hPAECs. SOX17 enhancer knockout in mice reduced lung SOX17 expression, resulting in more severe pulmonary vascular leak and hypoxia or SU5416/hypoxia-induced pulmonary hypertension. Conclusions: Common PAH risk variants upstream of the SOX17 promoter reduce endothelial SOX17 expression, at least in part, through differential binding of HOXA5 and ROR-α. Reduced SOX17 expression results in disturbed hPAEC function and PAH. Existing drug compounds can reverse the disturbed SOX17 pulmonary endothelial transcriptomic signature.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.046
GPT teacher head0.301
Teacher spread0.255 · 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 designBench or experimental
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

Citations39
Published2023
Admission routes1
Has abstractyes

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