Granger causality in integrated GC–MS and LC–MS metabolomics data reveals the interface of primary and secondary metabolism

Metabolomics has emerged as a key technique of modern life sciences in recent years. Two major techniques for metabolomics in the last 10 years are gas chromatography coupled to mass spectrometry (GC–MS) and liquid chromatography coupled to mass spectrometry (LC–MS). Each platform has a specific performance detecting subsets of metabolites. GC–MS in combination with derivatisation has a preference for small polar metabolites covering primary metabolism. In contrast, reversed phase LC–MS covers large hydrophobic metabolites predominant in secondary metabolism. Here, we present an integrative metabolomics platform providing a mean to reveal the interaction of primary and secondary metabolism in plants and other organisms. The strategy combines GC–MS and LC–MS analysis of the same sample, a novel alignment tool MetMAX and a statistical toolbox COVAIN for data integration and linkage of Granger Causality with metabolic modelling. For metabolic modelling we have implemented the combined GC–LC–MS metabolomics data covariance matrix and a stoichiometric matrix of the underlying biochemical reaction network. The changes in biochemical regulation are expressed as differential Jacobian matrices. Applying the Granger causality, a subset of secondary metabolites was detected with significant correlations to primary metabolites such as sugars and amino acids. These metabolic subsets were compiled into a stoichiometric matrix N. Using N the inverse calculation of a differential Jacobian J from metabolomics data was possible. Key points of regulation at the interface of primary and secondary metabolism were identified.     

Metabolomics technology and bioinformatics

Metabolomics is the global analysis of all or a large number of cellular metabolites. Like other functional genomics research, metabolomics generates large amounts of data. Handling, processing and analysis of this data is a clear challenge and requires specialized mathematical, statistical and bioinformatics tools. Metabolomics needs for bioinformatics span through data and information management, raw analytical data processing, metabolomics standards and ontology, statistical analysis and data mining, data integration and mathematical modelling of metabolic networks within a framework of systems biology. The major approaches in metabolomics, along with the modern analytical tools used for data generation, are reviewed in the context of these specific bioinformatics needs.     

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。     

Metabolomics Data Normalization with EigenMS

Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal intensity loss due to time-dependent effects of the LC column performance, accumulation of contaminants in the MS ion source and MS sensitivity among others. In this study we aimed to test a singular value decomposition-based method, called EigenMS, for normalization of metabolomics data. We analyzed a clinical human dataset where LC-MS serum metabolomics data and physiological measurements were collected from thirty nine healthy subjects and forty with type 2 diabetes and applied EigenMS to detect and correct for any systematic bias. EigenMS works in several stages. First, EigenMS preserves the treatment group differences in the metabolomics data by estimating treatment effects with an ANOVA model (multiple fixed effects can be estimated). Singular value decomposition of the residuals matrix is then used to determine bias trends in the data. The number of bias trends is then estimated via a permutation test and the effects of the bias trends are eliminated. EigenMS removed bias of unknown complexity from the LC-MS metabolomics data, allowing for increased sensitivity in differential analysis. Moreover, normalized samples better correlated with both other normalized samples and corresponding physiological data, such as blood glucose level, glycated haemoglobin, exercise central augmentation pressure normalized to heart rate of 75, and total cholesterol. We were able to report 2578 discriminatory metabolite peaks in the normalized data (p<0.05) as compared to only 1840 metabolite signals in the raw data. Our results support the use of singular value decomposition-based normalization for metabolomics data.     

Validation of a dual LC–HRMS platform for clinical metabolic diagnosis in serum,bridging quantitative analysis and untargeted metabolomics

Mass spectrometry-based metabolomics is a rapidly growing field in both research and diagnosis. Generally, the methodologies and types of instruments used for clinical and other absolute quantification experiments are different from those used for biomarkers discovery and untargeted analysis, as the former requires optimal sensitivity and dynamic range, while the latter requires high resolution and high mass accuracy. We used a Q-TOF mass spectrometer with two different types of pentafluorophenyl (PFP) stationary phases, employing both positive and negative ionization, to develop and validate a hybrid quantification and discovery platform using LC–HRMS. This dual-PFP LC–MS platform quantifies over 50 clinically relevant metabolites in serum (using both MS and MS/MS acquisitions) while simultaneously collecting high resolution and high mass accuracy full scans to monitor all other co-eluting non-targeted analytes. We demonstrate that the linearity, accuracy, and precision results for the quantification of a number of metabolites, including amino acids, organic acids, acylcarnitines and purines/pyrimidines, meets or exceeds normal bioanalytical standards over their respective physiological ranges. The chromatography resolved highly polar as well as hydrophobic analytes under reverse-phase conditions, enabling analysis of a wide range of chemicals, necessary for untargeted metabolomics experiments. Though previous LC–HRMS methods have demonstrated quantification capabilities for various drug and small molecule compounds, the present study provides an HRMS quant/qual platform tailored to metabolic disease; and covers a multitude of different metabolites including compounds normally quantified by a combination of separate instrumentation.     

