A comprehensive urinary metabolomic approach for identifying kidney cancerr

The diagnosis of cancer by examination of the urine has the potential to improve patient outcomes by means of earlier detection. Due to the fact that the urine contains metabolic signatures of many biochemical pathways, this biofluid is ideally suited for metabolomic analysis, especially involving diseases of the kidney and urinary system. In this pilot study, we test three independent analytical techniques for suitability for detection of renal cell carcinoma (RCC) in urine of affected patients. Hydrophilic interaction chromatography (HILIC-LC-MS), reversed-phase ultra performance liquid chromatography (RP-UPLC-MS), and gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) all were used as complementary separation techniques. The combination of these techniques is best suited to cover a very large part of the urine metabolome by enabling the detection of both lipophilic and hydrophilic metabolites present therein. In this study, it is demonstrated that sample pretreatment with urease dramatically alters the metabolome composition apart from removal of urea. Two new freely available peak alignment methods, MZmine and XCMS, are used for peak detection and retention time alignment. The results are analyzed by a feature selection algorithm with subsequent univariate analysis of variance (ANOVA) and a multivariate partial least squares (PLS) approach. From more than 2000 mass spectral features detected in the urine, we identify several significant components that lead to discrimination between RCC patients and controls despite the relatively small sample size. A feature selection process condensed the significant features to less than 30 components in each of the data sets. In future work, these potential biomarkers will be further validated with a larger patient cohort. Such investigation will likely lead to clinically applicable assays for earlier diagnosis of RCC, as well as other malignancies, and thereby improved patient prognosis.     

Development and optimization of a metabolomic method for analysis of adherent cell cultures

In this investigation, a gas chromatography/mass spectrometry (GC/MS)-based metabolomic protocol for adherent cell cultures was developed using statistical design of experiments. Cell disruption, metabolite extraction, and the GC/MS settings were optimized aiming at a gentle, unbiased, sensitive, and high-throughput metabolomic protocol. Due to the heterogeneity of the metabolome and the inherent selectivity of all analytical techniques, development of unbiased protocols is highly complex. Changing one parameter of the protocol may change the response of many groups of metabolites. In this investigation, statistical design of experiments and multivariate analysis also allowed such interaction effects to be taken into account. The protocol was validated with respect to linear range, precision, and limit of detection in a clonal rat insulinoma cell line (INS-1 832/13). The protocol allowed high-throughput profiling of metabolites covering the major metabolic pathways. The majority of metabolites displayed a linear range from a single well in a 96-well plate up to a 10 cm culture dish. The method allowed a total of 47 analyses to be performed in 24 h.     

INCA 2.0: A tool for integrated,dynamic modeling of NMR- and MS-based isotopomer measurements and rigorous metabolic flux analysis

Metabolic flux analysis (MFA) combines experimental measurements and computational modeling to determine biochemical reaction rates in live biological systems. Advancements in analytical instrumentation, such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), have facilitated chemical separation and quantification of isotopically enriched metabolites. However, no software packages have been previously described that can integrate isotopomer measurements from both MS and NMR analytical platforms and have the flexibility to estimate metabolic fluxes from either isotopic steady-state or dynamic labeling experiments. By applying physiologically relevant cardiac and hepatic metabolic models to assess NMR isotopomer measurements, we herein test and validate new modeling capabilities of our enhanced flux analysis software tool, INCA 2.0. We demonstrate that INCA 2.0 can simulate and regress steady-state ¹³C NMR datasets from perfused hearts with an accuracy comparable to other established flux assessment tools. Furthermore, by simulating the infusion of three different ¹³C acetate tracers, we show that MFA based on dynamic ¹³C NMR measurements can more precisely resolve cardiac fluxes compared to isotopically steady-state flux analysis. Finally, we show that estimation of hepatic fluxes using combined ¹³C NMR and MS datasets improves the precision of estimated fluxes by up to 50%. Overall, our results illustrate how the recently added NMR data modeling capabilities of INCA 2.0 can enable entirely new experimental designs that lead to improved flux resolution and can be applied to a wide range of biological systems and measurement time courses.     

Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry

The usual aim in metabolomic studies is to quantify the entire metabolome of each of a series of biological samples. To do this for complex biological matrices, e.g., plant tissues, efficient and reproducible extraction protocols must be developed. However, derivatization protocols must also be developed if GC/MS (one of the mostly widely used analytical methods for metabolomics) is involved. The aim of this study was to investigate how different chemical and physical factors (extraction solvent, derivatization reagents, and temperature) affect the extraction and derivatization of the metabolome from leaves of the plant Arabidopsis thaliana. Using design of experiment procedures, variation was systematically introduced, and the effects of this variation were analyzed using regression models. The results show that this approach allows a reliable protocol for metabolomic analysis of Arabidopsis to be determined with a relatively limited number of experiments. Following two different investigations an extraction and derivatization protocol was chosen. Further, the reproducibility of the analysis of 66 endogenous compounds was investigated, and it was shown that both hydrophilic and lipophilic compounds were detected with high reproducibility.     

MALDI/MS-based epitope mapping of antigens bound to immobilized antibodies

Proteolytic digestion of proteins bound to immobilized antibodies, combined with matrix assisted laser desorption (MALDI) mass spectrometric identification of the affinity-bound peptides, can be a powerful technique for epitope determination. Binding of the protein to the antibody is done while the protein is in its native, folded state. A purified protein is not required for this procedure, because only proteins containing the antigenic determinant will bind to the antibody in the initial step. The method makes use of the resistance of the antibody to enzymatic digestion. Enzymatic cleavage products of the antigenic protein not containing the epitope are washed off the beads, leaving the epitope-containing fragments affinity bound to the immobilized antibody. Dissociation of the antigen-antibody complex prior to mass spectrometric analysis is unnecessary because the affinity-bound peptides are released by the MALDI matrix crystallization process, although the antibody remains covalently attached to the sepharose beads. This epitope-mapping protocol has been used in the determination of both continuous and discontinuous epitopes on both glycosylated and unglycosylated proteins.     

Validation of onchocerciasis biomarker N-acetyltyramine-O-glucuronide (NATOG)

The Neglected Tropical Disease onchocerciasis is a parasitic disease. Despite many control programmes by the World Health Organization (WHO), large communities in West and Central Africa are still affected. Besides logistic challenges during biannual mass drug administration, the lack of a robust, point-of-care diagnostic is limiting successful eradication of onchocerciasis. Towards the implementation of a non-invasive and point-of-care diagnostic, we have recently reported the discovery of the biomarker N-acetyltyramine-O-glucuronide (NATOG) in human urine samples using a metabolomics-mining approach. NATOG’s biomarker value was enhanced during an investigation in a rodent model. Herein, we further detail the specificity of NATOG in active onchocerciasis infections as well as the co-infecting parasites Loa loa and Mansonella perstans. Our results measured by liquid chromatography coupled with mass spectrometry (LC-MS) reveal elevated NATOG values in mono- and co-infection samples only in the presence of the nematode Onchocerca volvulus. Metabolic pathway investigation of l-tyrosine/tyramine in all investigated nematodes uncovered an important link between the endosymbiotic bacterium Wolbachia and O. volvulus for the biosynthesis of NATOG. Based on these extended studies, we suggest NATOG as a biomarker for tracking active onchocerciasis infections and provide a threshold concentration value of NATOG for future diagnostic tool development.     

