HTLV-1 uses HSPG and neuropilin-1 for entry by molecular mimicry of VEGF165
Bibliographic record
Abstract
Human T-cell lymphotropic virus type 1 (HTLV-1) entry involves the interaction between the surface (SU) subunit of the Env proteins and cellular receptor(s). Previously, our laboratories demonstrated that heparan sulfate proteoglycans (HSPGs) and neuropilin-1 (NRP-1), a receptor of VEGF(165), are essential for HTLV-1 entry. Here we investigated whether, as when binding VEGF(165), HSPGs and NRP-1 work in concert during HTLV-1 entry. VEGF(165) binds to the b domain of NRP-1 through both HSPG-dependent and -independent interactions, the latter involving its exon 8. We show that VEGF(165) is a selective competitor of HTLV-1 entry and that HTLV-1 mimics VEGF(165) to recruit HSPGs and NRP-1: (1) the NRP-1 b domain is required for HTLV-1 binding; (2) SU binding to target cells is blocked by the HSPG-binding domain of VEGF(165); (3) the formation of Env/NRP-1 complexes is enhanced by HSPGs; and (4) the HTLV SU contains a motif homologous to VEGF(165) exon 8. This motif directly binds to NRP-1 and is essential for HTLV-1 binding to, internalization into, and infection of CD4(+) T cells and dendritic cells. These findings demonstrate that HSPGs and NRP-1 function as HTLV-1 receptors in a cooperative manner and reveal an unexpected mimicry mechanism that may have major implications in vivo.
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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".