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Research ArticleImmunologyOncology Open Access | 10.1172/jci.insight.192774

Targeting the pentose phosphate pathway mitigates graft-versus-host disease by rewiring alloreactive T cell metabolism

Saeed Daneshmandi,1,2 Eun Ko,3 Qi Yan,1 Jee Eun Choi,2 Prashant K. Singh,4 Richard M. Higashi,5 Andrew N. Lane,5 Teresa W.M. Fan,5 Jingxin Qiu,6 Sophia Hani,3 Keli L. Hippen,3 Jianmin Wang,7 Philip L. McCarthy,8 Bruce R. Blazar,3 and Hemn Mohammadpour1

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by Daneshmandi, S. in: PubMed | Google Scholar

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by Ko, E. in: PubMed | Google Scholar

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by Yan, Q. in: PubMed | Google Scholar

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by Choi, J. in: PubMed | Google Scholar

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by Singh, P. in: PubMed | Google Scholar

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by Higashi, R. in: PubMed | Google Scholar

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by Lane, A. in: PubMed | Google Scholar

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by Fan, T. in: PubMed | Google Scholar

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by Qiu, J. in: PubMed | Google Scholar

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by Hani, S. in: PubMed | Google Scholar

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by Hippen, K. in: PubMed | Google Scholar |

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by Wang, J. in: PubMed | Google Scholar |

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by McCarthy, P. in: PubMed | Google Scholar |

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by Blazar, B. in: PubMed | Google Scholar |

1Department of Cell Stress Biology, and

2Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

3Division of Blood & Marrow Transplant & Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.

4Department of Cancer Genetics & Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

5Department of Toxicology and Cancer Biology, Markey Cancer Center, Center for Environmental and Systems Biochemistry (CESB), Lexington, Kentucky, USA.

6Department of Pathology,

7Department of Biostatistics & Bioinformatics, and

8Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Find articles by Mohammadpour, H. in: PubMed | Google Scholar

Authorship note: SD and EK contributed equally to this work. PLM, BRB, and HM are joint supervisors of this work.

Published December 8, 2025 - More info

Published in Volume 10, Issue 23 on December 8, 2025
JCI Insight. 2025;10(23):e192774. https://doi.org/10.1172/jci.insight.192774.
© 2025 Daneshmandi et al. This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Published December 8, 2025 - Version history
Received: February 24, 2025; Accepted: October 15, 2025
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Abstract

Glycolysis fuels cytotoxic allogeneic T cells in acute graft-versus-host disease (aGvHD), but the downstream role of glucose metabolism in modulating aGvHD remains unclear. Targeting glycolysis or glucose receptors is toxic. Therefore, we explored alternative glucose-dependent pathways, focusing on the pentose phosphate pathway (PPP). Single-cell RNA sequencing revealed PPP upregulation in allogeneic T cells during allogeneic hematopoietic cell transplantation (allo-HCT). We showed that donor T cell deficiency in 6-phosphogluconate dehydrogenase (6PGD), the second rate-limiting enzyme in the PPP, significantly reduced aGvHD severity and mortality in murine models. Functional assays demonstrated that PPP blockade led to proliferation arrest without inducing apoptosis. PPP blockade shifted T cell metabolism away from T cell dependency on glycolysis for rapid T cell proliferation. Pharmacological inhibition of the PPP through 6PGD blockade with 6-aminonicotinamide (6AN) effectively reduced aGvHD severity, like donor 6PGD-deficient T cells in an allogeneic aGvHD model. Similarly, 6AN reduced xenogeneic GvHD lethality. 6PGD inhibition preserved the graft-versus-tumor (GvT) effect, with the generation of a small subset of granzyme Bhi effector T cells with potent antitumor activity. These findings highlight the PPP as a key regulator of allogeneic T cell proliferation and differentiation and identify 6PGD as a promising therapeutic target to mitigate aGvHD severity while preserving beneficial GvT effects.

Graphical Abstract
graphical abstract
Introduction

Graft-versus-host disease (GvHD) remains a leading cause of mortality and morbidity following allogeneic hematopoietic cell transplantation (allo-HCT), despite significant advances in therapies aimed at targeting allogeneic T cells (1, 2). Acute GvHD (aGvHD) is predominantly driven by the expansion of helper and cytotoxic T cells, which attack normal host tissues, primarily targeting organs such as the gut, liver, and skin (3, 4). Understanding allogeneic T cell biology in order to develop treatments that can effectively mitigate GvHD severity while preserving graft-versus-tumor (GvT) effects are fundamental for improving allo-HCT outcome.

Allogeneic T cells undergo significant metabolic changes following allo-HCT (5). T cells primarily use glucose as a fuel source, metabolizing it through glycolysis or the pentose phosphate pathway (PPP) (6, 7). Although the role of the T cell glycolysis pathway in aGvHD has been extensively examined (5, 8, 9), little is known about the role of the PPP in allogeneic T cell function.

The PPP, also referred to as the hexose monophosphate shunt, is a critical branch of glycolysis that contributes to nucleotide biosynthesis for energy, and NADPH regeneration and glutathione reduction that buffer oxidants produced during ATP production (10). The PPP pathway consists of a reversible non-oxidative and a largely irreversible oxidative branch (11). The oxidative branch is regulated by enzymes including glucose-6-phosphate dehydrogenase (G6PD), which is allosterically controlled by catalytic products and other metabolites (12). The flux through these branches is dynamically modulated in response to metabolic stress, determining the balance between NADPH and ribose-5-phosphate (R5P) production needed for growth and survival (13). Previous studies have shown that the gene expression of 6-phosphogluconate dehydrogenase (6PGD), the second major rate-limiting enzyme in the PPP, increases in donor T cells in a murine allo-HCT model (5). Our data also reveal that 6PGD is significantly upregulated in allogeneic T cells during the acute phase of GvHD. Because PPP generates nucleotide biosynthesis precursors and can divert glucose metabolism intermediates into the glycolysis pathway, PPP may support GVHD due to the higher need for energy that can be provided by glycolysis (9, 14, 15). In this study, we examined the contribution of the PPP to aGVHD pathobiology, aGvHD severity, efficacy of 6PGD checkpoint pharmacological inhibition, and GvT responses. Our findings offer valuable insights into allogeneic T cell metabolic activity and highlight potential, specific, therapeutic avenues for managing aGvHD while preserving GvT responses.

Results

6PGD gene expression is increased during aGvHD and regulates aGvHD. T cell metabolic reprogramming is entwined with their functional properties (5, 16). Previous studies reported a critical role for PPP after allo-HCT (5). To confirm these findings and to understand T cell metabolic changes after allo-HCT, and how they impact T cell function and allo-HCT outcomes, we employed unbiased single-cell RNA-seq (scRNA-seq) to characterize metabolic changes at days +7 and +14 after allo-HCT. We utilized the fully allogeneic model, C57BL/6→BALB/c, as described above. Donor splenic T cells (CD45+H2Kb+H2Kd–TCRβ+) were sorted on days +7 and +14 for scRNA-seq analysis to identify T cell clusters. Using Seurat analysis, a t-distributed stochastic neighbor embedding (t-SNE) plot revealed 9 T cell clusters (Figure 1A). Proportion analysis showed that naive and stem cell memory T cells were the most abundant, followed by effector/cytotoxic T cells, while central and effector memory T cells represented the smallest fractions (Supplemental Figure 1, A and B; supplemental material available online with this article; https://doi.org/10.1172/jci.insight.192774DS1) (17–22). The pathway enrichment analysis comparing day +14 to day +7 demonstrated upregulation of metabolic pathways, including glucose metabolism and glycolysis, and downregulation of oxidative phosphorylation (OXPHOS) among top 10 pathway changes as aGvHD progresses (Supplemental Figure 1C). Comparative analysis indicated elevated PPP gene signatures in stem cell memory T cells and proliferating clusters on day +14 when compared with day +7 (Figure 1B). An analysis of oxidative PPP during aGvHD initiation using G6pdx, G6pd2, Pgd, and Pgls gene sets revealed increased expression of oxidative PPP genes on day +7 when compared with day 0 for allogeneic, but not syngeneic, T cells (Figure 1C), particularly Pgd (which encodes 6PGD). This was confirmed by real-time PCR (Figure 1D). Western blot analysis further validated the enhanced 6PGD expression in alloreactive T cells (Supplemental Figure 2, A and B).

Blockade of oxidative PPP at the 6PGD metabolic checkpoint ameliorates theFigure 1

Blockade of oxidative PPP at the 6PGD metabolic checkpoint ameliorates the severity of and the mortality due to aGvHD. (A and B) BALB/c (H-2d) mice were lethally irradiated (8.5 Gy) on day –1 and transplanted with 3.5 × 106 T cell–depleted bone marrow (TCD-BM) cells and 0.7 × 106 purified splenic WT T cells from C57BL/6 (H-2b) mice on day 0. Splenic donor T cells (CD45+H2Kb+H2Kd–TCRβ+) were sorted and examined by scRNA-seq (A). The expression of oxidative PPP gene signatures was detected within the T cell clusters (B). T cells were isolated and pooled for analysis from 5 spleens. (C and D) Allo-HCT model was generated as in A and B using 0.2 × 106 purified splenic WT naive T cells. The expression levels of oxidative PPP pathway genes (C) and the Pgd gene (6PGD) (D) were examined in sorted alloreactive T cells (Allo: CD45+H2Kb+H2Kd–TCRβ+) and from BALB/c mice transplanted with syngeneic mice T cells (Syn: CD45+H2Kb–H2Kd+TCRβ+) on day +7. T cells were pooled from 5 spleens (1-way ANOVA). (E–G) BALB/c (H-2d) mice were lethally irradiated (8.5 Gy) on day –1 and transplanted with 3.5 × 106 TCD-BM cells with or without 0.2 × 106 splenic naive T cells from WT (Pgdfl/fl) or 6PGD-deficient (Pgdfl/flCd4Cre) C57BL/6 (H-2b) mice on day 0. Body weight loss (E) and clinical GvHD scores (F) are presented as mean ± SEM. n = 5 mice per group (2-way ANOVA). Survival data (G) are presented as percentage survival (log-rank Mantel-Cox test). (H and I) Allo-HCT model was generated as in E–G. GvHD progression was examined in the GI tract on days +7, +14, and +21. Representative images of H&E staining demonstrate infiltration of intraepithelial lymphocytes (blue arrows) and apoptosis (red arrows) (H). Original magnification, ×200. Semiquantitative GvHD scoring of small (top) and large (bottom) intestines are shown. n = 5 mice per group (Student’s t test). (J–L) C3H/SW (H-2b) mice were lethally irradiated (11.5 Gy) on day –1 and transplanted with 3.5 × 106 TCD-BM cells with or without 0.5 × 106 splenic naive T cells from Pgdfl/fl or Pgdfl/flCd4Cre C57BL/6 (H-2b) mice on day 0 as minor-mismatched model. Body weight loss (J) and clinical GvHD score (K) data are presented as mean ± SEM. Data are representative of 2 independent experiments (2-way ANOVA). Survival data are presented as percentage survival (log-rank Mantel-Cox test) (L). Data are shown as mean ± SEM. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

To explore the role of PPP, specifically 6PGD as a metabolic checkpoint regulating T cell function during aGvHD, we developed Cd4Cre × Pgdfl/fl mice, as described in the Methods. In these mice, 6PGD is inactivated in both CD4+ and CD8+ T cells since Cre is expressed at the CD4+CD8+ thymic development stage (15, 23).

