A statistical procedure to selectively detect metabolite signals in LC-MS data based on using variable isotope ratios

The tracing of metabolite signals in LC-MS data using stable isotope-labeled compounds has been described in the literature. However, the filtering efficiency and confidence when mining metabolite signals in complex LC-MS datasets can be improved. Here, we propose an additional statistical procedure to increase the compound-derived signal mining efficiency. This method also provides a highly confident approach to screen out metabolite signals because the correlation of varying concentration ratios of native/stable isotope-labeled compounds and their instrumental response ratio is used. An in-house computational program [signal mining algorithm with isotope tracing (SMAIT)] was developed to perform the statistical procedure. To illustrate the SMAIT concept and its effectiveness for mining metabolite signals in LC-MS data, the plasticizer, di-(2-ethylhexyl) phthalate (DEHP), was used as an example. The statistical procedure effectively filtered 15 probable metabolite signals from 3617 peaks in the LC-MS data. These probable metabolite signals were considered structurally related to DEHP. Results obtained here suggest that the statistical procedure could be used to confidently facilitate the detection of probable metabolites from a compound-derived precursor presented in a complex LC-MS dataset.     

The application of high-resolution mass spectrometry-based data-mining tools in tandem to metabolite profiling of a triple drug combination in humans

Patients are usually exposed to multiple drugs, and metabolite profiling of each drug in complex biological matrices is a big challenge. This study presented a new application of an improved high resolution mass spectrometry (HRMS)-based data-mining tools in tandem to fast and comprehensive metabolite identification of combination drugs in human. The model drug combination was metronidazole-pantoprazole-clarithromycin (MET-PAN-CLAR), which is widely used in clinic to treat ulcers caused by Helicobacter pylori. First, mass defect filter (MDF), as a targeted data processing tool, was able to recover all relevant metabolites of MET-PAN-CLAR in human plasma and urine from the full-scan MS dataset when appropriate MDF templates for each drug were defined. Second, the accurate mass-based background subtraction (BS), as an untargeted data-mining tool, worked effectively except for several trace metabolites, which were buried in the remaining background signals. Third, an integrated strategy, i.e., untargeted BS followed by improved MDF, was effective for metabolite identification of MET-PAN-CLAR. Most metabolites except for trace ones were found in the first step of BS-processed datasets, and the results led to the setup of appropriate metabolite MDF template for the subsequent MDF data processing. Trace metabolites were further recovered by MDF, which used both common MDF templates and the novel metabolite-based MDF templates. As a result, a total of 44 metabolites or related components were found for MET-PAN-CLAR in human plasma and urine using the integrated strategy. New metabolic pathways such as N-glucuronidation of PAN and dehydrogenation of CLAR were found. This study demonstrated that the combination of accurate mass-based multiple data-mining techniques in tandem, i.e., untargeted background subtraction followed by targeted mass defect filtering, can be a valuable tool for rapid metabolite profiling of combination drugs in vivo.     

基于液相色谱-质谱联用的代谢组学研究中代谢物的结构鉴定进展

基于液相色谱-质谱联用的代谢组学技术因其高效分离能力和高灵敏检测能力已成为生命科学研究的重要手段,但由于缺乏有效的通用标准谱图库,检测到的大量代谢物的结构难以鉴定。这制约了代谢组学覆盖度的提高和生物标志物的发现,造成化学和生物信息的严重丢失,成为代谢组学发展的主要技术瓶颈。随着质谱仪器及计算机技术的进步,基于大气压电离质谱（API-MS）的代谢物结构鉴定技术飞速发展,本文从质谱仪器、代谢物分子结构式判别、数据库及谱图检索以及计算机辅助谱图解析等方面,对代谢物结构鉴定的最新进展进行了综述。     

An integrated strategy for in vivo metabolite profiling using high-resolution mass spectrometry based data processing techniques

