New insights into cardiovascular and lipid metabolomics

Metabolomics is the study of metabolite profiles in biological samples, particularly urine, saliva, blood plasma and fat biopsies. The metabolome is now considered by some to be the most predictive phenotype: consequently, the comprehensive and quantitative study of metabolites is a desirable tool for diagnosing disease, identifying new therapeutic targets and enabling appropriate treatments. A wealth of information about metabolites has been accumulated with global profiling tools and several candidate technologies for metabolomic studies are now available. Many high-throughput metabolomics methodologies are currently under development and have yet to be applied in clinical practice on a routine basis. In the cardiovascular field, few recent metabolomic studies have been reported so far. This minireview provides an updated overview of alternative technical approaches for metabolomics studies and reviews initial applications of metabolomics that relate to both cardiovascular disease and lipid metabolism research.     

Small Molecule Metabolite Extraction Strategy for Improving LC/MS Detection of Cancer Cell Metabolome

Metabolomics is the comprehensive assessment of endogenous metabolites of a biological system. “Oncometabolomics” is a rapidly emerging field with potential for developing specific biomarkers for early detection, diagnosis, and disease prognosis. Given the power of this technology, the availability of standardized sample preparation methods for immortalized human cancer cell lines is critical toward augmenting research in this direction. Using MCF-7 cells as a model system, we describe an approach for intracellular metabolite extraction from cell cultures for reproducible and comprehensive metabolite extraction. The samples, when injected onto a reverse-phase 50 × 2.1 mm Acquity 1.7-μm C18 column, using an ultra performance liquid chromatography system (UPLC) coupled with electrospray ionization-quadrupole-time-of-flight-mass spectrometry (ESI-Q-TOF-MS) in positive and negative modes, yielded a data matrix with a total of 2600 features. This method, when compared with a water-extraction procedure described earlier, was found to yield significantly higher coverage and detection of molecular features. Finally, we successfully tested the performance of this method for an array of human cancer cell lines used widely in the cancer research field.     

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。     

Functional metabolomics: from biomarker discovery to metabolome reprogramming

Metabolomics is emerging as a powerful tool for studying metabolic processes, identifying crucial biomarkers responsible for metabolic characteristics and revealing metabolic mechanisms, which construct the content of discovery metabolomics. The crucial biomarkers can be used to reprogram a metabolome, leading to an aimed metabolic strategy to cope with alteration of internal and external environments, naming reprogramming metabolomics here. The striking feature on the similarity of the basic metabolic pathways and components among vastly differentspeciesmakesthe reprogrammingmetabolomics possible when the engineered metabolites play biological roles in cellular activity as a substrate of enzymes and a regulator to other molecules including proteins. The reprogramming metabolomics approach can be used to clarify metabolic mechanisms of responding to changed internal and external environmental factors and to establish a framework to develop targeted tools for dealing with the changes such as controlling and/or preventing infection with pathogens and enhancing host immunity against pathogens. This review introduces the current state and trends of discovery metabolomics and reprogramming metabolomics and highlights the importance of reprogramming metabolomics.     

植物代谢组学中几种重要的次生代谢物液质分析技术研究进展

代谢组学（metabolomics）主要是研究生物体、组织、细胞的代谢物组分及检测其动态变化过程,是继基因组和蛋白组学后新兴的一门组学技术。代谢物是细胞调节过程中的最终产物,其水平被视为生物系统对遗传或环境变化的最终反映。通过合适的分析平台,准确定性、定量在复杂的生物中具有化学多样性的次生代谢物是代谢组学的一项重要工作。液相色谱-串联质谱技术（liquid chromatography-tandem mass spectrometry,LC-MS/MS）是代谢物质检测平台最常用的方法,也为植物次生代谢物的广泛应用研究提供了基础。本文主要从植物激素类、叶酸类、黄酮类等次生代谢物方面进行阐述,结合液质联用技术,简要论述不同次生代谢物检测技术的研究进展。     

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。     

Metabolic signature associated with parameters of the complete blood count in apparently healthy individuals

Metabolomics studies now approach large sample sizes and the health characterization of the study population often include complete blood count (CBC) results. Upon careful interpretation the CBC aids diagnosis and provides insight into the health status of the patient within a clinical setting. Uncovering metabolic signatures associated with parameters of the CBC in apparently healthy individuals may facilitate interpretation of metabolomics studies in general and related to diseases. For this purpose 879 subjects from the population‐based Study of Health in Pomerania (SHIP)‐TREND were included. Using metabolomics data resulting from mass‐spectrometry based measurements in plasma samples associations of specific CBC parameters with metabolites were determined by linear regression models. In total, 118 metabolites significantly associated with at least one of the CBC parameters. Strongest associations were observed with metabolites of heme degradation and energy production/consumption. Inverse association seen with mean corpuscular volume and mean corpuscular haemoglobin comprised metabolites potentially related to kidney function. The presently identified metabolic signatures are likely derived from the general function and formation/elimination of blood cells. The wealth of associated metabolites strongly argues to consider CBC in the interpretation of metabolomics studies, in particular if mutual effects on those parameters by the disease of interest are known.     

