Kishore Kumar Pasikanti

Gas chromatography/mass spectrometry in metabolic profiling of biological fluids

One of the objectives of metabonomics is to identify subtle changes in metabolite profiles between biological systems of different physiological or pathological states. Gas chromatography mass spectrometry (GC/MS) is a widely used analytical tool for metabolic profiling in various biofluids, such as urine and blood due to its high sensitivity, peak resolution and reproducibility. The availability of the GC/MS electron impact (EI) spectral library further facilitates the identification of diagnostic biomarkers and aids the subsequent mechanistic elucidation of the biological or pathological variations. With the advent of new comprehensive two dimensional GC (GC x GC) coupled to time-of-flight mass spectrometry (TOFMS), it is possible to detect more than 1200 compounds in a single analytical run. In this review, we discuss the applications of GC/MS in the metabolic profiling of urine and blood, and discuss its advances in methodologies and technologies.

Global urinary metabolic profiling procedures using gas chromatography-mass spectrometry

The role of urinary metabolic profiling in systems biology research is expanding. This is because of the use of this technology for clinical diagnostic and mechanistic studies and for the development of new personalized health care and molecular epidemiology (population) studies. The methodologies commonly used for metabolic profiling are NMR spectroscopy, liquid chromatography mass spectrometry (LC/MS) and gas chromatography–mass spectrometry (GC/MS). In this protocol, we describe urine collection and storage, GC/MS and data preprocessing methods, chemometric data analysis and urinary marker metabolite identification. Results obtained using GC/MS are complementary to NMR and LC/MS. Sample preparation for GC/MS analysis involves the depletion of urea via treatment with urease, protein precipitation with methanol, and trimethylsilyl derivatization. The protocol described here facilitates the metabolic profiling of ∼400–600 metabolites in 120 urine samples per week.

Use of urine volatile organic compounds to discriminate tuberculosis patients from healthy subjects.

Development of noninvasive methods for tuberculosis (TB) diagnosis, with the potential to be administered in field situations, remains as an unmet challenge. A wide array of molecules are present in urine and reflect the pathophysiological condition of a subject. With infection, an alteration in the molecular constituents is anticipated, characterization of which may form a basis for TB diagnosis. In the present study volatile organic compounds (VOCs) in human urine derived from TB patients and healthy controls were identified and quantified using headspace gas chromatography/mass spectrometry (GC/MS). We found significant (p < 0.05) increase in the abundance of o-xylene (6.37) and isopropyl acetate (2.07) and decreased level of 3-pentanol (0.59), dimethylstyrene (0.37), and cymol (0.42) in TB patients compared to controls. These markers could discriminate TB from healthy controls and related diseases like lung cancer and chronic obstructive pulmonary disorder. This study suggests a possibility of using urinary VOCs for the diagnosis of human TB.

Development and validation of a gas chromatography/mass spectrometry metabonomic platform for the global profiling of urinary metabolites

This paper presents a simple and reliable gas chromatography/mass spectrometry (GC/MS) method for the metabonomic analysis of human urine samples. The sample preparation involved the depletion of excess urea via treatment with urease and subsequent protein precipitation using ice-cold ethanol. An aliquot of the mixture was separated, dried, trimethylsilyl (TMS)-derivatized and 1.0 microL of the derivatized extract was injected into the GC/MS system via split injection (1:10). Approximately 150 putative metabolites belonging to different chemical classes were identified from the pooled human urine samples. All the identified metabolites were selected to evaluate precision and stability of the GC/MS assay. More than 95% of the metabolites demonstrated good reproducibility, with intra-day and inter-day precision values below 15%. Metabolic profiling of 53 healthy male and female urine samples in combination with pattern recognition techniques was performed to further validate the GC/MS metabolite profiling assay. Principal component analysis (PCA) followed by orthogonal partial least squares analysis (OPLS) revealed differences between urinary metabolite profiles of healthy male and female subjects. This validated GC/MS metabolic profiling method may be further applied to the metabonomic screening of urinary biomarkers in clinical studies.

Trend analysis of metabonomics and systematic review of metabonomics-derived cancer marker metabolites

In this paper, trend analyses were performed to compare the different ‘omic’ technologies and the different analytical platforms and biological matrices exploited in metabonomic studies. While common and differential marker metabolites had been identified using various analytical platforms in metabonomics, little research was directed to review and consolidate marker metabolites in each disease state. A systematic review of metabonomics-derived marker metabolites in different cancers was performed to understand the significance of metabonomics in elucidating cancer biochemistry. The biological pathways associated with the cancer marker metabolites were further correlated to the pathology of cancers. Our trend analyses indicated that metabonomic publications increased exponentially in recent years, with nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography/mass spectrometry (LC/MS) being the most popular analytical platforms while blood, urine and tissue are the most commonly profiled biological matrices. Based on the consolidated cancer marker metabolites, it is reinforced that different cancers possess some common and yet distinct metabolic phenotypes, exhibiting numerous perturbed biochemical pathways related to their needs to support cell growth and proliferation and facilitate cancer cell survival.

