The dual-specificity kinase DYRK1A interacts with the Hepatitis B virus genome and regulates the production of viral RNA
Bibliographic record
Abstract
The genome of Hepatitis B virus (HBV) persists in infected hepatocytes as a nuclear episome (cccDNA) that is responsible for the transcription of viral genes and viral rebound, following antiviral treatment arrest in chronically infected patients. There is currently no clinically approved therapeutic strategy able to efficiently target cccDNA (Lucifora J 2016). The development of alternative strategies aiming at permanently abrogating HBV RNA production requires a thorough understanding of cccDNA transcriptional and post-transcriptional regulation. In a previous study, we discovered that 1C8, a compound that inhibits the phosphorylation of some cellular RNA-binding proteins, could decrease the level of HBV RNAs. Here, we aimed at identifying kinases responsible for this effect. Among the kinases targeted by 1C8, we focused on DYRK1A, a dual-specificity kinase that controls the transcription of cellular genes by phosphorylating transcription factors, histones, chromatin regulators as well as RNA polymerase II. The results of a combination of genetic and chemical approaches using HBV-infected hepatocytes, indicated that DYRK1A positively regulates the production of HBV RNAs. In addition, we found that DYRK1A associates with cccDNA, and stimulates the production of HBV nascent RNAs. Finally, reporter gene assays showed that DYRK1A up-regulates the activity of the HBV enhancer 1/X promoter in a sequence-dependent manner. Altogether, these results indicate that DYRK1A is a proviral factor that may participate in the HBV life cycle by stimulating the production of HBx, a viral factor absolutely required to trigger the complete cccDNA transcriptional program.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".