Recent Metabolic Advances for Preventing and Treating Acute and Chronic Graft Versus Host Disease
Bibliographic record
Abstract
The therapeutic efficacy of allogeneic hematopoietic stem cell transplantation (allo-HSCT) is limited by the development of graft-versus-host disease (GVHD). In GVHD, rigorous pre-conditioning regimen resets the immune landscape and inflammatory milieu causing immune dysregulation, characterized by an expansion of alloreactive cells and a reduction in immune regulatory cells. In acute GVHD (aGVHD), the release of damage- and pathogen- associated molecular patterns from damaged tissue caused by the conditioning regimen sets the stage for T cell priming, activation and expansion further exacerbating tissue injury and organ damage, particularly in the gastrointestinal tract. Studies have shown that donor T cells utilize multiple energetic and biosynthetic pathways to mediate GVHD that can be distinct from the pathways used by regulatory T cells for their suppressive function. In chronic GVHD (cGVHD), donor T cells may differentiate into IL-21 producing T follicular helper cells or tissue resident T helper cells that cooperate with germinal center B cells or memory B cells, respectively, to produce allo- and auto-reactive antibodies with subsequent tissue fibrosis. Alternatively, donor T cells can become IFN- γ/IL-17 cytokine expressing T cells that mediate sclerodermatous skin injury. Patients refractory to the first line standard regimens for GVHD treatment have a poor prognosis indicating an urgent need for new therapies to restore the balance between effector and regulatory immune cells while preserving the beneficial graft-versus-tumor effect. Emerging data points toward a role for metabolism in regulating these allo- and auto-immune responses. Here, we will discuss the preclinical and clinical data available on the distinct metabolic demands of acute and chronic GVHD and recent efforts in identifying therapeutic targets using metabolomics. Another dimension of this review will examine the changing microbiome after allo-HSCT and the role of microbial metabolites such as short chain fatty acids and long chain fatty acids on regulating immune responses. Lastly, we will examine the metabolic implications of coinhibitory pathway blockade and cellular therapies in allo-HSCT. In conclusion, greater understanding of metabolic pathways involved in immune cell dysregulation during allo-HSCT may pave the way to provide novel therapies to prevent and treat GVHD.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".