Narasimhamurthy Shanaiah

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.

Early Detection of Recurrent Breast Cancer Using Metabolite Profiling

We report on the development of a monitoring test for recurrent breast cancer, using metabolite-profiling methods. Using a combination of nuclear magnetic resonance (NMR) and two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) methods, we analyzed the metabolite profiles of 257 retrospective serial serum samples from 56 previously diagnosed and surgically treated breast cancer patients. One hundred sixteen of the serial samples were from 20 patients with recurrent breast cancer, and 141 samples were from 36 patients with no clinical evidence of the disease during ∼6 years of sample collection. NMR and GC×GC-MS data were analyzed by multivariate statistical methods to compare identified metabolite signals between the recurrence samples and those with no evidence of disease. Eleven metabolite markers (seven from NMR and four from GC×GC-MS) were shortlisted from an analysis of all patient samples by using logistic regression and 5-fold cross-validation. A partial least squares discriminant analysis model built using these markers with leave-one-out cross-validation provided a sensitivity of 86% and a specificity of 84% (area under the receiver operating characteristic curve = 0.88). Strikingly, 55% of the patients could be correctly predicted to have recurrence 13 months (on average) before the recurrence was clinically diagnosed, representing a large improvement over the current breast cancer-monitoring assay CA 27.29. To the best of our knowledge, this is the first study to develop and prevalidate a prediction model for early detection of recurrent breast cancer based on metabolic profiles. In particular, the combination of two advanced analytical methods, NMR and MS, provides a powerful approach for the early detection of recurrent breast cancer.

Correlative and quantitative 1H NMR-based metabolomics reveals specific metabolic pathway disturbances in diabetic rats

Type 1 diabetes was induced in Sprague-Dawley rats using streptozotocin. Rat urine samples (8 diabetic and 10 control) were analyzed by 1H nuclear magnetic resonance (NMR) spectroscopy. The derived metabolites using univariate and multivariate statistical analysis were subjected to correlative analysis. Plasma metabolites were measured by a series of bioassays. A total of 17 urinary metabolites were identified in the 1H NMR spectra and the loadings plots after principal components analysis. Diabetic rats showed significantly increased levels of glucose (P < 0.00001), alanine (P < 0.0002), lactate (P < 0.05), ethanol (P < 0.05), acetate (P < 0.05), and fumarate (P < 0.05) compared with controls. Plasma assays showed higher amounts of glucose, urea, triglycerides, and thiobarbituric acid-reacting substances in diabetic rats. Striking differences in the Pearsons correlation of the 17 NMR-detected metabolites were observed between control and diabetic rats. Detailed analysis of the altered metabolite levels and their correlations indicate a significant disturbance in the glucose metabolism and tricarboxylic acid (TCA) cycle and a contribution from gut microbial metabolism. Specific perturbed metabolic pathways include the glucose-alanine and Cori cycles, the acetate switch, and choline metabolism. Detection of the altered metabolic pathways and bacterial metabolites using this correlative and quantitative NMR-based metabolomics approach should help to further the understanding of diabetes-related mechanisms.

Class selection of amino acid metabolites in body fluids using chemical derivatization and their enhanced 13C NMR

We report a chemical derivatization method that selects a class of metabolites from a complex mixture and enhances their detection by 13C NMR. Acetylation of amines directly in aqueous medium with 1,1′-13C2 acetic anhydride is a simple method that creates a high sensitivity and quantitative label in complex biofluids with minimal sample pretreatment. Detection using either 1D or 2D 13C NMR experiments produces highly resolved spectra with improved sensitivity. Experiments to identify and compare amino acids and related metabolites in normal human urine and serum samples as well as in urine from patients with the inborn errors of metabolism tyrosinemia type II, argininosuccinic aciduria, homocystinuria, and phenylketonuria demonstrate the method. The use of metabolite derivatization and 13C NMR spectroscopy produces data suitable for metabolite profiling analysis of biofluids on a time scale that allows routine use. Extension of this approach to enhance the NMR detection of other classes of metabolites has also been accomplished. The improved detection of low-concentration metabolites shown here creates opportunities to improve the understanding of the biological processes and develop improved disease detection methodologies.

