Minimum reporting standards for plant biology context information in metabolomic studies

Plant metabolomics has matured over the past 8 years. Plant biologists routinely use comprehensive analyses of plant metabolites to discover new responses to genetic or environmental perturbation, or to validate initial hypotheses on the function and in vivo action of gene products. The wealth of scientific findings has increasingly provoked interest to share and review raw or processed data from plant metabolomics reports. We here suggest a minimum of parameters to be reported in order to define details of experimental study designs in plant metabolomics studies.     

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。     

A direct cell quenching method for cell-culture based metabolomics

A crucial step in metabolomic analysis of cellular extracts is the cell quenching process. The conventional method first uses trypsin to detach cells from their growth surface. This inevitably changes the profile of cellular metabolites since the detachment of cells from the extracellular matrix alters their physiology. This conventional method also includes time consuming wash/centrifuge steps after trypsinization, but prior to quenching cell activity. During this time, a considerable portion of intracellular metabolites are lost, rendering the conventional method less than ideal for application to metabolomics. We report here a novel sample preparation method for metabolomics applications using adherent mammalian cells, which eliminates the time consumption and physiological stress of the trypsinization and wash/centrifuge steps. This new method was evaluated in the study of metabolic changes caused by 17α-ethynylestradiol (EE2) in estrogen receptor (ER)-positive MCF-7 and ER-negative MDA-MB-231 human breast cancer cell lines using NMR spectroscopy. The results demonstrated that our direct cell quenching method is rapid, effective, and exhibits greater metabolite retention, providing an increase of approximately a factor of 50 compared to the conventional method.     

代谢物组学及其在微生物研究中的应用

代谢物组学(metabolomics)是继基因组学(genomics)、蛋白质组学(proteomics)后发展起来的一门新学科。对代谢物组学的含义,研究方法及流程,特别是其在微生物中的应用进行了介绍,包括使用代谢物组学中的NMR技术研究微生物在降解环境污染物中的作用;使用代谢物组学技术研究微生物代谢通量,从而在分析代谢通量的基础上通过代谢工程改变代谢通量,提高目的产物的得率;确定所获得基因库中沉默基因的功能;运用代谢物组学分析方法阐明生物体系对于环境变化的响应,从而协助我们确定最佳的取样时间及最佳分析组织,设计实验。随后简要对代谢物组学发展动态进行了展望。     

Extracellular metabolomics: a metabolic footprinting approach to assess fiber degradation in complex media

This work reports the implementation and optimization of a method for high-throughput analysis of metabolites produced by the breakdown of natural polysaccharides by microorganisms. Our simple protocol enables simultaneous separation and quantification of more than 40 different sugars and sugar derivatives, in addition to several organic acids in complex media, using 50-mul samples and a standard gas chromatography-mass spectrometry platform that was fully optimized for this purpose. As an implementation proof-of-concept, we assayed extracellular metabolite levels of three bacterial strains cultivated on complex medium rich in polysaccharides and under identical growth conditions. We demonstrate that the metabolic footprinting profile data distinguish among sample types such as typical metabolomics data. Moreover, we demonstrate that the differential metabolite-level data provide insight on specific fibrolytic activity of the different microbial strains and lay the groundwork for integrated proteome-metabolome studies of fiber-degrading microorganisms.     

Proposed minimum reporting standards for chemical analysis

There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at or . Further, community input related to this document can also be provided via this electronic forum. The contents of this paper do not necessarily reflect any position of the Government or the opinion of the Food and Drug Administration Sponsor: Metabolomics Society http://www.metabolomicssociety.org/ Reference: http://msi-workgroups.sourceforge.net/bio-metadata/reporting/pbc/ http://msi-workgroups.sourceforge.net/chemical-analysis/ Version: Revision: 5.1 Date: 09 January, 2007     

Metabolomic study reveals a selective accumulation of l-arginine in the d-ornithine treated tobacco cell suspension culture