An ongoing challenge of drug metabolite profiling is to detect and identify unknown or low-level metabolites in complex biological matrices. Here we present a generic strategy for metabolite detection using multiple accurate-mass-based data processing tools via the analysis of rat samples of two model drug candidates, AZD6280 and AZ12488024. First, the function of isotopic pattern recognition was proved to be highly effective in the detection of metabolites derived from [¹⁴C]-AZD6280 that possesses a distinct isotopic pattern. The metabolites revealed using this approach were in excellent qualitative correlation to those observed in radiochromatograms. Second, the effectiveness of accurate mass based untargeted data mining tools such as background subtraction, mass defect filtering, or a data mining package (MZmine) used for metabolomic analysis in detection of metabolites of [¹⁴C]-AZ12488024 in rat urine, feces, bile and plasma samples was examined and a total of 33 metabolites of AZ12488024 were detected. Among them, at least 16 metabolites were only detected by the aid of the data mining packages and not via radiochromatograms. New metabolic pathways such as S-oxidation and thiomethylation reactions occurring on the thiazole ring were proposed based on the processed data. The results of these experiments also demonstrated that accurate mass-based mass defect filtering (MDF) and data mining techniques used in metabolomics are complementary and can be valuable tools for delineating low-level metabolites in complex matrices. Furthermore, the application of distinct multiple data-mining algorithms in parallel, or in tandem, can be effective for rapidly profiling in vivo drug metabolites.     

基于气相色谱-飞行时间质谱技术的佐剂性关节炎大鼠尿液代谢组学的研究

采用弗氏完全佐剂(FCA)诱导佐剂性关节炎(AA)大鼠模型,观察大鼠足趾肿胀度和踝关节组织的病理学形态变化。应用气相色谱-飞行时间质谱(GC-TOF MS)技术检测AA大鼠尿液代谢物谱,并对数据进行主成分分析(PCA)、偏最小二乘法-判别分析(PLS-DA)及正交偏最小二乘法-判别分析(OPLS-DA),探讨可能的发病机制。通过变量重要性投影值(VIP>1)和P值(<0.05),筛选出尿液中的差异代谢物。在模型组大鼠的尿液中共发现异柠檬酸、α-酮戊二酸、柠康酸、肌酸、3-羟基丁酸等20种差异代谢物。推断AA代谢组学的发病机制可能与能量代谢、氨基酸代谢、脂肪酸代谢途径有关。     

基于HPLC/Q-TOF MS的4种农药联合暴露人群的代谢组学研究

采用基于高效液相色谱-飞行时间质谱联用(HPLC-TOF MS)的代谢组学方法,研究了啶虫脒、高效氯氟氰菊酯、联苯菊酯、甲氨基阿维菌素苯甲酸盐4种农药联合暴露所致的施药人群尿液中内源性代谢物的变化。采集30位农民喷洒4种复合农药前和喷洒农药期1,3,5,7 d的尿液进行检测。提取正常尿液中常见代谢物并通过质控样品评价手段进行分析,结果表明该方法具有良好的稳定性和精密度,可用于尿液中代谢物分析。多变量分析结果表明,暴露人群施药前后尿液的代谢物含量存在较大差异。对选取的36个差异离子进行鉴定,确定了8个生物标志物的结构。结果显示联合暴露组人群尿液中多巴胺、5-羟色胺、酪氨酸、色氨酸、牛磺酸和马尿酸的含量显著下降;犬尿素和肌酸的含量显著上升。4种农药联合暴露导致接触人群尿液中色氨酸代谢途径的中间产物含量降低,肝代谢和能量代谢相关的代谢物蓄积,可能与神经系统和肝脏功能的受损有关。     

A novel kernel Fisher discriminant analysis: constructing informative kernel by decision tree ensemble for metabolomics data analysis

Large amounts of data from high-throughput metabolomics experiments become commonly more and more complex, which brings an enormous amount of challenges to existing statistical modeling. Thus there is a need to develop statistically efficient approach for mining the underlying metabolite information contained by metabolomics data under investigation. In the work, we developed a novel kernel Fisher discriminant analysis (KFDA) algorithm by constructing an informative kernel based on decision tree ensemble. The constructed kernel can effectively encode the similarities of metabolomics samples between informative metabolites/biomarkers in specific parts of the measurement space. Simultaneously, informative metabolites or potential biomarkers can be successfully discovered by variable importance ranking in the process of building kernel. Moreover, KFDA can also deal with nonlinear relationship in the metabolomics data by such a kernel to some extent. Finally, two real metabolomics datasets together with a simulated data were used to demonstrate the performance of the proposed approach through the comparison of different approaches.     

