Role of Micro-RNAs in Regulation of Lentiviral Latency and Persistence
Bibliographic record
Abstract
Small interfering RNAs have been demonstrated to serve as a molecular defence against numerous retroviruses in plants and insects and, more recently, in primates. With the recent findings of micro-RNAs (miRNAs) that seem to play a pivotal role in the survival of the host, we have explored the role of miRNAs in lentiviral (LV) replication. We have previously hypothesized that, at least in the case of lentivirus infection, small interfering RNAs are involved in the inhibition of these types of viruses by the formation of intramolecular triplex formation (triplexes) between the polypurine tracks sequences of LV provirus and miRNAs and blocking the viral replication at the preintegration complex levels, placing these viruses into a suspended latency. Using several latently and chronically infected LV cell lines and human PBMCs from HIV-1-infected individuals, we show that perinuclear triplexes are formed in LV-infected cells. The number of triplexes decreased in cells with productive replication of LVs. Therefore, the degree of replication of HIV-1 and other LVs, both in the HIV-1 or other LV-infected cell lines and the HIV-1 infected PBMCs, inversely correlate with the number of cytoplasmic triplexes present in a particular cell. This correlation was further confirmed by the stimulation of PBMCs and LV-infected cell lines with appropriate mitogens. Treatment with Tagetin, a RNA polymerase III inhibitor, resulted in a significant decrease in triplexes and a dramatic increase in the LV replication. Our data suggest that triplex formation may be an important mechanism of LV latency mediated by endogenous miRNAs.
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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 | 0.000 | 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) | 0.001 | 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".