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Record W2912415330 · doi:10.1161/str.50.suppl_1.64

Abstract 64: Novel and Known Genes Elucidated in Cerebral Cavernous Malformation Through Comparative Transcriptomic Analysis of Multiple Model Species and Human Microdissected Lesional Endothelial Cells

2019· article· en· W2912415330 on OpenAlexaff
Janne Koskimäki, Romuald Girard, Yan Li, Hussein A. Zeineddine, Rhonda Lightle, Thomas Moore, Séan Lyne, Robert Shenkar, Laleh Saadat, Ying Cao, Sean P. Polster, Dongdong Zhang, Julián Carrión‐Penagos, Miguel Alejandro Lopez‐Ramirez, Eric M. Chapman, Alan T. Tang, Amy Akers, Pieter Faber, Jorge Andrade, Mark H. Ginsberg, Brent Derry, Mark L. Kahn, Douglas A. Marchuk, Issam A. Awad

Bibliographic record

VenueStroke · 2019
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations Diagnosis and Treatment
Canadian institutionsDouglas CollegeUniversity of Toronto
Fundersnot available
KeywordsTranscriptomeGenePathogenesisPopulationAngiogenesisPDGFRABiologyMedicineGeneticsCancer researchPathologyGene expression

Abstract

fetched live from OpenAlex

Cerebral cavernous malformations (CCMs) are vascular brain lesions predisposing 0.5% of the population to a lifetime risk of hemorrhagic stroke and seizures. The disease is associated to a mutation in one of the three CCM genes ( CCM1, CCM2 and CCM3 ). CCM pathogenesis has been shown to be endothelial autonomous, linked to angiogenic, adhesion, and inflammatory processes. Using RNA-Seq, we profiled the transcriptomes of lesional endothelial cells (ECs) extracted from 5 human CCMs. We also profiled the more common Ccm1, and the exceptionally aggressive Ccm3 genotypes in mouse brain microvascular endothelial cells (BMECs) and C. elegans . We first identified differently expressed genes (DEGs), gene ontology (GO) functions, and gene networks for each model separately. We then cross-compared the models and genotypes to identify the important and conserved genes likely contributing to pathogenesis of CCM disease. Nine hundred-fifteen DEGs in human microdissected lesional ECs, 1932 in Ccm1 ECKO and 524 in Ccm3 ECKO BMECs, as well as 1643 in ccm1 C. elegans and 1581 in ccm3 C. elegans were identified (p<0.05, FDR corrected, fold change≥1.2). FAT1 was commonly identified in the 5 models, while 7 other DEGs were common between human lesional ECs, mouse BMEC Ccm1 ECKO and ccm1 C. elegans: GNAO1 , SPARCL1 , PLXDC2 , PLCD3 , PDGFRA , FAXC , and UNC13A . Seventy-one DEGs were only identified in Ccm1 models, these genes were related to DNA repair, angiogenesis, microtubule functions and magnesium ion binding. Eleven DEGs were only found in Ccm3 models, and were related to rRNA processing, ribosome biogenesis and structural constituent, protein targeting to endoplasmic reticulum, protein intracellular targeting, and vesicle transportation to a cell membrane. We provide a comprehensive transcriptome library of CCM disease across species and genotypes. The results will be useful for validating putative mechanistic targets and biomarkers in this disease. For the first time, we also report fundamental transcriptomic differences between Ccm1 and Ccm3 genotypes, potentially explaining differences in CCM disease severity. Our results confirm several previously reported mechanisms, and suggest multiple novel gene candidates to be investigated in CCM pathogenesis.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.274
Teacher spread0.240 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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