We sought to determine the intrinsic impact of 6PGD deficiency on the resting splenic T cell phenotypes. Compared with WT controls, 6PGD-KO mice exhibited a lower frequency of CD4+ and CD8+ T cell subsets (Supplemental Figure 3A). Differentiation marker analysis revealed an increased frequency of effector T cells (CD62L–CD44+) among CD4+ (Supplemental Figure 3B) and CD8+ (Supplemental Figure 3C) populations. Resting-state 6PGD-KO T cells demonstrated a heightened expression of inflammatory cytokines, including IFN-γ (Supplemental Figure 3, D and E), TNF-α (Supplemental Figure 3, F and G), and GM-CSF (Supplemental Figure 3, H and I) when compared with WT T cells.

We therefore utilized purified naive T cells as donor T cells for the allo-HCT experiments to test the role of the PPP at the 6PGD checkpoint on aGvHD severity and mortality. We flow sorted naive T cells and found comparable CD4+ and CD8+ populations between the Pgdfl/flCd4Cre and Pgdfl/fl T cells with high purity (Supplemental Figure 4). To evaluate T cell 6PGD deficiency during aGvHD, WT BALB/c mice were irradiated (8.5 Gy with 137Cs) and transplanted i.v. with 3.5 × 106 total cells of T cell–depleted bone marrow (TCD-BM) from C57BL/6 mice with or without 0.2 × 106 naive T cells isolated from Pgdfl/fl or Pgdfl/flCd4Cre C56BL/6 mice. Compared with recipients of WT T cells, transplanted recipients of 6PGD-KO donor T cells showed reduced aGvHD severity, evidenced by decreased body weight loss (Figure 1E), lower clinical GvHD scores (Figure 1F), and improved survival (Figure 1G). As expected, recipients of TCD-BM alone did not develop aGvHD (Figure 1, E–G).

Histopathological examination of recipient target organs (small and large intestines) confirmed the clinical and survival data. Using a semiquantitative scoring system (24, 25), evaluator-blinded analysis of H&E-stained sections on days +7, +14, and +21 demonstrated lower aGvHD scores in recipients of Pgdfl/flCd4Cre T cells when compared with WT T cells, with reduced intraepithelial lymphocyte infiltration (blue arrows) and apoptosis (red arrows; nuclear and cytoplasm condensation) (Figure 1H). GI histopathology at lower and higher magnification on day +21 and liver histopathology on day +7 and day +14 are shown in Supplemental Figure 5. Semiquantitative scoring confirmed these observations (Figure 1I). Additionally, our previous reports showed the key role of 6PGD on regulation of endogenous (autologous) CD8+ T cell function (15). To examine the role of the PPP in CD8+ T cell–induced aGvHD, WT BALB/c mice were irradiated (8.5 Gy with 137Cs) and transplanted i.v. with 3.5 × 106 TCD-BM cells (from C57BL/6 mice) with or without 1 × 106 naive CD8+ T cells isolated from Pgdfl/fl or Pgdfl/flCd4Cre C56BL/6 mice. The results indicate that, like total T cells, 6PGD-KO donor CD8+ T cells showed reduced aGvHD severity, evidenced by decreased body weight loss (Supplemental Figure 6A), lower clinical GvHD scores (Supplemental Figure 6B), and improved survival (Supplemental Figure 6C).

To confirm the effects of 6PGD blockade in donor T cells in an MHC-matched, minor antigen–mismatched model, irradiated C3H/SW recipients were given 0.5 × 106 splenic naive T cells from either Pgdfl/flCd4Cre or Pgdfl/fl C57BL/6 donors, along with 3.5 × 106 TCD-BM cells (from C57BL/6 mice). C3H/SW recipients of 6PGD-KO T cells when compared with WT T cells displayed reduced body weight loss (Figure 1J), lower clinical GvHD scores (Figure 1K), and improved survival (Figure 1L) compared with controls.

Blockade of the 6PGD metabolic checkpoint prevents early expansion of allogeneic T cells while preserving a subset with an effector phenotype. We sought to understand possible mechanisms as to how 6PGD deficiency in donor naive T cells significantly reduces the severity and mortality of aGvHD. Using the MHC-mismatched (fully allogeneic) aGvHD model (C57BL/6 to BALB/c), we evaluated the impact of 6PGD deficiency on T cell subset dynamics, proliferation, and effector function. Flow cytometric analysis of splenic and hepatic donor T cells in recipients on days +7, +14, and +21 after allo-HCT revealed reduction in the frequencies of various CD4+ and CD8+ T cell subsets, including naive (Figure 2, A and F), stem cell memory (Figure 2, B and G), central memory (Figure 2, C and H), and effector memory (Figure 2, D and I). However, the frequency of effector CD4+ T cells increased, while no significant changes were observed in the effector CD8+ T cell population (Figure 2, E and J). The absolute CD4+ and CD8+ T cell numbers were significantly lower in 6PGD-deficient groups (Figure 2, K and L). Similar trends were observed when evaluating T cell subtype frequencies as percentages of total T cells within individual groups (Supplemental Figure 7). T cell subsets were identified according to the established markers in previous publications (26–29).

Blockade of the 6PGD metabolic checkpoint prevents alloreactive T cell expaFigure 2

Blockade of the 6PGD metabolic checkpoint prevents alloreactive T cell expansion while maintaining a portion of T cells with effector phenotype. (A–J) BALB/c (H-2d) mice were lethally irradiated (8.5 Gy) on day –1 and transplanted with 3.5 × 106 TCD-BM cells with or without 0.2 × 106 splenic naive T cells from Pgdfl/fl or Pgdfl/flCd4Cre C57BL/6 (H-2b) mice on day 0. The frequency of CD4+ T cells subsets as naive (A), stem cell memory (B), central memory (C), effector memory (D), and effector (E) was detected per spleen of the mice. The frequency of CD8+ T cells subsets as naive (F), stem cell memory (G), central memory (H), effector memory (I), and effector (J) was determined per spleen of the mice. n = 4–5 mice per group. Two-way ANOVA was used to analyze statistical significance among groups. The absolute number of CD4+ (K) and CD8+ (L) T cells was calculated per spleen on days +7, +14, and +21. n = 4–5 mice per group (2-way ANOVA). (M) T cells were isolated from Pgdfl/fl or Pgdfl/flCd4Cre spleens and activated in vitro with plate-bound anti-CD3 plus anti-CD28 mAbs. Apoptosis was determined by Annexin V staining. n = 4 data points per group. Data representative of 2 independent experiments (Student’s t test). (N and O) T cells were isolated from Pgdfl/fl or Pgdfl/flCd4Cre spleens, labeled with CFSE, and activated in vitro with plate-bound anti-CD3 plus anti-CD28 mAbs. The proliferation of CD4+ (top) and CD8+ (bottom) T cells was examined after 72 hours. Unstimulated T cells served as negative controls. n = 3 data points per group. Data representative of 3 independent experiments (Student’s t test) and are shown as mean ± SEM. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

We sought to determine whether the reduced T cell numbers were due to increased apoptosis or impaired proliferation. T cells were isolated from resting Pgdfl/fl and Pgdfl/flCd4Cre C57BL/6 mouse spleens and stimulated in vitro with anti-CD3 and anti-CD28 mAbs for 72 hours. Comparable viability in both groups was determined by Annexin V staining (Figure 2M). However, 6PGD-deficient T cells exhibited significantly lower proliferation (Figure 2, N and O). C57BL/6 donor T cell proliferation was assessed in vivo using a CFSE dilution assay with a 1:1 mixture of CD45.1+ WT and CD45.2+ Pgdfl/flCd4Cre naive T cells (0.2 × 106 total cells), transferred into lethally irradiated BALB/c mice. On day +3, CFSE dilution analysis confirmed that 6PGD-KO (CD45.2+) T cells proliferated significantly less compared with WT T cells (Figure 3, A–C). Evaluation of donor T cell expansion in spleen, mesenteric lymph nodes, and liver on day +7 showed significantly lower frequencies of CD45.2+ 6PGD-deficient T cells compared with CD45.1+ WT T cells (Figure 3, D and E). Time of cell injection is referred to as day 0 in these experiments.