基于液相色谱-质谱联用技术的心肌缺血大鼠血清和心肌组织代谢组学

采用异丙肾上腺素诱导心肌缺血大鼠模型,使用液相色谱-质谱法检测血清和心肌中的内源性成分,应用软件对已鉴定的40余种目标成分进行靶向提取,用主成分分析（PCA）、有监督偏最小二乘法判别分析（PLS-DA）对代谢组学数据进行多维度统计分析,筛选潜在生物标志物。与对照组相比,在心肌缺血模型组大鼠血清、组织中检测出18个差异代谢物,涉及精氨酸和脯氨酸代谢、甘氨酸、丝氨酸和苏氨酸代谢、谷氨酰胺和谷氨酸代谢、牛磺酸和亚牛磺酸代谢等多条代谢通路。代谢产物可作为心肌缺血研究中的重要标志物,该研究结果有助于揭示心肌缺血的发病机制,可为临床疾病诊断提供思路。     

Mass defect profiles of biological matrices and the general applicability of mass defect filtering for metabolite detection

Recent examples have demonstrated that the high-resolution liquid chromatography/mass spectrometry (LC/MS)-based mass defect filtering (MDF) technique was effective in selectively detecting drug metabolites regardless of their molecular weights or fragmentation patterns. The main objective of the current study was to evaluate the general applicability of MDF for drug metabolite detection in typical biological matrices. Mass defect profiles of commonly used biological matrices including plasma, urine, bile, and feces were obtained using an LTQ FT mass spectrometer and were compared with those of 115 commonly prescribed drugs. The mass defect profiles were presented as two-dimensional Y-X plots with the determined mass defects of components on the y-axis versus the corresponding m/z values on the x-axis. The mass defect profiles of the matrices appeared to be similar for each type of matrix across species, yet marked differences were apparent between matrices of a given species. The mass defect profiles of components in plasma, bile, and feces showed significant separation from most of the 115 drugs. The mass defect profiles of urine did not show such clean separation from that of the 115 drugs. The results suggest that MDF has a broad applicability for selective detection of drug metabolites in plasma, bile and feces although the selectivity for detecting urinary drug metabolites is not as good as in the other matrices. In addition, the mass defect profiles of the biological matrices allow for prediction of the effectiveness of MDF for certain applications, and for designing specific MDF windows for selective detection of drug metabolites.     

慢性心功能衰竭患者尿液的代谢组学研究

采用基于液相色谱-质谱联用的方法对慢性心力衰竭(Chronic heart failure, CHF)患者和正常对照(Control)人群的尿液进行分析, 筛选慢性心力衰竭患者尿液中的差异代谢物, 研究其发病机制, 并为临床治疗提供科学依据.选择15个慢性心力衰竭患者(年龄(62.27±3.14)岁)及15个正常人(年龄(65.41±4.63)岁), 采用高分辨度快速液相色谱-四极杆-飞行时间串联质谱(RRLC-QTOF/MS)技术对尿液代谢物进行分析, 采用主成分分析(PCA)对两组代谢物进行分类, 并筛选潜在生物标记物;运用偏最小二乘判别分析法(PLS-DA)建模, 考察生物标记物对疾病筛选的预测能力.研究结果表明, CHF组和Control组尿液代谢物谱能得到很好的区分, 发现并鉴定了2种潜在生物标记物尿苷及丙氨酰色氨酸, 提示嘧啶代谢和色氨酸代谢可能在心力衰竭发生发展中有重要作用.     

超高效液相色谱-四极杆-飞行时间质谱检测和鉴定猪尿中氯丙那林的主要代谢产物

采用超高效液相色谱-四极杆-飞行时间质谱(UPLC/Q-TOF MS)检测和鉴定了猪尿中氯丙那林的主要代谢产物,并讨论了氯丙那林在猪体内的主要代谢途径。按10 mg/kg(b. w.)的剂量口服灌食氯丙那林,分别采集给药前及给药后的猪尿液样品。采用UPLC/Q-TOF MS对样品进行分析,并应用质量亏损过滤和离子色谱峰提取等数据处理技术,在给药后24 h内的猪尿中检测和鉴定了9种氯丙那林的代谢产物,其中,Ⅰ相代谢产物2种,Ⅱ相代谢产物7种。然后,根据氯丙那林原形和代谢产物的碎片离子特征,对代谢产物的结构进行鉴定。最后,根据所鉴定的代谢产物,推测氯丙那林在猪体内的代谢途径包括苯环羟基化、β -羟基和仲氨基的葡萄糖醛酸轭合、羟基化后的葡萄糖醛酸和硫酸轭合等。研究结果表明,羟基化氯丙那林及其轭合产物的相对含量大于60%,明显高于氯丙那林原形及其轭合产物,是尿液中的主要代谢产物。本研究将为确定氯丙那林在动物体内的残留标示物及加强对氯丙那林非法使用的监控提供科学依据。     

Development of chemical isotope labeling liquid chromatography mass spectrometry for silkworm hemolymph metabolomics