Mass spectrometry methods in metabolomics

The review deals with metabolomics, a new and rapidly growing area directed to the comprehensive analysis of metabolites of biological objects. Metabolites are characterized by various physical and chemical properties, traditionally studied by methods of analytical chemistry focused on certain groups of chemical substances. However, current progress in mass spectrometry has led to formation of rather unified methods, such as metabolic fingerprinting and metabolomic profiling, which allow defining thousands of metabolites in one biological sample and therefore draw “a modern portrait of metabolomics.” This review describes basic characteristics of these methods, ways of metabolite separation, and analysis of metabolites by mass spectrometry. The examples shown in this review, allow to estimate these methods and to compare their advantages and disadvantages. Besides that, we consider the methods, which are of the most frequent use in metabolomics; these include the methods for data processing and the required resources, such as software for mass spectra processing and metabolite search database. In the conclusion, general suggestions for successful metabolomic experiments are given.     

Metabolites in Blood for Prediction of Bacteremic Sepsis in the Emergency Room

A metabolomics approach for prediction of bacteremic sepsis in patients in the emergency room (ER) was investigated. In a prospective study, whole blood samples from 65 patients with bacteremic sepsis and 49 ER controls were compared. The blood samples were analyzed using gas chromatography coupled to time-of-flight mass spectrometry. Multivariate and logistic regression modeling using metabolites identified by chromatography or using conventional laboratory parameters and clinical scores of infection were employed. A predictive model of bacteremic sepsis with 107 metabolites was developed and validated. The number of metabolites was reduced stepwise until identifying a set of 6 predictive metabolites. A 6-metabolite predictive logistic regression model showed a sensitivity of 0.91(95% CI 0.69–0.99) and a specificity 0.84 (95% CI 0.58–0.94) with an AUC of 0.93 (95% CI 0.89–1.01). Myristic acid was the single most predictive metabolite, with a sensitivity of 1.00 (95% CI 0.85–1.00) and specificity of 0.95 (95% CI 0.74–0.99), and performed better than various combinations of conventional laboratory and clinical parameters. We found that a metabolomics approach for analysis of acute blood samples was useful for identification of patients with bacteremic sepsis. Metabolomics should be further evaluated as a new tool for infection diagnostics.     

Widely Targeted Metabolomics Based on Large-Scale MS/MS Data for Elucidating Metabolite Accumulation Patterns in Plants

Metabolomics is an ‘omics’ approach that aims toanalyze all metabolites in a biological sample comprehensively.The detailed metabolite profiling of thousands of plant sampleshas great potential for directly elucidating plant metabolicprocesses. However, both a comprehensive analysis and a highthroughput are difficult to achieve at the same time due tothe wide diversity of metabolites in plants. Here, we have establisheda novel and practical metabolomics methodology for quantifyinghundreds of targeted metabolites in a high-throughput manner.Multiple reaction monitoring (MRM) using tandem quadrupole massspectrometry (TQMS), which monitors both the specific precursorions and product ions of each metabolite, is a standard techniquein targeted metabolomics, as it enables high sensitivity, reproducibilityand a broad dynamic range. In this study, we optimized the MRMconditions for specific compounds by performing automated flowinjection analyses with TQMS. Based on a total of 61,920 spectrafor 860 authentic compounds, the MRM conditions of 497 compoundswere successfully optimized. These were applied to high-throughputautomated analysis of biological samples using TQMS coupledwith ultra performance liquid chromatography (UPLC). By thisanalysis, approximately 100 metabolites were quantified in eachof 14 plant accessions from Brassicaceae, Gramineae and Fabaceae.A hierarchical cluster analysis based on the metabolite accumulationpatterns clearly showed differences among the plant families,and family-specific metabolites could be predicted using a batch-learningself-organizing map analysis. Thus, the automated widely targetedmetabolomics approach established here should pave the way forlarge-scale metabolite profiling and comparative metabolomics.     

