Involvement of Src and Syk Tyrosine Kinases in HIV-1 Transfer from Dendritic Cells to CD4+ T Lymphocytes
Bibliographic record
Abstract
Dendritic cells (DCs) are considered as key mediators of the early events in HIV-1 infection at mucosal sites. Although several aspects of the complex interactions between DCs and HIV-1 have been elucidated, there are still basic questions that remain to be answered about DCs/HIV-1 interplay. In this study, we examined the contribution of nonreceptor TKs in the known ability of DCs to efficiently transfer HIV-1 to CD4(+) T cells in trans. Experiments performed with specific inhibitors of Src and Syk family members indicate that these tyrosine kinases (TKs) are participating to HIV-1 transfer from immature monocyte-derived DCs (IM-MDDCs) to autologous CD4(+) T cells. Experiments with IM-MDDCs transfected with small interfering RNAs targeting Lyn and Syk confirmed the importance of these nonreceptor TKs in HIV-1 transmission. The Src- and Syk-mediated effect on virus transfer was linked with infection of IM-MDDCs in cis-as monitored by quantifying integrated viral DNA and de novo virus production. The process of HIV-1 transmission from IM-MDDCs to CD4(+) T cells was unaffected following treatment with protein kinase C and protein kinase A inhibitors. These data suggest that Src and Syk TKs play a functional role in productive HIV-1 infection of IM-MDDCs. Additional work is needed to facilitate our comprehension of the various mechanisms underlying the exact contribution of Src and Syk TKs to this phenomenon.
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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".