OGG1 inhibition suppresses African swine fever virus replication
Bibliographic record
Abstract
African swine fever virus (ASFV) is an important pathogen that causes a highly contagious and lethal disease in swine, for which neither a vaccine nor treatment is available. The DNA repair enzyme 8-oxoguanine DNA glycosylase 1 (OGG1), which excises the oxidative base lesion 8-oxo-7,8-dihydroguanine (8-oxoG), has been linked to the pathogenesis of different diseases associated with viral infections. However, the role of OGG1-base excision repair (BER) in ASFV infection has been poorly investigated. Our study aimed to characterize the alteration of host reactive oxygen species (ROS) and OGG1 and to analyse the role of OGG1 in ASFV infection. We found that ASFV infection induced high levels and dynamic changes in ROS and 8-oxoG and consistently increased the expression of OGG1. Viral yield, transcription level, and protein synthesis were reduced in ASFV-infected primary alveolar macrophages (PAMs) treated by TH5487 or SU0268 inhibiting OGG1. The expression of BER pathway associated proteins of ASFV was also suppressed in OGG1-inhibited PAMs. Furthermore, OGG1 was found to negatively regulate interferon β (IFN-β) production during ASFV infection and IFN-β could be activated by OGG1 inhibition with TH5487 and SU0268, which blocked OGG1 binding to 8-oxoG. Additionally, the interaction of OGG1 with viral MGF360-14-L protein could disturb IFN-β production to further affect ASFV replication. These results suggest that OGG1 plays the crucial role in successful viral infection and OGG1 inhibitors SU0268 or TH5487 could be used as antiviral agents for ASFV infection.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".