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Record W4213423229 · doi:10.1186/s12933-022-01458-z

MiR-30 promotes fatty acid beta-oxidation and endothelial cell dysfunction and is a circulating biomarker of coronary microvascular dysfunction in pre-clinical models of diabetes

2022· article· en· W4213423229 on OpenAlexafffund
Shawn Veitch, Makon‐Sébastien Njock, Mark Chandy, M. Ahsan Siraj, Lijun Chi, HaoQi Mak, Kai Yu, Kumaragurubaran Rathnakumar, Carmina Anjelica Perez-Romero, Zhiqi Chen, Faisal J. Alibhai, Dakota Gustafson, Sneha Raju, Ruilin Wu, Dorrin Zarrin Khat, Yaxu Wang, Amalia Caballero, Patrick Meagher, Edward Lau, Lejla Pepic, Henry S. Cheng, Natalie J. Galant, Kathryn L. Howe, Ren‐Ke Li, Kim A. Connelly, Mansoor Husain, Paul Delgado-Olguı́n, Jason E. Fish

Bibliographic record

VenueCardiovascular Diabetology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsPrincess Margaret Cancer CentreHospital for Sick ChildrenSt. Michael's HospitalToronto General HospitalUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchCanadian Vascular Network
KeywordsEndothelial dysfunctionMedicineInternal medicineDownregulation and upregulationDiastoleCardiologyDiabetes mellitusType 2 diabetesOxidative stressHeart failureEjection fractionBiomarkerEndocrinologyBiologyBlood pressureBiochemistryGene

Abstract

fetched live from OpenAlex

BACKGROUND: Type 2 diabetes (T2D) is associated with coronary microvascular dysfunction, which is thought to contribute to compromised diastolic function, ultimately culminating in heart failure with preserved ejection fraction (HFpEF). The molecular mechanisms remain incompletely understood, and no early diagnostics are available. We sought to gain insight into biomarkers and potential mechanisms of microvascular dysfunction in obese mouse (db/db) and lean rat (Goto-Kakizaki) pre-clinical models of T2D-associated diastolic dysfunction. METHODS: The microRNA (miRNA) content of circulating extracellular vesicles (EVs) was assessed in T2D models to identify biomarkers of coronary microvascular dysfunction/rarefaction. The potential source of circulating EV-encapsulated miRNAs was determined, and the mechanisms of induction and the function of candidate miRNAs were assessed in endothelial cells (ECs). RESULTS: We found an increase in miR-30d-5p and miR-30e-5p in circulating EVs that coincided with indices of coronary microvascular EC dysfunction (i.e., markers of oxidative stress, DNA damage/senescence) and rarefaction, and preceded echocardiographic evidence of diastolic dysfunction. These miRNAs may serve as biomarkers of coronary microvascular dysfunction as they are upregulated in ECs of the left ventricle of the heart, but not other organs, in db/db mice. Furthermore, the miR-30 family is secreted in EVs from senescent ECs in culture, and ECs with senescent-like characteristics are present in the db/db heart. Assessment of miR-30 target pathways revealed a network of genes involved in fatty acid biosynthesis and metabolism. Over-expression of miR-30e in cultured ECs increased fatty acid β-oxidation and the production of reactive oxygen species and lipid peroxidation, while inhibiting the miR-30 family decreased fatty acid β-oxidation. Additionally, miR-30e over-expression synergized with fatty acid exposure to down-regulate the expression of eNOS, a key regulator of microvascular and cardiomyocyte function. Finally, knock-down of the miR-30 family in db/db mice decreased markers of oxidative stress and DNA damage/senescence in the microvascular endothelium. CONCLUSIONS: MiR-30d/e represent early biomarkers and potential therapeutic targets that are indicative of the development of diastolic dysfunction and may reflect altered EC fatty acid metabolism and microvascular dysfunction in the diabetic heart.

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 score0.001
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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

Citations80
Published2022
Admission routes2
Has abstractyes

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