Recent and potential developments of biofluid analyses in metabolomics

Metabolomics, one of the ‘omic’ sciences in systems biology, is the global assessment and validation of endogenous small-molecule metabolites within a biologic system. Analysis of these key metabolites in body fluids has become an important role to monitor the state of biological organisms and is a widely used diagnostic tool for disease. A majority of these metabolites are being applied to metabolic profiling of the biological samples, for example, plasma and whole blood, serum, urine, saliva, cerebrospinal fluid, synovial fluid, semen, and tissue homogenates. However, the recognition of the need for a holistic approach to metabolism led to the application of metabolomics to biological fluids for disease diagnostics. A recent surge in metabolomic applications which are probably more accurate than routine clinical practice, dedicated to characterizing the biological fluids. While developments in the analysis of biofluid samples encompassing an important impediment, it must be emphasized that these biofluids are complementary. Metabolomics provides potential advantages that classical diagnostic approaches do not, based on following discovery of a suite of clinically relevant biomarkers that are simultaneously affected by the disease. Emerging as a promising biofocus, metabolomics will drive biofluid analyses and offer great benefits for public health in the long-term.     

电热蒸发-催化热解-原子吸收法直接测定茶叶中镉

重金属镉（Cd）一直是茶叶产品质量安全关注的重点。本研究基于电热蒸发-催化热解-原子吸收光谱仪（SS-ETV-AAS）,使用镍材质样品舟,在300 mL/min空气条件下,350 ℃干燥20 s,350~725 ℃灰化55 s;引入300 mL/min氢气与空气反应形成氮氢混合气氛,在725~800 ℃（50 s）下完成Cd的蒸发;之后,在高岭土填料催化热解炉800 ℃和准直管700 ℃条件下,氮氢火焰原子吸收测定镉的含量。方法检出限（LOD）为0.3 ng/g、定量限（LOQ）为1.0 ng/g,R2>0.998,多次测定的相对标准偏差（RSD）为1.8%~8.6%,多种茶叶样品中Cd的测定值与微波消解石墨炉原子吸收光谱法（GFAAS）无显著性差异（P>0.05）,Cd的回收率在92%~107%之间。试验结果表明,该方法灵敏度高、稳定性好、简单高效,且无需消解处理,样品分析时间仅为3min,适用于茶叶中Cd的快速检测。     

Metabolomics in the fight against malaria

Metabolomics uses high-resolution mass spectrometry to provide a chemical fingerprint of thousands of metabolites present in cells, tissues or body fluids. Such metabolic phenotyping has been successfully used to study various biologic processes and disease states. High-resolution metabolomics can shed new light on the intricacies of host-parasite interactions in each stage of the Plasmodium life cycle and the downstream ramifications on the host’s metabolism, pathogenesis and disease. Such data can become integrated with other large datasets generated using top-down systems biology approaches and be utilised by computational biologists to develop and enhance models of malaria pathogenesis relevant for identifying new drug targets or intervention strategies. Here, we focus on the promise of metabolomics to complement systems biology approaches in the quest for novel interventions in the fight against malaria. We introduce the Malaria Host-Pathogen Interaction Center (MaHPIC), a new systems biology research coalition. A primary goal of the MaHPIC is to generate systems biology datasets relating to human and non-human primate (NHP) malaria parasites and their hosts making these openly available from an online relational database. Metabolomic data from NHP infections and clinical malaria infections from around the world will comprise a unique global resource.     

基于气相色谱-质谱联用技术的代谢组学方法在转植酸酶基因玉米代谢物指纹图谱分析中的应用

目的:建立气相色谱-质谱联用技术(GC-MS)的代谢组学方法,初步研究转基因株系与对照株系之间代谢物指纹图谱的差异性,为转基因作物安全的评价提供参考。方法:优化提取条件,考察色谱条件,并采用主成分分析(PCA)数据处理方法对转基因株系及对照进行模式识别。结果:优化了提取条件及色谱条件,建立了GC-MS的代谢组学方法,获得了小分子的代谢产物的表达谱,发现转基因与其对照之间呈现出显著性差异。结论:优化的GC-MS的代谢组学方法可以从代谢水平检测转基因作物,找出差异性,为转基因作物的检测与评价提供技术支持。     