6PGD blockade prevents early expansion of alloreactive T cells in aGvHD modFigure 3

6PGD blockade prevents early expansion of alloreactive T cells in aGvHD models. (A–C) Naive T cells were isolated from CD45.1+ WT and CD45.2+ Pgdfl/flCd4Cre mice, mixed 1:1, and labeled with CFSE. TCD-BM cells (3.5 × 106) and 0.4 × 106 mixed CSFE-labeled T cells infused into lethally irradiated (8.5 Gy) BALB/c mice (A). Proliferation was determined on day +3 by CFSE dilution in splenic CD45.1+ and CD45.2+ T cells (B and C). n = 4–5 mice per group. Data represent one independent experiment (Student’s t test). (D and E) Isolated naive T cells from CD45.1+ WT and CD45.2+ Pgdfl/flCd4Cre mice were mixed 1:1. TCD-BM cells (3.5 × 106) and 0.4 × 106 mixed T cells were infused into lethally irradiated (8.5 Gy) BALB/c mice. The frequencies of CD45.1+ and CD45.2+ T cells in spleen, mesenteric lymph node, and liver were examined on day +7 after transplantation (D). The comparative frequencies of CD45.1+ and CD45.2+ T cells are given as the percentage of total T cells (E). Day 0 is the time of cell injection. n = 4–5 mice per group. Data represent 1 experiment (Student’s t test). Data are shown as mean ± SEM. **P < 0.01; ****P < 0.0001.

Pharmacological blockade of 6PGD with 6AN reduces GvHD severity in murine aGvHD and xenogeneic aGvHD models. To assess whether pharmacological inhibition of 6PGD could mitigate aGvHD lethality, we tested 6-aminonicotinamide (6AN), a small-molecule inhibitor of 6PGD, in both murine and xenogeneic GvHD models. In the C57BL/6→BALB/c aGvHD model, 6AN treatment (0.5 mg/kg, administered daily via i.p. injection starting on day 0 of allo-HCT for the experiment duration) significantly improved long-term survival, with 35% survival of the treated group on day +46 after allo-HCT compared with 0% survival in controls, with reduced clinical GvHD scores and improved weight loss after day +41 (Figure 4, A–C). In vitro studies indicated that 6AN impairs T cell proliferation, which is associated with disrupted cell-cycle progression. 6AN-treated T cells showed a 12%–18% reduction in the proportion of cells in the G2/M phase (Figure 4D) and a corresponding lower fraction of proliferating Ki67+ cells (Figure 4E). Importantly, 6AN treatment did not increase the preapoptotic Annexin V+ fixable viability dye–negative (FVD–) population (Figure 4F), indicating that the reduction in T cell proliferation is independent of increased cell death during the 72 hours of incubation. Instead, 6AN specifically inhibited proliferation (Figure 4G). These findings favor the hypothesis that 6PGD deficiency mitigates T cell proliferation, protecting against severe aGvHD.

6-Aminonicotinamide (6AN), a small-molecule inhibitor of 6PGD, reduces GvHDFigure 4

6-Aminonicotinamide (6AN), a small-molecule inhibitor of 6PGD, reduces GvHD severity in a fully allogeneic murine model and a xenogeneic GvHD model. (A–C) BALB/c (H-2d) mice were lethally irradiated (6 Gy) on day –1 and transplanted i.v. with 1 × 107 BM cells with or without 1 × 106 splenic T cells from WT C57BL/6 (H-2b) mice on day 0. Mice were injected i.p. with 0.5 mg/kg 6AN or vehicle (1% DMSO) daily. Mouse survival (A), GvHD clinical score (B), and body weight loss (C) are reported. n = 18–20 mice per group combined from 3 independent repeats (2-way ANOVA). (D) Splenic and lymph node T cells from WT C57BL/6 mice were activated in vitro with plate-bound anti-CD3 and anti-CD28 (10 μg/mL each) plus rmIL-2 (100 ng/mL) for 72 hours and examined by Vybrant DyeCycle (catalog V35003, Thermo Fisher Scientific) for cell cycle determination. (E and F) T cells were treated as in D and examined for Ki67 proliferation marker (E) and apoptosis rate (F). n = 3–6 data points per group. Data representative of 3 independent repeats (Student’s t test). (G) T cells were treated as in D after CFSE labeling and proliferation of CD4+ (top) and CD8+ (bottom) T cells was examined after 72 hours. n = 3 data points per group. Data representative of 3 independent repeats (Student’s t test). (H and I) NSG mice were sublethally irradiated (2.5 Gy) on day –1 and transplanted with 2 × 106 human PBMCs on day 0. Mice were injected i.p. with 1 mg/kg 6AN or vehicle every 2 days. Mouse survival (H) and GvHD clinical scores (I) are reported. n = 12 mice per group from 2 independent repeats (2-way ANOVA). (J and K) Mice were transplanted as in H and I and numbers of splenic CD4+ (J) and CD8+ (K) T cells were examined on day +14 after allo-HCT (Student’s t test). (L and M) Human T cells from healthy donors were activated with anti-CD3 and anti-CD28 (10 μg/mL each) plus rhIL-2 (100 ng/mL) and treated with 6AN or vehicle for 72 hours. Cell cycle status was determined by Vybrant DyeCycle (L) and apoptosis rate by Annexin V staining (M). n = 3 data points per group. Data representative of 2 independent repeats (Student’s t test). (N) Human T cells were isolated and treated as in L after CFSE labeling. The proliferation of CD4+ (top) and CD8+ (bottom) T cells was examined after 72 hours. n = 3 data points per group. Data representative of 2 independent repeats (Student’s t test). Data are shown as mean ± SEM. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

To evaluate the translational potential of 6AN, we employed a xenogeneic GvHD model using NSG mice. Recipients were sublethally irradiated (2.5 Gy, 137Cs source, day –1) and transplanted i.v. with 2 × 106 human PBMCs on day 0. Mice were treated 6AN or vehicle (1% DMSO) every 2 days (1 mg/kg), given every 2 days via i.p. injection beginning on day 0 of allo-HCT for the experiment duration. 6AN-treated recipients had significantly improved survival (Figure 4H; median survival of 23.5 days in the control group vs. 29.5 days in treated group, P = 0.008) and reduced clinical GvHD scores (Figure 4I). Analysis of donor human CD4+ and CD8+ T cells in treated mice revealed a significant reduction in cell numbers compared with vehicle-treated controls (Figure 4, J and K). Most T cell subsets were significantly reduced except for CD8+ effector memory T and CD4+ and CD8+ effector T cells (Supplemental Figure 8).

In vitro treatment of human T cells with 6AN led to a reduction in cell cycle progression (Figure 4L), without affecting cell viability (Figure 4M). 6AN-treated human T cells had a significant impairment in proliferation (Figure 4N), consistent with the murine in vitro results shown in Figure 4, D–F. The reduced capacity for donor T cell proliferation correlated with decreased alloreactivity and lower GvHD severity. In aggregate, the 6AN results highlight the potential of 6PGD inhibition as a therapeutic strategy for managing GvHD.

6PGD deficiency in T cells induces metabolic reprogramming toward reduced glycolysis and enhanced mitochondrial respiration. Isotope tracing precisely tracks cellular metabolic cascades (30). To determine the specificity of 6PGD blockade and understand the fate of glucose during 6PGD inhibition, we employed stable isotope–resolved metabolomics (SIRM) using D7-D-glucose labeling. Splenic naive T cells were activated with anti-CD3/anti-CD28 monoclonal antibodies and recombinant mouse IL-2 (rmIL-2), with or without 6AN treatment (Figure 5A). In 6AN-treated T cells, there was accumulation of 6-phosphogluconate (6PG), the substrate of 6PGD, confirming enzymatic blockade (Figure 5A). However, levels of R5P, a critical PPP product required for nucleotide biosynthesis, were not affected when compared to vehicle (DMSO) (Figure 5A). Instead, the non-oxidative arm of the PPP was upregulated, leading to increased intermediate levels of erythrose-4-phosphate (E4P), fructose-6-phosphate (F6P), and glyceraldehyde-3-phosphate (G3P) (Supplemental Figure 9). These results confirm that 6PGD blockade occurs following 6AN treatment and that the non-oxidative PPP is upregulated as a compensatory mechanism, providing R5P production for nucleotide biosynthesis.

6PGD blockade in T cells induces a metabolic reprogramming evident by reducFigure 5

6PGD blockade in T cells induces a metabolic reprogramming evident by reduced glycolysis and higher mitochondrial respiration. (A) Splenic naive T cells from WT C57BL/6 mice were activated in vitro with plate-bound anti-CD3 and anti-CD28 mabs (10 μg/mL each) plus rmIL-2 (100 ng/mL) for 4 days with 6AN (10 μM) or vehicle (DMSO) control, and then exposed to 10 mM D7-D-glucose. The 6-phosphogluconate (6PG) and ribose-5-phosphate (R5P) quantities generated from glucose were determined at 8 hours. n = 3 data points per group (Student’s t test). (B) Levels of NADPH and NADP+ in in vitro–activated T cells after 6AN or vehicle treatment for 72 hours are shown. n = 3 data points per group (Student’s t test). (C and D) The metabolic profiles of 6AN- and vehicle-treated T cells were analyzed by SCENITH single-cell translation profiling (C). 6AN-treated T cells showed lower glycolysis and higher mitochondrial function when compared with vehicle (D). n = 4–5 data points per group. Data representative of 2 independent repeats (2-way ANOVA). (E) In vitro–activated T cells in presence of 6AN or vehicle were examined for expression of the plasma membrane glucose transporter Glut1. n = 4–5 data points per group. Data representative of 2 independent repeats (Student’s t test). (F and G) Splenic naive T cells from Pgdfl/fl or Pgdfl/flCd4Cre mice were activated with plate-bound anti-CD3 and anti-CD28 (10 μg/mL each) plus rmIL-2 (100 ng/mL) for 4 days. For the pharmacologic experiment, naive T cells were isolated from WT mice and stimulated as above in the presence of 6AN (10 μM) or vehicle control. The harvested cells were examined for extracellular acidification rate (ECAR) (F) or oxygen consumption rate (OCR) (G) using the Seahorse assay. n = 4 data points per group. Data representative of 2 independent repeats. (H) T cells were prepared and activated as in F and G. The mitochondrial mass was determined by MitoTracker Deep Red staining. n = 4 data points per group. Data representative of 3 independent repeats (Student’s t test). (I and J) T cells were prepared as in F and G and examined by transmission electron microscopy. The mitochondria in the cells are highlighted by black arrows and mitochondrial number was counted in 20 random fields for each group (I). Scale bar: 1.5 μm. Mitochondrial activity was examined by evaluation of mitochondrial potential (ΔΨm) by tetramethylrhodamine ester (TMRE) (J). n = 4 data points per group. Data representative of 3 independent repeats (Student’s t test) and are shown as mean ± SEM. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

One of the effects of the PPP is NADPH production (12). To further confirm the impact of 6PGD blockade, we measured NADPH levels in T cells. As expected, NADPH production was significantly reduced in 6AN-treated cells, the functional consequence of 6PGD inhibition (Figure 5B).

