Ekaterina Nevedomskaya

Gas chromatography/atmospheric pressure chemical ionization-time of flight mass spectrometry: analytical validation and applicability to metabolic profiling.

Gas Chromatography (GC)-Mass Spectrometry (MS) with Atmospheric Pressure (AP) interface was introduced more than 30 years ago but never became a mainstream technique, mainly because of technical difficulties and cost of instrumentation. A recently introduced multipurpose AP source created the opportunity to reconsider the importance of AP ionization for GC. Here, we present an analytical evaluation of GC/APCI-MS showing the benefits of soft atmospheric pressure chemical ionization for GC in combination with a Time of Flight (TOF) mass analyzer. During this study, the complete analytical procedure was optimized and evaluated with respect to characteristic analytical parameters, such as repeatability, reproducibility, linearity, and detection limits. Limits of detection (LOD) were found within the range from 11.8 to 72.5 nM depending on the type of compound. The intraday and interday repeatability tests demonstrate relative standard deviations (RSDs) of peak areas between 0.7%-2.1% and 3.8%-6.4% correspondingly. Finally, we applied the developed method to the analysis of human cerebrospinal fluid (CSF) samples to check the potential of this new analytical combination for metabolic profiling.

Alignment of capillary electrophoresis–mass spectrometry datasets using accurate mass information

Capillary electrophoresis–mass spectrometry (CE–MS) is a powerful technique for the analysis of small soluble compounds in biological fluids. A major drawback of CE is the poor migration time reproducibility, which makes it difficult to combine data from different experiments and correctly assign compounds. A number of alignment algorithms have been developed but not all of them can cope with large and irregular time shifts between CE–MS runs. Here we present a genetic algorithm designed for alignment of CE–MS data using accurate mass information. The utility of the algorithm was demonstrated on real data, and the results were compared with one of the existing packages. The new algorithm showed a significant reduction of elution time variation in the aligned datasets. The importance of mass accuracy for the performance of the algorithm was also demonstrated by comparing alignments of datasets from a standard time-of-flight (TOF) instrument with those from the new ultrahigh resolution TOF maXis (Bruker Daltonics).

CE-MS for metabolic profiling of volume-limited urine samples: application to accelerated aging TTD mice.

Metabolic profiling of biological samples is increasingly used to obtain more insight into the pathophysiology of diseases. For translational studies, biological samples from animal models are explored; however, the volume of these samples can be a limiting factor for metabolic profiling studies. For instance, only a few microliters of urine is often available from small animals like mice. Hence, there is a need for a tailor-made analytical method for metabolic profiling of volume-limited samples. In the present study, the feasibility of capillary electrophoresis time-of-flight mass spectrometry (CE-ToF-MS) for metabolic profiling of urine from mice is evaluated. Special attention is paid to the analytical workflow; that is, such aspects as sample preparation, analysis, and data treatment are discussed from the metabolomics viewpoint. We show that metabolites belonging to several chemical families can be analyzed in mouse urine with the CE-ToF-MS method using minimal sample pretreatment and an in-capillary preconcentration procedure. This exemplifies the advantages of CE-ToF-MS for metabolic profiling of volume-limited samples as loss of material is minimized. The feasibility of the CE-ToF-MS-based workflow for metabolic profiling is illustrated by the analysis of urine samples from wild-type as well as from TTD mutant mice, which are a model for the accelerated aging, with osteoporosis being one of the main hallmarks.

Explorative analysis of urine by capillary electrophoresis-mass spectrometry in chronic patients with complex regional pain syndrome.

Complex Regional Pain Syndrome (CRPS) is characterized by various combinations of sensory, autonomic and motor disturbances. Pain disproportionate to the severity and duration of the inciting event is the most devastating symptom. Diagnosis of CRPS is difficult as the underlying mechanisms remain unclear. To try to derive metabolic indicators potentially characteristic for CRPS, we applied capillary electrophoresis time-of-flight mass spectrometry (CE-ToF-MS) to the explorative analysis of urine. The CE-ToF-MS method provided fast and stable metabolic profiles of urine samples. The mean intraday and interday CVs were <2% and <9% for migration times and peak areas, respectively, demonstrating robustness of the method. With the use of multivariate chemometric analysis, discrimination between urine samples from CRPS patients and controls was obtained, emphasizing differences in metabolic signatures between CRPS-diseased patients and controls. Several compounds, such as 3-methylhistidine, were responsible for discriminating the samples. The biological relevance of these compounds with regard to CRPS is discussed. Thus, CE-ToF-MS-based metabolic profiling of urine from CRPS patients and controls revealed metabolites that differentiate between diseased and control, illustrating the usefulness of this approach to get more insight into the pathology underlying CRPS.

1H NMR-based metabolic profiling of urinary tract infection: combining multiple statistical models and clinical data

Urinary tract infection (UTI) encompasses a variety of clinical syndromes ranging from mild to life-threatening conditions. As such, it represents an interesting model for the development of an analytically based scoring system of disease severity and/or host response. Here we test the feasibility of this concept using 1H NMR based metabolomics as the analytical platform. Using an exhaustively clinically characterized cohort and taking advantage of the multi-level study design, which opens possibilities for case–control and longitudinal modeling, we were able to identify molecular discriminators that characterize UTI patients. Among those discriminators a number (e.g. acetate, trimethylamine and others) showed association with the degree of bacterial contamination of urine, whereas others, such as, for instance, scyllo-inositol and para-aminohippuric acid, are more likely to be the markers of morbidity.

