基于冠心病心肌缺血血瘀证小型猪血清核磁共振代谢组学的研究

通过左冠状动脉前降支放置Ameriod环制备冠心病慢性心肌缺血动物模型,并结合冠脉造影等方法确立中医血瘀证证候,采用现代核磁共振(NMR)和模式识别技术,分析冠心病心肌缺血血瘀证模型与假手术组小型猪4星期血清的共振氢谱,及相应代谢物谱群的变化,开展中医血瘀证候学的研究.结果显示,该方法制备的模型4星期后确立为稳定的冠心...     

Evaluation of automated sample preparation, retention time locked gas chromatography-mass spectrometry and data analysis methods for the metabolomic study of Arabidopsis species

In this paper, automated sample preparation, retention time locked gas chromatography-mass spectrometry (GC-MS) and data analysis methods for the metabolomics study were evaluated. A miniaturized and automated derivatisation method using sequential oximation and silylation was applied to a polar extract of 4 types (2 types×2 ages) of Arabidopsis thaliana, a popular model organism often used in plant sciences and genetics. Automation of the derivatisation process offers excellent repeatability, and the time between sample preparation and analysis was short and constant, reducing artifact formation. Retention time locked (RTL) gas chromatography-mass spectrometry was used, resulting in reproducible retention times and GC-MS profiles. Two approaches were used for data analysis. XCMS followed by principal component analysis (approach 1) and AMDIS deconvolution combined with a commercially available program (Mass Profiler Professional) followed by principal component analysis (approach 2) were compared. Several features that were up- or down-regulated in the different types were detected.     

Separation strategies for untargeted metabolomics

Metabolomics has rapidly become a profiling technique of choice for biomarker elucidation and molecular diagnostics in addition to studies focused on understanding disease pathogenesis. Key to the success of metabolomics in these areas has been the techniques to separate and analyze the chemically diverse group of compounds comprising the metabolome by using global and untargeted approaches. Untargeted metabolomic efforts have the goal of examining as many metabolites as possible simultaneously and most frequently use an LC/MS-based approach. Here, the importance of LC in an untargeted metabolomic workflow is outlined and separation strategies for optimization are reviewed within the context of these criteria.     

Use of ultra-performance liquid chromatography/time-of-flight mass spectrometry with nozzle-skimmer fragmentation for comprehensive quantitative analysis of secondary metabolites in Arabidopsis thaliana

This study sought to develop techniques for LC/MS-based metabolomics and to verify that an MS/MS spectral tag (MS2T) could be used in practical secondary metabolite profiling. The retention time (RT), precursor ions, and fragment ions generated by nozzle-skimmer fragmentation were determined using ultra-performance liquid chromatography/time-of-flight mass spectrometry (UPLC/TOF-MS) and compared with the MS2T. A standard mix was analyzed with UPLC/TOF-MS under the same conditions as were used to construct the MS2T. The difference in RT for the standards was less than 0.15 min and the average RSD was about 2.8%, suggesting that the analysis was highly repeatable. Both precursor ions and fragment ions were observed when the cone voltage was 75 V. Experimental data and fragmentation pattern in the MS2T annotation list were highly similar. Wild-type and cas-1 mutant Arabidopsis thaliana samples treated with an elicitor were analyzed using UPLC/TOF-MS. Sixty-five peaks were successfully annotated. Fragment ions were observed with nozzle-skimmer fragmentation in 50 of 65 (77%) peaks. The reliability of annotation may have increased as a result of fragment ions. Results of multivariate analysis suggested that cas-1 was related to induction of the biosynthesis of these flavonoids. The devised method facilitated practical secondary metabolite profiling.     

Ultra-high performance liquid chromatography tandem mass spectrometry for comprehensive analysis of urinary acylcarnitines

We report an enabling mass spectrometric method for the analysis of lipid metabolites in order to define better the lipid metabolome in terms of chemical diversity and generate fragment ion spectra of these metabolites as a potential resource for unknown metabolite identification. This work focuses on the analysis of one important class of lipid metabolites, the acylcarnitines. Current analytical methods have only detected and identified a limited number of these metabolites. The method described herein provides the most comprehensive acylcarnitine profile in urine of healthy individuals up to date. It involves an optimized solid phase extraction technique for selective analyte extraction using cartridges containing both lipophilic and cation-exchange properties. The captured analytes are then subjected to ultra-high performance liquid chromatography (UPLC) separation, followed by tandem mass spectrometry (MS/MS) analysis using information-dependent acquisitions and selected reaction monitoring (SRM). The urine of six healthy individuals was analyzed using this method. A total of 355 acylcarnitines were detected; only 43 of them have been previously reported in the urine of healthy individuals. Detection of this large number of acylcarnitines illustrates the great diversity of the lipid metabolome as well as the usefulness of the method for profiling acylcarnitines. Furthermore, the MS/MS spectra of the 355 acylcarnitines will be uploaded to a public human metabolome database as a mass spectrometric resource for unknown metabolite identification.     

