Cardiac glycoside/aglycones inhibit HIV-1 gene expression by a mechanism requiring MEK1/2-ERK1/2 signaling
Bibliographic record
Abstract
Abstract The capacity of HIV-1 to develop resistance to current drugs calls for innovative strategies to control this infection. We aimed at developing novel inhibitors of HIV-1 replication by targeting viral RNA processing—a stage dependent on conserved host processes. We previously reported that digoxin is a potent inhibitor of this stage. Herein, we identify 12 other cardiac glycoside/aglycones or cardiotonic steroids (CSs) that impede HIV growth in HIV-infected T cells from clinical patients at IC50s (1.1–1.3 nM) that are 2–26 times below concentrations used in patients with heart conditions. We subsequently demonstrate that CSs inhibit HIV-1 gene expression in part through modulation of MEK1/2-ERK1/2 signaling via interaction with the Na+/K+-ATPase, independent of alterations in intracellular Ca2+. Supporting this hypothesis, depletion of the Na+/K+-ATPase or addition of a MEK1/2-ERK1/2 activator also impairs HIV-1 gene expression. Similar to digoxin, all CSs tested induce oversplicing of HIV-1 RNAs, reducing unspliced (Gag) and singly spliced RNAs (Env/p14-Tat) encoding essential HIV-1 structural/regulatory proteins. Furthermore, all CSs cause nuclear retention of genomic/unspliced RNAs, supporting viral RNA processing as the underlying mechanism for their disruption of HIV-1 replication. These findings call for further in vivo validation and supports the targeting of cellular processes to control HIV-1 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 | 0.000 |
| 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".