Signal Intensities Derived from Different NMR Probes and Parameters Contribute to Variations in Quantification of Metabolites

We discovered that serious issues could arise that may complicate interpretation of metabolomic data when identical samples are analyzed at more than one NMR facility, or using slightly different NMR parameters on the same instrument. This is important because cross-center validation metabolomics studies are essential for the reliable application of metabolomics to clinical biomarker discovery. To test the reproducibility of quantified metabolite data at multiple sites, technical replicates of urine samples were assayed by 1D-¹H-NMR at the University of Alberta and the University of Michigan. Urine samples were obtained from healthy controls under a standard operating procedure for collection and processing. Subsequent analysis using standard statistical techniques revealed that quantitative data across sites can be achieved, but also that previously unrecognized NMR parameter differences can dramatically and widely perturb results. We present here a confirmed validation of NMR analysis at two sites, and report the range and magnitude that common NMR parameters involved in solvent suppression can have on quantitated metabolomics data. Specifically, saturation power levels greatly influenced peak height intensities in a frequency-dependent manner for a number of metabolites, which markedly impacted the quantification of metabolites. We also investigated other NMR parameters to determine their effects on further quantitative accuracy and precision. Collectively, these findings highlight the importance of and need for consistent use of NMR parameter settings within and across centers in order to generate reliable, reproducible quantified NMR metabolomics data.     

Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information

Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these “unknown metabolites” is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype–metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.     

植物应答非生物胁迫的代谢组学研究进展

代谢组学技术是研究植物代谢的理想平台, 通过现代检测分析技术对胁迫环境下植物中代谢产物进行定性和定量分析, 可以监测其随时间变化的规律。而各种组学平台包括基因组学、转录组学及代谢组学的整合, 更是一个强有力的工具箱, 将所获得的不同组学的信息联系起来, 有利于从整体研究生物系统对基因或环境变化的响应, 如可判断代谢物的变化是从哪一个层面开始发生的, 帮助人们揭开复杂的植物胁迫应答机制。该文对近期代谢组学技术及其与蛋白质组学、基因组学技术相结合探索植物应答非生物胁迫的研究进行了综述。代谢组学的应用, 拓展了对植物耐受非生物胁迫分子机制的认识, 开展更多这方面的研究, 再通过植物代谢组学、转录组学、蛋白质组学和基因组学整合, 有助于从整体水平上把握植物胁迫应答机制。     

The potential of metabolomics tools in bioremediation studies

As a post-genomics tool, metabolomics is a young and vibrant field of science in its exponential growth phase. Metabolome analysis has become very popular recently, and novel techniques for acquiring and analyzing metabolomics data continue to emerge that are useful for a variety of biological studies. The bioremediation field has a lot to gain from the advances in this emerging area. Thus, this review article focuses on the potential of various experimental and conceptual approaches developed for metabolomics to be applied in bioremediation research, such as strategies for elucidation of biodegradation pathways using isotope distribution analysis and molecular connectivity analysis, the assessment of mineralization process using metabolic footprinting analysis, and the improvement of the biodegradation process via metabolic engineering. We demonstrate how the use of metabolomics tools can significantly extend and enhance the power of existing bioremediation approaches by providing a better overview of the biodegradation process.     

Normalization techniques for PARAFAC modeling of urine metabolomic data

Introduction

One of the body fluids often used in metabolomics studies is urine. The concentrations of metabolites in urine are affected by hydration status of an individual, resulting in dilution differences. This requires therefore normalization of the data to correct for such differences. Two normalization techniques are commonly applied to urine samples prior to their further statistical analysis. First, AUC normalization aims to normalize a group of signals with peaks by standardizing the area under the curve (AUC) within a sample to the median, mean or any other proper representation of the amount of dilution. The second approach uses specific end-product metabolites such as creatinine and all intensities within a sample are expressed relative to the creatinine intensity.

Objectives

Another way of looking at urine metabolomics data is by realizing that the ratios between peak intensities are the information-carrying features. This opens up possibilities to use another class of data analysis techniques designed to deal with such ratios: compositional data analysis. The aim of this paper is to develop PARAFAC modeling of three-way urine metabolomics data in the context of compositional data analysis and compare this with standard normalization techniques.

Methods

In the compositional data analysis approach, special coordinate systems are defined to deal with the ratio problem. In essence, it comes down to using other distance measures than the Euclidian Distance that is used in the conventional analysis of metabolomic data.

Results

We illustrate using this type of approach in combination with three-way methods (i.e. PARAFAC) of a longitudinal urine metabolomics study and two simulations. In both cases, the advantage of the compositional approach is established in terms of improved interpretability of the scores and loadings of the PARAFAC model.

Conclusion

For urine metabolomics studies, we advocate the use of compositional data analysis approaches. They are easy to use, well established and proof to give reliable results.

     

生态代谢组学研究进展

代谢组学指某一生物系统中产生的或已存在的代谢物组的研究,以质谱和核磁共振技术为分析平台,以信息建模与系统整合为目标。随着代谢组学中的研究方法与技术成为生态学研究的有力工具,生态代谢组学概念应运而生,即研究某一个生物体对环境变化的代谢物组水平的响应。理清代谢组学与生态代谢组学学科发展的脉络,综述代谢组学研究中的常用技术及其优势与局限性,论述代谢组学技术在生态学研究中的应用现状,展望代谢组学技术与其他系统生物学组学技术的结合在生态学中的应用前景,提出生态代谢组学研究者未来要完成的任务和面对的挑战。     

