PubMed

Multi-omic signature of body weight change: results from a population-based cohort study. BMC Med. 2015;13(1):48 Authors: Wahl S, Vogt S, Stückler F, Krumsiek J, Bartel J, Kacprowski T, Schramm K, Carstensen M, Rathmann W, Roden M, Jourdan C, Kangas AJ, Soininen P, Ala-Korpela M, Nöthlings U, Boeing H, Theis FJ, Meisinger C, Waldenberger M, Suhre K, Homuth G, Gieger C, Kastenmüller G, Illig T, Linseisen J, Peters A, Prokisch H, Herder C, Thorand B, Grallert H Abstract BACKGROUND: Excess body weight is a major risk factor for cardiometabolic diseases. The complex molecular mechanisms of body weight change-induced metabolic perturbations are not fully understood. Specifically, in-depth molecular characterization of long-term body weight change in the general population is lacking. Here, we pursued a multi-omic approach to comprehensively study metabolic consequences of body weight change during a seven-year follow-up in a large prospective study. METHODS: We used data from the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort. At follow-up (F4), two-platform serum metabolomics and whole blood gene expression measurements were obtained for 1,631 and 689 participants, respectively. Using weighted correlation network analysis, omics data were clustered into modules of closely connected molecules, followed by the formation of a partial correlation network from the modules. Association of the omics modules with previous annual percentage weight change was then determined using linear models. In addition, we performed pathway enrichment analyses, stability analyses, and assessed the relation of the omics modules with clinical traits. RESULTS: Four metabolite and two gene expression modules were significantly and stably associated with body weight change (P-values ranging from 1.9 × 10-4 to 1.2 × 10-24). The four metabolite modules covered major branches of metabolism, with VLDL, LDL and large HDL subclasses, triglycerides, branched-chain amino acids and markers of energy metabolism among the main representative molecules. One gene expression module suggests a role of weight change in red blood cell development. The other gene expression module largely overlaps with the lipid-leukocyte (LL) module previously reported to interact with serum metabolites, for which we identify additional co-expressed genes. The omics modules were interrelated and showed cross-sectional associations with clinical traits. Moreover, weight gain and weight loss showed largely opposing associations with the omics modules. CONCLUSIONS: Long-term weight change in the general population globally associates with serum metabolite concentrations. An integrated metabolomics and transcriptomics approach improved the understanding of molecular mechanisms underlying the association of weight gain with changes in lipid and amino acid metabolism, insulin sensitivity, mitochondrial function as well as blood cell development and function. PMID: 25857605 [PubMed - in process]

Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography-mass spectrometry data. J Chromatogr A. 2015 Mar 7; Authors: Bean HD, Hill JE, Dimandja JM Abstract The potential of high-resolution analytical technologies like GC×GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC×GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC×GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly-resolved peaks, especially those at the extremes of the detector linear range, and no influence on well-chromatographed peaks. Therefore, optimized chromatography is required for proper GC×GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses. PMID: 25857541 [PubMed - as supplied by publisher]

Antiphospholipid Antibodies Alter Cell-Death-Regulating Lipid Metabolites in First and Third Trimester Human Placentae. Am J Reprod Immunol. 2015 Apr 9; Authors: Pantham P, Heazell AE, Mullard G, Begley P, Chen Q, Brown M, Dunn WB, Chamley LW Abstract PROBLEM: Antiphospholipid antibodies (aPL) are maternal autoantibodies that increase the risk of a woman developing preeclampsia 10-fold. aPL are internalized into the syncytiotrophoblast and increase extrusion of necrotic trophoblast debris into the maternal blood. This necrotic trophoblast debris may trigger endothelial cell dysfunction contributing to the pathogenesis of preeclampsia. We hypothesize that aPL directly affect placental metabolism, leading to increased syncytiotrophoblast death. METHODS OF STUDY: First and third trimester human placental explants were cultured with aPL, a control antibody, or media only, and placental conditioned culture media was examined by mass spectroscopy. Molecular targets of interest were investigated using qRTPCR and immunohistochemistry. RESULTS: The levels of 79 and 132 metabolites, respectively, were altered due to the treatment of first and third trimester placental explants with aPL. These included ceramides and diacylglycerols, which play important roles in cell death regulatory pathways. Antiphospholipid antibodies also decreased the expression of protein kinase C-epsilon (PRKCE) in placental explants, possibly due to the disrupted balance between ceramides and diacylglycerols caused by aPL. CONCLUSION: One mechanism by which aPL cause aberrant cell death in the syncytiotrophoblast in the first and third trimester is by disruption of placental lipid signaling and decreased expression of PRKCE. PMID: 25856778 [PubMed - as supplied by publisher]

