Endogenous metabolites have gained increasing interest over the past 5 years largely for their implications in diagnostic and pharmaceutical biomarker discovery. METLIN (http://metlin.scripps.edu), a freely accessible web-based data repository, has been developed to assist in a broad array of metabolite research and to facilitate metabolite identification through mass analysis. METLINincludes an annotated list of known metabolite structural information that is easily cross-correlated with its catalogue of high-resolution Fourier transform mass spectrometry (FTMS) spectra, tandem mass spectrometry (MS/MS) spectra, and LC/MS data.

The aim of metabolite profiling is to monitor all metabolites within a biological sample for applications in basic biochemical research as well as pharmacokinetic studies and biomarker discovery. Here, novel data analysis software, XCMS, was used to monitor all metabolite features detected from an array of serum extraction methods, with application to metabolite profiling using electrospray liquid chromatography/mass spectrometry (ESI-LC/MS). The XCMS software enabled the comparison of methods with regard to reproducibility, the number and type of metabolite features detected, and the similarity of these features between different extraction methods. Extraction efficiency with regard to metabolite feature hydrophobicity was examined through the generation of unique feature density distribution plots, displaying feature distribution along chromatographic time. Hierarchical clustering was performed to highlight similarities in the metabolite features observed between the extraction methods. Protein extraction efficiency was determined using the Bradford assay, and the residual proteins were identified using nano-LC/MS/MS. Additionally, the identification of four of the most intensely ionized serum metabolites using FTMS and tandem mass spectrometry was reported. The extraction methods, ranging from organic solvents and acids to heat denaturation, varied widely in both protein removal efficiency and the number of mass spectral features detected. Methanol protein precipitation followed by centrifugation was found to be the most effective, straightforward, and reproducible approach, resulting in serum extracts containing over 2000 detected metabolite features and less than 2% residual protein. Interestingly, the combination of all approaches produced over 10,000 unique metabolite features, a number that is indicative of the complexity of the human metabolome and the potential of metabolomics in biomarker discovery.

A nonlinear alignment strategy was examined for the quantitative analysis of serum metabolites. Two small-molecule mixtures with a difference in relative concentration of 20-100% for 10 of the compounds were added to human serum. The metabolomics protocol using UPLC and XCMS for LC-MS data alignment could readily identify 8 of 10 spiked differences among more than 2700 features detected. Normalization of data against a single factor obtained through averaging the XCMS integrated response areas of spiked standards increased the number of identified differences. The original data structure was well preserved using XCMS, but reintegration of identified differences in the original data reduced the number of false positives. Using UPLC for separation resulted in 20% more detected components compared to HPLC. The length of the chromatographic separation also proved to be a crucial parameter for a number of detected features. Moreover, UPLC displayed better retention time reproducibility and signal-to-noise ratios for spiked compounds over HPLC, making this technology more suitable for nontargeted metabolomics applications.

Abstract.Comprehensive detection and quantitation of metabolites from a biological source constitute the major challenges of current metabolomics research. Two chemical derivatization methodologies, butylation and amination, were applied to human serum for ionization enhancement of a broad spectrum of metabolite classes, including steroids and amino acids. LC-ESI-MS analysis of the derivatized serum samples provided a significant signal elevation across the total ion chromatogram to over a 100-fold increase in ionization efficiency. It was also demonstrated that derivatization combined with isotopically labeled reagents facilitated the relative quantitation of derivatized metabolites from individual as well as pooled samples.

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Serum analysis with LC/MS can yield thousands of potential metabolites. However, in metabolomics, biomarkers of interest will often be of low abundance, and ionization suppression from high abundance endogenous metabolites such as phospholipids may prevent the detection of these metabolites. Here a cerium-modified column and methyl-tert-butyl-ether (MTBE) liquid-liquid extraction were employed to remove phospholipids from serum in order to obtain a more comprehensive metabolite profile. XCMS, an in-house developed data analysis software platform, showed that the intensity of existing endogenous metabolites increased, and that new metabolites were observed. This application of phospholipid capture in combination with XCMS non-linear data processing has enormous potential in metabolite profiling, for biomarker detection and quantitation.

In this paper, a fractional-order memristive model with infinite coexisting attractors is investigated. The numerical solution of the system is derived based on the Adomian decomposition method (ADM), and its dynamic behaviors are analyzed by means of phase diagrams, bifurcation diagrams, Lyapunov exponent spectrum (LEs), dynamic map based on SE complexity and maximum Lyapunov exponent (MLE). Simulation results show that it has rich dynamic characteristics, including asymmetric coexisting attractors with different structures and offset boosting. Finally, the digital signal processor (DSP) implementation verifies the correctness of the solution algorithm and the physical feasibility of the system.