O4-S1.05 RNA helicases, p72 and p68 and HDAC1 interact with HIV-1 integrase and affect viral replication
Bibliographic record
Abstract
Background Human immunodeficiency virus type-1 (HIV-1) infection is one of the leading causes of death worldwide. Current anti-HIV-1 therapy, referred as highly active antiretroviral therapy (HAART), is based on the use of combination of drugs directed against viral enzymes mainly reverse transcriptase and protease and more recently integrase. Indeed, HAART has dramatically improved the clinical course of the disease. However, the emergence of multi-drug resistant virus strains during treatment highlights the urgent need to develop novel antiretroviral drugs against new HIV-1 targets. HIV-1 is able to hijack cellular machinery for its replication through protein-protein interactions between viral and host cell factors and a rising strategy against HIV-1 infection is to inhibit key virus-cell interactions. Integrase that catalyses HIV-1 viral DNA integration into the host cell genome is currently a focus for the development of new drugs. Several cellular partners of integrase have been identified using different methods. Based on a different strategy, our study aimed to identify new integrase cellular partners. Methods We used a biotinylated oligonucleotide derived from the viral U3 LTR end as bait to isolate integrase in a streptavidin beads magnetic separation. Proteins co-purified with integrase were analysed by mass spectrometry. Results Interestingly, our method allowed the identification of new cellular proteins notably p72 and p68 RNA helicases and histone deacetylase 1 (HDAC1) as integrase partners in addition to proteins already reported in literature. The interaction of p72, p68 and HDAC1 proteins with integrase was confirmed by co-immunoprecipitation. In addition, specific knockdown of p72 and p68 RNA helicases and HDAC1 were shown to affect HIV-1 replication. Conclusions Our data suggest that cellular proteins, p72 and p68 RNA helicases and HDAC1 facilitate HIV-1 replication through interaction with integrase.
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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) | 0.000 | 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 | 0.000 |
| 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".