Altered Plasma Lysophosphatidylcholines and Amides in Non-Obese and Non-Diabetic Subjects with Borderline-To-Moderate Hypertriglyceridemia: A Case-Control Study. PLoS One. 2015;10(4):e0123306 Authors: Lee SY, Kim M, Jung S, Lee SH, Lee JH Abstract Hypertriglyceridemia (HTG) is a risk factor for atherosclerotic cardiovascular disease (CVD). We investigated alterations in plasma metabolites associated with borderline-to-moderate HTG (triglycerides (TG) 150-500 mg/dL). Using UPLC-LTQ-Orbitrap mass spectrometry analysis, the metabolomics profiles of 111 non-diabetic and non-obese individuals with borderline-to-moderate HTG were compared with those of 111 age- and sex-matched controls with normotriglyceridemia (NTG, TG <150 mg/dL). When compared to the NTG control group, the HTG group exhibited higher plasma levels of lysophosphatidylcholines (lysoPCs), including C14:0 (q = 0.001) and C16:0 (q = 1.8E-05), and several amides, including N-ethyldodecanamide (q = 2.9E-05), N-propyldodecanamide (q = 3.5E-05), palmitoleamide (q = 2.9E-06), and palmitic amide (q = 0.019). The metabolomic profiles of the HTG group also exhibited lower plasma levels of cis-4-octenedioic acid (q<1.0E-9) and docosanamide (q = 0.002) compared with those of the NTG controls. LysoPC 16:0 and palmitoleamide emerged as the primary metabolites able to discriminate the HTG group from the NTG group in a partial least-squares discriminant analysis and were positively associated with the fasting triglyceride levels. We identified alterations in lysoPCs, amides, and cis-4-octenedioic acid among non-diabetic and non-obese individuals with borderline-to-moderate HTG. These results provide novel insights into the metabolic alterations that occur in the early metabolic stages of HTG. This information may facilitate the design of early interventions to prevent disease progression. PMID: 25856314 [PubMed - as supplied by publisher]

Metabolomics analysis and biomarker identification for the brain of rats exposed subchronically to the mixtures of low dose cadmium and chlorpyrifos. Chem Res Toxicol. 2015 Apr 9; Authors: Xu MY, Sun YJ, Wang P, Xu HY, Chen LP, Zhu L, Wu YJ Abstract Cadmium (Cd) and chlorpyrifos (CPF) are widespread harmful environmental pollutants with neurotoxicity to mammalians. Although the exposure to Cd and CPF at the same time may pose significant risk to human health, the subchronic combined neurotoxicity of these two chemicals at low-level in the brain is poorly understood. In this study, we treated rats with three doses (low, middle, and high) of Cd, CPF, or their mixtures for 90 days. No obvious symptom was observed in the treated animals except those treated with high dose CPF. Histological results showed that middle and high doses of the chemicals caused neuronal cell damage in brains. GC-MS-based metabonomics analysis revealed that energy and amino acid metabolism was disturbed in the brains of rats exposed to the two chemicals and their combinations even at low doses. We further identified the unique brain metabolite biomarkers for Cd-, CPF-, or their combination-treated rats. Two amino acids tyrosine and L-leucine were identified as the biomarkers for Cd and CPF treatment, respectively. In addition, a set of five unique biomarkers (1,2-propanediol-1-phosphate, D-gluconic acid, 9H-purine, serine, 2-ketoisovaleric acid) was identified for the mixtures of Cd and CPF. Therefore, the metabolomics analysis is more sensitive than regular clinical observation and pathological examination to detect the neurotoxicity of the individual and combined Cd and CPF at low levels. Overall, these results identified the unique biomarkers for Cd and CPF exposure, which provide new insights to the mechanism of their joint toxicity. PMID: 25856237 [PubMed - as supplied by publisher]

