Kaposi Sarcoma-associated Herpesvirus Latency-associated Nuclear Antigen Inhibits Interferon (IFN) β Expression by Competing with IFN Regulatory Factor-3 for Binding to IFNB Promoter
Bibliographic record
Abstract
Host cells respond to viral infections by synthesizing and producing antiviral molecules such as type I interferons (IFN). The Kaposi sarcoma-associated herpesvirus (KSHV) encodes multiple proteins expressed during the lytic replication cycle that alter the antiviral response of the host. Considering that in Kaposi sarcoma lesions and primary effusion lymphoma cells KSHV is latent in the vast majority of cells, we were interested in determining whether latently expressed viral proteins have the ability to modulate IFN synthesis. The latency-associated nuclear antigen (LANA-1) is a large nuclear protein that plays a role in the establishment and maintenance of latent KSHV episome in the nucleus of infected cells. LANA-1 is also described to modulate the cellular transcription. Here, we report that LANA-1 inhibits IFN-beta transcription and synthesis by competing with the binding of interferon regulatory factor-3 (IRF3) to the IFNB promoter. Using mutants of LANA-1, we have identified the central acidic repeated region as the domain essential for interfering with the binding of IRF3 to the positive regulatory domains I-III of the IFNB promoter. In addition, the nuclear localization of LANA-1 proved essential for IFN-beta inhibition. Thus, LANA-1 interferes with the formation of IFN-beta enhanceosome by competing with the fixation of IRF3 and by inhibiting the expression of the CREB-binding protein. The ability of LANA-1 to inhibit IFNB gene expression highlights a new role for this protein in cellular gene modulation and immune evasion strategies.
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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".