MRCQuant- an accurate LC-MS relative isotopic quantification algorithm on TOF instruments

Background  

Relative isotope abundance quantification, which can be used for peptide identification and differential peptide quantification, plays an important role in liquid chromatography-mass spectrometry (LC-MS)-based proteomics. However, several major issues exist in the relative isotopic quantification of peptides on time-of-flight (TOF) instruments: LC peak boundary detection, thermal noise suppression, interference removal and mass drift correction. We propose to use the Maximum Ratio Combining (MRC) method to extract MS signal templates for interference detection/removal and LC peak boundary detection. In our method, MRCQuant, MS templates are extracted directly from experimental values, and the mass drift in each LC-MS run is automatically captured and compensated. We compared the quantification accuracy of MRCQuant to that of another representative LC-MS quantification algorithm (msInspect) using datasets downloaded from a public data repository.     

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。     

Separating the wheat from the chaff: a prioritisation pipeline for the analysis of metabolomics datasets

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful and widely applied method for the study of biological systems, biomarker discovery and pharmacological interventions. LC-MS measurements are, however, significantly complicated by several technical challenges, including: (1) ionisation suppression/enhancement, disturbing the correct quantification of analytes, and (2) the detection of large amounts of separate derivative ions, increasing the complexity of the spectra, but not their information content. Here we introduce an experimental and analytical strategy that leads to robust metabolome profiles in the face of these challenges. Our method is based on rigorous filtering of the measured signals based on a series of sample dilutions. Such data sets have the additional characteristic that they allow a more robust assessment of detection signal quality for each metabolite. Using our method, almost 80% of the recorded signals can be discarded as uninformative, while important information is retained. As a consequence, we obtain a broader understanding of the information content of our analyses and a better assessment of the metabolites detected in the analyzed data sets. We illustrate the applicability of this method using standard mixtures, as well as cell extracts from bacterial samples. It is evident that this method can be applied in many types of LC-MS analyses and more specifically in untargeted metabolomics.     

液相质谱联用的代谢组方法优化及其在蓝细菌分析中的应用

为了准确鉴定光合蓝细菌中的各种代谢物,需要对基于液相色谱–质谱联用仪(LC-MS)的代谢组学分析方法进行有针对性的优化。本研究选取了24种涉及中心碳代谢和能量代谢的代谢物作为LC-MS的检测目标,获得了每个代谢物的最适色谱分离条件和质谱参数;同时以光合蓝细菌Synechocystis sp.PCC6803为主要对象,针对性地优化了样品前处理条件,结果显示适当延长梯度洗脱顺序表的时间并将流速设为0.2 m L/min可以得到最佳的分离效果,同时选择80%(V/V)甲醇(-80?C)作为代谢物萃取剂。分析结果证明这一代谢组分析技术可以成功地应用到光合蓝细菌的研究中。     

Exploring the protective effects of Danqi Tongmai tablet on acute myocardial ischemia rats by comprehensive metabolomics profiling

BackgroundDanqi Tongmai tablet (DQTM), a combination of salvianolic acids (SA) and panax notoginsenosides (PNS), is now in phase II clinical trial developed for the treatment of cardiovascular diseases. However, the mechanisms of its protective effects through regulating endogenous metabolites remain unclear.PurposeThe purpose of this study was to explore the protective effects of DQTM on acute myocardial ischemia rats by comprehensive metabolomics profiling.Study designThe rats were divided into three groups: sham-operating, acute myocardial ischemia (AMI) and DQTM groups. The plasma and heart were collected and profiled by LC-MS based metabolomics and lipidomics. Based on the identified differential metabolites, the pathway analysis results were obtained and further validated using the network pharmacology approach.MethodsThe AMI model was induced by ligating the left anterior descending coronary artery. The metabolomics and lipidomics profiling were based on two established LC–QTOF/MS analysis methods. The raw data were processed using XCMS Online, then the differential metabolites with nonparametric t-test p value less than 0.05 were selected and identified using HMDB and METLIN. The pathway analysis was conducted using MetaboAnalyst and validated with the predicted network results obtained by BATMAN-TCM.ResultsThe metabolomics and lipidomics profiles of plasma and heart in response to AMI and DQTM were significantly different. The AMI operation had a serious influence on metabolites in heart ischemia region, while DQTM had a greater impact on lipids in heart non-ischemia region. A total of 151 differential metabolites were identified, including mainly amino acids and fatty acids. Multiple metabolic pathways were disturbed after AMI and could be restored by DQTM, of which arachidonic acid metabolism was further validated with the predicted results of network pharmacology.ConclusionThe protective effects of DQTM on acute myocardial ischemia rats could be achieved through the regulation of multiple metabolic pathways.     

