HIV-1 RNA in Large and Small Plasmatic Extracellular Vesicles: a Novel Parameter for Monitoring Immune Activation and Virological Failure
Notice bibliographique
Résumé
Abstract Background Antiretroviral therapy (ART) suppresses viral replication in most people living with HIV-1 (PLWH). However, PLWH remain at risk of viral rebound. HIV-1 infection modifies the content of extracellular vesicles (EVs). The changes in microRNA content in EVs are biomarkers of immune activation and viral replication in PLWH. Moreover, viral molecules are enclosed in EVs produced from infected cells. Our objective was to assess the value of EV-associated HIV-1 RNA as a biomarker of immune activation and viral replication in PLWH. Methods Plasma samples were obtained from a cohort of 53 PLWH with a detectable viremia. Large and small EVs were respectively purified by plasma centrifugation at 17,000 x g and by precipitation with ExoQuick™. HIV-1 RNA and microRNAs were quantified in the EV subtypes by RT-qPCR. Findings HIV-1 RNA content was higher in large EVs of ART-naive PLWH. Small EVs HIV-1 RNA was equivalent in ART-naive and ART-treated PLWH and positively correlated with CD4/CD8 T cell ratio. In ART-naive PLWH, HIV-1 RNA content of large EVs correlated with small EV-associated miR-29a, miR-146a and miR-155, biomarkers of viral replication and immune activation. A receiver operating characteristics analysis showed that HIV-1 RNA in large EVs discriminated PLWH with a high CD8 T cell count. Interpretation HIV-1 RNA in large EVs was associated with viral replication and immune activation biomarkers. Inversely, HIV-1 RNA in small EVs was related to immune restoration. Overall, these results suggest that HIV-1 RNA quantification in purified EVs could be a useful parameter to monitor HIV-1 infection. Funding Canadian Institutes of Health Research (CIHR) grants MOP-391232; MOP-188726; MOP-267056 (HIV/AIDS initiative) Research in context Evidence before this study Antiretroviral therapy (ART) suppress viral replication to make HIV-1 infection manageable, but fails to clear the virus from people living with HIV-1 (PLWH). Hence, the infection becomes a chronic condition characterized by a dysfunction of the immune system caused by repeated activation and a persistent risk of a resurgence of viral replication (viral rebound). New biomarkers are required to improve the care of PLWH by identifying the individuals with a greater immune dysfunction and/or a higher risk of viral rebound. HIV-1 infection modifies the abundance, size and content of plasmatic extracellular vesicles (EVs). Specific host microRNAs enrcichment in EVs correlates with immune activation and viral rebound. In addition, viral proteins and genomic material are found within EVs. Various EV subtypes are released by infected cells, all using different biogenesis machinery. The distribution of HIV-1 RNA in EV subtypes has never been assessed and this novel parameter could provide information on the infection progression. Added value of this study This study provides the first quantification of HIV-1 RNA in two EV subtypes, large and small, from the plasma of PLWH. Large EVs HIV-1 RNA was lower in ART-treated PLWH and decreased with the duration of treatment. HIV-1 RNA associated to large EVs was a better predictor of immune activation than the standard plasma viral load. Inversely, the HIV-1 RNA concentration in small EVs was unaffected by ART and linked to better immune functions. Overall, the results presented in this study suggest that HIV-1 RNA in large EVs originates from ongoing viral replication, while HIV-1 in small EVs is the produce of proviral transcription. Implications of all the evidence The standard procedure for the clinical care of PLWH is to quantify HIV-1 RNA in the whole plasma, disregarding the context of its production. We show that the differential distribution of HIV-1 RNA in large and small EVs seems to be an indicator of disease progression. The purification of plasmatic EVs is considered as a non-invasive liquid biopsy to assess the progression of diseases. PLWH could benefit from the analysis of their plasmatic EVs to monitor the infection with an improved precision.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 ».