Exometabolomics and MSI: deconstructing how cells interact to transform their small molecule environment. Curr Opin Biotechnol. 2015 Apr 4;34:209-216 Authors: Silva LP, Northen TR Abstract Metabolism is at the heart of many biotechnologies from biofuels to medical diagnostics. Metabolomic methods that provide glimpses into cellular metabolism have rapidly developed into a critical component of the biotechnological development process. Most metabolomics methods have focused on what is happening inside the cell. Equally important are the biochemical transformations of the cell, and their effect on other cells and their environment; the exometabolome. Exometabolomics is therefore gaining popularity as a robust approach for obtaining rich phenotypic data, and being used in bioprocessing and biofuel development. Mass spectrometry imaging approaches, including several nanotechnologies, provide complimentary information by localizing metabolic processes within complex biological matrices. Together, the two technologies can provide new insights into the metabolism and interactions of cells. PMID: 25855407 [PubMed - as supplied by publisher]

SHMT2 drives glioma cell survival in ischaemia but imposes a dependence on glycine clearance. Nature. 2015 Apr 8; Authors: Kim D, Fiske BP, Birsoy K, Freinkman E, Kami K, Possemato RL, Chudnovsky Y, Pacold ME, Chen WW, Cantor JR, Shelton LM, Gui DY, Kwon M, Ramkissoon SH, Ligon KL, Kang SW, Snuderl M, Vander Heiden MG, Sabatini DM Abstract Cancer cells adapt their metabolic processes to support rapid proliferation, but less is known about how cancer cells alter metabolism to promote cell survival in a poorly vascularized tumour microenvironment. Here we identify a key role for serine and glycine metabolism in the survival of brain cancer cells within the ischaemic zones of gliomas. In human glioblastoma multiforme, mitochondrial serine hydroxymethyltransferase (SHMT2) and glycine decarboxylase (GLDC) are highly expressed in the pseudopalisading cells that surround necrotic foci. We find that SHMT2 activity limits that of pyruvate kinase (PKM2) and reduces oxygen consumption, eliciting a metabolic state that confers a profound survival advantage to cells in poorly vascularized tumour regions. GLDC inhibition impairs cells with high SHMT2 levels as the excess glycine not metabolized by GLDC can be converted to the toxic molecules aminoacetone and methylglyoxal. Thus, SHMT2 is required for cancer cells to adapt to the tumour environment, but also renders these cells sensitive to glycine cleavage system inhibition. PMID: 25855294 [PubMed - as supplied by publisher]

Identification of endogenous metabolites in human sperm cells using (1) H-NMR and GC-MS. Andrology. 2015 Apr 8; Authors: Paiva C, Amaral A, Rodriguez M, Canyellas N, Correig X, Ballescà JL, Ramalho-Santos J, Oliva R Abstract The objective of this study was to contribute to the first comprehensive metabolomic characterization of the human sperm cell through the application of two untargeted platforms based on proton nuclear magnetic resonance ((1) H-NMR) spectroscopy and gas chromatography coupled to mass spectrometry (GC-MS). Using these two complementary strategies, we were able to identify a total of 69 metabolites, of which 42 were identified using NMR, 27 using GC-MS and 4 by both techniques. The identity of some of these metabolites was further confirmed by two-dimensional (1) H-(1) H homonuclear correlation spectroscopy (COSY) and (1) H-(13) C heteronuclear single-quantum correlation (HSQC) spectroscopy. Most of the metabolites identified are reported here for the first time in mature human spermatozoa. The relationship between the metabolites identified and the previously reported sperm proteome was also explored. Interestingly, overrepresented pathways included not only the metabolism of carbohydrates, but also of lipids and lipoproteins. Of note, a large number of the metabolites identified belonged to the amino acids, peptides and analogues super class. The identification of this initial set of metabolites represents an important first step to further study their function in male gamete physiology and to explore potential reasons for dysfunction in future studies. We also demonstrate that the application of NMR and MS provides complementary results, thus constituting a promising strategy towards the completion of the human sperm cell metabolome. PMID: 25854681 [PubMed - as supplied by publisher]