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

Quantitative analysis of blood plasma metabolites using isotope enhanced NMR methods

NMR spectroscopy is a powerful analytical tool for both qualitative and quantitative analysis. However, accurate quantitative analysis in complex fluids such as human blood plasma is challenging, and analysis using one-dimensional NMR is limited by signal overlap. It is impractical to use heteronuclear experiments involving natural abundance (13)C on a routine basis due to low sensitivity, despite their improved resolution. Focusing on circumventing such bottlenecks, this study demonstrates the utility of a combination of isotope enhanced NMR experiments to analyze metabolites in human blood plasma. (1)H-(15)N HSQC and (1)H-(13)C HSQC experiments on the isotope tagged samples combined with the conventional (1)H one-dimensional and (1)H-(1)H TOCSY experiments provide quantitative information on a large number of metabolites in plasma. The methods were first tested on a mixture of 28 synthetic analogues of metabolites commonly present in human blood; 27 metabolites in a standard NIST (National Institute of Standards and Technology) human blood plasma were then identified and quantified with an average coefficient of variation of 2.4% for 17 metabolites and 5.6% when all the metabolites were considered. Carboxylic acids and amines represent a majority of the metabolites in body fluids, and their analysis by isotope tagging enables a significant enhancement of the metabolic pool for biomarker discovery applications. Improved sensitivity and resolution of NMR experiments imparted by (15)N and (13)C isotope tagging are attractive for both the enhancement of the detectable metabolic pool and accurate analysis of plasma metabolites. The approach can be easily extended to many additional metabolites in almost any biological mixture.

Chemoselective 15N tag for sensitive and high-resolution nuclear magnetic resonance profiling of the carboxyl-containing metabolome

Metabolic profiling has received increasing recognition as an indispensable complement to genomics and proteomics for probing biological systems and for clinical applications. (1)H nuclear magnetic resonance (NMR) is widely used in the field but is challenged by spectral complexity and overlap. Improved and simple methods that quantitatively profile a large number of metabolites are sought to make further progress. Here, we demonstrate a simple isotope tagging strategy, in which metabolites with carboxyl groups are chemically tagged with (15)N-ethanolamine and detected using a 2D heteronuclear correlation NMR experiment. This method is capable of detecting over 100 metabolites at concentrations as low as a few micromolar in biological samples, both quantitatively and reproducibly. Carboxyl-containing compounds are found in almost all metabolic pathways, and thus this new approach should find a variety of applications.

Application of 31P NMR spectroscopy and chemical derivatization for metabolite profiling of lipophilic compounds in human serum.

New methods for obtaining metabolic fingerprints of biological samples with improved resolution and sensitivity are highly sought for early disease detection, studies of human health and pathophysiology, and for better understanding systems biology. Considering the complexity of biological samples, interest in biochemical class selection through the use of chemoselective probes for improved resolution and quantitation is increasing. Considering the role of lipids in the pathogenesis of a number of diseases, in this study fingerprinting of lipid metabolites was achieved by 31P labeling using the derivatizing agent 2‐chloro‐4,4,5,5‐tetramethyldioxaphospholane. Lipids containing hydroxyl, aldehyde and carboxyl groups were selectively tagged with 31P and then detected with good resolution using 31P NMR by exploiting the 100% natural abundance and wide chemical shift range of 31P. After standardizing the reaction conditions using representative compounds, the derivatization approach was used to profile lipids in human serum. The results show that the 31P derivatization approach is simple, reproducible and highly quantitative, and has the potential to profile a number of important lipids in complex biological samples. Copyright

Use of EDTA to minimize ionic strength dependent frequency shifts in the 1H NMR spectra of urine

The 1H NMR spectrum of urine exhibits a large number of detectable metabolites and is, therefore, highly suitable for the study of perturbations caused by disease, toxicity, nutrition or environmental factors in humans and animals. However, variations in the chemical shifts and intensities due to altered pH and ionic strength present a challenge in NMR-based studies. With a view towards understanding and minimizing the effects of these variations, we have extensively studied the effects of ionic strength and pH on the chemical shifts of common urine metabolites and their possible reduction using EDTA (ethylenediaminetetraacetic acid). 1H NMR chemical shifts for alanine, citrate, creatinine, dimethylamine, glycine, histidine, hippurate, formate and the internal reference, TSP (trimethylsilylpropionic acid-d4, sodium salt) obtained under different conditions were used to assess each effect individually. EDTA minimizes the frequency shifts of the metabolites that have a propensity for metal binding. Chelation of such metal ions is evident from the appearance of signals from EDTA complexed to divalent metal ions such as calcium and magnesium. Not surprisingly, increasing the buffer concentration or buffer volume also minimizes pH dependent frequency shifts. The combination of EDTA and an appropriate buffer effectively minimizes both pH dependent frequency shifts and ionic strength dependent intensity variations in urine NMR spectra.

Ibuprofen metabolite profiling using a combination of SPE/column-trapping and HPLC-micro-coil NMR.

Solid-phase extraction and column-trapping preconcentration are combined to enhance HPLC-nuclear magnetic resonance (HPLC-NMR) and applied to metabolite profiling in biological samples. Combining the two signal enhancement techniques improved the NMR signal substantially such that we were able to identify 2-hydroxyibuprofen, carboxyibuprofen, and unmetabolized ibuprofen molecules from a small urine sample after a therapeutic dose of ibuprofen. The hyphenated SPE/column-trapping method resulted in an excellent overall signal enhancement of up to 90-fold.