The non-protein amino acid ornithine (Orn) plays essential roles in regulation of the urea cycle and polyamines biosynthesis in tobacco. Herein, we show that d-enantiomer of Orn, can actively participate in metabolites production in tobacco cells, functioning a positive role in plant cells metabolism, as opposed to the common l-enantiomer. Using a comprehensive amino acids and amines profiling method by liquid chromatography–electrospray ionization triple quadruple mass spectrometry (LC–ESI-QqQ-MS) in combination with chiral LC–ESI-MS, it was shown that d-Orn has a potential advantage in promoting selective and large accumulation of l-arginine (l-Arg) in tobacco cells. Exogenous d-Orn resulted in a selective up-regulation of l-Arg by 80-fold, while l-Orn slightly increased the levels of all amino acids. Changes of all the urea cycle related intermediates, e.g. citrulline, Arg and Orn were also shown to be critical following change of Orn's stereochemistry. GC/MS profiling of the metabolites revealed that high nicotine production was the dominant change driven by l-Orn treatments. From these observations, d-Orn was shown to be a selective regulator of l-Arg biosynthesis and the urea cycle. We propose that d-Orn has a potential function in the tobacco cells which through some previously unidentified mechanism result in l-Arg accumulation.     

Application of Two‐Dimensional NMR Spectroscopy to Metabotyping Laboratory Escherichia coli Strains

NMR Spectroscopy has been established as a major tool for identification and quantification of metabolites in a living system. Since the metabolomics era began, one‐dimensional NMR spectroscopy has been intensively employed due to its simplicity and quickness. However, it has suffered from an inevitable overlap of signals, thus leading to inaccuracy in identification and quantification of metabolites. Two‐dimensional (2D) NMR has emerged as a viable alternative because it can offer higher accuracy in a reasonable amount of time. We employed ¹H,¹³C‐HSQC to profile metabolites of six different laboratory E. coli strains. We identified 18 metabolites and observed clustering of six strains according to their metabolites. We compared the metabolites among the strains, and found that a) the strains specialized for protein production were segregated; b) XL1‐Blue separated itself from others by accumulating amino acids such as alanine, aspartate, glutamate, methionine, proline, and lysine; c) the strains specialized for cloning purpose were spread out from one another; and d) the strains originating from B strain were characterized by succinate accumulation. This work shows that 2D‐NMR can be applied to identify a strain from metabolite analysis, offering a possible alternative to genetic analysis to identify E. coli strains.     

Metabolomics of forage plants: a review

Background

Forage plant breeding is under increasing pressure to deliver new cultivars with improved yield, quality and persistence to the pastoral industry. New innovations in DNA sequencing technologies mean that quantitative trait loci analysis and marker-assisted selection approaches are becoming faster and cheaper, and are increasingly used in the breeding process with the aim to speed it up and improve its precision. High-throughput phenotyping is currently a major bottle neck and emerging technologies such as metabolomics are being developed to bridge the gap between genotype and phenotype; metabolomics studies on forages are reviewed in this article.

Scope

Major challenges for pasture production arise from the reduced availability of resources, mainly water, nitrogen and phosphorus, and metabolomics studies on metabolic responses to these abiotic stresses in Lolium perenne and Lotus species will be discussed here. Many forage plants can be associated with symbiotic microorganisms such as legumes with nitrogen fixing rhizobia, grasses and legumes with phosphorus-solubilizing arbuscular mycorrhizal fungi, and cool temperate grasses with fungal anti-herbivorous alkaloid-producing Neotyphodium endophytes and metabolomics studies have shown that these associations can significantly affect the metabolic composition of forage plants. The combination of genetics and metabolomics, also known as genetical metabolomics can be a powerful tool to identify genetic regions related to specific metabolites or metabolic profiles, but this approach has not been widely adopted for forages yet, and we argue here that more studies are needed to improve our chances of success in forage breeding.

Conclusions

Metabolomics combined with other ‘-omics’ technologies and genome sequencing can be invaluable tools for large-scale geno- and phenotyping of breeding populations, although the implementation of these approaches in forage breeding programmes still lags behind. The majority of studies using metabolomics approaches have been performed with model species or cereals and findings from these studies are not easily translated to forage species. To be most effective these approaches should be accompanied by whole-plant physiology and proof of concept (modelling) studies. Wider considerations of possible consequences of novel traits on the fitness of new cultivars and symbiotic associations need also to be taken into account.