Complexity and pitfalls of mass spectrometry-based targeted metabolomics in brain research

Current quantitative metabolomic research in brain tissue is challenged by several analytical issues. To compare data of metabolite pattern, ratios of individual metabolite concentrations and composed classifiers characterizing a distinct state, standardized workup conditions, and extraction medium are crucial. Differences in physicochemical properties of individual compounds and compound classes such as polarity determine extraction yields and, thus, ratios of compounds with varying properties. Also, variations in suppressive effects related to coextracted matrix components affect standards or references and their concentration-dependent responses.The selection of a common tissue extraction protocol is an ill-posed problem because it can be regarded as a multiple objective decision depending on factors such as sample handling practicability, measurement precision, control of matrix effects, and relevance of the chemical assay. This study systematically evaluates the impact of extraction solvents and the impact of the complex brain tissue on measured metabolite levels, taking into account ionization efficiency as well as challenges encountered in the trace-level quantification of the analytes in brain matrices. In comparison with previous studies that relied on nontargeted platforms, consequently emphasizing the global behavior of the metabolomic fingerprint, here we focus on several series of metabolites spanning over extensive polarity, concentration, and molecular mass ranges.     

Feature extraction in the analysis of proteomic mass spectra

Feature extraction or biomarker selection is a critical step in disease diagnosis and knowledge discovery based on protein MS. Many studies have discussed the classification methods applied in proteomics; however, few could be found to address feature extraction in detail. In this paper, we developed a systematic approach for the extraction of mass spectrum peak apex and peak area with special emphasis on noise filtration and peak calibration. Application to a head and neck cancer data generated at the Eastern Virginia Medical School [Wadsworth, J. T., Somers, K. D., Cazares, L. H., Malik, G. et al.., Clin. Cancer Res. 2004, 10, 1625-1632] revealed that the new feature extraction method would yield consistent and highly discriminatory biomarkers.     

Microbial metabolomics: past,present and future methodologies

Microbial metabolomics has received much attention in recent years mainly because it supports and complements a wide range of microbial research areas from new drug discovery efforts to metabolic engineering. Broadly, the term metabolomics refers to the comprehensive (qualitative and quantitative) analysis of the complete set of all low molecular weight metabolites present in and around growing cells at a given time during their growth or production cycle. This review focuses on the past, current and future development of various experimental protocols in the rapid developing area of metabolomics in the ongoing quest to reliably quantify microbial metabolites formed under defined physiological conditions. These developments range from rapid sample collection, instant quenching of microbial metabolic activity, extraction of the relevant intracellular metabolites as well as quantification of these metabolites using enzyme based and or modern high tech hyphenated analytical protocols, mainly chromatographic techniques coupled to mass spectrometry (LC-MSⁿ, GC-MSⁿ, CE-MSⁿ), where n indicates the number of tandem mass spectrometry, and nuclear magnetic resonance spectroscopy (NMR).     

Non-polar and polar chemical profiling of six Casearia species (Salicaceae)

Six species of Casearia (C. decandra, C. grandiflora, C. javitensis, C. arborea, C. lasiophylla and C. ulmifolia) were chemically investigated in their non-polar and polar constituents. To this purpose, leaf extracts were analyzed by Gas- (GC) and Liquid-Chromatography (LC) coupled with Mass Spectrometry (MS). Twenty compounds, mainly identified as terpenes, fatty acids and hydrocarbons, and differentially expressed in the six species, were detected in the non-polar extracts. Fourteen compounds, among which glycosylated flavonoids and clerodane-type diterpenes, were tentatively identified by LC-MS/MS analysis in the leaf ethanol extracts.     

PAXgene fixation enables comprehensive metabolomic and proteomic analyses of tissue specimens by MALDI MSI

