Quantile Normalization Approach for Liquid Chromatography— Mass Spectrometry-based Metabolomic Data from Healthy Human Volunteers

Lee, Joomi; Park, Jeong-Hyeon; Lim, Myung-Seop; Seong, Sook Jin; Seo, Jae‐Hyun; Park, Sung Min; Lee, Hae Won; Yoon, Young‐Ran

doi:10.2116/analsci.28.801

Cited by 44 publications

(34 citation statements)

References 16 publications

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“…Peak extraction and quantification of ion intensities were performed by an adaptive processing software package (apLCMS) designed for use with LC-FTMS data, 26 which provided tables containing m/z values, retention time, and integrated ion intensity for each m/z feature. The data were log-transformed, median centered, scaled to have unit variance, and quantile normalized 27,28 prior to statistical and bioinformatic analyses.…”

Section: Metabolomicsmentioning

confidence: 99%

Metabolomics of Bronchoalveolar Lavage Differentiate Healthy HIV-1-Infected Subjects from Controls

Cribbs

Park

Guidot

et al. 2014

AIDS Research and Human Retroviruses

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Despite antiretroviral therapy, pneumonias from pathogens such as pneumococcus continue to cause significant morbidity and mortality in HIV-1-infected individuals. Respiratory infections occur despite high CD4 counts and low viral loads; therefore, better understanding of lung immunity and infection predictors is necessary. We tested whether metabolomics, an integrated biosystems approach to molecular fingerprinting, could differentiate such individual characteristics. Bronchoalveolar lavage fluid (BALf ) was collected from otherwise healthy HIV-1-infected individuals and healthy controls. A liquid chromatography-high-resolution mass spectrometry method was used to detect metabolites in BALf. Statistical and bioinformatic analyses used false discovery rate (FDR) and orthogonally corrected partial least-squares discriminant analysis (OPLS-DA) to identify groupwise discriminatory factors as the top 5% of metabolites contributing to 95% separation of HIV-1 and control. We enrolled 24 subjects with HIV-1 (median CD4 = 432) and 24 controls. A total of 115 accurate mass m/z features from C 18 and AE analysis were significantly different between HIV-1 subjects and controls (FDR = 0.05). Hierarchical cluster analysis revealed clusters of metabolites, which discriminated the samples according to HIV-1 status (FDR = 0.05). Several of these did not match any metabolites in metabolomics databases; mass-tocharge 325.065 ([M + H] + ) was significantly higher (FDR = 0.05) in the BAL of HIV-1-infected subjects and matched pyochelin, a siderophore-produced Pseudomonas aeruginosa. Metabolic profiles in BALf differentiated healthy HIV-1-infected subjects and controls. The lack of association with known human metabolites and inclusion of a match to a bacterial metabolite suggest that the differences could reflect the host's lung microbiome and/or be related to subclinical infection in HIV-1-infected patients.

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Section: Metabolomicsmentioning

confidence: 99%

Metabolomics of Bronchoalveolar Lavage Differentiate Healthy HIV-1-Infected Subjects from Controls

Cribbs

Park

Guidot

et al. 2014

AIDS Research and Human Retroviruses

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“…Finally, the intensities of the 1,178 peaks were normalised using a quantile normalization algorithm [55] to remove systematic errors from sample preparations and LC–MS analyses. The peak intensities represent a metabolic phenotype for each sample, which varied among samples and thus represented the individual variation in the plasma metabolome.…”

Section: Resultsmentioning

confidence: 99%

“…Each of the test samples was analysed by LC–MS in the positive ionisation mode to obtain metabolite profiles. The analysis involved the following steps: plasma sample preparation, full-scan (50–900 m/z ) LC–MS analysis, data preprocessing, peak detection and alignment (using XCMS software) [52], [53], peak intensity normalization (using the quantile normalization algorithm of the preprocessCore package [54] with R language, version 2.11.1) [55], and the creation of an export annotated peak data table along with unique peak identifiers and normalised peak intensities for further multivariate statistical analysis.…”