Metabolic Profiling of Accelerated Aging ERCC1d/− Mice

Aging is a fundamental biological process for which the mechanism is still largely unknown due to its complex and multifactorial nature. Animal models allow us to simplify this complexity and to study individual factors separately. As there are many causative links between DNA repair deficiency and aging, we studied the ERCC1(d/-) mouse, which has a modified ERCC1 gene, involved in the Nucleotide Excision Repair, and as a result has a premature aging phenotype. Profiling of these mice on different levels can give an insight into the mechanisms underlying the aging phenotype. In the current study, we have performed metabolic profiling of serum and urine of these mice in comparison to wild type and in relation to aging by (1)H NMR spectroscopy. Analysis of metabolic trajectories of animals from 8 to 20 weeks suggested that wild type and ERCC1(d/-) mutants have similar age-related patterns of changes; however, the difference between genotypes becomes more prominent with age. The main differences between these two genetically diverse groups of mice were found to be associated with altered lipid and energy metabolism, transition to ketosis, and attenuated functions of the liver and kidney.

Fibrinogen alpha chain O-glycopeptides as possible markers of urinary tract infection

Urinary tract infection (UTI) is the most common bacterial infection leading to substantial morbidity and considerable health care expenditures across all ages. Here we present an exploratory UPLC-MS study of human urine in the context of febrile, complicated urinary tract infection aimed to reveal and identify possible markers of a host response on infection. A UPLC-MS based workflow, taking advantage of Ultra High Resolution (UHR) Qq-ToF-MS, and multivariate data handling were applied to a carefully selected group of 39 subjects with culture-confirmed febrile Escherichia coli UTI. Using a combination of unsupervised and supervised multivariate modeling we have pinpointed a number of peptides specific for UTI. An unequivocal structural identification of these peptides, as O-glycosylated fragments of the human fibrinogen alpha 1 chain, required MS2 and MS3 experiments on two different MS platforms: ESI-UHR-Qq-ToF and ESI-ion trap, a blast search and, finally, confirmation was achieved by matching experimental tandem mass spectra with those of custom synthesized candidate-peptides. In conclusion, exploiting non-targeted UPLC-MS based approach for the investigation of UTI related changes in urine, we have identified and structurally characterized unique O-glycopeptides, which are, to our knowledge, the first demonstration of O-glycosylation of human fibrinogen alpha 1-chain.

Systematic integration of molecular profiles identifies miR-22 as a regulator of lipid and folate metabolism in breast cancer cells

Dysregulated microRNA (miRNA) mediate malignant phenotypes, including metabolic reprogramming. By performing an integrative analysis of miRNA and metabolome data for the NCI-60 cell line panel, we identified an miRNA cluster strongly associated with both c-Myc expression and global metabolic variation. Within this cluster the cancer-associated and cardioprotective miR-22 was shown to repress fatty acid synthesis and elongation in tumour cells by targeting ATP citrate lyase and fatty acid elongase 6, as well as impairing mitochondrial one-carbon metabolism by suppression of methylene tetrahydrofolate dehydrogenase/cyclohydrolase. Across several data sets, expression of these target genes were associated with poorer outcomes in breast cancer patients. Importantly, a beneficial effect of miR-22 on clinical outcomes in breast cancer was shown to depend on the expression levels of the identified target genes, demonstrating the relevance of miRNA/mRNA interactions to disease progression in vivo. Our systematic analysis establishes miR-22 as a novel regulator of tumour cell metabolism, a function that could contribute to the role of this miRNA in cellular differentiation and cancer development. Moreover, we provide a paradigmatic example of effect modification in outcome analysis as a consequence of miRNA-directed gene targeting, a phenomenon that could be exploited to improve patient prognosis and treatment.

Effect of Suboptimal Sampling and Handling Conditions on Urinary Metabolic Profiles

Collection and storage of the clinical samples are crucial factors in the metabolomic workflows. However, with the expansion of metabolomics into the clinical domain and towards the large field studies in particular, the high sampling/storage standards practiced in the tightly controlled hospital environment cannot always be guaranteed. Thus, if the samples are exposed to suboptimal conditions and their integrity is compromised should they be discarded? Or such samples retain physiologically relevant information and can be of use? To explore the options we analyzed 117 urine samples that were collected under two different conditions. Part of the samples were collected within a clinical setting under optimal conditions, another part by patients at home and shipped to the hospital by mail. All samples were analyzed by liquid chromatography–mass spectrometry (LC–MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy. Multivariate modelling revealed clear differences between the two sampling conditions for both LC–MS and 1H NMR data sets. However, the differential metabolites appeared to be platform-specific, which clearly emphasizes the complementary nature of both techniques. The analysis of the samples that were exposed to suboptimal conditions revealed that age and body mass index remain as dominant traits of the metabolic profile, although their influence was stronger for LC–MS data. In conclusion, although it is important to ensure adequate sample collection and storage conditions, urine samples that do not fulfil these criteria still retain valuable physiological information and as such thus they could be of use for metabolomic studies when no alternative is available.