To explore how 6PGD blockade alters the metabolic capacity of T cells, we performed SCENITH single-cell translation profiling (31), a flow cytometry–based method to assess energy metabolism. This revealed reduced overall metabolic capacity in 6AN-treated T cells, consistent with shifting to a resting phenotype (Figure 5C). SCENITH analysis showed that the glucose analog 2-deoxy-D-glucose (2DG) significantly decreased glycolysis, while oligomycin, which blocks OXPHOS by inhibiting membrane-bound mitochondrial ATP synthetase, had a significant but modest impact on WT T cells, consistent with a strong reliance of WT T cells on glycolysis. Conversely, in 6AN-treated T cells, the inhibition of OXPHOS using oligomycin had the most significant impact on T cell metabolism, indicating that these T cells rely on OXPHOS for their energy metabolism (Figure 5C). Metabolic profiling further demonstrated a shift from glycolysis to mitochondrial respiration, with decreased glucose dependency and enhanced mitochondrial activity (Figure 5D). This metabolic reprogramming was accompanied by a reduced surface expression of the glucose transporter Glut1, which facilitates glucose transport across the plasma membrane (Figure 5E). Seahorse flux analysis confirmed these findings, showing lower glycolytic activity and increased mitochondrial respiration in T cells without 6PGD in Pgdfl/flCd4Cre mice or WT mice given 6AN for 6PGD pharmacologic blockade (Figure 5, F and G).

As noted above, mitochondrial function is enhanced in 6PGD-KO (Pgdfl/flCd4Cre) T cells. These cells exhibited increased mitochondrial mass and mitochondrial numbers per cell, with elevated mitochondrial membrane potential (Figure 5, H–J). 6PGD blockade reprograms T cell metabolism to prioritize the non-oxidative PPP for nucleotide precursor production (Supplemental Figure 9), while shifting energy generation from glycolysis to mitochondrial respiration (Figure 5).

Previous reports showed the importance of glycolysis in donor T cell survival (32). Our studies confirm that 6PGD blockade does not induce T cell death in the allo-HCT model in vivo (Supplemental Figure 10, A and B) or in T cells activated in vitro as early as 24 hours after stimulation (Supplemental Figure 10, C–E). These results suggest that 6PGD blockade reprograms T cell metabolism to switch from glycolysis to mitochondrial respiration, providing energy for T cell survival and function. The metabolic shift decreases T cell proliferation through diminished glycolysis and reprograms T cells toward a resting state, contributing to the reduced severity and mortality of aGvHD. Thus, 6PGD is central to the integration of metabolic pathways to regulate T cell function and may serve as a therapeutic target.

6PGD blockade preserves GvT responses while ameliorating aGvHD. The reduction in T cell proliferation in 6PGD-KO T cells led us to investigate their capacity to sustain the GvT effect to control malignancy. To evaluate the effect of 6PGD blockade, BALB/c recipients underwent lethal irradiation (8.5 Gy, 137Cs, day –1). On day 0, lethally irradiated mice were injected with 0.1 × 106 luciferase-expressing A20 (A20-Luc+) BALB/c tumor cells 4 hours before transplantation with 3.5 × 106 TCD-BM cells from C57BL/6 WT mice and 0.2 × 106 splenic naive T cells from Pgdfl/fl or Pgdfl/flCd4Cre donors. Controls receiving only tumor cells and TCD-BM exhibited significant tumor growth by bioluminescence imaging (BLI) (Figure 6, A and B). Tumor growth was controlled in recipients of T cells from both Pgdfl/fl and Pgdfl/flCd4Cre donors, indicating that 6PGD-KO T cells retained GvT activity (Figure 6, A and B). Pgdfl/flCd4Cre T cell recipients also exhibited reduced aGvHD severity compared with those receiving Pgdfl/fl T cells, as demonstrated by improved body weight (Figure 6C), lower clinical GvHD scores (Figure 6D), and enhanced survival (Figure 6E). These results indicated that 6PGD blockade in donor T cells achieves the dual benefit of mitigating aGvHD by inhibiting the T cells, which causes GvHD while preserving the GvT efficacy even with a reduction in cytotoxic T cells.

6PGD blockade preserved GvT responses while ameliorating aGvHD.Figure 6

6PGD blockade preserved GvT responses while ameliorating aGvHD. (A and B) BALB/c (H-2d) mice were lethally irradiated (8.5 Gy) on day –1 and transplanted i.v. with 3.5 × 106 TCD-BM cells with or without 0.2 × 106 splenic naive T cells from Pgdfl/fl or Pgdfl/flCd4Cre C57BL/6 (H-2b) mice plus 0.1 × 106 A20-Luc+ tumor cells on day 0. Tumor growth was monitored by bioluminescence imaging (BLI) (A). Tumor growth was quantified by detection of bioluminescent signals (B). n = 5 mice per group. Data representative of 2 independent repeats (2-way ANOVA). (C–E) In the mice treated as in A and B, body weight loss (C) and clinical GvHD score (D) were measured and are presented as mean ± SEM. Two-way ANOVA was used to analyze statistical significance among groups. (E) Survival of mice was monitored and is presented as percentage survival. n = 5 mice per group. Data representative of 2 independent repeats (log-rank Mantel-Cox test). (F) For tumor killing by activated T cells, splenic naive T cells were isolated from Pgdfl/fl or Pgdfl/flCd4Cre mice and activated with plate-bound anti-CD3 and anti-CD28 (10 μg/mL each) plus rmIL-2 (100 ng/mL) for 4 days. Harvested T cells were cocultured with A20-Luc+ tumor cells and tumor killing was measured after 18 hours using BLI. Tumor cell survival was quantified by detection of bioluminescent signals. n = 3 time points per group. Data representative of 3 independent repeats (2-way ANOVA). (G) T cells were activated as in F. T cell granzyme B expression was detected by flow cytometry. n = 3 time points per group. Data representative of 3 independent repeats (Student’s t test) and are shown as mean ± SEM. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

To confirm the antitumor capacity of 6PGD-deficient T cells, splenic naive T cells were isolated from Pgdfl/fl or Pgdfl/flCd4Cre mice, activated in vitro with anti-CD3/anti-CD28 mAbs, and cocultured with A20-Luc+ tumor cells. Tumor killing was assessed at 18 hours using BLI based on our previous publication (24, 33). Pgdfl/flCd4Cre T cells exhibited enhanced tumor-killing capacity compared with Pgdfl/fl T cells (Figures 6F). Increased cytotoxicity of 6PGD-deficient T cells against A20-Luc+ tumor cell was associated with higher granzyme B expression (Figure 6G), a known factor for T cell-mediate A20-Luc+ tumor cell lysis (34). Previous studies reported that generation of lactic acid by tumor cells disrupts the metabolic fitness of T cells, including reduced glycolysis, resulting in defective T cell antitumor responses (32). Our results suggest that 6PGD blockade reprograms T cell metabolism to switch from glycolysis to mitochondrial respiration. This metabolic switch prevents adverse effects (toxicity) while inducing a tumor-killing phenotype in T cells.

To further confirm these findings and to explore the potential clinical translation of 6PGD inhibition on GvT effects, we treated mice in the GvT model with 6AN. BALB/c mice received lethal irradiation (8.5 Gy, 137Cs) on day –1 and on day 0 were injected with A20-Luc+ tumor cells (0.1 × 106 cells) 4 hours before transplantation with 3.5 × 106 TCD-BM cells from C57BL/6 WT mice and 0.2 × 106 splenic naive T cells from WT donors. The mice were treated with vehicle or 6AN (0.5 mg/kg, administered daily via i.p. injection), starting on day 0 until the endpoint. Examination of tumor growth showed that control mice receiving only tumor cells and TCD-BM treated with either vehicle or 6AN exhibited significant tumor growth (Figure 7A). Tumor growth was controlled in recipients of T cells from both vehicle and 6AN treatment, indicating that the T cells retained GvT activity (Figure 7A). In mice with donor T cells plus tumor cells, 6AN-treated recipients further exhibited reduced aGvHD severity compared with those receiving vehicle, as shown by higher body weight (Figure 7B), lower clinical GvHD scores (Figure 7C), and enhanced survival (Figure 7D). These results show the effect of 6PGD inhibition on donor T cells to mitigate aGvHD while maintaining the GvT effect. Next, we examined the direct effect of 6AN on tumor cells. We have previously shown that 6PGD blockade enhances the antitumor function of endogenous (autologous) T cells (15). Other investigators have shown that 6PGD function supports tumor cell growth (35, 36). We used a syngeneic GvT murine model with and without 6AN, showing that 6AN treatment reduces tumor growth, resulting in increased survival, without changes in body weight or GvHD clinical score (Supplemental Figure 11, A–C). We showed that in vitro exposure of A20 tumor cells to increased concentration of 6AN (5, 10, and 20 μM) resulted in a higher apoptotic rate (Supplemental Figure 11D) and lower expression of the Ki67 proliferation marker (Supplemental Figure 11E). These results confirm the direct additive effect of 6AN on tumor control along with the enhanced antitumor function of donor T cells, while ameliorating the aGvHD severity, highlighting the potential clinical translatability of 6AN treatment after allo-HCT.