Section: Methodsmentioning

confidence: 99%

Pharmacometabolomic Approach to Predict QT Prolongation in Guinea Pigs

et al. 2013

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Drug-induced torsades de pointes (TdP), a life-threatening arrhythmia associated with prolongation of the QT interval, has been a significant reason for withdrawal of several medicines from the market. Prolongation of the QT interval is considered as the best biomarker for predicting the torsadogenic risk of a new chemical entity. Because of the difficulty assessing the risk for TdP during drug development, we evaluated the metabolic phenotype for predicting QT prolongation induced by sparfloxacin, and elucidated the metabolic pathway related to the QT prolongation. We performed electrocardiography analysis and liquid chromatography–mass spectroscopy-based metabolic profiling of plasma samples obtained from 15 guinea pigs after administration of sparfloxacin at doses of 33.3, 100, and 300 mg/kg. Principal component analysis and partial least squares modelling were conducted to select the metabolites that substantially contributed to the prediction of QT prolongation. QTc increased significantly with increasing dose (r = 0.93). From the PLS analysis, the key metabolites that showed the highest variable importance in the projection values (>1.5) were selected, identified, and used to determine the metabolic network. In particular, cytidine-5′-diphosphate (CDP), deoxycorticosterone, L-aspartic acid and stearic acid were found to be final metabolomic phenotypes for the prediction of QT prolongation. Metabolomic phenotypes for predicting drug-induced QT prolongation of sparfloxacin were developed and can be applied to cardiac toxicity screening of other drugs. In addition, this integrative pharmacometabolomic approach would serve as a good tool for predicting pharmacodynamic or toxicological effects caused by changes in dose.

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“…[16] Quantile normalization was also conducted by R for metabolomic data processing. [17] The preprocessed dataset was imported to SIMCA software (version 14, Umetrics, Umeå, Sweden) and a multivariate statistical analysis was performed. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used according to Bylesjö et al [18] Variable importance in the projection (VIP) value was used to account for which metabolite contributed to the alteration after the glimepiride administration.…”

Section: Discussionmentioning

confidence: 99%

Metabolomic analysis of healthy human urine following administration of glimepiride using a liquid chromatography-tandem mass spectrometry

Young

Gwon

Kim

et al. 2017

Transl Clin Pharmacol

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Glimepiride, a third generation sulfonylurea, is an antihyperglycemic agent widely used to treat type 2 diabetes mellitus. In this study, an untargeted urinary metabolomic analysis was performed to identify endogenous metabolites affected by glimepiride administration. Urine samples of twelve healthy male volunteers were collected before and after administration of 2 mg glimepiride. These samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and then subjected to multivariate data analysis including principal component analysis and orthogonal partial least squares discriminant analysis. Through this metabolomic profiling, we identified several endogenous metabolites such as adenosine 3', 5'-cyclic monophosphate (cAMP), quercetin, tyramine, and urocanic acid, which exhibit significant metabolomic changes between pre-and posturine samples. Among these, cAMP, which is known to be related to insulin secretion, was the most significantly altered metabolite following glimepiride administration. In addition, the pathway analysis showed that purine, tyrosine, and histidine metabolism was affected by pharmacological responses to glimepiride. Together, the results suggest that the pharmacometabolomic approach, based on LC-MS/MS, is useful in understanding the alterations in biochemical pathways associated with glimepiride action.

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Quantile Normalization Approach for Liquid Chromatography— Mass Spectrometry-based Metabolomic Data from Healthy Human Volunteers

Cited by 44 publications

References 16 publications

Metabolomics of Bronchoalveolar Lavage Differentiate Healthy HIV-1-Infected Subjects from Controls

Metabolomics of Bronchoalveolar Lavage Differentiate Healthy HIV-1-Infected Subjects from Controls

Pharmacometabolomic Approach to Predict QT Prolongation in Guinea Pigs

Metabolomic analysis of healthy human urine following administration of glimepiride using a liquid chromatography-tandem mass spectrometry

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