Silkworm (Bombyx mori) is a very useful target insect for evaluation of endocrine disruptor chemicals (EDCs) due to mature breeding techniques, complete endocrine system and broad basic knowledge on developmental biology. Comparative metabolomics of silkworms with and without EDC exposure offers another dimension of studying EDCs. In this work, we report a workflow on metabolomic profiling of silkworm hemolymph based on high-performance chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) and demonstrate its application in studying the metabolic changes associated with the pesticide dichlorodiphenyltrichloroethane (DDT) exposure in silkworm. Hemolymph samples were taken from mature silkworms after growing on diet that contained DDT at four different concentrations (1, 0.1, 0.01, 0.001 ppm) as well as on diet without DDT as controls. They were subjected to differential ¹²C-/¹³C-dansyl labeling of the amine/phenol submetabolome, LC-UV quantification of the total amount of labeled metabolites for sample normalization, and LC-MS detection and relative quantification of individual metabolites in comparative samples. The total concentration of labeled metabolites did not show any significant change between four DDT-treatment groups and one control group. Multivariate statistical analysis of the metabolome data set showed that there was a distinct metabolomic separation between the five groups. Out of the 2044 detected peak pairs, 338 and 1471 metabolites have been putatively identified against the HMDB database and the EML library, respectively. 65 metabolites were identified by the dansyl library searching based on the accurate mass and retention time. Among the 65 identified metabolites, 33 positive metabolites had changes of greater than 1.20-fold or less than 0.83-fold in one or more groups with p-value of smaller than 0.05. Several useful biomarkers including serine, methionine, tryptophan, asymmetric dimethylarginine, N-Methyl-D-aspartic and tyrosine were identified. The changes of these biomarkers were likely due to the disruption of the endocrine system of silkworm by DDT. This work illustrates that the method of CIL LC-MS is useful to generate quantitative submetabolome profiles from a small volume of silkworm hemolymph with much higher coverage than conventional LC-MS methods, thereby facilitating the discovery of potential metabolite biomarkers related to EDC or other chemical exposure.     

多环芳烃暴露的生物标志物——尿中羟基多环芳烃

多环芳烃(PAHs) 是典型的持久性有机污染物,在职业高PAHs 暴露环境下,容易诱发肺癌、皮肤癌等癌症。对PAHs 的暴露评价可为流行病学研究和污染物风险评价等提供有效的数据。由于暴露途径的复杂化,采用尿样中PAHs 的代谢产物———羟基多环芳烃作为标志物来综合评价人体对PAHs 的内暴露情况已经成为研究的热点。本文系统介绍了多环芳烃的吸收、代谢、尿中PAHs 代谢产物的主要存在形式、主要的生物标志物以及它们的主要影响因素。     

Real-Time NMR Spectroscopy for Studying Metabolism

Current metabolomics approaches utilize cellular metabolite extracts, are destructive, and require high cell numbers. We introduce here an approach that enables the monitoring of cellular metabolism at lower cell numbers by observing the consumption/production of different metabolites over several kinetic data points of up to 48 hours. Our approach does not influence cellular viability, as we optimized the cellular matrix in comparison to other materials used in a variety of in-cell NMR spectroscopy experiments. We are able to monitor real-time metabolism of primary patient cells, which are extremely sensitive to external stress. Measurements are set up in an interleaved manner with short acquisition times (approximately 7 minutes per sample), which allows the monitoring of up to 15 patient samples simultaneously. Further, we implemented our approach for performing tracer-based assays. Our approach will be important not only in the metabolomics fields, but also in individualized diagnostics.     

基于液相色谱-质谱联用的代谢组学技术研究Pokemon诱导的胞内脂代谢变化

Pokemon是一种转录抑制因子,能够通过影响染色质的重组或直接与抑癌基因结合而抑制抑癌基因的转录,促使肿瘤形成。该文利用基于液相色谱-质谱联用的代谢组学技术研究了Pokemon在肝癌中调控细胞代谢的作用机制。通过脂质转染,获得了Pokemon高表达的HL7702细胞,分别收集转染后不同时间点的细胞。利用基于液相色谱-质谱联用技术的代谢组学方法,分析胞内代谢物的成分。根据多元统计分析的结果选出差异显著的候选代谢物,通过数据库（METLIN和HMDB）检索、二级图谱比对进行结构解析,确证了36种代谢物。通过KEGG数据库检索发现这些代谢物主要与脂质合成相关。进一步分析发现脂质合成途径中乙酰辅酶羧化酶和脂肪酸合成酶均被激活。结果显示,Pokemon可通过激活细胞中脂质合成通路而影响细胞的代谢。     

