HIV-1 RNA in Large and Small Plasmatic Extracellular Vesicles: a Novel Parameter for Monitoring Immune Activation and Virological Failure
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".