Impact of in vitro HIV infection on human thymic regulatory T cell differentiation
Bibliographic record
Abstract
Background The differentiation and function of immunosuppressive regulatory T cells (Tregs) is dictated by the master transcription factor FoxP3. During HIV infection, there is an increase in Treg frequencies in the peripheral blood and lymphoid tissues. This accentuates immune dysfunction and disease progression. Expression of FoxP3 by thymic Tregs (tTregs) is partially controlled by TGF-β. This cytokine also contributes to Treg development in the peripheral blood and lymphoid tissues. Although TGF-β mediates lymphoid tissue fibrosis and peripheral Treg differentiation in HIV-infected individuals, its role in the induction and maintenance of Tregs within the thymus during HIV infection remains unclear. Methods Thymocytes were isolated from fresh human thymic tissues obtained from pediatric patients undergoing cardiac surgery. Infection by both R5- and X4-tropic HIV-1 strains and TGF-β treatment of human thymocytes was performed in an in vitro co-culture model with OP9-DL1 cells expressing Notch ligand delta-like 1 without T cell receptor (TCR) activation. Results Despite high expression of CCR5 and CXCR4 by tTregs, FoxP3 + CD3highCD8- thymocytes were much less prone to in vitro infection with R5- and X4-tropic HIV strains compared to FoxP3-CD3highCD8- thymocytes. As expected, CD3highCD4+ thymocytes, when treated with TGF-β1, upregulated CD127 and this treatment resulted in increased FoxP3 expression and Treg differentiation, but did not affect the rate of HIV infection. FoxP3 expression and Treg frequencies remained unchanged following in vitro HIV infection alone or in combination with TGF-β1. Conclusion FoxP3 expression and tTreg differentiation is not affected by in vitro HIV infection alone or the combination of in vitro HIV infection and TGF-β treatment.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".