微生物代谢组学及其在工业中的应用

微生物代谢组学是系统生物学的重要组成部分,其与基因组学、转录组学和蛋白质组学相互补充,近年来受到越来越多人的重视。其主要对细胞生长或生长周期某一时刻细胞内外所有低分子量代谢物进行定性和定量分析,直接反映了细胞的生理状态,对理解细胞功能十分重要。由于代谢物的复杂性,研究者需根据不同的目的及对象选择合适的分析方法。对微生物代谢组学近年来的研究方法进行综述,包括样品处理、分析手段、数据分析,并讨论了微生物代谢组学在工业中的应用及所面临的挑战。     

乳酸菌代谢组学研究进展

代谢组学作为系统生物学的重要分支,近年来在微生物研究领域受到广泛关注,并取得了重要进展。目前乳酸菌代谢组学正日益成为研究的热点,就乳酸菌代谢组学研究中有关样品的制备、分析鉴定和数据分析等涉及的主要方法进行概述,并介绍一些乳酸菌代谢组学应用的典型实例,对乳酸菌代谢组学研究中潜在的问题和未来发展趋势进行讨论。     

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided.     

Metabolomics and its role in understanding cellular responses in plants

A natural shift is taking place in the approaches being adopted by plant scientists in response to the accessibility of systems-based technology platforms. Metabolomics is one such field, which involves a comprehensive non-biased analysis of metabolites in a given cell at a specific time. This review briefly introduces the emerging field and a range of analytical techniques that are most useful in metabolomics when combined with computational approaches in data analyses. Using cases from Arabidopsis and other selected plant systems, this review highlights how information can be integrated from metabolomics and other functional genomics platforms to obtain a global picture of plant cellular responses. We discuss how metabolomics is enabling large-scale and parallel interrogation of cell states under different stages of development and defined environmental conditions to uncover novel interactions among various pathways. Finally, we discuss selected applications of metabolomics. This special review article is dedicated to the commemoration of the retirement of Dr. Oluf L. Gamborg after 25 years of service as Founding Managing Editor of Plant Cell Reports. RB and KN have contributed equally to this review.     

Metabolomics assisted metabolic network modeling and network wide analysis of metabolites in microbiology

Metabolomics is the science of qualitatively and quantitatively analyzing low molecular weight metabolites occur in a given biological system. It provides valuable information to elucidate the functional roles and relations of different metabolites in a metabolic pathway. In recent years, a large amount of research on microbial metabolomics has been conducted. It has become a useful tool for achieving highly efficient synthesis of target metabolites. At the same time, many studies have been conducted over the years in order to integrate metabolomics data into metabolic network modeling, which has yielded many exciting results. Additionally, metabolomics also shows great advantages in analyzing the relationship of metabolites network wide. Integrating metabolomics data into metabolic network construction and applying it in network wide analysis of cell metabolism would further improve our ability to control cellular metabolism and optimize the design of cell factories for the overproduction of valuable biochemicals. This review will examine recent progress in the application of metabolomics approaches in metabolic network modeling and network wide analysis of microbial cell metabolism.     

代谢组学及其应用

对代谢组学的概念、特性、发展历史做了简要介绍,综述了当前代谢组学研究中的数据采集、数据分析中采用的技术,及代谢组学在疾病诊断、药物毒性研究、植物和微生物等邻域的应用,并对代谢组学的发展作了展望。     

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis.?Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.     

Multivariate classification analysis of metabolomic data for candidate biomarker discovery in type 2 diabetes mellitus

Recent advances in genomics, metabolomics and proteomics have made it possible to interrogate disease pathophysiology and drug response on a systems level. The analysis and interpretation of the complex data obtained using these techniques is potentially fertile but equally challenging. We conducted a small clinical trial to explore the application of metabolomics data in candidate biomarker discovery. Specifically, serum and urine samples from patients with type 2 diabetes mellitus (T2DM) were profiled on metabolomics platforms before and after 8 weeks of treatment with one of three commonly used oral antidiabetic agents, the sulfonyurea glyburide, the biguanide metformin, or the thiazolidinedione rosiglitazone. Multivariate classification techniques were used to detect serum or urine analytes, obtained at baseline (pre-treatment) that could predict a significant treatment response after 8 weeks. Using this approach, we identified three analytes, measured at baseline, that were associated with response to a thiazolidinedione after 8 weeks of treatment. Although larger and longer-term studies are required to validate any of the candidate biomarkers, pharmacometabolomic profiling, in combination with multivariate classification, is worthy of further exploration as an adjunct to clinical decision making regarding treatment selection and for patient stratification within clinical trials. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

代谢组学及其在微藻研究中的应用

代谢组学以完整的生物体为研究对象,运用合适的分析测试手段检测靶向或非靶向代谢物,结合统计模型进行分析解释。随着微藻研究的深入,微藻与代谢组学结合探究分子作用机理的研究日益增多。本文总结代谢组学的发展概况、研究流程及常用分析技术特点和代谢组学在微藻领域的研究进展,展望代谢组学在微藻研究的应用前景与发展趋势,并提出实际应用中所面临的困难与挑战。     

An introduction to metabolomics and its potential application in veterinary science

Metabolomics has been found to be applicable to a wide range of fields, including the study of gene function, toxicology, plant sciences, environmental analysis, clinical diagnostics, nutrition, and the discrimination of organism genotypes. This approach combines high-throughput sample analysis with computer-assisted multivariate pattern-recognition techniques. It is increasingly being deployed in toxico- and pharmacokinetic studies in the pharmaceutical industry, especially during the safety assessment of candidate drugs in human medicine. However, despite the potential of this technique to reduce both costs and the numbers of animals used for research, examples of the application of metabolomics in veterinary research are, thus far, rare. Here we give an introduction to metabolomics and discuss its potential in the field of veterinary science.