An alcohol-based non-crosslinking tissue fixative, PAXgene Tissue System, has been proposed as alternative fixation method to formalin, providing superior and morphological preservation. To date, metabolites have not been assessed in PAXgene-fixed tissues. The study focuses on a comparison between PAXgene and standard formalin fixation for metabolomic analysis by MALDI mass spectrometry imaging. Therefore, fifty-six samples from seven mice organs were fixed with PAXgene (PFPE) or formalin (FFPE), embedded in paraffin, and processed to a tissue microarray. PAXgene was able to spatially preserve metabolites in organs achieving an overlap of common metabolites ranging from 34 to 78% with FFPE. Highly similar signal intensities and visualization of molecules demonstrated negligible differences for metabolite imaging on PFPE compared to FFPE tissues. In addition, we performed proteomic analysis of intact proteins and peptides derived from enzymatic digestion. An overlap of 33 to 58% was found between FFPE and PFPE tissue samples in peptide analysis with a higher number of PFPE-specific peaks. Analysis of intact proteins achieved an overlap in the range of 0 to 28% owing to the poor detectability of cross-linked proteins in formalin-fixed tissues. Furthermore, metabolite and peptide profiles obtained from PFPE tissues were able to correctly classify organs independent of the fixation method, whereas a distinction of organs by protein profiles was only achieved by PAXgene fixation. Finally, we applied MALDI MSI to human biopsies by sequentially analyzing metabolites and peptides within the same tissue section. Concerning prospective studies, PAXgene can be used as an alternative fixative for multi-omic tissue analysis.     

Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform

Mass spectrometry is being used to find disease-related patterns in mixtures of proteins derived from biological fluids. Questions have been raised about the reproducibility and reliability of peak quantifications using this technology. We collected nipple aspirate fluid from breast cancer patients and healthy women, pooled them into a quality control sample, and produced 24 replicate SELDI spectra. We developed a novel algorithm to process the spectra, denoising with the undecimated discrete wavelet transform (UDWT), and evaluated it for consistency and reproducibility. UDWT efficiently decomposes spectra into noise and signal. The noise is consistent and uncorrelated. Baseline correction produces isolated peak clusters separated by flat regions. Our method reproducibly detects more peaks than the method implemented in Ciphergen software. After normalization and log transformation, the mean coefficient of variation of peak heights is 10.6%. Our method to process spectra provides improvements over existing methods. Denoising using the UDWT appears to be an important step toward obtaining results that are more accurate. It improves the reproducibility of quantifications and supplies tools for investigation of the variations in the technology more carefully. Further study will be required, because we do not have a gold standard providing an objective assessment of which peaks are present in the samples.     

Methods for mapping of interaction networks involving membrane proteins

Nearly one-third of all genes in various organisms encode membrane-associated proteins that participate in numerous protein-protein interactions important to the processes of life. However, membrane protein interactions pose significant challenges due to the need to solubilize membranes without disrupting protein-protein interactions. Traditionally, analysis of isolated protein complexes by high-resolution 2D gel electrophoresis has been the main method used to obtain an overall picture of proteome constituents and interactions. However, this method is time consuming, labor intensive, detects only abundant proteins and is limited with respect to the coverage required to elucidate large interaction networks. In this review, we discuss the application of various methods to elucidate interactions involving membrane proteins. These techniques include methods for the direct isolation of single complexes or interactors as well as methods for characterization of entire subcellular and cellular interactomes.     

Development of a metabolomic approach based on urine samples and direct infusion mass spectrometry

The analysis of urine by direct infusion mass spectrometry suffers from ion suppression due to its high salt content and inter-sample variability caused by the differences in urine volume between persons. Thus, urine metabolomics requires a careful selection of the sample preparation procedure and a normalization strategy to deal with these problems. Several approaches were tested for metabolomic analysis of urine samples by direct infusion electrospray mass spectrometry (DI–ESI–MS), including solid phase extraction, liquid–liquid extraction, and sample dilution. In addition, normalization of results based on conductivity values and statistical treatment was performed to minimize sample variability. Both urine dilution and solid phase extraction with mixed mode sorbent considerably reduced the salt content in urine, providing comprehensive metabolomic fingerprints. Moreover, statistical data normalization enabled the correction of inter-sample physiological variability, improving the quality of results obtained. Therefore, high-throughput DI–ESI–MS fingerprinting of urine samples can be achieved with simple pretreatment procedures allowing the use of this noninvasive sampling in metabolomics. Finally, the optimized approach was tested in a pilot metabolomic investigation of urine samples from transgenic mice models of Alzheimer’s disease (APP/PS1) in order to illustrate the potential of the methodology.     