Pharmacological inhibition of 6PGD suppresses aGvHD while maintaining GvT rFigure 7

Pharmacological inhibition of 6PGD suppresses aGvHD while maintaining GvT responses. (A–D) BALB/c (H-2d) mice were lethally irradiated (8.5 Gy) on day –1 and transplanted i.v. with 3.5 × 106 TCD-BM cells with or without 0.2 × 106 splenic naive T cells from WT C57BL/6 (H-2b) mice plus 0.1 × 106 A20-Luc+ tumor cells on day 0. Mice were injected i.p. with 0.5 mg/kg 6-aminonicotinamide (6AN) or vehicle (1% DMSO) daily. Tumor growth was quantified by detection of bioluminescent signals (A). Treated mice body weight loss (B) and clinical GvHD score (C) were measured and are presented as mean ± SEM (2-way ANOVA). (D) Survival of mice was monitored and is presented as percentage survival. n = 5 mice per group (log-rank Mantel-Cox test). Data are shown as mean ± SEM. *P < 0.05; **P < 0.01; ***P < 0.001.

The ability to preferentially modulate T cell responses through metabolic reprogramming at the 6PGD checkpoint provides a promising therapeutic strategy for achieving improved outcomes in allo-HCT.

Discussion

In this study, we demonstrate that blocking 6PGD, the second rate-limiting enzyme in the PPP, significantly decreases the severity of aGvHD. Inhibiting 6PGD by selective genetic deletion in T cells or by pharmacologic inhibition by 6AN led to decreased aGvHD severity, with no impact on the GvT effect after allo-HCT.

The increase in PPP pathway activity in allogeneic T cells aligns with previous findings that demonstrate increased glucose metabolism in GvHD (5, 8). Elevated uptake of glucose analogs correlates with donor cell infiltration and GvHD severity (37). Preclinical models have confirmed the important role of glycolysis in allogeneic T cells. Glycolytic inhibition through 2DG or Glut1 deficiency in donor T cells reduces aGvHD (5, 8). In this study, the selective deletion of 6PGD in donor T cells during allo-HCT led to changes in the ratio of effector to naive T cells in Pgdfl/flCd4Cre mice compared with WT (Pgdfl/fl) mice. Therefore, naive T cells were used as the source of T cells for allo-HCT. However, in vitro and in vivo assays showed that deficiency of 6PGD in T cells caused reduced T cell proliferation, manifested by a 25%–75% reduction in in vitro CD4+ and CD8+ proliferation and 40%–60% reductions in vivo in CD4+ and CD8+ subsets, especially on days +7 and +14 (detailed in Figure 2). This emphasizes the critical dependence of allogeneic T cells on glycolysis (9, 38), particularly the PPP, to meet proliferative and cytotoxic demands.

We have further elucidated how allogeneic T cells utilize glucose through the PPP during T cell proliferation and expansion. Specifically, 6PGD is upregulated in allogeneic T cells 7 days after allo-HCT. Blocking 6PGD, either genetically or pharmacologically, significantly reduced T cell proliferation without apoptosis. Thus, 6PGD activity is essential for T cell progression through the cell cycle. Our results are consistent with earlier studies showing that PPP inhibition leads to G2/M cell cycle arrest in cancer cells (39). While previous research attributed this arrest to insufficient R5P levels for DNA synthesis (40), we found that R5P levels remain unaffected. In allogeneic T cells, there is compensation by the non-oxidative branch of the PPP. NADPH production is significantly reduced with PPP inhibition, leading to elevated superoxide levels that may trigger downstream signaling resulting in cell cycle arrest, reduced T cell proliferation, and diminished T cell absolute numbers. There was no significant difference in apoptosis between WT and 6PGD-deficient T cells, indicating that sufficient NADPH production via G6PD prevented excessive superoxide accumulation that could cause cell death. We also observed that 6PGD blockade induces metabolic reprogramming in allogeneic T cells, shifting their metabolism from glycolysis to OXPHOS, predominantly utilized by naive and resting T cells. However, activated T cells exhibit an increase in OXPHOS alongside an upregulation of glycolysis (41–43). This metabolic shift following 6PGD blockade is likely a result of PPP inhibition: redirecting glucose into the non-oxidative PPP and OXPHOS pathways. Additionally, most T cells remain in a naive state after 6PGD blockade, relying primarily on OXPHOS for energy. This metabolic switch after PPP inhibition represents an advantage over strategies that disrupt T cell metabolism or that are toxic for T cells such as targeting glycolysis (32).

Since allo-HCT is employed to treat patients with hematologic malignancies, it was critical to understand how PPP inhibition affected the GvT effect. 6PGD-deficient T cells during allo-HCT retained their ability to improve recipient survival by inhibiting tumor growth without inducing severe aGvHD. Previous studies found that CD8+ T cell cytotoxic granule expression (e.g., granzyme B and perforin) is not regulated by glycolysis, contributing to a preserved GvT effect (8). We demonstrated in vitro that 6PGD-deficient T cells generated improved tumor killing, with increased granzyme B expression. Although granzyme B has been associated with GvHD, the absolute number of effector T cells in 6PGD-deficient mice was lower than in the WT group. This suggests that there may not be sufficient granzyme B–positive T cells to induce severe GvHD (44), but their numbers are adequate to maintain a GvT effect. Further studies are needed to evaluate the impact of 6AN on in vitro and in vivo cytotoxic T cell generation and function, by assessing the expression of cytotoxic molecules including Fas/FasL, granzymes A and B, perforin, and proinflammatory cytokines such as IFN-γ and TNF-α. Additionally, it is crucial to investigate the cytolytic capacity of these cytotoxic T cells to determine whether 6AN affects their functional cytotoxicity. Clinical studies have proposed that the allogeneic T cell dose required to induce aGvHD is approximately 10-fold higher than the dose needed to achieve a GvT effect; however, this is dependent on the different T cell subsets (45). Thus, a small population of 6PGD-deficient T cells appears to be sufficient to control tumor progression without triggering severe aGvHD.

To evaluate the translational potential of targeting 6PGD, we used 6AN, a selective 6PGD inhibitor, in murine and xenogeneic allo-HCT models. 6AN significantly reduced aGvHD severity, mirroring the phenotype observed with 6PGD-deficient T cells. These findings underscore the translational value of targeting the PPP to ameliorate GvHD severity and mortality. 6AN was used in this study but had been found to have minimal antitumor effects at lower doses and toxicity when dose-escalated in patients with advanced cancers (46). Development of novel and less toxic 6PGD inhibitors will be critical for further evaluation to minimize GvHD while preserving the GvT effect.

In summary, using conditional 6PGD-deficient murine models and a selective 6PGD inhibitor, we demonstrate that the PPP is critical for alloreactive T cell proliferation and cell cycle progression during aGvHD. Targeting 6PGD effectively mitigates aGvHD while preserving GvT activity. Future efforts should focus on testing selective 6PGD inhibitors to optimize clinical outcomes in allo-HCT. Our study establishes the PPP as a viable target for preventing aGvHD without compromising GvT efficacy.

Methods

Sex as a biological variable

Both male and female mice were used in all the in vivo studies.

Mice and A20-Luc+ cell line

The mouse strains C57BL/6J (B6, H-2b, CD45.2+), B6.SJL-Ptprca Pepcb/BoyJ (B6, H-2b, CD45.1+), B6.Cg-Tg1Cwi/BfluJ (Cd4Cre), C3.SW-H2b/SnJ (C3H.H2b), BALB/c (H-2d), and NOD.Cg-Prkdcscid Il2rgtm1Wjl Tg(HLA-A/H2-D/B2M)1Dvs/SzJ (NSG) were purchased from The Jackson Laboratory. Pgdfl/fl mice were provided by Pankaj Seth (Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA) and crossed with Cd4Cre mice to generate Pgdfl/flCd4Cre mice. Female mice aged 6 to 12 weeks were age matched across different groups. All mice were housed in specific pathogen–free (SPF) facilities at the University of Minnesota and the Roswell Park Comparative Oncology Shared Resources (COSR). The A20-Luc+ cell line was provided by Xuefang Cao (Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, Maryland, USA) and cultured in RPMI 1640 (with L-glutamine and 2000 mg/L D-glucose, Corning) supplemented with 10% fetal bovine serum (FBS; Gibco), and 1% penicillin/streptomycin (MilliporeSigma).

Murine T cell isolation and culture

Murine CD4+ and CD8+ T cells were purified from spleens and lymph nodes by negative selection as previously described (47). Pan naive T cells were isolated using EasySep Mouse Pan-Naive T Cell Isolation Kit (STEMCELL Technologies). Isolated T cells were cultured in RPMI 1640 (with L-glutamine and 2000 mg/L D-glucose, Corning) supplemented with 100 U/mL recombinant human IL-2 (rhIL-2, BioLegend), 10% FBS (Gibco), 1 mM HEPES (MilliporeSigma), 2 mM GlutaMax (Thermo Fisher Scientific), 1 mM sodium pyruvate (Thermo Fisher Scientific), 1× penicillin/streptomycin (MilliporeSigma), 50 μg/mL gentamicin (gentamicin sulfate, liquid, Corning), and 55 mM 2-mercaptoethanol (MilliporeSigma). For stimulated cultures, the culture plate was coated with 10 μg/mL functional-grade anti-CD3ε (catalog 14-0031-82, eBioscience) and 10 μg/mL functional-grade anti-CD28 (catalog 14-0281-82, eBioscience) and washed with PBS prior to seeding.