Matrix effects break the LC behavior rule for analytes in LC-MS/MS analysis of biological samples

High-performance liquid chromatography (HPLC) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) are generally accepted as the preferred techniques for detecting and quantitating analytes of interest in biological matrices on the basis of the rule that one chemical compound yields one LC-peak with reliable retention time (Rt.). However, in the current study, we have found that under the same LC-MS conditions, the Rt. and shape of LC-peaks of bile acids in urine samples from animals fed dissimilar diets differed significantly among each other. To verify this matrix effect, 17 authentic bile acid standards were dissolved in pure methanol or in methanol containing extracts of urine from pigs consuming either breast milk or infant formula and analyzed by LC-MS/MS. The matrix components in urine from piglets fed formula significantly reduced the LC-peak Rt. and areas of bile acids. This is the first characterization of this matrix effect on Rt. in the literature. Moreover, the matrix effect resulted in an unexpected LC behavior: one single compound yielded two LC-peaks, which broke the rule of one LC-peak for one compound. The three bile acid standards which exhibited this unconventional LC behavior were chenodeoxycholic acid, deoxycholic acid, and glycocholic acid. One possible explanation for this effect is that some matrix components may have loosely bonded to analytes, which changed the time analytes were retained on a chromatography column and interfered with the ionization of analytes in the MS ion source to alter the peak area. This study indicates that a comprehensive understanding of matrix effects is needed towards improving the use of HPLC and LC-MS/MS techniques for qualitative and quantitative analyses of analytes in pharmacokinetics, proteomics/metabolomics, drug development, and sports drug testing, especially when LC-MS/MS data are analyzed by automation software where identification of an analyte is based on its exact molecular weight and Rt.     

Metabolomics-based identification of apoptosis-inducing metabolites in recombinant fed-batch CHO culture media

A liquid chromatography-mass spectrometry (LC-MS) based metabolomics platform was previously established to identify and profile extracellular metabolites in culture media of mammalian cells. This presented an opportunity to isolate novel apoptosis-inducing metabolites accumulating in the media of antibody-producing Chinese hamster ovary (CHO mAb) fed-batch bioreactor cultures. Media from triplicate cultures were collected daily for the metabolomics analysis. Concurrently, cell pellets were obtained for determination of intracellular caspase activity. Metabolite profiles from the LC-MS data were subsequently examined for their degree of correlation with the caspase activity. A panel of extracellular metabolites, the majority of which were nucleotides/nucleosides and amino acid derivatives, exhibited good (R² > 0.8) and reproducible correlation. Some of these metabolites, such as oxidized glutathione, AMP and GMP, were later shown to induce apoptosis when introduced to fresh CHO mAb cultures. Finally, metabolic engineering targets were proposed to potentially counter the harmful effects of these metabolites.     

Separating the wheat from the chaff: a prioritisation pipeline for the analysis of metabolomics datasets

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful and widely applied method for the study of biological systems, biomarker discovery and pharmacological interventions. LC-MS measurements are, however, significantly complicated by several technical challenges, including: (1) ionisation suppression/enhancement, disturbing the correct quantification of analytes, and (2) the detection of large amounts of separate derivative ions, increasing the complexity of the spectra, but not their information content. Here we introduce an experimental and analytical strategy that leads to robust metabolome profiles in the face of these challenges. Our method is based on rigorous filtering of the measured signals based on a series of sample dilutions. Such data sets have the additional characteristic that they allow a more robust assessment of detection signal quality for each metabolite. Using our method, almost 80% of the recorded signals can be discarded as uninformative, while important information is retained. As a consequence, we obtain a broader understanding of the information content of our analyses and a better assessment of the metabolites detected in the analyzed data sets. We illustrate the applicability of this method using standard mixtures, as well as cell extracts from bacterial samples. It is evident that this method can be applied in many types of LC-MS analyses and more specifically in untargeted metabolomics.

