Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy

Amino acid metabolomics and machine learning-driven assessment of future liver remnant growth after hepatectomy in livers of various backgrounds

Accurate assessment of future liver remnant growth after partial hepatectomy (PH) in patients with different liver backgrounds is a pressing clinical issue. Amino acid (AA) metabolism plays a crucial role in liver regeneration. In this study, we combined metabolomics and machine learning (ML) to develop a generalized future liver remnant assessment model for multiple liver backgrounds. The liver index was calculated at 0, 6, 24, 48, 72 and 168 h after 70 % PH in healthy mice and mice with nonalcoholic steatohepatitis or liver fibrosis. The serum levels of 39 amino acids (AAs) were measured using UPLC-MS/MS. The dataset was randomly divided into training and testing sets at a 2:1 ratio, and orthogonal partial least squares regression (OPLS) and minimally biased variable selection in R (MUVR) were used to select a metabolite signature of AAs. To assess liver remnant growth, nine ML models were built, and evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The post-Pareto technique for order preference by similarity to the ideal solution (TOPSIS) was employed for ranking the ML algorithms, and a stacking technique was utilized to establish consensus among the superior algorithms. Compared with those of OPLS, the signature AAs set identified by MUVR (Thr, Arg, EtN, Phe, Asa, 3MHis, Abu, Asp, Tyr, Leu, Ser, and bAib) are more concise. Post-Pareto TOPSIS ranking demonstrated that the majority of ML algorithm in combinations with MUVR outperformed those with OPLS. The established SVM-KNN consensus model performed best, with an R2 of 0.79, an MAE of 0.0029, and an RMSE of 0.0035 for the testing set. This study identified a metabolite signature of 12 AAs and constructed an SVM-KNN consensus model to assess future liver remnant growth after PH in mice with different liver backgrounds. Our preclinical study is anticipated to establish an alternative and generalized assessment method for liver regeneration.

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

Objective

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).

Methods

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.

Results

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.

Conclusion

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.

Serum metabolomics characteristics and fatty-acid-related mechanism of cirrhosis with histological response in chronic hepatitis B

Background and aims: The serum metabolites changes in patients with hepatitis B virus (HBV)-related cirrhosis as progression. Peroxisome proliferator-activated receptor gamma (PPARγ) is closely related to lipid metabolism in cirrhotic liver. However, the relationship between fatty acids and the expression of hepatic PPARγ during cirrhosis regression remains unknown. In this study, we explored the serum metabolic characteristics and expression of PPARγ in patients with histological response to treatment with entecavir. Methods: Sixty patients with HBV-related cirrhosis were selected as the training cohort with thirty patients each in the regression (R) group and non-regression (NR) group based on their pathological changes after 48-week treatment with entecavir. Another 72 patients with HBV-related cirrhosis and treated with entecavir were collected as the validation cohort. All of the serum samples were tested using ultra-performance liquid chromatography coupled to tandem mass spectrometry. Data were processed through principal component analysis and orthogonal partial least square discriminant analysis. Hepatic PPARγ expression was observed using immunohistochemistry. The relationship between serum fatty acids and PPARγ was calculated using Pearson's or Spearman's correlation analysis. Results: A total of 189 metabolites were identified and 13 differential metabolites were screened. Compared to the non-regression group, the serum level of fatty acids was higher in the R group. At baseline, the expression of PPARγ in hepatic stellate cells was positively correlated with adrenic acid (r 2 = 0.451, p = 0.046). The expression of PPARγ in both groups increased after treatment, and the expression of PPARγ in the R group was restored in HSCs much more than that in the NR group (p = 0.042). The adrenic acid and arachidonic acid (AA) in the R group also upgraded more than the NR group after treatment (p = 0.037 and 0.014). Conclusion: Baseline serum differential metabolites, especially fatty acids, were identified in patients with HBV-related cirrhosis patients who achieved cirrhosis regression. Upregulation of adrenic acid and arachidonic acid in serum and re-expression of PPARγ in HSCs may play a crucial role in liver fibrosis improvement.

Integration of metabolomics and machine learning revealed tryptophan metabolites are sensitive biomarkers of pemetrexed efficacy in non-small cell lung cancer

BACKGROUND

Anti-folate drug pemetrexed is a vital chemotherapy medication for non-small cell lung cancer (NSCLC). Its response varies widely and often develops resistance to the treatment. Therefore, it is urgent to identify biomarkers and establish models for drug efficacy evaluation and prediction for rational drug use.

METHODS

A total of 360 subjects were screened and 323 subjects were recruited. Using metabolomics in combination with machine learning methods, we are trying to select potential biomarkers to diagnose NSCLC and evaluate the efficacy of pemetrexed in treating NSCLC. Furtherly, we measured the concentration of eight metabolites in the tryptophan metabolism pathway in the validation set containing 201 subjects using a targeted metabolomics method with UPLC-MS/MS.