Review on the analysis of 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine and its phase I and phase II metabolites in biological matrices, foodstuff and beverages

The heterocyclic aromatic amine, 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), has been shown to be carcinogenic in rodents, mice and rats. Following phase I N-hydroxylation and phase II esterification PhIP exerts its carcinogenic effect by binding to DNA purines. Quantitative and qualitative analysis of its bioactivated metabolites as well as it detoxification products is important in studying its biological effects and inter- and intra-individual exposures. A review is presented with an extensive coverage of publications specifically reporting on the analysis of PhIP and its phase I and II metabolites in biological matrices, foodstuff and beverages. Analytical techniques such as liquid and gas chromatography coupled with various detection techniques (mass spectrometry, ultraviolet or fluorescence detection) were mostly applied. We conclude that since the initial identification of PhIP in 1986 a large set of assays has been developed for the analysis of PhIP and its phase I and phase II metabolites in a wide range of matrices, these included food products and biological samples such as plasma, urine and faeces. In addition, it was shown that numerous metabolites were recovered and identified. Thus, we conclude that liquid chromatography coupled to mass spectrometry is clearly the method of choice for sensitive qualitative as well as quantitative analysis with high selectivity and reaching lower quantification levels in the sub pg/mL range. The main aim of this review is that it can be used by other researchers as a resource for method development and optimization of analytical methods of PhIP and its carcinogenic or detoxification products.     

Dynamic metabolic change is indicative of inflammation-induced transformation of hepatic cells

The observation that prolonged inflammation plays a causative role in cancer development has been well documented. However, an incremental process that leads from healthy to malignant phenotypes has not yet been described. Experimentally induced hepatocellular carcinoma is considered one of the representative laboratory models for studying this process. Hepatic exposure to viral infection or toxic reagents leads to chronic inflammation and gradual transformation into hepatocellular carcinoma. Here we present metabolomic profiles of hepatic cells at different stages during inflammation-induced cellular transformation by N-nitrosodiethylamine. Using gas chromatography–mass spectrometry, we quantitatively assessed the changes in cellular metabolites during the transformation process in hepatitis and liver cirrhosis. Further pathway analysis of the differentially expressed metabolites showed that carbohydrate metabolism and lipid metabolism were greatly altered in hepatitis and liver cirrhosis, respectively. Additionally, the enhanced inflammation in cirrhosis was associated with a shift from carbohydrate metabolism to lipid and amino acid metabolism. Among the differentially expressed metabolites found in diseased mouse livers, d-glucose and d-mannitol showed the most significant changes, highlighting them as potential early-diagnostic biomarkers of hepatocellular carcinoma development. Taken together, these investigations into the dynamic metabolic changes that occur during the precancerous stages of hepatocellular carcinoma add to and refine understanding of how chronic inflammation ultimately leads to cancer. Furthermore, the findings set the stage for identifying metabolites that may serve as early-diagnostic indicators of these unfolding events.     

Technical and clinical aspects of cortisol as a biochemical marker of chronic stress

Stress is now recognized as a universal premorbid factor associated with many risk factors of various chronic diseases. Acute stress may induce an individual’s adaptive response to environmental demands. However, chronic, excessive stress causes cumulative negative impacts on health outcomes through “allostatic load”. Thus, monitoring the quantified levels of long-term stress mediators would provide a timely opportunity for prevention or earlier intervention of stressrelated chronic illnesses. Although either acute or chronic stress could be quantified through measurement of changes in physiological parameters such as heart rate, blood pressure, and levels of various metabolic hormones, it is still elusive to interpret whether the changes in circulating levels of stress mediators such as cortisol can reflect the acute, chronic, or diurnal variations. Both serum and salivary cortisol levels reveal acute changes at a single point in time, but the overall long-term systemic cortisol exposure is difficult to evaluate due to circadian variations and its protein-binding capacity. Scalp hair has a fairy predictable growth rate of approximately 1 cm/month, and the most 1 cm segment approximates the last month’s cortisol production as the mean value. The analysis of cortisol in hair is a highly promising technique for the retrospective assessment of chronic stress. [BMB Reports 2015; 48(4): 209-216]     

