Untargeted Metabolomics and Polyamine Profiling in Serum before and after Surgery in Colorectal Cancer Patients

Lee, Yu Ra; An, Ki-Yong; Jeon, Justin Y.; Kim, Nam Kyu; Lee, Ji Won; Hong, Jongki; Chung, Bong Chul

doi:10.3390/metabo10120487

Cited by 13 publications

(10 citation statements)

References 31 publications

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“…The variable influence on projection (VIP) values presents the overall influence of each x -variable in the OPLS-DA model on y -variables. The two groups’ differential metabolites were identified using a statistically significant threshold value of VIP > 1 ( Q -value < 0.05) obtained from the OPLS-DA model and univariate analysis ( Lee et al, 2020 ; Li et al, 2021b ). The information of the identified distinctive metabolites was then input to the MetaboAnalyst platform to obtain the significantly perturbed metabolic pathways related to AD.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“… 47 The polyamines are synthesized from arginine and proline metabolism, and the polyamine pathway is a potential target for cancer chemoprevention. 48 , 49 The gene of ODC in the polyamine pathway making polyamine synthesis associated with cancer polyamine function for the therapeutic intervention. 50 , 51 We propose that EiKCS in plants related to polyamine synthesis may be of medicinal value.…”

Section: Discussionmentioning

confidence: 99%

Overexpression of EiKCS confers paraquat‐resistance in rice (Oryza sativaL.) by promoting the polyamine pathway

Luo

Chen

Zhu

et al. 2021

Pest Management Science

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BACKGROUND: Paraquat is used widely as one of the bipyridine herbicides, which generates reactive oxygen species to cause cell death. With a growing number of paraquat-resistant weeds, the mechanism of paraquat-resistance in plants remains unclear. This research verified the functions of a previously confirmed putative paraquat-resistant gene, EiKCS, from paraquat-resistant goosegrass by genetic engineering in a single overexpressing line in rice.RESULTS: Overexpression of EiKCS improved paraquat resistance in transgenic rice (KCSox). Pre-applied (12 h) exogenous spermidine (1.5 mmol L −1 ), alleviated the injury of paraquat in rice. Paraquat induced injury in KCSox was 19.57%, which was lower than 32.22% injury it induced in wild-type (WT) rice. The paraquat-resistant mechanism was through the increased activity of antioxidant enzymes and the overproduction of endogenous polyamines. The spermine content in KCSox was more than 30 ∼g mL −1 , while that in WT rice was less than 5 ∼g mL −1 . Quantitative proteomics showed that ⊎-ketoacyl-coenzyme A (CoA) synthase ( 51.81 folds) encoded by the transgenic EiKCS gene promoted the synthesis of the proteins involved with the polyamine pathway. The synthesized putrescine was promoted by the arginine decarboxylase (ADC) pathway. The spermidine synthase I (1.10-fold) and three eceriferum cofactors (CERs) were responsive to the paraquat stress. We validated putrescine (C 18 H 20 N 2 O 2 ) spermidine (C 28 H 31 N 3 O 3 ), and spermine (C 38 H 42 N 4 O 4 ) in this study.CONCLUSION: EiKCS encoding ⊎-ketoacyl-CoA synthase from goosegrass has been shown as an ideal candidate gene for engineering genetically modified organism (GMO) crops, as its overexpression does not only bring paraquat-resistance, but also have potential benefits without decreasing yield and rice grain quality.

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“…The experimental conditions were the same as those previously established [ 47 , 48 ]. Table 3 shows detailed information of experimental conditions used.…”

Section: Methodsmentioning

confidence: 99%

“…Statistical analysis was performed as described previously [ 47 , 48 ]. Retention time and m/z were calculated using MassLynx 4.1 software.…”

Section: Methodsmentioning

confidence: 99%

Alterations in Pattern Baldness According to Sex: Hair Metabolomics Approach

et al. 2021

Self Cite

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Pattern baldness has been associated with the male hormone, dihydrotestosterone. In this study, we tried to determine how the overall metabolic pathways of pattern baldness differ in patients and in normal controls. Our study aimed to identify alterations in hair metabolomic profiles in order to identify possible markers of pattern baldness according to sex. Untargeted metabolomics profiling in pattern baldness patients and control subjects was conducted using ultra-performance liquid chromatography-mass spectrometry. To identify significantly altered metabolic pathways, partial least squares discriminant analysis was performed. Our analysis indicated differences in steroid biosynthesis pathway in both males and females. However, there was a remarkable difference in the androgen metabolic pathway in males, and the estrogen metabolic and arachidonic acid pathways in females. For the first time, we were able to confirm the metabolic pathway in pattern baldness patients using hair samples. Our finding improves understanding of pattern baldness and highlights the need to link pattern baldness and sex-related differences.

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Untargeted Metabolomics and Polyamine Profiling in Serum before and after Surgery in Colorectal Cancer Patients

Cited by 13 publications

References 31 publications

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

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

Overexpression of EiKCS confers paraquat‐resistance in rice (Oryza sativaL.) by promoting the polyamine pathway

Alterations in Pattern Baldness According to Sex: Hair Metabolomics Approach

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