Antiviral Activity of PLK1-Targeting siRNA Delivered by Lipid Nanoparticles in HBV-Infected Hepatocytes
Bibliographic record
Abstract
BACKGROUND: A link between HBV and PLK1 was clearly evidenced in HBV-driven carcinogenesis, and we have also recently shown that PLK1 is a proviral factor in the early phases of HBV infection. Moreover, we have shown that BI-2536, a small molecule PLK1 inhibitor, was very efficient at inhibiting HBV DNA neosynthesis, notably by affecting nucleocapsid assembly as a result of the modulation of HBc phosphorylation. Yet, as small molecule kinase inhibitors often feature poor selectivity, a more specific and safer strategy to target PLK1 would be needed for a potential development against chronic HBV infections. METHODS: Here, we analysed using both freshly isolated primary human hepatocytes and differentiated HepaRG, the anti-HBV properties of an LNP-encapsulated PLK1-targeting siRNA. Standard assays were used to monitor the effect of LNP siPLK1, or controls (LNP siHBV and LNP siNon-targeting), on HBV replication and cell viability. RESULTS: A dose as low as 100 ng/ml of LNP-siPLK1 resulted in a >75% decrease in secreted HBV DNA (viral particles), which was comparable to that obtained with LNP siHBV or 10 µM of tenofovir (TFV), without affecting cell viability. Interestingly, and in contrast to that obtained with TFV, a strong inhibition of viral RNA and HBe/HBsAg secretions was also observed under LNP siPLK1 treatment. This correlated with a significant intracellular decrease of vRNA accumulation, which was independent of any change in cccDNA levels, thus suggesting a transcriptional or post-transcriptional modulation. Such an effect was not obtained with a biochemical approach of PLK1 inhibition, suggesting an enzymatic-independent role of PLK1. CONCLUSIONS: This study emphasizes that a specific PLK1 inhibition could help in achieving an improved HBsAg loss in CHB patients, likely in combination with other HBsAg-targeting strategies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".