International immunopharmacology · 2022
Metabolomic profiling for drug-induced liver injury with autoantibodies.
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Han Yan-Zhong, Ma Zhi-Tao, Zhou Ming-Xi, Niu Ming, Zhao Xu, Guo Yu-Ming, Song Xin-Hua, Lu Ya-Wen, Bai Zhao-Fang, Li Zhen, Gao Han, Zhao Yong-Kang, Wang Jia-Bo, Xiao Xiao-He, Jing Jing
Аннотация
BACKGROUNDS: Drug induced liver injury (DILI) is sometimes similar to autoimmune hepatitis (AIH) in serology and histology. Clinicians empirically screened DILI with significant autoimmune characteristics to implement clinical intervention. We tried to characterize DILI with autoantibodies by metabolomics.
METHODS: Untargeted metabolomics coupled with pattern recognition approaches were performed on sera samples including AIH (n = 59), DILI with autoantibodies (DILIAb+, n = 68), and DILI without autoantibodies (DILIAb-, n = 75). The differential metabolites and fingerprint metabolites between AIH and DILIAb- were screened by orthogonal partial least squares-discriminant analysis and hierarchical clustering respectively.
RESULTS: Of the 388 annotated differential metabolites between AIH and DILIAb-, 74 fingerprint metabolites were screened. The eigenmetabolite compressed from the fingerprint possessed high discrimination efficacy (AUC:0.891; 95 %CI, 0.838-0.944). In the fingerprint-based PCA model, AIH and DILIAb- were separated into three regions: the "pure region" of AIH (Region 1), the "pure region" of DILIAb- (Region 3), the mixture region of AIH and DILIAb- (Region 2). After incorporated into the PCA model, DILIAb+ samples were distributed into the three regions, indicating that DILIAb+ samples had different etiological tendencies. Moreover, the fingerprint-based radar model verified the results of PCA model characterizing DILIAb+. Notably, the antibody titers of DILIAb+ in the three regions did not differ significantly, while the response rates for glucocorticoids were obviously different. The metabolic difference among DILIAb+ in different regions mainly lies in energy metabolism.
CONCLUSIONS: In terms of metabolic signature, DILIAb+ may not be a community of same pathogenesis, including AIH-inclined parts. Which deserves further study.
METHODS: Untargeted metabolomics coupled with pattern recognition approaches were performed on sera samples including AIH (n = 59), DILI with autoantibodies (DILIAb+, n = 68), and DILI without autoantibodies (DILIAb-, n = 75). The differential metabolites and fingerprint metabolites between AIH and DILIAb- were screened by orthogonal partial least squares-discriminant analysis and hierarchical clustering respectively.
RESULTS: Of the 388 annotated differential metabolites between AIH and DILIAb-, 74 fingerprint metabolites were screened. The eigenmetabolite compressed from the fingerprint possessed high discrimination efficacy (AUC:0.891; 95 %CI, 0.838-0.944). In the fingerprint-based PCA model, AIH and DILIAb- were separated into three regions: the "pure region" of AIH (Region 1), the "pure region" of DILIAb- (Region 3), the mixture region of AIH and DILIAb- (Region 2). After incorporated into the PCA model, DILIAb+ samples were distributed into the three regions, indicating that DILIAb+ samples had different etiological tendencies. Moreover, the fingerprint-based radar model verified the results of PCA model characterizing DILIAb+. Notably, the antibody titers of DILIAb+ in the three regions did not differ significantly, while the response rates for glucocorticoids were obviously different. The metabolic difference among DILIAb+ in different regions mainly lies in energy metabolism.
CONCLUSIONS: In terms of metabolic signature, DILIAb+ may not be a community of same pathogenesis, including AIH-inclined parts. Which deserves further study.