Temporal changes in inflammatory mitochondria-enriched microRNAs following traumatic brain injury and effects of miR-146a nanoparticle delivery
Notice bibliographique
Résumé
MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate post-transcriptional gene expression and contribute to all aspects of cellular function. We previously reported that the activities of several mitochondria-enriched miRNAs regulating inflammation (i.e., miR-142-3p, miR-142-5p, and miR-146a) are altered in the hippocampus at 3-12 hours following a severe traumatic brain injury. In the present study, we investigated the temporal expression profile of these inflammatory miRNAs in mitochondria and cytosol fractions at more chronic post-injury times following severe controlled cortical impact injury in rats. In addition, several inflammatory genes were analyzed in the cytosol fractions. The analysis showed that while elevated levels were observed in cytoplasm, the mitochondria-enriched miRNAs, miR-142-3p and miR-142-5p continued to be significantly reduced in mitochondria from injured hippocampi for at least 3 days and returned to near normal levels at 7 days post-injury. Although not statistically significant, miR-146a also remained at reduced levels for up to 3 days following controlled cortical impact injury, and recovered by 7 days. In contrast, miRNAs that are not enriched in mitochondria, including miR-124a, miR-150, miR-19b, miR-155, and miR-223 were either increased or demonstrated no change in their levels in mitochondrial fractions for 7 days. The one exception was that miR-223 levels were reduced in mitochondria at 1 day following injury. No major alterations were observed in sham operated animals. This temporal pattern was unique to mitochondria-enriched miRNAs and correlated with injury-induced changes in mitochondrial bioenergetics as well as expression levels of several inflammatory markers. These observations suggested a potential compartmental re-distribution of the mitochondria-enriched inflammatory miRNAs and may reflect an intracellular mechanism by which specific miRNAs regulate injury-induced inflammatory signaling. To test this, we utilized a novel peptide-based nanoparticle strategy for in vitro and in vivo delivery of a miR-146a mimic as a potential therapeutic strategy for targeting nuclear factor-kappaB inflammatory modulators in the injured brain. Nanoparticle delivery of miR-146a to BV-2 or SH-SY5Y cells significantly reduced expression of TNF receptor-associated factor 6 (TRAF6) and interleukin-1 receptor-associated kinase 1 (IRAK1), two important modulators of the nuclear factor-kappaB (NF-κB) pro-inflammatory pathway. Moreover, injections of miR-146a containing nanoparticles into the brain immediately following controlled cortical impact injury significantly reduced hippocampal TNF receptor-associated factor 6 and interleukin-1 receptor-associated kinase 1 levels. Taken together, our studies demonstrate the subcellular alteration of inflammatory miRNAs after traumatic brain injury and establish proof of principle that nanoparticle delivery of miR-146a has therapeutic potential for modulating pro-inflammatory effectors in the injured brain. All of the studies performed were approved by the University of Kentucky Institutional Animal Care and Usage Committee (IACUC protocol # 2014-1300) on August 17, 2017.
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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 | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».