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Background: Although colorectal cancer (CRC) is one of the most common causes of cancer mortality, early-stage detection improves survival rates dramatically. Because cancer impacts important metabolic pathways, the alteration of metabolite levels as a potential biomarker of early-stage cancer has been the focus of many studies. Here, we used CE-TOFMS, a novel and promising method with small injection volume and high resolution, to separate and detect ionic compounds based on the different migration rates of charged metabolites in order to detect metabolic biomarkers in patients with CRC.Methods: A total of 56 patients with CRC (n = 14 each of Stages I-IV), 60 healthy controls, and 59 patients with colonic adenoma were included in this study. Metabolome analysis was conducted by CE-TOFMS on serum samples of patients and controls using the Advanced Scan package (Human Metabolome Technologies).Results: We obtained 334 metabolites in the serum, of which 139 were identified as known substances. Among these 139 known metabolites, 16 were correlated with CRC stage by upregulation and 44 by downregulation, with benzoic acid (r = -0.649, t = 11.653, p = 6.07599E-24), octanoic acid (r = 0.557, t = 9.183, p = 7.9557E-17), decanoic acid (r = 0.539, t = 8.749, p = 1.24352E-15), and histidine (r = -0.513, t = 8.194, p = 3.90224E-14) exhibiting significant correlation.Conclusions: To the best of our knowledge, this is the first report to determine the correlation between serum metabolites and CRC stage using CE-TOFMS. Our results show that benzoic acid exhibited excellent diagnostic power and could potentially serve as a novel disease biomarker for CRC diagnosis. [ABSTRACT FROM AUTHOR]

In a previous study, it was demonstrated that the toxic impact of titanium dioxide nanoparticles on Escherichia coli starts at 10 ppm and is closely related to the presence of little aggregates. It was also assumed that only a part of the bacterial population is able to adapt to this stress and attempts to survive. Proteomic analyses, supported by results from metabolomics, reveal that exposure of E. coli to nano-TiO2 induces two main effects on bacterial metabolism: firstly, the up-regulation of proteins and the increase of metabolites related to energy and growth metabolism; secondly, the down-regulation of other proteins resulting in an increase of metabolites, particularly amino acids. Some proteins, e.g. chaperonin 1 or isocitrate dehydrogenase, and some metabolites, e.g. phenylalanine or valine, might be used as biomarkers of nanoparticles stress. Astonishingly, the ATP content gradually rises in relation with the nano-TiO2 concentration in the medium, indicating a dramatic release of ATP by the damaged cells. These apparently contradictory results accredit the thesis of a heterogeneity of the bacterial population. This heterogeneity is also confirmed by SEM images which show that while some bacteria are fully covered by nano-TiO2, the major part of the bacterial population remains free from nanoparticles, resulting in a difference of proteome and metabolome. The use of combined–omics has allowed to better understand the heterogeneous bacterial response to nano-TiO2 stress due to heterogeneous contacts between the protagonists under environmental conditions. [ABSTRACT FROM AUTHOR]

Metabolomics is a promising avenue for biomarker discovery. Although the quality of metabolomic analyses, especially global metabolomics (G-Met) using mass spectrometry (MS), largely depends on the instrumentation, potential bottlenecks still exist at several basic levels in the metabolomics workflow. Therefore, we established a precise protocol initially for the G-Met analyses of human blood plasma to overcome some these difficulties. In our protocol, samples are deproteinized in a 96-well plate using an automated liquid-handling system, and conducted either using a UHPLC-QTOF/MS system equipped with a reverse phase column or a LC-FTMS system equipped with a normal phase column. A normalization protocol of G-Met data was also developed to compensate for intra- and inter-batch differences, and the variations were significantly reduced along with our normalization, especially for the UHPLC-QTOF/MS data with a C18 reverse-phase column for positive ions. Secondly, we examined the changes in metabolomic profiles caused by the storage of EDTA-blood specimens to identify quality markers for the evaluation of the specimens’ pre-analytical conditions. Forty quality markers, including lysophospholipids, dipeptides, fatty acids, succinic acid, amino acids, glucose, and uric acid were identified by G-Met for the evaluation of plasma sample quality and established the equation of calculating the quality score. We applied our quality markers to a small-scale study to evaluate the quality of clinical samples. The G-Met protocols and quality markers established here should prove useful for the discovery and development of biomarkers for a wider range of diseases. [ABSTRACT FROM AUTHOR]