Targeted Metabolomics for Biomarker Discovery

Griffiths, William J.; Koal, Therese; Wang, Yuqin; Kohl, Matthias; Enot, David; Deigner, Hans‐Peter

doi:10.1002/anie.200905579

Cited by 338 publications

(262 citation statements)

References 178 publications

Supporting

Mentioning

250

Contrasting

Unclassified

Order By: Relevance

“…be measured both in the plasma and urine 33 . We analysed the liver gene expression data obtained from NASH (severe stage of NAFLD) patients (Supplementary Fig.…”

Section: Article Nature Communications | Doi: 101038/ncomms4083mentioning

confidence: 99%

Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease

et al. 2014

View full text Add to dashboard Cite

Several liver disorders result from perturbations in the metabolism of hepatocytes, and their underlying mechanisms can be outlined through the use of genome-scale metabolic models (GEMs). Here we reconstruct a consensus GEM for hepatocytes, which we call iHepatocytes2322, that extends previous models by including an extensive description of lipid metabolism. We build iHepatocytes2322 using Human Metabolic Reaction 2.0 database and proteomics data in Human Protein Atlas, which experimentally validates the incorporated reactions. The reconstruction process enables improved annotation of the proteomics data using the network centric view of iHepatocytes2322. We then use iHepatocytes2322 to analyse transcriptomics data obtained from patients with non-alcoholic fatty liver disease. We show that blood concentrations of chondroitin and heparan sulphates are suitable for diagnosing non-alcoholic steatohepatitis and for the staging of non-alcoholic fatty liver disease. Furthermore, we observe serine deficiency in patients with NASH and identify PSPH, SHMT1 and BCAT1 as potential therapeutic targets for the treatment of non-alcoholic steatohepatitis.

show abstract

“…be measured both in the plasma and urine 33 . We analysed the liver gene expression data obtained from NASH (severe stage of NAFLD) patients (Supplementary Fig.…”

Section: Article Nature Communications | Doi: 101038/ncomms4083mentioning

confidence: 99%

Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Metabolomics uses analytical chemical techniques such as proton nuclear magnetic resonance spectroscopy ( 1 H NMR) or mass spectrometry to obtain information about the identity and abundance of metabolites in a particular sample at much higher resolution (e.g., about the individual triglycerides) than would be possible using classical clinical chemistry measures (such as enzymatically determined total triglyceride level). Metabolomics analyses can be classified into targeted and untargeted (global) approaches (Griffiths et al, 2010). The targeted approaches are used to obtain good quantification of known metabolites, whereas the global approaches are used to obtain a broad overview of all metabolites (both known and unknown) that are present in a particular sample.…”

mentioning

confidence: 99%

Familial Resemblance for Serum Metabolite Concentrations

et al. 2013

View full text Add to dashboard Cite

Metabolomics is the comprehensive study of metabolites, which are the substrates, intermediate, and end products of cellular metabolism. The heritability of the concentrations of circulating metabolites bears relevance for evaluating their suitability as biomarkers for disease. We report aspects of familial resemblance for the concentrations in human serum of more than 100 metabolites, measured using a targeted metabolomics platform. Age-and sex-corrected monozygotic twin correlations, midparent-offspring regression coefficients, and spouse correlations in subjects from two independent cohorts (Netherlands Twin Register and Leiden Longevity Study) were estimated for each metabolite. In the Netherlands Twin Register subjects, who were largely fasting, we found significant monozygotic twin correlations for 121 out of 123 metabolites. Heritability was confirmed by midparent-offspring regression. For most detected metabolites, the correlations between spouses were considerably lower than those between twins, indicating a contribution of genetic effects to familial resemblance. Remarkably high heritability was observed for free carnitine (monozygotic twin correlation 0.66), for the amino acids serine (monozygotic twin correlation 0.77) and threonine (monozygotic twin correlation 0.64), and for phosphatidylcholine acyl-alkyl C40:3 (monozygotic twin correlation 0.77). For octenoylcarnitine, a consistent point estimate of approximately 0.50 was found for the spouse correlations in the two cohorts as well as for the monozygotic twin correlation, suggesting that familiality for this metabolite is explained by shared environment. We conclude that for the majority of metabolites targeted by the used metabolomics platform, the familial resemblance of serum concentrations is largely genetic. Our results contribute to the knowledge of the heritability of fasting serum metabolite concentrations, which is relevant for biomarker research.

show abstract

“…In addition, when this technique is applied into imaging MS (IMS) metabolomics, the primary challenge is strengthened by IMS inherent low sensitivity [37,38]. In comparison, targeted LC-MS strategy not only can assure high-sensitivity and high-quality MS/MS data which provide high confidence for data interpretation [39][40][41], but also make raw data processing easier and more accurate than the former. Of course, the realization of replacing untargeted metabolome analysis with targeted LC-MS metabolomics requires continuous and extensive development of methodology and metabolite data library.…”

Section: Future Perspectivesmentioning

confidence: 99%

Untargeted LC–MS Data Preprocessing in Metabolomics

Tian

Shui

2017

J. Anal. Test.

View full text Add to dashboard Cite

Liquid chromatography-mass spectrometry (LC-MS) has enabled the detection of thousands of metabolite features from a single biological sample that produces large and complex datasets. One of the key issues in LC-MS-based metabolomics is comprehensive and accurate analysis of enormous amount of data. Many free data preprocessing tools, such as XCMS, MZmine, MAVEN, and MetaboAnalyst, as well as commercial software, have been developed to facilitate data processing. However, researchers are challenged by the inevitable and unconquerable yields of numerous false-positive peaks, and human errors while manually removing such false peaks. Even with continuous improvements of data processing tools, there can still be many mistakes generated during data preprocessing. In addition, many data preprocessing software exist, and every tool has its own advantages and disadvantages. Thereby, a researcher needs to judge what kind of software or tools to choose that most suit their vendor proprietary formats and goal of downstream analysis. Here, we provided a brief introduction of the general steps of raw MS data processing, and properties of automated data processing tools. Then, characteristics of mainly free data preprocessing software were summarized for researchers' consideration in conducting metabolomics study.

show abstract

Targeted Metabolomics for Biomarker Discovery

Cited by 338 publications

References 178 publications

Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease

Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease

Familial Resemblance for Serum Metabolite Concentrations

Untargeted LC–MS Data Preprocessing in Metabolomics

Contact Info

Product

Resources

About