Flow cytometry analysis

The following procedure was used for murine cell isolation: Spleens were harvested, mechanically dissociated, passed through a 70 μm pore cell strainer, and the RBCs were lysed by ammonium-chloride-potassium (ACK) lysis buffer according to manufacturer’s protocol (Thermo Fisher Scientific). Livers were harvested, mechanically dissociated, passed through a 70 μm pore cell strainer, resuspended in 40% Percoll PLUS (Catalog 17544502, Cytiva) density gradient media, and centrifuged at 600g for 20 minutes. The cell pellet was collected, and RBCs were lysed by ACK lysis buffer. For in vitro cultured cells, T cells were harvested on day 4 after activation. The harvested cells were suspended in FACS buffer (1% FBS in PBS) and stained for surface markers for 30 minutes at 4°C. An Aqua LIVE/DEAD Fixable Dead Cell Stain dye (Thermo Fisher Scientific) was used to separate live and dead cells. For cell cycle dye staining, Vybrant DyeCycle Violet Stain (catalog V35003, Thermo Fisher Scientific) was used, according to manufacturer’s instruction.Intracellular cytokine staining was performed using a fixation/permeabilization kit (BD Biosciences). For cytokine staining, cells were stimulated in vitro with phorbol myristate acetate (50 ng/mL; Sigma-Aldrich) and ionomycin (1 μg/mL; Sigma-Aldrich) in the presence of GolgiPlug (BD Biosciences) for 4 hours prior to staining. In our studies, BUV395-CD8a (catalog 563786, BD Biosciences), BUV661-CD122 (catalog 741493, BD Biosciences), BUV805-CCR7 (catalog 742065, BD Biosciences), Brilliant Violet 421–CXCR3 (catalog 155907, BioLegend), BV605-CD45RA (catalog 740350, BD Biosciences), FITC–TNF-α (catalog 506304, BioLegend), NovaFluor Blue 610-70S–CD44 (catalog M010T02B06, eBioscience), PerCp-Cy5.5–H2Kb (catalog 116516, BioLegend), PE–GM-CSF (catalog 505406, BioLegend), PE/Dazzle 594–Sca-1 (catalog 108138, BioLegend), Alexa Fluor 647–IFN-γ (catalog 557735, BD Biosciences), and Alexa Fluor 700–CD62L (catalog 104426, BioLegend) were used.

aGvHD models

Fully allogeneic model. BALB/c (H-2d, The Jackson Laboratory) mice were lethally irradiated (8.5 Gy;137Cs source, single fraction, day –1) and given i.v. 3.5 × 106 TCD-BM cells from C57BL/6 (H-2b, The Jackson Laboratory) with or without 0.2 × 106 purified naive splenic pan-T cells from fully allogeneic donor Pgdfl/fl or Pgdfl/flCd4Cre C57BL/6 mice (15). Pgdfl/fl mice were provided by Pankaj Seth (Beth Israel Deaconess Medical Center, Boston Massachusetts, USA) through TWMF. 6PGD conditional KO mice were generated by crossing Pgdfl/fl mice with Cd4Cre mice (The Jackson Laboratory) (15), generating Pgdfl/flCd4Cre transgenic mice.

Minor histocompatibility antigen disparate model. C3H/SW mice (H-2b, The Jackson Laboratory) were lethally irradiated (11 Gy; 137Cs source, single fraction, day –1) and transplanted i.v. with 3.5 × 106 TCD-BM cells with or without 0.5 × 106 splenic naive T cells from Pgdfl/fl or Pgdfl/flCd4Cre C57BL/6 mice on day 0. In all GvHD models, monitored survival is reported as percentage survival. GvHD clinical scores based on posture, motility, hair loss, skin integrity, and weight loss were monitored twice weekly (24, 25). The primary experimental endpoint was survival, with mice monitored daily for clinical signs of disease progression. Mice were euthanized upon reaching a moribund state, defined as unresponsiveness to gentle stimulation, in accordance with institutional animal care guidelines. Additionally, body weight was monitored twice a week and served as another indicator of disease severity and general health status. These criteria allowed consistent assessment of treatment outcomes.

Pharmacological blockade of 6PGD

Pharmacological blockade of 6PGD was tested in lethally irradiated (6 Gy; x-ray source, day –1) BALB/c mice transplanted i.v. with 1 × 107 fully allogeneic BM cells with or without 1 × 106 splenic T cells from WT C57BL/6 mice on day 0 (48). Mice were i.p. injected daily with 0.5 mg/kg 6AN (MilliporeSigma) or vehicle (DMSO) from day 0 until day 60. For xenogeneic GVHD studies, NSG mice were sublethally irradiated with a single fraction of 2.5 Gy (137Cs source) on day –1 and transplanted i.v. with 2 × 106 human PBMCs on day 0. Fresh deidentified PBMCs were provided by the Data Bank and BioRepository (DBBR) at Roswell Park Comprehensive Cancer Center. In xenogeneic murine models, 6AN (1 mg/kg; MilliporeSigma) or vehicle (DMSO) was administered i.p. every 2 days.

GvT model

BALB/c mice were lethally irradiated (8.5 Gy, 137Cs source, day –1). On day 0, hosts were injected i.v. with 3.5 × 106 TCD-BM cells with or without 0.2 × 106 purified naive splenic pan-T cells (Negative Selection Isolation Kit, STEMCELL Technologies) from Pgdfl/fl or Pgdfl/flCd4Cre C57BL/6 mice. On day 0, experimental cohorts were also injected i.v. with 0.1 × 106 A20-Luc+ (BALB/c) tumor cells. Tumor burden was measured by BLI weekly (24, 25). Body weight loss, clinical GvHD score, and survival were monitored daily.

These experiments were conducted in 2 different centers (Roswell Park and the University of Minnesota) and included 2 different aGvHD and GvT models. These settings strengthen the results and confirm the role of 6PGD inhibition in allo-HCT outcomes independent of transplantation model.

scRNA-seq

To examine the metabolic changes in allogeneic T cells, allo-HCT in the fully allogeneic model was conducted as described above. Splenic donor T cells (CD45+H2Kb+H2Kd–TCRβ+) were harvested, sorted on days +7 and +14, and examined for T cell clusters using scRNA-seq. Sorted cells were analyzed with AOPI stain (Revvity Health Sciences) and a Cellometer K2 automated cell counter (Nexcelom) to assess concentration, viability, and to confirm the absence of clumping and debris. scRNA-seq was performed as previously described (49). Bioinformatic analyses were conducted using the Seurat (v5.0.0) R package for scRNA-seq (50). Oxidative PPP gene signatures were examined within T cell clusters.

scRNA-seq analysis workflow: For the 10× Genomics Chromium libraries, the raw sequencing data were processed using Cellranger software (Cell Ranger Single-Cell Software Suite) (http://software.10xgenomics.com/single-cell/overview/welcome). Then, the filtered gene-barcode matrices, which contain barcodes with the unique molecular identifier (UMI) count that passed the cell detection algorithm, were used for further analysis. Doublet detection was performed using Scrublet (51) for each sample, and the cells with a doublet score higher than the predicted threshold were excluded in the analysis. All the downstream analyses were performed using the Seurat single-cell data analysis R package (50). First, cells with low RNA content and with a low number of detected genes (<500), very high number of detected genes (>7500) due to doublets, or higher mitochondrial RNA content (>15%) were filtered out from the analysis. Then, the normalized and scaled UMI counts were calculated using the SCTransform method in Seurat. Dimension reductions including principal component analysis (PCA), uniform manifold approximation and projection (UMAP), and t-SNE were carried out using highly variable genes. Data clustering was identified using shared nearest neighbor–based (SNN-based) clustering on the first 10–30 principal components. Subsequently, the cell clusters can be annotated by the known gene markers or by identifying differentially expressed genes for each cluster. Gene set enrichment analysis (GSEA) was done using the fgsea (52) R package with average log2(fold change)calculated with FindMarkers from Seurat. The hallmark and canonical pathways from the Molecular Signatures Database (MSigDB; https://www.gsea-msigdb.org/gsea/msigdb) were used in GSEA analysis.

Co-adaptive transfer during allo-HCT

To examine the proliferative capacity of 6PGD-deficient T cells during allo-HCT, naive T cells were isolated from CD45.1+ WT and CD45.2+ Pgdfl/flCD4Cre mice, mixed at a 1:1 ratio, and labeled with CFSE (Thermo Fisher Scientific) for 30 minutes at 37°C. On day 0, 3.5 × 106 TCD-BM cells from C57BL/6 and 0.4 × 106 mixed CFSE-labeled T cells were injected i.v. into lethally irradiated (8.5 Gy; 137Cs source) BALB/c mice. Proliferation was determined on day +3 by CFSE dilution in splenic CD45.1+ and CD45.2+ T cells. The frequencies of CD45.1+ WT and CD45.2+ Pgdfl/flCd4Cre T cells in spleen, mesenteric lymph nodes, and liver were examined on day +7 after transplantation. Target tissues were collected, mechanically dissociated, passed through a 70 μm cell strainer, RBCs were lysed with ACK lysis buffer according to the manufacturer’s protocol (Thermo Fisher Scientific), and remaining cells examined by flow cytometry.

Gene expression by qPCR

To confirm the screening scRNA-seq data, WT BALB/c mice were irradiated (8.5 Gy, 137Cs source) and transplanted i.v. with 3.5 × 106 TCD-BM cells (isolated from C57BL/6 mice) with or without 0.2 × 106 splenic naive C56BL/6 T cells. Alloreactive T cells (CD45+H2Kb+H2Kd–TCRβ+) were sorted using a SONY MA900 sorter on day +7 after allo-HCT. The expression of the Pgd gene (6PGD) was examined in sorted T cells on day +7 compared to day 0 naive T cells by real-time quantitative PCR using the ABI 7300 Real-Time PCR system (Applied Biosystems). Total RNA was extracted with RNeasy Mini Kit (QIAGEN). Total DNA-free RNA was used for mRNA isolation and library construction. The expression levels of G6pdx (TaqMan probe: Mm04260097_m1), Pgls (TaqMan probe: Mm00452601_m1), and Pgd (TaqMan probe: Mm01263703_m1) were measured using the TaqMan RNA-to-CT 1-Step Kit (Thermo Fisher Scientific). The gene expression was normalized to18S rRNA housekeeping gene expression as ΔCt = Ct (target gene) – Ct (18S rRNA). Changes between day 0 and day +7 were calculated as fold change = 2ΔΔCt, where ΔΔCt = Ct (target gene) – Ct (target gene at baseline).

NADPH measurement

NADP+/NADPH levels were measured by a colorimetric assay according to the manufacturer’s instructions (Abcam). Briefly, T cells were cultured for 72 hours with vehicle or 6AN. The cells were lysed, incubated with the reaction mixture, followed by monitoring the absorbance at 450 nm, which is proportional to NADPH concentration.

