Screening of Biomarkers in Rat Urine Using LC/Electrospray Ionization-MS and Two-Way Data Analysis

Idborg, Helena; Edlund, Per-Olof; Kvalheim, Olav M.; Schuppe-Koistinen, Ina; Jacobsson, Sven P.

doi:10.1021/ac0341618

Cited by 161 publications

(119 citation statements)

References 24 publications

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“…Cholestasis-induced bile duct cell necrosis and subsequent membrane breakdown caused by ANIT and as the result of the serum examinations may account for the elevation of urinary bile acids at 7-55 hours. PAG has been reported to be a potential biomarker for phospholipidosis (Dieterle et al 2006;Espina et al 2001;Idborg-Bjorkman et al 2003;Nicholls et al 2000), and alteration of PAG levels has been found in urinary metabolic fingerprinting for ANIT-induced intrahepatic cholestasis using LC-MS (La et al 2005). PAG is the end product of phenylalanine metabolism in rodents (James et al 1972).…”

Section: Discussionmentioning

confidence: 99%

Urinary Metabolic Fingerprinting for α-naphthylisothiocyanate-induced Intrahepatic Cholestasis in Rats Using Fourier Transform-ion Cyclotron Resonance Mass Spectrometry

et al. 2008

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Urinary metabolic fingerprinting with Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) was performed to monitor metabolic changes in an α-naphthylisothiocyanate (ANIT)-induced rat model of intrahepatic cholestasis and to investigate the relationships among metabolic changes, histopathology, and blood chemistry. ANIT was administered orally as a single dose of 100 mg/kg. Urine samples were collected predose (-31 to -24 hours) and postdose at 0-7, 7-24, 24-31, 31-48, 48-55, 55-72, and 72-96 hours, and serum samples were collected on days 1, 2, and 4 postdose. Increased levels of alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, and total bilirubin were seen on day 2. The negative ion profiles for urine samples collected after 7-24, 24-31, 31-48, and 48-55 hours differed from the predose profile based on principal component analysis. Onset of recovery was observed after 24-31 hours, when the urinary composition reverted toward the predose position. In conclusion, it is possible to monitor the progression of and recovery from drug-induced hepatotoxicity by urinary metabolic fingerprinting with FT-ICR MS and to search for potential biomarkers involved in intrahepatic cholestasis.

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Section: Discussionmentioning

confidence: 99%

Urinary Metabolic Fingerprinting for α-naphthylisothiocyanate-induced Intrahepatic Cholestasis in Rats Using Fourier Transform-ion Cyclotron Resonance Mass Spectrometry

et al. 2008

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“…Major efforts have been made to improve the feature extraction, quantification, and identification. A number of algorithms and software have been developed that greatly facilitate the data processing of LC/MS data [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] . As this is not the focus of the review, we will skip the details.…”

Section: Bottom-up and Top-down Metabolomicsmentioning

confidence: 99%

Analyzing LC/MS Metabolic Profiling Data in the Context of Existing Metabolic Networks

Yu¹,

Bai²

2012

CMB

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Metabolic profiling is the unbiased detection and quantification of low molecular-weight metabolites in a living system. It is rapidly developing in biological and translational research, contributing to disease mechanism elucidation, environmental chemical surveillance, biomarker detection, and health outcome prediction. Recent developments in experimental and computational technology allow more and more known metabolites to be detected and quantified from complex samples. As the coverage of the metabolic network improves, it has become feasible to examine metabolic profiling data from a systems perspective, i.e. interpreting the data and performing statistical inference in the context of pathways and genome-scale metabolic networks. Recently a number of methods have been developed in this area, and much improvement in algorithms and databases are still needed. In this review, we survey some methods for the analysis of metabolic profiling data based on metabolic networks.

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“…33 for an overview of Chemometrics analysis techniques) to extract the major spectral components in LC-MS profiling of urine (34,35), a notably simpler mixture than tissue or serum. In this manner, a data matrix, X, defining a single LC-MS experiment, with rows corresponding to time points and columns to m/z values, was resolved into a set of "pure" spectral profiles.…”

Section: Mid-level Processing-peak Detection and Quantificationmentioning

confidence: 99%

Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry

Listgarten

Emili

2005

Molecular & Cellular Proteomics

278

213

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The combined method of LC-MS/MS is increasingly being used to explore differences in the proteomic composition of complex biological systems. The reliability and utility of such comparative protein expression profiling studies is critically dependent on an accurate and rigorous assessment of quantitative changes in the relative abundance of the myriad of proteins typically present in a biological sample such as blood or tissue. In this review, we provide an overview of key statistical and computational issues relevant to bottom-up shotgun global proteomic analysis, with an emphasis on methods that can be applied to improve the dependability of biological inferences drawn from large proteomic datasets. Focusing on a start-tofinish approach, we address the following topics: 1) lowlevel data processing steps, such as formation of a data matrix, filtering, and baseline subtraction to minimize noise, 2) mid-level processing steps, such as data normalization, alignment in time, peak detection, peak quantification, peak matching, and error models, to facilitate profile comparisons; and, 3) high-level processing steps such as sample classification and biomarker discovery, and related topics such as significance testing, multiple testing, and choice of feature space. We report on approaches that have recently been developed for these steps, discussing their merits and limitations, and propose areas deserving of further research. Molecular & Cellular Proteomics 4:419 -434, 2005.With the sequencing of the human genome largely complete and publicly available, emphasis in molecular biology is shifting away from DNA sequencing and related problems toward a systematic evaluation of how the myriad of encoded gene products operate together to mediate the biological mechanisms that sustain life, and how these processes become perturbed in response to disease. Comprehensive systems-wide biological studies have been greatly facilitated by the advent of large-scale genomic, proteomic, and informatic technologies, such as DNA microarrays, ultra-sensitive highthroughput protein MS, and robust statistical and machinelearning methods developed for very large datasets. Evaluation, interpretation, and integration of data produced by these respective platforms represent major ongoing challenges and areas of active research.The field of expression proteomics seeks to answer the following questions: 1) which proteins and variant isoforms are expressed during the lifecycle of an organism; 2) which post-translational modifications occur in each of these proteins; 3) how do these patterns differ in different cell types and tissues and under different developmental, physiological, and disease conditions; and 4) how can biologists make use of this information to better understand the molecular basis for fundamental biological processes as well as for monitoring the course of disease so as to improve clinical diagnosis and treatment (1-3). These questions are made all the more difficult by the complexity of most biological systems, which increases...

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Screening of Biomarkers in Rat Urine Using LC/Electrospray Ionization-MS and Two-Way Data Analysis

Cited by 161 publications

References 24 publications

Urinary Metabolic Fingerprinting for α-naphthylisothiocyanate-induced Intrahepatic Cholestasis in Rats Using Fourier Transform-ion Cyclotron Resonance Mass Spectrometry

Urinary Metabolic Fingerprinting for α-naphthylisothiocyanate-induced Intrahepatic Cholestasis in Rats Using Fourier Transform-ion Cyclotron Resonance Mass Spectrometry

Analyzing LC/MS Metabolic Profiling Data in the Context of Existing Metabolic Networks

Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry

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