RESULTS

In the discovery set containing 122 subjects, the metabolic profile of healthy controls (H), newly diagnosed NSCLC patients (ND), patients who responded well to pemetrexed treatment (S) and pemetrexed-resistant patients (R) differed significantly on the PLS-DA scores plot. Pathway analysis showed that glycine, serine and threonine metabolism occurred in every two group comparisons. TCA cycle, pyruvate metabolism and glycerolipid metabolism are the most significantly changed pathways between ND and H group, pyruvate metabolism was the most altered pathway between S and ND group, and tryptophan metabolism was the most changed pathway between S and R group. We found Random forest method had the maximum area under the curve (AUC) and can be easily interpreted. The AUC is 0.981 for diagnosing patients with NSCLC and 0.954 for evaluating pemetrexed efficiency.

CONCLUSION

We compared eight mathematical models to evaluate pemetrexed efficiency for treating NSCLC. The Random forest model established with metabolic markers tryptophan, kynurenine and xanthurenic acidcan accurately diagnose NSCLC and evaluate the response of pemetrexed.

Identification of Biomarkers for Methamphetamine Exposure Time Prediction in Mice Using Metabolomics and Machine Learning Approaches

Methamphetamine (METH) abuse has become a global public health and safety problem. More information is needed to identify the time of drug abuse. In this study, methamphetamine was administered to male C57BL/6J mice with increasing doses from 5 to 30 mg kg^-1 (once a day, i.p.) for 20 days. Serum and urine samples were collected for metabolomics studies using gas chromatography-mass spectrometry (GC-MS). Six machine learning models were used to infer the time of drug abuse and the best model was selected to predict administration time preliminarily. The metabolic changes caused by methamphetamine were explored. As results, the metabolic patterns of methamphetamine exposure mice were quite different from the control group and changed over time. Specifically, serum metabolomics showed enhanced amino acid metabolism and increased fatty acid consumption, while urine metabolomics showed slowed metabolism of the tricarboxylic acid (TCA) cycle, increased organic acid excretion, and abnormal purine metabolism. Phenylalanine in serum and glutamine in urine increased, while palmitic acid, 5-HT, and monopalmitin in serum and gamma-aminobutyric acid in urine decreased significantly. Among the six machine learning models, the random forest model was the best to predict the exposure time (serum: MAE = 1.482, RMSE = 1.69, R squared = 0.981; urine: MAE = 2.369, RMSE = 1.926, R squared = 0.946). The potential biomarker set containing four metabolites in the serum (palmitic acid, 5-hydroxytryptamine, monopalmitin, and phenylalanine) facilitated the identification of methamphetamine exposure. The random forest model helped predict the methamphetamine exposure time based on these potential biomarkers.

The role of gut microbiota in liver regeneration

The liver has unique regeneration potential, which ensures the continuous dependence of the human body on hepatic functions. As the composition and function of gut microbiota has been gradually elucidated, the vital role of gut microbiota in liver regeneration through gut-liver axis has recently been accepted. In the process of liver regeneration, gut microbiota composition is changed. Moreover, gut microbiota can contribute to the regulation of the liver immune microenvironment, thereby modulating the release of inflammatory factors including IL-6, TNF-α, HGF, IFN-γ and TGF-β, which involve in different phases of liver regeneration. And previous research have demonstrated that through enterohepatic circulation, bile acids (BAs), lipopolysaccharide, short-chain fatty acids and other metabolites of gut microbiota associate with liver and may promote liver regeneration through various pathways. In this perspective, by summarizing gut microbiota-derived signaling pathways that promote liver regeneration, we unveil the role of gut microbiota in liver regeneration and provide feasible strategies to promote liver regeneration by altering gut microbiota composition.

iTRAQ-based quantitative proteomic analysis of the liver regeneration termination phase after partial hepatectomy in mice

Liver regeneration (LR) is an important biological process after liver injury. As the "brake" in the process of LR, the termination phase of LR not only suppresses the continuous increase in liver volume but also effectively promotes the recovery of liver function. However, the mechanisms underlying the termination phase of LR are still not clear. In our study, we used isobaric tags for relative and absolute quantification (iTRAQ)-based quantitative proteomic analysis to determine the protein expression profiles of livers in the termination phase of mouse LR after partial hepatectomy (PH). We found that the expression of 197 proteins increased gradually during LR; in addition, 187 proteins were upregulated and 264 proteins were downregulated specifically in the termination phase of LR. The GO analysis of the proteins revealed the upregulation of "cell-cell adhesion" and "translation" and the downregulation of the "oxidation-reduction process". The KEGG pathway analysis showed that "biosynthesis of antibiotics" and "ribosomes" were significantly upregulated, while "metabolic pathways" were significantly downregulated. These analyses indicated that the termination phase of LR mainly focuses on restoring cellular structure and function. Differentially expressed proteins such as SNX5 were also screened out from biological processes. SIGNIFICANCE: The key regulatory factors in the termination phase of LR were studied by iTRAQ-based proteomics to lay a foundation for further study of the molecular mechanism and biomarkers of the termination phase of LR. This study will guide the clinical perioperative management of patients after hepatectomy.