SCENITH

Vehicle- or 6AN-treated T cells were analyzed according to the standard procedure described in the original publication (31). Briefly, activated T cells treated with vehicle or 6AN for 72 hours were harvested from culture plates, washed, and incubated with metabolic inhibitors (100 mM 2DG, 1 μM oligomycin, and a combination of 100 mM 2DG and 1 μM oligomycin) in complete RPMI 1640 for 15 minutes. Puromycin (10 μg/mL) was added to the media containing 6AN, and the cells were incubated for an additional 30 minutes. After washing, the cells were stained for surface markers and puromycin. Translational changes during metabolic inhibition were determined by calculating the geometric mean fluorescence intensity (gMFI) of puromycin and analyzed for glucose and mitochondrial dependence.

Seahorse bioenergetics analysis

Splenic naive T cells were isolated from Pgdfl/fl or Pgdfl/flCd4Cre mice and activated with plate-bound anti-CD3 and anti-CD28 (10 μg/mL each) plus rmIL-2 (100 ng/mL) for 4 days. In another set, naive T cells were stimulated in presence of 6AN (10 μM) or vehicle control. On day 4, the harvested cells were examined in a XFe96 Extracellular Flux Analyzer (Seahorse Bioscience) for glycolysis function by examination of the extracellular acidification rate (ECAR; mpH/min), and for the mitochondrial function by examining the mitochondrial oxygen consumption rate (OCR; O2 pmol/min). For this, 2 × 105 cells/well were seeded on Cell-Tak-coated Seahorse XFe96 (Agilent) culture plates in assay media. The ECAR was analyzed in 4 consecutive stages: basal, glycolysis induction (by adding 10 mM glucose), maximal glycolysis (by adding 2 mM oligomycin), and inhibition of glycolysis (by adding 100 mM 2DG). The OCR was measured in the following stages: basal respiration, inhibition of complex V (by adding 2 mM oligomycin), maximal respiration (by adding 1 mM carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone [FCCP]), and inhibition of the electron transport chain (by adding 1 mM rotenone plus 1 mM antimycin A).

Mitochondrial mass and mitochondrial potential analysis

Mitochondrial phenotype also was examined by flow cytometry. For this, splenic naive T cells from Pgdfl/fl or Pgdfl/flCd4Cre mice were activated in vitro by plate-bound anti-CD3 and anti-CD28 (10 μg/mL each) plus rmIL-2 (100 ng/mL) for 4 days. Harvested cells were stained with 200 nM MitoTracker Deep Red FM (Thermo Fisher Scientific) for mitochondrial mass and 200 nM tetramethylrhodamine ester (TMRE) (Thermo Fisher Scientific) for mitochondrial membrane potential for 30 minutes at 37°C. The stained cells were analyzed by the cells were analyzed using the BD LSRFortessa cell analyzer (BD Biosciences).

Transmission electron microscopy

Splenic naive T cells were isolated from Pgdfl/fl or Pgdfl/flCd4Cre mice and activated with anti-CD3 and anti-CD28 (10 μg/mL each) plus rmIL-2 (100 ng/mL) for 4 days. The harvested T cells on day 4 were fixed in 4% glutaraldehyde and stained for electron microscopy. Ultrathin section cuts of fixed cells were placed on a copper grid, stained with lead citrate, and examined in a JEOL 1200EX transmission electron microscope or a Tecnai G2 Spirit BioTWIN.

SIRM analysis

To investigate glucose consumption following the 6PGD blockade, T cells were activated in the presence of 6AN (10 μM) or vehicle control. On day 4, T cells were harvested and treated with tracing media consisting of glucose-free RPMI 1640 media enriched with 10% dialyzed FBS (Life Technologies), 20 mM HEPES, 0.05 mM 2-mercaptoethanol, and 1% penicillin-streptomycin. This medium also included the stable isotope–enriched nutrient D7-D-glucose (10 mM; Cambridge Isotope Laboratories). Cells were collected at the 8-hour time point, and the metabolite concentrations were determined via ion chromatography-ultra high-resolution mass spectrometry (IC-UHR-MS) using previously established methods (15). The concentration of each metabolite was computed and normalized to total protein abundance (μmole/g protein).

Western blotting

For Western blot analyses, T cells (CD45+H2Kb+H2Kd–TCRβ+) were sorted using a SONY MA900 sorter on day +7 after allo-HCT and lysed in radioimmunoprecipitation assay (RIPA) buffer (Thermo Fisher Scientific). The original T cells injected on day 0 served as baseline control. The RIPA buffer was supplemented with 1% protease and phosphatase inhibitor (Thermo Fisher Scientific) according to the manufacturer’s instructions. Samples were run in SDS-PAGE (Bio-Rad) to separate the target proteins transferred to a PVDF membrane (Bio-Rad). The membranes were blocked with EveryBlot Blocking Buffer (Bio-Rad) and treated with primary antibodies of rabbit anti-PGD (catalog 14718-1-AP, Proteintech) and rabbit anti-Vinculin (catalog 13901, Cell Signaling Technology) at 1:1000 dilution and then HRP-conjugated anti-rabbit IgG secondary antibody (catalog 7074, Cell Signaling Technology) at 1:3000 dilution. The membranes were developed with Clarity Max Western ECL Substrate (Bio-Rad). The images were captured using an Odyssey Fc (LI-COR).

Statistics

Statistical evaluations were conducted using Prism 10 (GraphPad). Group differences were assessed using the 2-tailed Student’s t test for comparisons between 2 groups and 1-way ANOVA for comparisons involving more than 2 groups, followed by Tukey’s post hoc test. Tumor growth was analyzed using 2-way ANOVA with Tukey’s post hoc analysis. A P value of less than 0.05 was considered indicative of statistical significance between groups. Data presented in the figures are expressed as mean ± SEM.

Study approval. Animal studies were conducted in accordance with a protocol reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Minnesota (2403-41944A) and Roswell Park (1143M). Pgdfl/fl or Pgdfl/flCd4Cre C57BL/6 mice were bred and maintained in Roswell Park animal facility under IACUC protocol no. 1140.

Data availability

Values for all data points found in graphs are in the Supporting Data Values file. The scRNA-seq data can be accessed through the NCBI Gene Expression Omnibus (GEO GSE289995).

Author contributions

Experimental design and execution were conducted by SD, EK, BRB, and HM. Data interpretation and analysis was performed by SD, EK, PLM, BRB, and HM. QY, JEC, SH, and KLH helped with in vitro and in vivo experiments. Histopathology evaluations were done by JQ. Bioinformatical and scRNA-seq transcriptomics analysis were done by PKS and JW. Tracing studies were performed by SD, RMH, ANL, and TWMF. Xenogeneic GVHD studies were performed by SH and KLH. The manuscript was written and edited by SD, EK, PLM, BRB, and HM.

Funding support

This work is the result of NIH funding, in whole or in part, and is subject to the NIH Public Access Policy. Through acceptance of this federal funding, the NIH has been given a right to make the work publicly available in PubMed Central.

  • NIH grant R00 HL155792 (to HM).
  • V Foundation (to HM).
  • Roswell Park Alliance Foundation (to HM).
  • American Society of Hematology Graduate Hematology Award (to EK).
  • NIH grants R37AI34495, R01HL155114, and P01HL158505 (to BRB).
  • Brendan and Elise McCarthy (to PLM).
  • National Cancer Institute grant P30CA016056.
  • NIH grant P30CA177558 (to BM Evers, Metabolism Shared Resources at the University of Kentucky); partial support for IC-UHR-MS data acquisition.
Supplemental material

View Supplemental data

View Unedited blot and gel images

View Supporting data values

Acknowledgments

The authors thank Feng Li and Eugene Kononov for technical assistance, and the Genomics Shared Resource, Comparative Oncology Shared Resources (COSR), and the Flow Cytometry Core Facility at Roswell Park for expert support. We thank Pankaj Seth for providing Pgdfl/fl mouse breeding pairs.

Address correspondence to: Hemn Mohammadpour, CGP-L3-315, Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, New York, USA. Email: Hemn.mohammadpour@roswellpark.org.

Footnotes

Conflict of interest: PLM reports advisory board/consulting for BlueBird Biotech, Bristol-Myers Squibb, Celgene, Fate Therapeutics, Janssen, Juno, Karyopharm, Magenta Therapeutics, Sanofi, and Takeda. PLM reports honoraria from BlueBird Biotech, Bristol-Myers Squibb, Celgene, Fate Therapeutics, Janssen, Juno, Karyopharm, Magenta Therapeutics, Sanofi, and Takeda. BRB reports research funding from BlueRock Therapeutics and Carisma Therapeutics and consulting fees from BlueRock Therapeutics, Editas Medicine, Janssen Oncology, Sandoz, Sanofi, Legend Biotech, GentiBio Inc., and Magenta Therapeutics.

Copyright: © 2025, Daneshmandi et al. This is an open access article published under the terms of the Creative Commons Attribution 4.0 International License.