     

Metabolite profiling of plant carotenoids using the matrix-assisted laser desorption ionization time-of-flight mass spectrometry

Although modern MS has facilitated the advent of metabolomics, some natural products such as carotenoids are not readily compatible to detection by MS. In the present article, we describe how matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI/TOF-MS) can be utilized to acquire mass spectra of carotenoids effectively. The procedure is sensitive (pmole range), reduces 'spot to spot' variation and provides high mass accuracy, thus aiding identification. The technique has been applied in vivo to the analysis of carotenoids in isolated plant cells and in vitro to three applications: (i) to show compatibility with purification methods such as LC, TLC and HPLC; (ii) for the rapid identification and quantification (by isotope dilution) of carotenoids present in crude extracts from plant tissues and whole cells; (iii) simultaneous semi-quantitative determination of carotenoids metabolites (m/z values) in crude plant extracts. Multivariate analysis of the recorded m/z values shows the effectiveness of the procedure in distinguishing genotypes from each other. In addition, the utility of the technique has been demonstrated on two mutant tomato populations, to determine alterations in carotenoid content, and a comparison made with traditional HPLC-photodiode array analysis. These data show that MALDI/TOF-MS can be used to rapidly profile, identify and quantify plant carotenoids reproducibly, as well as detecting other metabolites (m/z) in complex biological systems.     

Precursor mass prediction by clustering ionization products in LC-MS-based metabolomics

Liquid chromatography-mass spectrometry (LC-MS) is becoming the dominant technology in metabolomics, involving the comprehensive analysis of small molecules in biological systems. However, its use is still limited mainly by challenges in global high-throughput identification of metabolites: LC-MS data is highly complex, particularly due to the formation of multiple ionization products from individual metabolites. To address the limitation in metabolite identification, we developed a principled approach, designed to exploit the multi-dimensional information hidden in the data. The workflow first clusters candidate ionization products of the same metabolite together which typically have similar retention time, then searches for mass relationships among them in order to determine their ion types and metabolite identity. The robustness of our approach was demonstrated by its application to the LC-MS profiles of cell culture supernatant, which accurately predicted most of the known media components in the samples. Compared to conventional methods, our approach was able to generate significantly fewer candidate metabolites without missing out valid ones, thus reducing false-positive matches. Additionally, improved confidence in identification is achieved since each prediction comes with a probable combination of known ion types. Hence, our integrative workflow provides precursor mass predictions with high confidence by identifying various ionization products which account for a large proportion of detected peaks, thus minimizing false positives.     

LC-MS metabolic profiling of Arabidopsis thaliana plant leaves and cell cultures: optimization of pre-LC-MS procedure parameters

This study treats the optimization of methods for homogenizing Arabidopsis thaliana plant leaves as well as cell cultures, and extracting their metabolites for metabolomics analysis by conventional liquid chromatography electrospray ionization mass spectrometry (LC-ESI/MS). Absolute recovery, process efficiency and procedure repeatability have been compared between different pre-LC-MS homogenization/extraction procedures through the use of samples fortified before extraction with a range of representative metabolites. Hereby, the magnitude of the matrix effect observed in the ensuing LC-MS based metabolomics analysis was evaluated. Based on relative recovery and repeatability of key metabolites, comprehensiveness of extraction (number of m/z-retention time pairs) and clean-up potential of the approach (minimum matrix effects), the most appropriate sample pre-treatment was adopted. It combines liquid nitrogen homogenization for plant leaves with thermomixer based extraction using MeOH/H(2)O 80/20. As such, an efficient and highly reproducible LC-MS plant metabolomics set-up is achieved, as illustrated by the obtained results for both LC-MS (8.88%+/-5.16 versus 7.05%+/-4.45) and technical variability (12.53%+/-11.21 versus 9.31%+/-6.65) data in a comparative investigation of A. thaliana plant leaves and cell cultures, respectively.     

Simplified absolute metabolite quantification by gas chromatography-isotope dilution mass spectrometry on the basis of commercially available source material

In the field of metabolomics, GC-MS has rather established itself as a tool for semi-quantitative strategies like metabolic fingerprinting or metabolic profiling. Absolute quantification of intra- or extracellular metabolites is nowadays mostly accomplished by application of diverse LC-MS techniques. Only few groups have so far adopted GC-MS technology for this exceptionally challenging task. Besides numerous and deeply investigated problems related to sample generation, the pronounced matrix effects in biological samples have led to the almost mandatory application of isotope dilution mass spectrometry (IDMS) for the accurate determination of absolute metabolite concentrations. Nevertheless, access to stable isotope labeled internal standards (ILIS), which are in many cases commercially unavailable, is quite laborious and very expensive. Here we present an improved and simplified gas chromatography-isotope dilution mass spectrometry (GC-IDMS) protocol for the absolute determination of intra- and extracellular metabolite levels. Commercially available (13)C-labeled algal cells were used as a convenient source for the preparation of internal standards. Advantages as well as limitations of the described method are discussed.