JAK-STAT Signaling Pathways and Inhibitors Affect Reversion of Envelope-Mutated HIV-1
Bibliographic record
Abstract
ABSTRACT HIV can spread by both cell-free and cell-to-cell transmission. Here, we show that many of the amino acid changes in Env that are close to the CD4 binding pocket can affect HIV replication. We generated a number of mutant viruses that were unable to infect T cells as cell-free viruses but were nevertheless able to infect certain T cell lines as cell-associated viruses, which was followed by reversion to the wild type. However, the activation of JAK-STAT signaling pathways caused the inhibition of such cell-to-cell infection as well as the reversion of multiple HIV Env mutants that displayed differences in their abilities to bind to the CD4 receptor. Specifically, two T cell activators, interleukin-2 (IL-2) and phorbol 12-myristate 13-acetate (PMA), both capable of activation of JAK-STAT pathways, were able to inhibit cell-to-cell viral transmission. In contrast, but consistent with the above result, a number of JAK-STAT and mTOR inhibitors actually promoted HIV-1 transmission and reversion. Hence, JAK-STAT signaling pathways may differentially affect the replication of a variety of HIV Env mutants in ways that differ from the role that these pathways play in the replication of wild-type viruses. IMPORTANCE Specific alterations in HIV Env close to the CD4 binding site can differentially change the ability of HIV to mediate infection for cell-free and cell-associated viruses. However, such differences are dependent to some extent on the types of target cells used. JAK-STAT signaling pathways are able to play major roles in these processes. This work sheds new light on factors that can govern HIV infection of target cells.
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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) | 0.002 | 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".