Reference information: JCI Insight. 2025;10(23):e192774.https://doi.org/10.1172/jci.insight.192774

References
  1. Blazar BR, et al. Dissecting the biology of allogeneic HSCT to enhance the GvT effect whilst minimizing GvHD. Nat Rev Clin Oncol. 2020;17(8):475–492.
    View this article via: CrossRef PubMed Google Scholar
  2. Hill GR, et al. Current concepts and advances in graft-versus-host disease immunology. Annu Rev Immunol. 2021;39:19–49.
    View this article via: CrossRef PubMed Google Scholar
  3. Zeiser R, Blazar BR. Acute graft-versus-host disease - biologic process, prevention, and therapy. N Engl J Med. 2017;377(22):2167–2179.
    View this article via: CrossRef PubMed Google Scholar
  4. Welniak LA, et al. Immunobiology of allogeneic hematopoietic stem cell transplantation. Annu Rev Immunol. 2007;25:139–170.
    View this article via: CrossRef PubMed Google Scholar
  5. Nguyen HD, et al. Metabolic reprogramming of alloantigen-activated T cells after hematopoietic cell transplantation. J Clin Invest. 2016;126(4):1337–1352.
    View this article via: JCI CrossRef PubMed Google Scholar
  6. Nguyen HD, et al. T-cell metabolism in hematopoietic cell transplantation. Front Immunol. 2018;9:176.
    View this article via: CrossRef PubMed Google Scholar
  7. Le Bourgeois T, et al. Targeting T cell metabolism for improvement of cancer immunotherapy. Front Oncol. 2018;8:237.
    View this article via: CrossRef PubMed Google Scholar
  8. Huang Y, et al. Targeting glycolysis in alloreactive t cells to prevent acute graft-versus-host disease while preserving graft-versus-leukemia effect. Front Immunol. 2022;13:751296.
    View this article via: CrossRef PubMed Google Scholar
  9. Assmann JC, et al. Glycolytic metabolism of pathogenic T cells enables early detection of GVHD by 13C-MRI. Blood. 2021;137(1):126–137.
    View this article via: CrossRef PubMed Google Scholar
  10. TeSlaa T, et al. The pentose phosphate pathway in health and disease. Nat Metab. 2023;5(8):1275–1289.
    View this article via: CrossRef PubMed Google Scholar
  11. Ge T, et al. The role of the pentose phosphate pathway in diabetes and cancer. Front Endocrinol (Lausanne). 2020;11:365.
    View this article via: CrossRef PubMed Google Scholar
  12. Riganti C, et al. The pentose phosphate pathway: an antioxidant defense and a crossroad in tumor cell fate. Free Radic Biol Med. 2012;53(3):421–436.
    View this article via: CrossRef PubMed Google Scholar
  13. Jin L, Zhou Y. Crucial role of the pentose phosphate pathway in malignant tumors. Oncol Lett. 2019;17(5):4213–4221.
    View this article via: PubMed CrossRef Google Scholar
  14. Watanabe T, et al. Reprogramming of pyrimidine nucleotide metabolism supports vigorous cell proliferation of normal and malignant T cells. Blood Adv. 2024;8(6):1345–1358.
    View this article via: CrossRef PubMed Google Scholar
  15. Daneshmandi S, et al. Blockade of 6-phosphogluconate dehydrogenase generates CD8+ effector T cells with enhanced anti-tumor function. Cell Rep. 2021;34(10):108831.
    View this article via: CrossRef PubMed Google Scholar
  16. Slack M, et al. T cell metabolic reprogramming and plasticity. Mol Immunol. 2015;68(2 pt c):507–512.
    View this article via: CrossRef PubMed Google Scholar
  17. Jeon CH, et al. Induction of S100A4, S100A6, and galectin-1 during the lineage commitment of CD4+CD8+ thymocyte cell line is suppressed by 2,3,7,8-tetrachlorodibenzo-p-dioxin. Toxicol Lett. 2009;187(3):157–163.
    View this article via: CrossRef PubMed Google Scholar
  18. Andreatta M, et al. Interpretation of T cell states from single-cell transcriptomics data using reference atlases. Nat Commun. 2021;12(1):2965.
    View this article via: CrossRef PubMed Google Scholar
  19. Wang DH, et al. Single-cell sequencing and multiple machine learning algorithms to identify key T-cell differentiation gene for progression of NAFLD cirrhosis to hepatocellular carcinoma. Front Mol Biosci. 2024;11:1301099.
    View this article via: CrossRef PubMed Google Scholar
  20. Chen J, et al. Single-cell transcriptomics reveal the intratumoral landscape of infiltrated T-cell subpopulations in oral squamous cell carcinoma. Mol Oncol. 2021;15(4):866–886.
    View this article via: CrossRef PubMed Google Scholar
  21. Miller BC, et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat Immunol. 2019;20(3):326–336.
    View this article via: CrossRef PubMed Google Scholar
  22. Miragaia RJ, et al. Single-cell transcriptomics of regulatory T cells reveals trajectories of tissue adaptation. Immunity. 2019;50(2):493–504.
    View this article via: CrossRef PubMed Google Scholar
  23. Lee PP, et al. A critical role for Dnmt1 and DNA methylation in T cell development, function, and survival. Immunity. 2001;15(5):763–774.
    View this article via: CrossRef PubMed Google Scholar
  24. Mohammadpour H, et al. Galectin-3 expression in donor T cells reduces GvHD severity and lethality after allogeneic hematopoietic cell transplantation. Cell Rep. 2023;42(3):112250.
    View this article via: CrossRef PubMed Google Scholar
  25. Mohammadpour H, et al. β2-Adrenergic receptor activation on donor cells ameliorates acute GvHD. JCI Insight. 2021;5(12):e137788.
    View this article via: JCI Insight CrossRef PubMed Google Scholar
  26. Gattinoni L, et al. T memory stem cells in health and disease. Nat Med. 2017;23(1):18–27.
    View this article via: CrossRef PubMed Google Scholar
  27. Gattinoni L, et al. Wnt signaling arrests effector T cell differentiation and generates CD8+ memory stem cells. Nat Med. 2009;15(7):808–813.
    View this article via: CrossRef PubMed Google Scholar
  28. Gattinoni L, et al. A human memory T cell subset with stem cell-like properties. Nat Med. 2011;17(10):1290–1297.
    View this article via: CrossRef PubMed Google Scholar
  29. Choi H, et al. The roles of CCR7 for the homing of memory CD8+ T cells into their survival niches. Immune Netw. 2020;20(3):e20.
    View this article via: CrossRef PubMed Google Scholar
  30. Jang C, et al. Metabolomics and isotope tracing. Cell. 2018;173(4):822–837.
    View this article via: CrossRef PubMed Google Scholar
  31. Argüello RJ, et al. SCENITH: a flow cytometry-based method to functionally profile energy metabolism with single-cell resolution. Cell Metab. 2020;32(6):1063–1075.
    View this article via: CrossRef PubMed Google Scholar
  32. Uhl FM, et al. Metabolic reprogramming of donor T cells enhances graft-versus-leukemia effects in mice and humans. Sci Transl Med. 2020;12(567):eabb8969.
    View this article via: CrossRef PubMed Google Scholar
  33. Mohammadpour H, et al. Blockade of host β2-adrenergic receptor enhances graft-versus-tumor effect through modulating APCs. J Immunol. 2018;200(7):2479–2488.
    View this article via: CrossRef PubMed Google Scholar
  34. Du W, et al. Granzyme B contributes to the optimal graft-versus-tumor effect mediated by conventional CD4+ T cells. J Immunol Res Ther. 2016;1(1):22–28.
    View this article via: PubMed Google Scholar
  35. Qiao R, et al. Critical role of 6-phosphogluconate dehydrogenase in TAp73-mediated cancer cell proliferation. Mol Cancer Res. 2023;21(8):825–835.
    View this article via: CrossRef PubMed Google Scholar
  36. Wang B, et al. Inhibition of 6-phosphogluconate dehydrogenase suppresses esophageal squamous cell carcinoma growth and enhances the anti-tumor effects of metformin via the AMPK/mTOR pathway. Mol Cancer. 2025;24(1):97.
    View this article via: CrossRef PubMed Google Scholar
  37. Stelljes M, et al. Clinical molecular imaging in intestinal graft-versus-host disease: mapping of disease activity, prediction, and monitoring of treatment efficiency by positron emission tomography. Blood. 2008;111(5):2909–2918.
    View this article via: CrossRef PubMed Google Scholar
  38. Hippen KL, et al. Distinct regulatory and effector T cell metabolic demands during graft-versus-host disease. Trends Immunol. 2020;41(1):77–91.
    View this article via: CrossRef PubMed Google Scholar
  39. Mele L, et al. A new inhibitor of glucose-6-phosphate dehydrogenase blocks pentose phosphate pathway and suppresses malignant proliferation and metastasis in vivo. Cell Death Dis. 2018;9(5):572.
    View this article via: CrossRef PubMed Google Scholar
  40. Sobanski T, et al. Cell metabolism and DNA repair pathways: implications for cancer therapy. Front Cell Dev Biol. 2021;9:633305.
    View this article via: CrossRef PubMed Google Scholar
  41. Corrado M, Pearce EL. Targeting memory T cell metabolism to improve immunity. J Clin Invest. 2022;132(1):e148546.
    View this article via: JCI CrossRef PubMed Google Scholar
  42. Patsoukis N, et al. The role of metabolic reprogramming in T cell fate and function. Curr Trends Immunol. 2016;17:1–12.
    View this article via: PubMed Google Scholar
  43. Buck MD, et al. Mitochondrial dynamics controls T cell fate through metabolic programming. Cell. 2016;166(1):63–76.
    View this article via: CrossRef PubMed Google Scholar
  44. Graubert TA, et al. The role of granzyme B in murine models of acute graft-versus-host disease and graft rejection. Blood. 1996;87(4):1232–1237.
    View this article via: CrossRef PubMed Google Scholar
  45. Negrin RS. Graft-versus-host disease versus graft-versus-leukemia. Hematology Am Soc Hematol Educ Program. 2015;2015(1):225–230.
    View this article via: CrossRef PubMed Google Scholar
  46. Perlia CP, et al. Clinical use of 6-aminonicotinamide in patients with disseminated neoplastic disease. Cancer. 1961;14:644–648.
    View this article via: CrossRef PubMed Google Scholar
  47. Bolivar-Wagers S, et al. Murine CAR19 Tregs suppress acute graft-versus-host disease and maintain graft-versus-tumor responses. JCI Insight. 2022;7(17):e160674.
    View this article via: JCI Insight CrossRef PubMed Google Scholar
  48. Larson JH, et al. Enforced gut homing of murine regulatory T cells reduces early graft-versus-host disease severity. Am J Transplant. 2023;23(8):1102–1115.
    View this article via: CrossRef PubMed Google Scholar
  49. Daneshmandi S, et al. Exportin 1 governs the immunosuppressive functions of myeloid-derived suppressor cells in tumors through ERK1/2 nuclear export. Cell Mol Immunol. 2024;21(8):873–891.
    View this article via: CrossRef PubMed Google Scholar
  50. Satija R, et al. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33(5):495–502.
    View this article via: CrossRef PubMed Google Scholar
  51. Wolock SL, et al. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 2019;8(4):281–291.
    View this article via: CrossRef PubMed Google Scholar
  52. Korotkevich G, et al. Fast gene set enrichment analysis [preprint]. https://doi.org/10.1101/060012 Posted on bioRxiv February 1, 2021.
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