An improved pseudotargeted GC–MS/MS-based metabolomics method and its application in radiation-induced hepatic injury in a rat model

Wu, Hanxu; Xu, Chao; Gu, Yifeng; Yang, Shugao; Wang, Yarong; Wang, Chang

doi:10.1016/j.jchromb.2020.122250

Cited by 12 publications

(6 citation statements)

References 40 publications

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“…As asparagine is anapleurotic to the tricarboxylic acid cycle, and serine and alanine are in equilibrium with pyruvate, this may suggest inadequate mitochondrial compensation and possibly higher protein turnover rate early during SBRT, both of which may portend clinical liver toxicity. Preclinical studies in rats have previously demonstrated dysregulation of serine and alanine metabolism in the liver of mice after 6 Gy of whole body irradiation [23] , consistent with our findings. Early increase in the expression of hydroxyproline, a marker for liver fibrosis [26] , was also observed in HCC patients with the greatest decline in liver function at three months post SBRT in the current study.…”

Section: Discussionsupporting

confidence: 92%

“…Following only one to two fractions of SBRT, higher plasma levels of the glycerophospholipids such as PC aa C30:0 and PC ae C30:0 were detected in the HCC patients with higher baseline CP score and ALBI grade compared to their lower score/grade counterparts, suggesting increased radiation-induced lipid turnover in the impaired livers. In line with our findings, some preclinical studies previously demonstrated significant dysregulation of various glycerophospholipids in the liver of non-human primates and rats at early (3 days) and late (60 days) time points following a single dose of whole body irradiation (which included the whole liver) compared to that of healthy controls who did not receive whole body irradiation [ 22 , 23 ]. Dysregulation of glycerophospholipids has also been reported in prostate cancer patients who developed urinary symptoms following prostate SBRT [24] .…”

Section: Discussionsupporting

confidence: 91%

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Plasma metabolomic profiles in liver cancer patients following stereotactic body radiotherapy

Jang

Kurland

et al. 2020

eBioMedicine

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Background Stereotactic body radiotherapy (SBRT) is an effective treatment for hepatocellular carcinoma (HCC). This study sought to identify differentially expressed plasma metabolites in HCC patients at baseline and early during SBRT, and to explore if changes in these metabolites early during SBRT may serve as biomarkers for radiation-induced liver injury and/or tumour response. Methods Forty-seven HCC patients were treated with SBRT on previously published prospective trials. Plasma samples were collected at baseline and after one to two fractions of SBRT, and analysed by GC/MS and LC/MS for untargeted and targeted metabolomics profiling, respectively. Findings Sixty-nine metabolites at baseline and 62 metabolites after one to two fractions of SBRT were differentially expressed, and strongly separated the Child Pugh (CP) B from the CP A HCC patients. These metabolites are associated with oxidative stress and alterations in hepatic cellular metabolism. Differential upregulation of serine, alanine, taurine, and lipid metabolites early during SBRT from baseline was noted in the HCC patients who demonstrated the greatest increase in CP scores at three months post SBRT, suggesting that high protein and lipid turnover early during SBRT may portend increased clinical liver toxicity. Twenty annotated metabolites including fatty acids, glycerophospholipids, and acylcarnitines were differentially upregulated early during SBRT from baseline and separated patients with complete/partial response from those with stable disease at three months post SBRT. Interpretation Dysregulation of amino acid and lipid metabolism detected early during SBRT are associated with subsequent clinical liver injury and tumour response in HCC.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 91%

Plasma metabolomic profiles in liver cancer patients following stereotactic body radiotherapy

Jang

Kurland

et al. 2020

eBioMedicine

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“…Metabolomics is a high-throughput tool for quantitatively analyzing the small-molecule metabolites in biospecimens such as blood, tissue, urine, or saliva ( Cheng et al, 2018 ). It has been increasingly applied to discovering potential biomarkers and related metabolic pathways ( Johnson et al, 2016 ), the investigations of polypharmacological mechanisms of drug combination therapy ( Li et al, 2021a ), and the host response to the drug therapy ( Wang et al, 2018 ; Li et al, 2020 ), and explorations of complicated pathophysiologic mechanisms of diseases ( Johnson et al, 2016 ; Wu et al, 2020 ). Generally, the widely targeted metabolomics method can achieve accurate quantification of targeted metabolites by defining ion-pairs information derived from untargeted metabolomics or obtained from relevant references and existing mass spectrum public databases ( Heikkinen et al, 2019 ; Zhou et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?

Zhu

Huang

et al. 2021

Front. Mol. Biosci.

