Editorial: MiRNAs as pivotal components of ncRNA networks associated with CNS injuries and neurodegeneration, and their therapeutic potential
Notice bibliographique
Résumé
Since miRNA discovery 30 years ago, studies have been focused on understanding miRNA mechanisms of action and biogenesis, as well as the pathways they regulate and their potential role in diagnosis and therapy of different diseases (Rani and Sengar, 2022, Dexheimer and Cochella, 2020, Matulic et al., 2022, Diener et al., 2022. Many studies have revealed alterations in the expression of miRNAs under different physiological and pathological conditions, supporting their participation in all cellular processes (DeVeale et al., 2021).Evidence shows that miRNAs respond to different environmental stimuli, such as cellular stress and viral infections, regulating gene expression in different cell types and organisms (Gebert andMacRae, 2019, DeVeale et al., 2021). The high stability and rapid expression of miRNAs compared to other RNA species make them ideal candidates in charge of the fine-tuning of mRNA and protein levels, and ultimately of the cellular response to diverse clues (Campos-Melo, 2022). These characteristics and the regulatory networks that miRNAs establish interacting with other non-coding RNAs (ncRNAs) such as long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), are critical for neuronal function, death, and survival and are increasingly recognized as associated with human neurological disorders, such as neurodegeneration, spinal cord injury, and stroke (Yamamura et al., 2018, Khan et al., 2022, Nuzziello and Liguori, 2019, Li et al., 2022, Xu et al., 2021, Silvestro and Mazzon, 2022. This research topic clusters five articles from experts interested in in silico and in vitro miRNA characterization, miRNA functional interactions with other ncRNA molecules, and potential therapeutic roles of miRNAs in neurological diseases. Megret et al. (2022) reviewed the main families of machine learning methods for the analysis of miRNA regulation in neurodegenerative disorders. The authors argue that the challenge in system-level studies to analyze the role of miRNA regulation in neurodegenerative diseases is due to problems of insufficient and inhomogeneous data and in the accuracy of system-level modeling. The authors propose shape analysis of complex omics data as a promising approach to construct improved models of high-level precision in matching miRNA-mRNA profiles in neurodegeneration. Rysenkova et al. (2022). MiRNAs Plaur-miR1-3p and Plaur-miR1-5p, were described to be expressed in mouse brains and target Mef2D, a gene that encodes a protein that regulates gene expression in embryogenesis and brain architecture maintenance, and contributes to the regulation of neuronal apoptosis, neurogenesis, and differentiation.In recent years, several articles have studied regulatory networks and interactions between miRNAs and other ncRNAs in vitro and in vivo. Both, lncRNAs and circRNAs function as miRNA sponges or decoys altering the availability of miRNAs and their downstream regulatory effects (Sharma et al., 2023, Tang et al., 2023, Liu and Chen, 2022. For this research topic, Lan et al. ( 2022) reviewed lncRNAs that are involved in Alzheimer's disease (AD).LncRNAs modulate the transcription of target genes in cis or in trans, and mRNA stability and processing, by regulating the assembly of multi-molecular complexes. This article reviews evidence that lncRNAs contribute to the pathogenesis of AD by regulating tau hyperphosphorylation, Aβ plague formation, mitochondrial and synaptic function, neuroinflammation, and neuronal apoptosis. From viruses to humans, the regulatory role of miRNAs and their wiring networks with other ncRNAs is only beginning to be elucidated. Beyond the experimental characterization of most miRNAs, we need to understand where, when, and how miRNAs exert their actions over specific targets in the cell. This is particularly relevant in animals because the partial base pairing between the miRNA seed region and 3'UTR sequences creates a high repertoire of targets.Studies of spatiotemporal expression and function of miRNAs and their ncRNA partners within subcellular compartments, in different tissue cell types, and ultimately in the whole organism, will allow the understanding of the importance of genome regulation by miRNAs and their link to human diseases of the CNS.
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 | 0,003 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,009 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,013 |
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 ».