Haiwei Gu

Metabolomics-based methods for early disease diagnostics

The emerging field of metabolomics, in which a large number of small-molecule metabolites from body fluids or tissues are detected quantitatively in a single step, promises immense potential for early diagnosis, therapy monitoring and for understanding the pathogenesis of many diseases. Metabolomics methods are mostly focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry (MS). Analysis of the data from these high-resolution methods using advanced chemometric approaches provides a powerful platform for translational and clinical research and diagnostic applications. In this review, the current trends and recent advances in NMR- and MS-based metabolomics are described with a focus on the development of advanced NMR and MS methods, improved multivariate statistical data analysis and recent applications in the area of cancer, diabetes, inborn errors of metabolism and cardiovascular diseases.

Altered proteome turnover and remodeling by short-term caloric restriction or rapamycin rejuvenate the aging heart

Chronic caloric restriction (CR) and rapamycin inhibit the mechanistic target of rapamycin (mTOR) signaling, thereby regulating metabolism and suppressing protein synthesis. Caloric restriction or rapamycin extends murine lifespan and ameliorates many aging‐associated disorders; however, the beneficial effects of shorter treatment on cardiac aging are not as well understood. Using a recently developed deuterated‐leucine labeling method, we investigated the effect of short‐term (10 weeks) CR or rapamycin on the proteomics turnover and remodeling of the aging mouse heart. Functionally, we observed that short‐term CR and rapamycin both reversed the pre‐existing age‐dependent cardiac hypertrophy and diastolic dysfunction. There was no significant change in the cardiac global proteome (823 proteins) turnover with age, with a median half‐life 9.1 days in the 5‐month‐old hearts and 8.8 days in the 27‐month‐old hearts. However, proteome half‐lives of old hearts significantly increased after short‐term CR (30%) or rapamycin (12%). This was accompanied by attenuation of age‐dependent protein oxidative damage and ubiquitination. Quantitative proteomics and pathway analysis revealed an age‐dependent decreased abundance of proteins involved in mitochondrial function, electron transport chain, citric acid cycle, and fatty acid metabolism as well as increased abundance of proteins involved in glycolysis and oxidative stress response. This age‐dependent cardiac proteome remodeling was significantly reversed by short‐term CR or rapamycin, demonstrating a concordance with the beneficial effect on cardiac physiology. The metabolic shift induced by rapamycin was confirmed by metabolomic analysis.

Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: Application to the detection of breast cancer

Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two most commonly used analytical tools in metabolomics, and their complementary nature makes the combination particularly attractive. A combined analytical approach can improve the potential for providing reliable methods to detect metabolic profile alterations in biofluids or tissues caused by disease, toxicity, etc. In this paper, (1)H NMR spectroscopy and direct analysis in real time (DART)-MS were used for the metabolomics analysis of serum samples from breast cancer patients and healthy controls. Principal component analysis (PCA) of the NMR data showed that the first principal component (PC1) scores could be used to separate cancer from normal samples. However, no such obvious clustering could be observed in the PCA score plot of DART-MS data, even though DART-MS can provide a rich and informative metabolic profile. Using a modified multivariate statistical approach, the DART-MS data were then reevaluated by orthogonal signal correction (OSC) pretreated partial least squares (PLS), in which the Y matrix in the regression was set to the PC1 score values from the NMR data analysis. This approach, and a similar one using the first latent variable from PLS-DA of the NMR data resulted in a significant improvement of the separation between the disease samples and normals, and a metabolic profile related to breast cancer could be extracted from DART-MS. The new approach allows the disease classification to be expressed on a continuum as opposed to a binary scale and thus better represents the disease and healthy classifications. An improved metabolic profile obtained by combining MS and NMR by this approach may be useful to achieve more accurate disease detection and gain more insight regarding disease mechanisms and biology.

Deregulated Myc Requires MondoA/Mlx for Metabolic Reprogramming and Tumorigenesis

Deregulated Myc transcriptionally reprograms cell metabolism to promote neoplasia. Here we show that oncogenic Myc requires the Myc superfamily member MondoA, a nutrient-sensing transcription factor, for tumorigenesis. Knockdown of MondoA, or its dimerization partner Mlx, blocks Myc-induced reprogramming of multiple metabolic pathways, resulting in apoptosis. Identification and knockdown of genes coregulated by Myc and MondoA have allowed us to define metabolic functions required by deregulated Myc and demonstrate a critical role for lipid biosynthesis in survival of Myc-driven cancer. Furthermore, overexpression of a subset of Myc and MondoA coregulated genes correlates with poor outcome of patients with diverse cancers. Coregulation of cancer metabolism by Myc and MondoA provides the potential for therapeutics aimed at inhibiting MondoA and its target genes.

Colorectal cancer detection using targeted serum metabolic profiling.

Colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world. Despite an expanding knowledge of its molecular pathogenesis during the past two decades, robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC are still lacking. In this study, we present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying biomarker candidates that could enable highly sensitive and specific CRC detection using human serum samples. In this targeted approach, 158 metabolites from 25 metabolic pathways of potential significance were monitored in 234 serum samples from three groups of patients (66 CRC patients, 76 polyp patients, and 92 healthy controls). Partial least-squares-discriminant analysis (PLS-DA) models were established, which proved to be powerful for distinguishing CRC patients from both healthy controls and polyp patients. Receiver operating characteristic curves generated based on these PLS-DA models showed high sensitivities (0.96 and 0.89, respectively, for differentiating CRC patients from healthy controls or polyp patients), good specificities (0.80 and 0.88), and excellent areas under the curve (0.93 and 0.95). Monte Carlo cross validation was also applied, demonstrating the robust diagnostic power of this metabolic profiling approach.

