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Enregistrement W4410188272 · doi:10.3389/fcell.2025.1615951

Editorial: Decoding cell fate: the critical roles of extracellular vesicles

2025· editorial· en· W4410188272 sur OpenAlexaff
Yongqiang Chen, Saeid Ghavami, Joy Irobi

Notice bibliographique

RevueFrontiers in Cell and Developmental Biology · 2025
Typeeditorial
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueExtracellular vesicles in disease
Établissements canadiensCancerCare ManitobaResearch Institute in Oncology and HematologyUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
Organismes subventionnairesnon disponible
Mots-clésExtracellular vesiclesExtracellularCell biologyMicrovesiclesCell fate determinationBiologyCellChemistryComputational biologymicroRNAGeneticsTranscription factorGene

Résumé

récupéré en direct d'OpenAlex

Once considered passive byproducts of cellular metabolism, extracellular vesicles (EVs) have emerged as finely tuned intercellular communication messengers that orchestrate biological processes, such as immune responses and disease progression (Kumar et al., 2024;Yanez-Mo et al., 2015;Yim et al., 2020). These nano-and micro-sized vesicles range from 30-5,000 nanometers and mainly include exosomes, microvesicles, and apoptotic bodies; they are secreted by prokaryotic and eukaryotic cells, transporting a wide range of biologically active molecules, such as proteins, lipids, and nucleic acids (Mobarak et al., 2024).While traditional studies have focused primarily on intracellular pathways and direct environmental stressors as key regulators of cell fate, this Research Topic shifts the lens toward the emerging influence of EVs in dictating whether a cell survives or dies and how disease can be treated. Recent advances suggest that EVs are not merely passive information conveyors but active participants in the dynamic balance between cellular resilience and vulnerability (Xia et al., 2022;Sulek, 2024). They mediate protective functions under stress, modulate autophagy, apoptosis, and immune responses, and have been implicated in a range of pathophysiological conditions, such as neurodegeneration, cancer, fibrosis, and autoimmune disease (Wilczak et al., 2024;Zhang et al., 2025).This Topic has collected one Brief Research Report article and three review articles. Gade et al. (Gade et al., 2025) reported paradigm-shifting original findings related to secretory mitophagy. Under oxidative stress, when damaged mitochondria exceed the degradation capacity of the lysosome, cancer cells adopt a surprising survival mechanism. Rather than undergoing degradation via the lysosome, dysfunctional mitochondria are expelled out of the cell through EVs enriched with the mitophagy regulator PINK1 (PTEN (phosphatase and tensin homolog deleted on chromosome 10)-induced kinase 1). The EV-mediated removal of "toxic" defective mitochondria can help prevent cancer cells from dying. The study identified a novel, EV-mediated resilience pathway in tumor cells that could be exploited to sensitize cancer cells to oxidative therapies.Longfei et al. (Longfei et al., 2025) EVs have become hot research topics in disease diagnosis and treatment. Articles collected in this Research Topic highlight the multifaceted roles of EVs in cellular decision-making. From cancer cell survival, antioxidant defense, and immune modulation to OA and SSc treatment, EVs are more than vesicular bystanders-they are functional effectors capable of tipping the scales between degeneration and regeneration, as well as disease progression and therapeutic success.EVs from parental cells can promote cell survival or death in recipient cells (Sanwlani and Gangoda, 2021) (Figure 1). This Research Topic reveals a new mechanism that parental cells can use their EVs to transport "toxic" intracellular components (e.g., damaged mitochondria) outside to prolong survival. More studies are needed to evaluate whether different "toxic" elements can be expelled out of the cell to support survival.EVs can also play a dual role (promoting or worsening) in disease treatment, depending upon the context (Figure 1). They could have opposite effects on treating the same disease (e.g., OA) if derived from different sources of cells in the same organism. Regarding the same disease (e.g., OA), EVs from healthy cells (e.g., healthy chondrocytes) could help its treatment, whereas those from "diseased" cells (e.g., degenerative chondrocytes) do not. This observation might be extrapolated to other diseases, although more studies are needed to verify the hypothesis. When EVs are derived from the same type of cells (e.g., synovial fibroblasts), their components (e.g., the types of miRNAs) could contribute to different outcomes (e.g., promoting cartilage repair or damage). The possibility of using TCM-derived EVs for OA treatment suggests that EVs from different kingdoms or species of organisms can be explored to treat human diseases.As the field progresses, understanding the precise bioactive cargo, delivery mechanisms, and contextspecific effects of EVs will be essential. The promise they hold across disciplines, from oncology to rheumatology to regenerative medicine, invites a rethinking of how we approach complex diseases.The Topic provides an overview of the current breakthroughs and lays a compelling foundation for future research that positions EVs at the center of next-generation diagnostics and therapies.FIGURE 1 Graphical model of the effects of extracellular vesicles on cell survival or death and disease treatment. The effects are context-dependent on the types of (parental and recipient) cells, the types of extracellular vesicles (EVs), the composition of EVs, and the disease.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,015
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,048

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,015
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0030,001
Études des sciences et des technologies0,0020,002
Communication savante0,0050,005
Science ouverte0,0030,001
Intégrité de la recherche0,0100,013
Charge utile insuffisante (le modèle a refusé de juger)0,0140,010

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.

Tête enseignante Opus0,004
Tête enseignante GPT0,239
Écart entre enseignants0,235 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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