Related Articles Monitoring alcoholic fermentation: an untargeted approach. J Agric Food Chem. 2014 Jul 16;62(28):6784-93 Authors: Ferreira AC, Monforte AR, Teixeira CS, Martins R, Fairbairn S, Bauer FF Abstract This work describes the utility and efficiency of a metabolic profiling pipeline that relies on an unsupervised and untargeted approach applied to a HS-SPME/GC-MS data. This noninvasive and high throughput methodology enables "real time" monitoring of the metabolic changes inherent to the biochemical dynamics of a perturbed complex biological system and the extraction of molecular candidates that are latter validated on its biochemical context. To evaluate the efficiency of the pipeline five different fermentations, carried on a synthetic media and whose perturbation was the nitrogen source, were performed in 5 and 500 mL. The smaller volume fermentations were monitored online by HS-SPME/GC-MS, allowing to obtain metabolic profiles and molecular candidates time expression. Nontarget analysis was applied using MS data in two ways: (i) one dimension (1D), where the total ion chromatogram per sample was used, (ii) two dimensions (2D), where the integrity time vs m/z per sample was used. Results indicate that the 2D procedure captured the relevant information more efficiently than the 1D. It was also seen that although there were differences in the fermentation performance in different scales, the metabolic pathways responsible for production of metabolites that impact the quality of the volatile fraction was unaffected, so the proposed pipeline is suitable for the study of different fermentation systems that can undergo subsequent sensory validation on a larger scale. PMID: 24976138 [PubMed - indexed for MEDLINE]

Metabolomic Method: UPLC-q-ToF Polar and Non-Polar Metabolites in the Healthy Rat Cerebellum Using an In-Vial Dual Extraction. PLoS One. 2015;10(4):e0122883 Authors: Ebshiana AA, Snowden SG, Thambisetty M, Parsons R, Hye A, Legido-Quigley C Abstract Unbiased metabolomic analysis of biological samples is a powerful and increasingly commonly utilised tool, especially for the analysis of bio-fluids to identify candidate biomarkers. To date however only a small number of metabolomic studies have been applied to studying the metabolite composition of tissue samples, this is due, in part to a number of technical challenges including scarcity of material and difficulty in extracting metabolites. The aim of this study was to develop a method for maximising the biological information obtained from small tissue samples by optimising sample preparation, LC-MS analysis and metabolite identification. Here we describe an in-vial dual extraction (IVDE) method, with reversed phase and hydrophilic liquid interaction chromatography (HILIC) which reproducibly measured over 4,000 metabolite features from as little as 3mg of brain tissue. The aqueous phase was analysed in positive and negative modes following HILIC separation in which 2,838 metabolite features were consistently measured including amino acids, sugars and purine bases. The non-aqueous phase was also analysed in positive and negative modes following reversed phase separation gradients respectively from which 1,183 metabolite features were consistently measured representing metabolites such as phosphatidylcholines, sphingolipids and triacylglycerides. The described metabolomics method includes a database for 200 metabolites, retention time, mass and relative intensity, and presents the basal metabolite composition for brain tissue in the healthy rat cerebellum. PMID: 25853858 [PubMed - as supplied by publisher]