Laboratory diagnosis of melioidosis: Past,present and future

Melioidosis is an emerging, potentially fatal disease caused by Burkholderia pseudomallei, which requires prolonged antibiotic treatment to prevent disease relapse. However, difficulties in laboratory diagnosis of melioidosis may delay treatment and affect disease outcomes. Isolation of B. pseudomallei from clinical specimens has been improved with the use of selective media. However, even with positive cultures, identification of B. pseudomallei can be difficult in clinical microbiology laboratories, especially in non-endemic areas where clinical suspicion is low. Commercial identification systems may fail to distinguish between B. pseudomallei and closely related species such as Burkholderia thailandensis. Genotypic identification of suspected isolates can be achieved by sequencing of gene targets such as groEL which offer higher discriminative power than 16S rRNA. Specific PCR-based identification of B. pseudomallei has also been developed using B. pseudomallei-specific gene targets such as Type III secretion system and Tat-domain protein. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry, a revolutionary technique for pathogen identification, has been shown to be potentially useful for rapid identification of B. pseudomallei, although existing databases require optimization by adding reference spectra for B. pseudomallei. Despite these advances in bacterial identification, diagnostic problems encountered in culture-negative cases remain largely unresolved. Although various serological tests have been developed, they are generally unstandardized “in house” assays and have low sensitivities and specificities. Although specific PCR assays have been applied to direct clinical and environmental specimens, the sensitivities for diagnosis remain to be evaluated. Metabolomics is an uprising tool for studying infectious diseases and may offer a novel approach for exploring potential diagnostic biomarkers. The metabolomics profiles of B. pseudomallei culture supernatants can be potentially distinguished from those of related bacterial species including B. thailandensis. Further studies using bacterial cultures and direct patient samples are required to evaluate the potential of metabolomics for improving diagnosis of melioidosis.     

代谢物组学方法及其在植物学研究中的应用

代谢物是生物体受遗传控制和环境影响的最终表达产物,以全体代谢物(代谢物组)为研究对象的代谢物组学是继基因组学和蛋白质组学后必然出现的又一门"组学"技术。该文综述了代谢物组的检测、数据的处理和分析等以及这些技术在植物目标分析、基因功能、代谢途径和代谢工程、整合植物学、信号转导等研究中的应用和前景。     

The biological interpretation of metabolomic data can be misled by the extraction method used

The field of metabolomics is getting more and more popular and a wide range of different sample preparation procedures are in use by different laboratories. Chemical extraction methods using one or more organic solvents as the extraction agent are the most commonly used approach to extract intracellular metabolites and generate metabolite profiles. Metabolite profiles are the scaffold supporting the biological interpretation in metabolomics. Therefore, we aimed to address the following fundamental question: can we obtain similar metabolomic results and, consequently, reach the same biological interpretation by using different protocols for extraction of intracellular metabolites? We have used four different methods for extraction of intracellular metabolites using four different microbial cell types (Gram negative bacterium, Gram positive bacterium, yeast, and a filamentous fungus). All the quenched samples were pooled together before extraction, and, therefore, they were identical. After extraction and GC?CMS analysis of metabolites, we did not only detect different numbers of compounds depending on the extraction method used and regardless of the cell type tested, but we also obtained distinct metabolite levels for the compounds commonly detected by all methods (P-value?<?0.001). These differences between methods resulted in contradictory biological interpretation regarding the activity of different metabolic pathways. Therefore, our results show that different solvent-based extraction methods can yield significantly different metabolite profiles, which impact substantially in the biological interpretation of metabolomics data. Thus, development of alternative extraction protocols and, most importantly, standardization of sample preparation methods for metabolomics should be seriously pursued by the scientific community.     

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.     

代谢组学在肿瘤药物靶点筛选中的应用进展

肿瘤是一种多因素参与造成机体各系统功能平衡紊乱的代谢性疾病,代谢重编程是恶性肿瘤的重要特征之一.研究"代谢指纹图谱"的代谢组学,通过揭示肿瘤或药物引起的宿主内源性代谢物的变化,为肿瘤药物靶点的筛选提供了可能.但目前对代谢组在肿瘤药物靶点筛选中的整体性综述并不多见,因此,本文在介绍了代谢组学筛选肿瘤药物靶点的流程的基础上...     

代谢组学技术在微藻研究中的应用

代谢组学(metabolomics/metabonomics)是系统生物学的重要组成部分之一,主要通过分析生物体受环境刺激、病理生理或基因变异等因素引起的内源性小分子代谢物变化来研究其生理功能与代谢之间的关系,进而揭示代谢物变化背后的代谢调控机制与机理。代谢组学技术具有灵敏度高、选择范围广和分析速度快等特点,逐渐在微藻研究领域发挥出优势。本文介绍了代谢组学的研究流程、分析方法以及代谢组学在微藻领域的应用和发展趋势,并提出了代谢组学在微藻研究过程中面临的问题和挑战,以期为代谢组学技术在微藻生物能源利用、环境保护、生产高价值产物等方面的应用提供新的思路和选择。