Metabonomics investigation of human urine after ingestion of green tea with gas chromatography/mass spectrometry, liquid chromatography/mass spectrometry and 1H NMR spectroscopy

A method using gas chromatography/mass spectrometry (GC/MS), liquid chromatography/mass spectrometry (LC/MS) and (1)H NMR with pattern recognition tools such as principle components analysis (PCA) was used to study the human urinary metabolic profiles after the intake of green tea. From the normalized peak areas obtained from GC/MS and LC/MS and peak heights from (1)H NMR, statistical analyses were used in the identification of potential biomarkers. Metabolic profiling by GC/MS provided a different set of quantitative signatures of metabolites that can be used to characterize the molecular changes in human urine samples. A comparison of normalized metabonomics data for selected metabolites in human urine samples in the presence of potential overlapping peaks after tea ingestion from LC/MS and (1)H NMR showed the reliability of the current approach and method of normalization. The close agreements of LC/MS with (1)H NMR data showed that the effects of ion suppression in LC/MS for early eluting metabolites were not significant. Concurrently, the specificity of detecting the stated metabolites by (1)H NMR and LC/MS was demonstrated. Our data showed that a number of metabolites involved in glucose metabolism, citric acid cycle and amino acid metabolism were affected immediately after the intake of green tea. The proposed approach provided a more comprehensive picture of the metabolic changes after intake of green tea in human urine. The multiple analytical approach together with pattern recognition tools is a useful platform to study metabolic profiles after ingestion of botanicals and medicinal plants.

Comparative evaluation of software for retention time alignment of gas chromatography/time-of-flight mass spectrometry-based metabonomic data

In chromatography-based metabonomic research, retention time (RT) alignment of chromatographic peaks poses a challenge for the accurate profiling of biomarkers. Although a number of RT alignment software has been reported, the performance of these software packages have not been comprehensively evaluated. This study aimed to evaluate the RT alignment accuracy of publicly available and commercial RT alignment software. Two gas chromatography/mass spectrometry (GC/MS) datasets acquired from a mixture of standard metabolites and human bladder cancer urine samples, were used to assess three publicly available software packages, MetAlign, MZmine and TagFinder, and two commercial applications comprising the Calibration feature and Statistical Compare of ChromaTOF software. The overall RT alignment accuracies in aligning standard compounds mixture were 93, 92, 74, 73 and 42% for Calibration feature, MZmine, MetAlign, Statistical Compare and TagFinder, respectively. Additionally, unique trends were observed for the individual software with regards to the different experimental conditions related to extent and direction of RT shifts. Conflicting performance was observed for human urine samples suggesting that RT misalignments still occurred despite the use of RT alignment software. While RT alignment remains an inevitable step in data preprocessing, metabonomic researchers are recommended to perform manual check on the RT alignment of important biomarkers as part of their validation process.

Metabonomic Profiling of Bladder Cancer

Early diagnosis and life-long surveillance are clinically important to improve the long-term survival of bladder cancer patients. Currently, a noninvasive biomarker that is as sensitive and specific as cystoscopy in detecting bladder tumors is lacking. Metabonomics is a complementary approach for identifying perturbed metabolic pathways in bladder cancer. Significant progress has been made using modern metabonomic techniques to characterize and distinguish bladder cancer patients from control subjects, identify marker metabolites, and shed insights on the disease biology and potential therapeutic targets. With its rapid development, metabonomics has the potential to impact the clinical management of bladder cancer patients in the future by revolutionizing the diagnosis and life-long surveillance strategies and stratifying patients for diagnostic, surgical, and therapeutic clinical trials. An introduction to metabonomics, typical metabonomic workflow, and critical evaluation of metabonomic investigations in identifying biomarkers for the diagnosis of bladder cancer are presented.

Metabonomic diagnosis of bladder cancer: what are the advantages and considerations?

Bladder cancer (BC) is the fifth most common cancer and is one of the leading causes of death worldwide [1]. The 5-year survival rate for BC is approximately 94% if detected early; thus timely diagnosis and intervention of BC increase patient’s survival rate dramatically [2]. However, BC has a high recurrence rate and patients are subjected to life-long surveillance. As a result, the lifetime economic burden per BC patient is higher than other cancer types. Currently, the standard diagnoses of BC are cystoscopy and urinary cytology [3]. While cystoscopy is clinically reliable, it is expensive, invasive and associated with a definite risk of morbidity. On the other hand, noninvasive urinary cytology demonstrates poor sensitivity in detecting low-grade BC (4–31%) [4]. In recent years, several proteinaceous urinebased bladder tumor markers (UBBTMs) have been evaluated for the diagnosis of BC, such as bladder tumor antigen (BTA), NMP22, FDP and ImmunoCyt [5]. However, the specificity and sensitivity of current molecular biomarkers are not adequate to replace cystoscopy [6]. Therefore, there is an impetus to develop sensitive and specific noninvasive biomarkers for the diagnosis and surveillance of BC. Dysregulated molecular pathways, associated with tumor genes, secrete specific modulated levels of metabolites into biological fluids that may be detectable prior to clinical symptoms, rendering them potential early biomarkers for cancer diagnosis. Metabonomics measures the dynamic multiparametric responses of systems biological metabolome to genetic modifications or pathophysiological stimuli such as cancers [7]. It allows scientists to survey global dysregulated metabolic pathways and gain holistic insights into cancer etiology and progression. Significant progress has been made using modern metabonomics techniques to characterize BC patients, identify marker metabolites and generate new knowledge in disease biology and potential therapeutic targets [5].

An automated Pearson's correlation change classification (APC3) approach for GC/MS metabonomic data using total ion chromatograms (TICs)

A fully automated and computationally efficient Pearsons correlation change classification (APC3) approach is proposed and shown to have overall comparable performance with both an average accuracy and an average AUC of 0.89 ± 0.08 but is 3.9 to 7 times faster, easier to use and have low outlier susceptibility in contrast to other dimensional reduction and classification combinations using only the total ion chromatogram (TIC) intensities of GC/MS data. The use of only the TIC permits the possible application of APC3 to other metabonomic data such as LC/MS TICs or NMR spectra. A RapidMiner implementation is available for download at http://padel.nus.edu.sg/software/padelapc3.