Design and analysis of experiments with high throughput biological assay data

The design and analysis of experiments using gene expression microarrays is a topic of considerable current research, and work is beginning to appear on the analysis of proteomics and metabolomics data by mass spectrometry and NMR spectroscopy. The literature in this area is evolving rapidly, and commercial software for analysis of array or proteomics data is rarely up to date, and is essentially nonexistent for metabolomics data. In this paper, I review some of the issues that should concern any biologists planning to use such high-throughput biological assay data in an experimental investigation. Technical details are kept to a minimum, and may be found in the referenced literature, as well as in the many excellent papers which space limitations prevent my describing. There are usually a number of viable options for design and analysis of such experiments, but unfortunately, there are even more non-viable ones that have been used even in the published literature. This is an area in which up-to-date knowledge of the literature is indispensable for efficient and effective design and analysis of these experiments. In general, we concentrate on relatively simple analyses, often focusing on identifying differentially expressed genes and the comparable issues in mass spectrometry and NMR spectroscopy (consistent differences in peak heights or areas for example). Complex multivariate and pattern recognition methods also need much attention, but the issues we describe in this paper must be dealt with first. The literature on analysis of proteomics and metabolomics data is as yet sparse, so the main focus of this paper will be on methods devised for analysis of gene expression data that generalize to proteomics and metabolomics, with some specific comments near the end on analysis of metabolomics data by mass spectrometry and NMR spectroscopy.     

A review of characterization of tocotrienols from plant oils and foods

Tocotrienols, members of the vitamin E family, are natural compounds found in a number of vegetable oils, wheat germ, barley and certain types of nuts and grains. Vegetable oils provide the best sources of these vitamin E forms, particularly palm oil and rice bran oil contain higher amounts of tocotrienols. Other sources of tocotrienols include grape fruit seed oil, oats, hazelnuts, maize, olive oil, buckthorn berry, rye, flax seed oil, poppy seed oil and sunflower oil. Tocotrienols are of four types, viz. alpha (α), beta (β), gamma (γ) and delta (δ). Unlike tocopherols, tocotrienols are unsaturated and possess an isoprenoid side chain. A number of researchers have developed methods for the extraction, analysis, identification and quantification of different types of vitamin E compounds. This article constitutes an in-depth review of the chemistry and extraction of the unsaturated vitamin E derivatives, tocotrienols, from various sources using different methods. This review article lists the different techniques that are used in the characterization and purification of tocotrienols such as soxhlet and solid–liquid extractions, saponification method, chromatography (thin layer, column chromatography, gas chromatography, supercritical fluid, high performance), capillary electrochromatography and mass spectrometry. Some of the methods described were able to identify one form or type while others could analyse all the analogues of tocotrienol molecules. Hence, this article will be helpful in understanding the various methods used in the characterization of this lesser known vitamin E variant.     

Parallelized small-scale production of uniformly C-labeled cell extract for quantitative metabolome analysis

The need for quantitative intracellular metabolome information is central to modern applied biotechnology and systems biology. In most cases, sample preparation and metabolite analysis result in degradation of metabolites and signal suppression due to metabolite instability and matrix effects during LC–MS analysis. Therefore the application of uniformly (U) ¹³C-labeled cell extract as an internal standard has gained interest in recent years. In this study a multiple-step protocol has been developed for efficient preparation of U-¹³C-labeled Escherichia coli cell extracts in stirred-tank bioreactors on a milliliter scale with a minimal supply of costly ¹³C-labeled substrate. Significant reduction of fermentation medium salt concentration in the U-¹³C-labeled cell extract was achieved to reduce ion-suppression effects during mass-spectrometric analysis. Additionally, variation of reaction conditions in parallel-operated stirred-tank bioreactors on a milliliter scale enables the simultaneous preparation of U-¹³C-labeled cell extracts with varying metabolite concentrations, which is shown by an example of the labeled phosphoenolpyruvate level in E. coli.