Amino acid metabolomics and machine learning for assessment of post-hepatectomy liver regeneration

• Published in: Frontiers in pharmacology Vol. 15; p. 1345099
• Main Authors: Yan, Yuqing, Chen, Qianping, Dai, Xiaoming, Xiang, Zhiqiang, Long, Zhangtao, Wu, Yachen, Jiang, Hui, Zou, Jianjun, Wang, Mu, Zhu, Zhu
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 24.05.2024
• Subjects: hepatectomy; liver regeneration; machine learning; metabolomics; non-alcoholic steatohepatitis (NASH); Pharmacology; hepatectomy; machine learning; metabolomics; liver regeneration; non-alcoholic steatohepatitis (NASH)
• ISSN: 1663-9812; 1663-9812
• DOI: 10.3389/fphar.2024.1345099

Amino acid (AA) metabolism plays a vital role in liver regeneration. However, its measuring utility for post-hepatectomy liver regeneration under different conditions remains unclear. We aimed to combine machine learning (ML) models with AA metabolomics to assess liver regeneration in health and non-alcoholic steatohepatitis (NASH). The liver index (liver weight/body weight) was calculated following 70% hepatectomy in healthy and NASH mice. The serum levels of 39 amino acids were measured using ultra-high performance liquid chromatography-tandem mass spectrometry analysis. We used orthogonal partial least squares discriminant analysis to determine differential AAs and disturbed metabolic pathways during liver regeneration. The SHapley Additive exPlanations algorithm was performed to identify potential AA signatures, and five ML models including least absolute shrinkage and selection operator, random forest, K-nearest neighbor (KNN), support vector regression, and extreme gradient boosting were utilized to assess the liver index. Eleven and twenty-two differential AAs were identified in the healthy and NASH groups, respectively. Among these metabolites, arginine and proline metabolism were commonly disturbed metabolic pathways related to liver regeneration in both groups. Five AA signatures were identified, including hydroxylysine, L-serine, 3-methylhistidine, L-tyrosine, and homocitrulline in healthy group, and L-arginine, 2-aminobutyric acid, sarcosine, beta-alanine, and L-cysteine in NASH group. The KNN model demonstrated the best evaluation performance with mean absolute error, root mean square error, and coefficient of determination values of 0.0037, 0.0047, 0.79 and 0.0028, 0.0034, 0.71 for the healthy and NASH groups, respectively. The KNN model based on five AA signatures performed best, which suggests that it may be a valuable tool for assessing post-hepatectomy liver regeneration in health and NASH.