Stromal-Derived Factor-1α and Interleukin-7 Treatment Improves Homeostatic Proliferation of Naïve CD4+ T Cells after Allogeneic Stem Cell Transplantation
Bibliographic record
Abstract
Graft-versus-host disease (GVHD) impairs immune reconstitution after allogeneic stem cell transplantation (allo-SCT) and effective therapies aimed at restoring T cell counts in GVHD patients have yet to be developed. During GVHD, CD4(+) T cell reconstitution is particularly affected and current models hold that GVHD insult to the peripheral lymphoid niche is responsible for this effect. Here, we show that naïve CD4(+) T cell homeostatic proliferation (HP) is lost during GVHD because of low systemic IL-7 and impaired dendritic cell (DC) regeneration. We assessed factors involved in DC differentiation and found that although fms-like tyrosine kinase 3 ligand (Flt3-L) levels were normal, stromal-derived factor-1α (SDF-1α) was diminished in the blood of GVHD mice. Unlike Flt3-L treatment, the administration of SDF-1α specifically increased CD8α(+) DC numbers and did not worsen GVHD. Importantly, CD4(+) T cell HP was enhanced only when IL-7 and SDF-1α or Flt3L were coadministered, confirming the crucial role of DCs and IL-7 in restoring CD4(+) T cell regeneration during GVHD. Altogether, our results indicate that CD8α(+) DCs are part of the peripheral niche that controls CD4(+) T cell HP and that their depletion, combined with low systemic IL-7, explains how GVHD constrains naïve CD4(+) T cell reconstitution after allo-SCT.
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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) | 0.001 | 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".