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Alcohol dependence (AD) is a condition of alcohol use disorder in which the drinkers frequently develop emotional symptoms associated with a continuous alcohol intake. AD characterized by metabolic disturbances can be quantitatively analyzed by metabolomics to identify the alterations in metabolic pathways. This study aimed to: i) compare the plasma metabolic profiling between healthy and AD-diagnosed individuals to reveal the altered metabolic profiles in AD, and ii) identify potential biological correlates of alcohol-dependent inpatients based on metabolomics and interpretable machine learning. Plasma samples were obtained from healthy (n = 42) and AD-diagnosed individuals (n = 43). The plasma metabolic differences between them were investigated using liquid chromatography-tandem mass spectrometry (AB SCIEX® QTRAP 4500 system) in different electrospray ionization modes with scheduled multiple reaction monitoring scans. In total, 59 and 52 compounds were semi-quantitatively measured in positive and negative ionization modes, respectively. In addition, 39 metabolites were identified as important variables to contribute to the classifications using an orthogonal partial least squares-discriminant analysis (OPLS-DA) (VIP > 1) and also significantly different between healthy and AD-diagnosed individuals using univariate analysis (p-value < 0.05 and false discovery rate < 0.05). Among the identified metabolites, indole-3-carboxylic acid, quinolinic acid, hydroxy-tryptophan, and serotonin were involved in the tryptophan metabolism along the indole, kynurenine, and serotonin pathways. Metabolic pathway analysis revealed significant changes or imbalances in alanine, aspartate, glutamate metabolism, which was possibly the main altered pathway related to AD. Tryptophan metabolism interactively influenced other metabolic pathways, such as nicotinate and nicotinamide metabolism. Furthermore, among the OPLS-DA-identified metabolites, normetanephrine and ascorbic acid were demonstrated as suitable biological correlates of AD inpatients from our model using an interpretable, supervised decision tree classifier algorithm. These findings indicate that the discriminatory metabolic profiles between healthy and AD-diagnosed individuals may benefit researchers in illustrating the underlying molecular mechanisms of AD. This study also highlights the approach of combining metabolomics and interpretable machine learning as a valuable tool to uncover potential biological correlates. Future studies should focus on the global analysis of the possible roles of these differential metabolites and disordered metabolic pathways in the pathophysiology of AD.

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“…Mass spectrometry is an analytical method that measures the ion-to-mass ratio based on the ionization of components in the samples by an ion source, and is widely used in the detection of metabolites ( 54 – 56 ). The sample can be directly analyzed by mass spectrometry, or in tandem with other separation methods to obtain mass spectra, such as liquid chromatography (LC) ( 57 – 59 ), gas chromatography (GC) ( 60 , 61 ), hydrophilic interaction chromatography-mass spectrometry (HILIC-MS) ( 62 ), Flow-injection analysis-mass spectrometry(FIA–MS) ( 63 ), or capillary electrophoresis (CE) ( 64 , 65 ). It should be noted that no single method can separate all metabolites simultaneously, as some metabolites are difficult to ionize, and in some cases, mass number limitations prevent mass spectrometry techniques from measuring all metabolites ( 66 ).…”

Section: Metabolomics Analysis Platformmentioning

confidence: 99%

A Hypothesis From Metabolomics Analysis of Diabetic Retinopathy: Arginine-Creatine Metabolic Pathway May Be a New Treatment Strategy for Diabetic Retinopathy

et al. 2022

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Diabetic retinopathy is one of the serious complications of diabetes, which the leading causes of blindness worldwide, and its irreversibility renders the existing treatment methods unsatisfactory. Early detection and timely intervention can effectively reduce the damage caused by diabetic retinopathy. Metabolomics is a branch of systems biology and a powerful tool for studying pathophysiological processes, which can help identify the characteristic metabolic changes marking the progression of diabetic retinopathy, discover potential biomarkers to inform clinical diagnosis and treatment. This review provides an update on the known metabolomics biomarkers of diabetic retinopathy. Through comprehensive analysis of biomarkers, we found that the arginine biosynthesis is closely related to diabetic retinopathy. Meanwhile, creatine, a metabolite with arginine as a precursor, has attracted our attention due to its important correlation with diabetic retinopathy. We discuss the possibility of the arginine-creatine metabolic pathway as a therapeutic strategy for diabetic retinopathy.

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An improved pseudotargeted GC–MS/MS-based metabolomics method and its application in radiation-induced hepatic injury in a rat model

Cited by 12 publications

References 40 publications

Plasma metabolomic profiles in liver cancer patients following stereotactic body radiotherapy

Plasma metabolomic profiles in liver cancer patients following stereotactic body radiotherapy

Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?

A Hypothesis From Metabolomics Analysis of Diabetic Retinopathy: Arginine-Creatine Metabolic Pathway May Be a New Treatment Strategy for Diabetic Retinopathy

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