Endothelial cells secrete small extracellular vesicles bidirectionally containing distinct cargo to uniquely reprogram vascular cells in the circulation and vessel wall
Notice bibliographique
Résumé
Abstract Rationale: Extracellular vesicles (EVs) contain bioactive cargo including microRNAs (miRNAs) and proteins that are released by cells as a form of cell-cell communication. Endothelial cells (ECs) form the innermost lining of all blood vessels and thereby interface with cells in the circulation as well as cells residing in the vascular wall. It is unknown whether ECs have the capacity to release EVs capable of governing recipient cells within two separate compartments, and how this is affected by endothelial activation commonly seen in atheroprone regions. Objective: Given their boundary location, we propose that ECs utilize bidirectional release of distinct EV cargo in quiescent and activated states to communicate with cells within the circulation and blood vessel wall. Methods and Results: EVs were isolated from primary human aortic endothelial cells (ECs) (+/- IL-1β activation), quantified, and analysed by miRNA transcriptomics and proteomics. Compared to quiescent ECs, activated ECs increased EV release, with miRNA and protein cargo that were related to atherosclerosis. RNA sequencing of EV-treated monocytes and smooth muscle cells (SMCs) revealed that EVs from activated ECs altered pathways that were pro-inflammatory and atherogenic. Apical and basolateral EV release was assessed using ECs on transwells. ECs released more EVs apically, which increased with activation. Apical and basolateral EV cargo contained distinct transcriptomes and proteomes that were altered by EC activation. Notably, basolateral EC-EVs displayed greater changes in the EV secretome, with pathways specific to atherosclerosis. In silico analysis determined that compartment-specific cargo released by the apical and basolateral surfaces of ECs can reprogram monocytes and SMCs, respectively. Conclusions: The demonstration that ECs are capable of polarized EV cargo loading and directional EV secretion reveals a novel paradigm for endothelial communication, which may ultimately enhance our ability to design endothelial-based therapeutics for cardiovascular diseases such as atherosclerosis where ECs are persistently activated. Non-standard Abbreviations and Acronyms cryo-EM cryogenic electron microscopy EC endothelial cell EV extracellular vesicle GO gene ontology HAEC human aortic endothelial cell SMC human aortic vascular smooth muscle cell IL-1β interleukin 1 beta KEGG Kyoto encyclopedia of genes and genomes LC-MS label-free liquid-chromatography mass spectrometry MVB multivesicular body miRNA microRNA RNAseq RNA sequencing TEM transmission electron microscopy TIRF total interal reflection fluorescence microscopy miRNA microRNA Graphical abstract: Polarized endothelial extracellular vesicle communication with luminal and abluminal vascular cells Endothelial cell small extracellular vesicle (EC-EV) release from apical (luminal) and basolateral (abluminal) surfaces in quiescence and after endothelial activation. Quiescent EC-EVs are depicted in blue (bright blue=apical, light blue=basolateral), while activated EC-EVs are depicted in red (bright red=apical, light red=basolateral). Luminal monocyte is represented in purple with upregulation of pro-inflammatory transcripts (bright purple) after uptake of activated EC-EVs from the apical surface, compared to uptake of quiescent apical EC-EVs (light purple). Basolateral EC-EVs are taken up by an abluminal resident smooth muscle cell depicted in yellow. Smooth muscle cell uptake of activated basolateral EC-EVs with upregulation of pro-inflammatory/pro-atherogenic transcripts (bright yellow), as compared to uptake of quiescent EC-EVs (light yellow).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».