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Record W4403904412 · doi:10.1016/j.isci.2024.111244

CBP/p300 lysine acetyltransferases inhibit HIV-1 expression in latently infected T cells

2024· article· en· W4403904412 on OpenAlexafffund
Riley M. Horvath, Ivan Sadowski

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsAcetyltransferasesLysineHuman immunodeficiency virus (HIV)Histone AcetyltransferasesChemistryBiologyVirologyCell biologyAcetylationComputational biologyMolecular biologyBiochemistryGeneAmino acid

Abstract

fetched live from OpenAlex

HIV-1 latency is regulated by chromatin modifying enzymes, and histone deacetylase inhibitors (HDACi) cause reactivation of provirus expression. Surprisingly, we observed that inhibitors of the CBP/p300 acetyltransferases also cause reversal of latency in T cells. CBP/p300 inhibitors synergize with various latency reversing agents to cause HIV-1 reactivation. In contrast, inhibition of CBP/p300 impaired reversal of latency by the HDACi SAHA, indicating that CBP/p300 must contribute to acetylation on the HIV-1 LTR associated with HDACi-mediated latency reversal. CBP/p300 inhibition caused loss of H3K27ac and H3K4me3 from the LTR, but did not affect association of the inhibitor protein BRD4. Furthermore, inhibition of the additional lysine acetyltransferases PCAF/GCN5 or KAT6A/KAT6B also caused reversal of latency, suggesting that protein acetylation has an inhibitory effect on HIV-1 expression. Collectively, these observations indicate that transcription from the HIV-1 LTR is controlled both positively and negatively by protein acetylation, likely including both histone and non-histone regulatory targets.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.005

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.

Opus teacher head0.014
GPT teacher head0.264
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2024
Admission routes2
Has abstractyes

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