Cooperation of TNF Family Members CD40 Ligand, Receptor Activator of NF-κB Ligand, and TNF-α in the Activation of Dendritic Cells and the Expansion of Viral Specific CD8+ T Cell Memory Responses in HIV-1-Infected and HIV-1-Uninfected Individuals
Bibliographic record
Abstract
Members of the TNF superfamily have been shown to be instrumental in enhancing cell-mediated immune responses, primarily through their interactions with dendritic cells (DCs). We systematically evaluated the ability of three TNF superfamily molecules, CD40 ligand (CD40L), receptor activator of NF-kappaB ligand (RANKL), and TNF-alpha, to expand ex vivo EBV-specific CTL responses in healthy human individuals and ex vivo HIV-1-specific CTL responses in HIV-1-infected individuals. In both groups of individuals, we found that all three TNF family molecules could expand CTL responses, albeit at differing degrees. CD40L treatment alone was better than RANKL or TNF-alpha alone to mature DCs and to expand CTL. In healthy volunteers, TNF-alpha or RANKL could cooperate with CD40L to maximize the ability of DCs to expand virus-specific CTL responses. In HIV-1 infection, cooperative effects between TNF-alpha or RANKL in combination with CD40L were variable. TNF-alpha and RANKL cooperated with CD40L via differing mechanisms, i.e., TNF-alpha enhanced IL-12 production, whereas RANKL enhanced survival of CD40L-stimulated DCs. These findings demonstrate that optimal maturation of DCs requires multiple signals by TNF superfamily members that include CD40L. In HIV-1 infection, DCs may only require CD40L to maximally expand CTL. Finally, CTL responses were higher in CD4(+) T cell-containing conditions even in the presence of TNF family molecules, suggesting that CD4(+) T cells can provide help to CD8(+) T cells independently of CD40L, RANKL, or TNF-alpha.
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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) | 0.000 | 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".