A distinct glycerophospholipid metabolism signature of acute graft versus host disease with predictive value
Yue Liu, Aijie Huang, Qi Chen, Xiaofei Chen, Yang Fei, Xiaoming Zhao, Weiping Zhang, Zhanying Hong, Zhenyu Zhu, Jianmin Yang, Yifeng Chai, Jianmin Wang, Xiaoxia Hu
Yue Liu, Aijie Huang, Qi Chen, Xiaofei Chen, Yang Fei, Xiaoming Zhao, Weiping Zhang, Zhanying Hong, Zhenyu Zhu, Jianmin Yang, Yifeng Chai, Jianmin Wang, Xiaoxia Hu
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Clinical Research and Public Health Metabolism Transplantation

A distinct glycerophospholipid metabolism signature of acute graft versus host disease with predictive value

Abstract

BACKGROUND Acute graft versus host disease (aGvHD) is a major factor that limits the successful outcomes of allogeneic hematopoietic cell transplantation (alloHSCT). Currently, there are few validated biomarkers that can help predict the risk of aGvHD in clinical settings.METHODS We performed an integrated metabolomics and transcriptomics study and identified biomarkers that distinguish alloHSCT recipients with aGvHD from alloHSCT recipients without aGvHD in 2 separate cohorts.RESULTS Pathway analysis of 38 significantly altered metabolites and 1,148 differentially expressed genes uncovered a distinctly altered glycerophospholipid (GPL) metabolism network. Subsequently, we developed an aGvHD risk score (GRS) based on 5 metabolite markers from GPL metabolism to predict the risk of aGvHD. GRS showed a positive predictive value of 92.2% and 89.6% in the training and validation cohorts, respectively. In addition, high GRS was correlated with poor overall survival. Gene expressions of GPL-related lipases were significantly altered in aGvHD samples, leading to dysregulated GPLs.CONCLUSION Using integrative “Omic” analysis, we unraveled a comprehensive view of the molecular perturbations underlying the pathogenesis of aGvHD. Our work represents an initial investigation of a unique metabolic and transcriptomic network that may help identify aGvHD at an early stage and facilitate preemptive therapy.FUNDING National Natural Science Foundation of China (NSFC; 81530047, 81870143, 81470321, 81770160, 81270567, 81270638, 81573396, 81703674) provided funding for material, processing, metabolomics, and transcriptomics studies. This work was also supported by Shanghai Sailing Program from Science and Technology Commission Shanghai Municipality (17YF1424700), the Scholarship from Shanghai Municipal Health and Family Planning Commission (2017BR012), and the Special Clinical Research in Health Industry in Shanghai (20184Y0054). The projects supporting young scholars are 17YF1424700 (to YL), 2017BR012 (to XH), and 20184Y0054 (to QC).

Authors

Yue Liu, Aijie Huang, Qi Chen, Xiaofei Chen, Yang Fei, Xiaoming Zhao, Weiping Zhang, Zhanying Hong, Zhenyu Zhu, Jianmin Yang, Yifeng Chai, Jianmin Wang, Xiaoxia Hu

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Figure 5

Five metabolites exhibited a strong correlation with the development and severity of aGvHD.

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Five metabolites exhibited a strong correlation with the development and...
(A) The heatmap of 5 metabolites (C16:0 PAF, LysoPC [22:6], PE [20:4/20:3], PC [13:0/0:0], LysoPE [0:0/22:4]) selected from 17 GPL metabolites. (B) ROC curve analysis of the ability of plasma metabolites, including C16:0 PAF, LysoPC (22:6), PE (20:4/20:3), PC (13:0/0:0), and LysoPE (0:0/22:4), to predict the development of aGvHD based on both the training (n = 57) and validation sets (n = 50). The area under the ROC curve was 0.922 (95% CI, 0.849–0.966) in the training set, and 0.896 (95% CI, 0.811–0.981) in the validation set. (C) Box plots showing fluctuations in relative signal intensities of 5 metabolites. Five metabolites within the panel were restored to normal levels at varying degrees after aGvHD in remission after medication. The comparisons were made between control vs. aGvHD, and aGvHDΔ vs. aGvHD, respectively. Bars indicate median value. P value refers to 1-way ANOVA. *P < 0.05, **P < 0.01. aGvHDΔ, aGvHD in remission after medication.