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.     

Comparison of two algorithmic data processing strategies for metabolic fingerprinting by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry

The alignment algorithm Statistical Compare (SC) developed by LECO Corporation for the processing of comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS) data was validated and compared to the in-house developed retention time correction and data alignment tool INCA (Integrative Normalization and Comparative Analysis) by a spike-in experiment and the comparative metabolic fingerprinting of a wild type versus a double mutant strain of Escherichia coli (E. coli). Starting with the same peak lists generated by LECO's ChromaTOF software, the accuracy of peak alignment and detection of 1.1- to 4-fold changes in metabolite concentration was assessed by spiking 20 standard compounds into an aqueous methanol extract of E. coli. To provide the same quality input signals for both alignment routines, the universal m/z 73 trace of the trimethylsilyl (TMS) group was used as a quantitative measure for all features. The performance of data processing and alignment was evaluated and illustrated by ROC curves. Statistical Compare performed marginally better at the lower fold changes, while INCA did so at the higher fold changes. Using SC, quantitative precision could be improved substantially by exploiting the signal intensities of metabolite-specific unique (U) m/z ion traces rather than the universal m/z 73 trace. A list of 56 features that distinguished the two E. coli strains was obtained by the SC alignment using m/z U with an estimated false discovery rate (FDR) of <0.05. Ultimately, 23 metabolites could be identified, one additional and five less than with INCA due to the failure of SC to extract unitized m/z U's across all fingerprints with suitable spectral intensities for the latter metabolites.     

LC-MS-based multi-omics analysis of brain tissue for the evaluation of the anti-ischemic stroke potential of Tribulus terrestris L. fruit extract in MCAO rats

This study aimed to investigate the chemical composition of Tribulus terrestris L. fruit (TT) extract named TT15 and its protective effect against ischemic stroke (IS) as well as corresponding mechanisms. The chemical composition of TT15 was analyzed by liquid chromatography-mass spectrometry (LC-MS), and the compound identification was conducted via searching the in-house database. The LC-MS-based multi-omics approach was applied to search the differential metabolites and differential proteins in rat brain tissue and to explore the biomarker and molecular mechanism of TT15 against middle cerebral artery occlusion (MCAO). A total of 20 compounds were identified from TT15, mainly including alkaloids, flavonoids, phenols, quinones, and esters. These 20 compounds significantly affected the metabolism of 44 metabolites and the expression of 51 proteins. Joint pathway analysis showed that these metabolites and proteins were mainly involved in the response to elevated platelet cytosolic Ca²⁺ and platelet activation, which inferred that TT15 may exert a protective effect against cerebral ischemic injury via regulating platelet function. This study provides useful information for further exploration of the mechanisms of TT extract against IS.     

Metabolites identification of oil palm roots infected with Ganoderma boninense using GC–MS-based metabolomics

An approach to metabolomics profiling of non-infected and Ganoderma boninense (G. boninsense) infected oil palm roots crude extracts that utilize gas chromatography-mass spectrometry (GC–MS) and multivariate statistics of principal component analysis (PCA) have been tested. This combination has provided a rapid approach in investigating the changes in the metabolite variations of non-infected and infected oil palm roots at 14 and 30 days post-infection. The extracts were prepared by using 80% (v/v) of methanol. In identifying the metabolites responsible for each differentiation, PCA model was generated in loading bi-plot. Dimethyl benzene-1,4-dicarboxylate, methyl 3-(3,5-ditert-butyl-4-hydroxyphenyl)propanoate, ergost-5-en-3-ol, (3β), stigmast-5-en-3-ol, (3β), stigmasterol, methyl hexadecanoate, methyl (9Z,12Z)-octadeca-9,12-dienoate, methyl octadecanoate, 2-(hydroxymethyl)-2-nitropropane-1,3-diol, methyl (Z)-octadec-6-enoate and (E)-icos-5-ene were found more abundant in G. boninense infected roots than in non-infected roots. Steroidal compounds and fatty acid derivatives which has been determined in the non-infected and G. boninense infected roots regulate a variety of responses to the G. boninense. The abundant of these metabolites in G. boninense infected roots are due to the crucial roles in pathogen defence.     

Identification of the new chymotrypsin inhibitor micropeptin 996 by metabolomics-guided analysis

An untargeted metabolomics approach was used to investigate a cultured strain of Microcystis aeruginosa (UTEX LB2386) known to be a prolific producer of a diverse class of cyanopeptides. Identification of a putative new compound with a molecular weight of 996 led to the purification and structure elucidation of this new member of the micropeptin class of cyanopeptides. Micropeptin 996 displayed potent inhibition of the serine protease enzyme chymotrypisin relative to structurally related members of this class.