1H NMR metabolomics study of age profiling in children

Metabolic profiling of urine provides a fingerprint of personalized endogenous metabolite markers that correlate to a number of factors such as gender, disease, diet, toxicity, medication, and age. It is important to study these factors individually, if possible to unravel their unique contributions. In this study, age‐related metabolic changes in children of age 12 years and below were analyzed by 1H NMR spectroscopy of urine. The effect of age on the urinary metabolite profile was observed as a distinct age‐dependent clustering even from the unsupervised principal component analysis. Further analysis, using partial least squares with orthogonal signal correction regression with respect to age, resulted in the identification of an age‐related metabolic profile. Metabolites that correlated with age included creatinine, creatine, glycine, betaine/TMAO, citrate, succinate, and acetone. Although creatinine increased with age, all the other metabolites decreased. These results may be potentially useful in assessing the biological age (as opposed to chronological) of young humans as well as in providing a deeper understanding of the confounding factors in the application of metabolomics. Copyright

Peak capacity optimization in comprehensive two dimensional liquid chromatography: A practical approach

In this work we develop a practical approach to optimization in comprehensive two dimensional liquid chromatography (LC x LC) which incorporates the important under-sampling correction and is based on the previously developed gradient implementation of the Poppe approach to optimizing peak capacity. The Poppe method allows the determination of the column length, flow rate as well as initial and final eluent compositions that maximize the peak capacity at a given gradient time. It was assumed that gradient elution is applied in both dimensions and that various practical constraints are imposed on both the initial and final mobile phase composition in the first dimension separation. It was convenient to consider four different classes of solute sets differing in their retention properties. The major finding of this study is that the under-sampling effect is very important and causes some unexpected results including the important counter-intuitive observation that under certain conditions the optimum effective LC x LC peak capacity is obtained when the first dimension is deliberately run under sub-optimal conditions. In addition, we found that the optimum sampling rate in this study is rather slower than reported in previous studies and that it increases with longer first dimension gradient times.

15N-cholamine--a smart isotope tag for combining NMR- and MS-based metabolite profiling.

Recently, the enhanced resolution and sensitivity offered by chemoselective isotope tags have enabled new and enhanced methods for detecting hundreds of quantifiable metabolites in biofluids using nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry. However, the inability to effectively detect the same metabolites using both complementary analytical techniques has hindered the correlation of data derived from the two powerful platforms and thereby the maximization of their combined strengths for applications such as biomarker discovery and the identification of unknown metabolites. With the goal of alleviating this bottleneck, we describe a smart isotope tag, (15)N-cholamine, which possesses two important properties: an NMR sensitive isotope and a permanent charge for MS sensitivity. Using this tag, we demonstrate the detection of carboxyl group containing metabolites in both human serum and urine. By combining the individual strengths of the (15)N label and permanent charge, the smart isotope tag facilitates effective detection of the carboxyl-containing metabolome by both analytical methods. This study demonstrates a unique approach to exploit the combined strength of MS and NMR in the field of metabolomics.

Selective detection of diethylene glycol in toothpaste products using neutral desorption reactive extractive electrospray ionization tandem mass spectrometry.

A rapid, sensitive method based on neutral desorption (ND) reactive extractive electrospray ionization mass spectrometry (EESI-MS) has been established for the selective quantitative detection of diethylene glycol (DEG) in toothpaste products without any sample pretreatment. The sensitivity and specificity of DEG detection were enhanced by implementing selective ion/molecule reactions in the EESI process, featuring the EESI mass spectra with the characteristic signals of DEG. The method provided a low limit of detection (LOD) (approximately 0.00002%, weight percent of DEG in toothpaste), reasonable recovery (97.6-102.4%), and acceptable relative standard deviations (RSD < 8%, n = 8) for direct measuring of DEG in the spiked toothpaste samples. Trace amounts of DEG in commercial toothpaste products have been quantitatively detected without any sample manipulation. The results demonstrate that nonvolatile compounds such as DEG can be sensitively liberated using the neutral gas beam for quantitative detection from the extremely viscous toothpaste containing solid nanoparticles, showing that ND-EESI-MS is a useful tool for the rapid characterization of highly complex and/or viscous samples at molecular levels.

Statistical Analysis and Modeling of Mass Spectrometry-Based Metabolomics Data

Multivariate statistical techniques are used extensively in metabolomics studies, ranging from biomarker selection to model building and validation. Two model independent variable selection techniques, principal component analysis and two sample t-tests are discussed in this chapter, as well as classification and regression models and model related variable selection techniques, including partial least squares, logistic regression, support vector machine, and random forest. Model evaluation and validation methods, such as leave-one-out cross-validation, Monte Carlo cross-validation, and receiver operating characteristic analysis, are introduced with an emphasis to avoid over-fitting the data. The advantages and the limitations of the statistical techniques are also discussed in this chapter.