Metabolism of synthetic cannabinoids PB-22 and its 5-fluoro analog, 5F-PB-22, by human hepatocyte incubation and high-resolution mass spectrometry

Background

PB-22 (1-pentyl-8-quinolinyl ester-1H-indole-3-carboxylic acid) and 5F-PB-22 (1-(5-fluoropentyl)-8-quinolinyl ester-1H-indole-3-carboxylic acid) are new synthetic cannabinoids with a quinoline substructure and the first marketed substances with an ester bond linkage. No human metabolism data are currently available, making it difficult to document PB-22 and 5F-PB-22 intake from urine analysis, and complicating assessment of the drugs’ pharmacodynamic and toxicological properties.

Methods

We incubated 10 μmol/l PB-22 and 5F-PB-22 with pooled cryopreserved human hepatocytes up to 3 h and analyzed samples on a TripleTOF 5600+ high-resolution mass spectrometer. Data were acquired via TOF scan, followed by information-dependent acquisition triggered product ion scans with mass defect filtering (MDF). The accurate mass full scan MS and MS/MS metabolite datasets were analyzed with multiple data processing techniques, including MDF, neutral loss and product ion filtering.

Results

The predominant metabolic pathway for PB-22 and 5F-PB-22 was ester hydrolysis yielding a wide variety of (5-fluoro)pentylindole-3-carboxylic acid metabolites. Twenty metabolites for PB-22 and 22 metabolites for 5F-PB-22 were identified, with the majority generated by oxidation with or without glucuronidation. For 5F-PB-22, oxidative defluorination occurred forming PB-22 metabolites. Both compounds underwent epoxide formation followed by internal hydrolysis and also produced a cysteine conjugate.

Conclusion

Human hepatic metabolic profiles were generated for PB-22 and 5F-PB-22. Pentylindole-3-carboxylic acid, hydroxypentyl-PB-22 and PB-22 pentanoic acid for PB-22, and 5′-fluoropentylindole-3-carboxylic acid, PB-22 pentanoic acid and the hydroxy-5F-PB-22 metabolite with oxidation at the quinoline system for 5F-PB-22 are likely the best targets to incorporate into analytical methods for urine to document PB-22 and 5F-PB-22 intake.

Quantification of azoxystrobin and identification of two novel metabolites in lettuce via liquid chromatography–quadrupole-linear ion trap (QTRAP) mass spectrometry

Pesticide metabolite identification is gaining increased attention because of the interest in potential metabolite toxicity. Azoxystrobin is one of the most prevalent pesticide residues in foods in Europe. The majority of azoxystrobin metabolites have been identified using radiolabelled standards, which are either expensive or not readily available. Thus, alternative approaches for metabolite identification are desirable. Here, an LC-MS/MS method for quantifying azoxystrobin and identifying its metabolites using quadrupole-linear ion trap mass spectrometry is reported. Seven metabolites of azoxystrobin were identified 2 and 4 weeks after spraying lettuce with azoxystrobin. Among them, two metabolites are reported for the first time. The hydrolysis, reduction, hydroxylation, photoisomerisation and hydrolytic cleavage of ether bonds are identified as biotransformation processes involved in azoxystrobin metabolism in lettuce.     

Data processing for mass spectrometry-based metabolomics

Modern analytical technologies afford comprehensive and quantitative investigation of a multitude of different metabolites. Typical metabolomic experiments can therefore produce large amounts of data. Handling such complex datasets is an important step that has big impact on extent and quality at which the metabolite identification and quantification can be made, and thus on the ultimate biological interpretation of results. Increasing interest in metabolomics thus led to resurgence of interest in related data processing. A wide variety of methods and software tools have been developed for metabolomics during recent years, and this trend is likely to continue. In this paper we overview the key steps of metabolomic data processing and focus on reviewing recent literature related to this topic, particularly on methods for handling data from liquid chromatography mass spectrometry (LC-MS) experiments.     

Training in metabolomics research. I. Designing the experiment,collecting and extracting samples and generating metabolomics data

The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program's goals, the University of Alabama at Birmingham has hosted an annual 4‐day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. The complete set of training materials including slide sets and videos can be viewed at http://www.uab.edu/proteomics/metabolomics/workshop/workshop_june_2015.php . Copyright © 2016 John Wiley & Sons, Ltd.