Chromatin-mediated epigenetic regulation of HSV-1 transcription as a potential target in antiviral therapy
Bibliographic record
Abstract
The ability to establish, and reactivate from, latent infections is central to the biology and pathogenesis of HSV-1. It also poses a strong challenge to antiviral therapy, as latent HSV-1 genomes do not replicate or express any protein to be targeted. Although the processes regulating the establishment and maintenance of, and reactivation from, latency are not fully elucidated, the current general consensus is that epigenetics play a major role. A unifying model postulates that whereas HSV-1 avoids or counteracts chromatin silencing in lytic infections, it becomes silenced during latency, silencing which is somewhat disrupted during reactivation. Many years of work by different groups using a variety of approaches have also shown that the lytic HSV-1 chromatin is distinct and has unique biophysical properties not shared with most cellular chromatin. Nonetheless, the lytic and latent viral chromatins are typically enriched in post translational modifications or histone variants characteristic of active or repressed transcription, respectively. Moreover, a variety of small molecule epigenetic modulators inhibit viral replication and reactivation from latency. Despite these successes in culture and animal models, it is not obvious how epigenetic modulation would be used in antiviral therapy if the same epigenetic mechanisms governed viral and cellular gene expression. Recent work has highlighted several important differences between the viral and cellular chromatins, which appear to be of consequence to their respective epigenetic regulations. In this review, we will discuss the distinctiveness of the viral chromatin, and explore whether it is regulated by mechanisms unique enough to be exploited in antiviral therapy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".