Immune Cells Release MicroRNA-155 Enriched Extracellular Vesicles That Promote HIV-1 Infection
Bibliographic record
Abstract
The hallmark of HIV-1 infection is the rapid dysregulation of immune functions. Recent investigations for biomarkers of such dysregulation in people living with HIV (PLWH) reveal a strong correlation between viral rebound and immune activation with an increased abundance of extracellular vesicles (EVs) enriched with microRNA-155. We propose that the activation of peripheral blood mononuclear cells (PBMCs) leads to an increased miR-155 expression and production of miR-155-rich extracellular vesicles (miR-155-rich EVs), which can exacerbate HIV-1 infection by promoting viral replication. PBMCs were incubated with either HIV-1 (NL4.3Balenv), a TLR-7/8 agonist, or TNF. EVs were harvested from the cell culture supernatant by differential centrifugation, and RT-qPCR quantified miR-155 in cells and derived EVs. The effect of miR-155-rich EVs on replication of HIV-1 in incubated PBMCs was then measured by viral RNA and DNA quantification. HIV-1, TLR7/8 agonist, and TNF each induced the release of miR-155-rich EVs by PBMCs. These miR-155-rich EVs increased viral replication in PBMCs infected in vitro. Infection with HIV-1 and inflammation promote the production of miR-155-rich EVs, enhancing viral replication. Such autocrine loops, therefore, could influence the course of HIV-1 infection by promoting viral replication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".