Inhibitors of Tax1‐PDZ Interactions Block HTLV‐1 Viral Transmission by Changing EV Composition
Bibliographic record
Abstract
Extracellular vesicles (EVs) are known to facilitate infection by enveloped RNA viruses including the Human T-cell leukemia virus type-1 (HTLV-1). HTLV-1-encoded proteins, like the transactivator and oncoprotein Tax-1, are loaded into EVs but their precise impact on EV cargos is not yet known. Here, we report a comprehensive interaction map between Tax-1 and the human PDZ (PSD95/DLG/ZO-1) proteins that regulate EVs formation and composition. We show that Tax-1 interacts with more than one-third of hPDZome components, including proteins involved in cell cycle, cell-cell junctions, cytoskeleton organization and membrane complex assembly. We extensively characterized Tax-1 interaction with syntenin-1, an evolutionary conserved PDZ hub that controls EV biogenesis. Using nuclear magnetic resonance (NMR) spectroscopy, we have determined the structural basis of the interaction between the C-terminal PDZ binding motif of Tax-1, and two PDZ domains of syntenin-1. Importantly, we show that a small molecule able to inhibit HTLV-1 cell-to-cell transmission breaks the Tax-1/syntenin-1 interaction, impacts the levels of syntenin-1 and viral proteins in EVs, and shifts the EV composition toward cellular antiviral proteins and microRNAs, including the miR-320 family. Consequently, we demonstrate that mimics of miR-320c, encapsulated into EVs, have antiviral activities with a potential to be used against HTLV-1 induced diseases.
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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".