Learning to Classify Organic and Conventional Wheat - A Machine Learning Driven Approach Using the MeltDB 2.0 Metabolomics Analysis Platform. Front Bioeng Biotechnol. 2015;3:35 Authors: Kessler N, Bonte A, Albaum SP, Mäder P, Messmer M, Goesmann A, Niehaus K, Langenkämper G, Nattkemper TW Abstract We present results of our machine learning approach to the problem of classifying GC-MS data originating from wheat grains of different farming systems. The aim is to investigate the potential of learning algorithms to classify GC-MS data to be either from conventionally grown or from organically grown samples and considering different cultivars. The motivation of our work is rather obvious nowadays: increased demand for organic food in post-industrialized societies and the necessity to prove organic food authenticity. The background of our data set is given by up to 11 wheat cultivars that have been cultivated in both farming systems, organic and conventional, throughout 3 years. More than 300 GC-MS measurements were recorded and subsequently processed and analyzed in the MeltDB 2.0 metabolomics analysis platform, being briefly outlined in this paper. We further describe how unsupervised (t-SNE, PCA) and supervised (SVM) methods can be applied for sample visualization and classification. Our results clearly show that years have most and wheat cultivars have second-most influence on the metabolic composition of a sample. We can also show that for a given year and cultivar, organic and conventional cultivation can be distinguished by machine-learning algorithms. PMID: 25853128 [PubMed]

The neural stem cell fate determinant TRIM32 regulates complex behavioral traits. Front Cell Neurosci. 2015;9:75 Authors: Hillje AL, Beckmann E, Pavlou MA, Jaeger C, Pacheco MP, Sauter T, Schwamborn JC, Lewejohann L Abstract In mammals, new neurons are generated throughout the entire lifespan in two restricted areas of the brain, the dentate gyrus (DG) of the hippocampus and the subventricular zone (SVZ)-olfactory bulb (OB) system. In both regions newborn neurons display unique properties that clearly distinguish them from mature neurons. Enhanced excitability and increased synaptic plasticity enables them to add specific properties to information processing by modulating the existing local circuitry of already established mature neurons. Hippocampal neurogenesis has been suggested to play a role in spatial-navigation learning, spatial memory, and spatial pattern separation. Cumulative evidences implicate that adult-born OB neurons contribute to learning processes and odor memory. We recently demonstrated that the cell fate determinant TRIM32 is upregulated in differentiating neuroblasts of the SVZ-OB system in the adult mouse brain. The absence of TRIM32 leads to increased progenitor cell proliferation and less cell death. Both effects accumulate in an overproduction of adult-generated OB neurons. Here, we present novel data from behavioral studies showing that such an enhancement of OB neurogenesis not necessarily leads to increased olfactory performance but in contrast even results in impaired olfactory capabilities. In addition, we show at the cellular level that TRIM32 protein levels increase during differentiation of neural stem cells (NSCs). At the molecular level, several metabolic intermediates that are connected to glycolysis, glycine, or cysteine metabolism are deregulated in TRIM32 knockout mice brain tissue. These metabolomics pathways are directly or indirectly linked to anxiety or depression like behavior. In summary, our study provides comprehensive data on how the impairment of neurogenesis caused by the loss of the cell fate determinant TRIM32 causes a decrease of olfactory performance as well as a deregulation of metabolomic pathways that are linked to mood disorders. PMID: 25852471 [PubMed]

Systemic Alterations in the Metabolome of Diabetic NOD Mice Delineate Increased Oxidative Stress Accompanied by Reduced Inflammation and Hypertriglyceridemia. Am J Physiol Endocrinol Metab. 2015 Apr 7;:ajpendo.00019.2015 Authors: Fahrmann J, Grapov D, Yang J, Hammock B, Fiehn O, Bell GI, Hara M Abstract Non-obese diabetic (NOD) mice are a commonly-used model of type 1 diabetes (T1D). However, not all animals will develop overt diabetes despite undergoing similar autoimmune insult. In this study, a comprehensive metabolomic approach, consisting of gas chromatography time-of-flight (GC-TOF) mass spectrometry (MS), ultra high performance liquid chromatography accurate mass quadruple time-of-flight (UHPLC-qTOF) MS and targeted UHPLC-tandem mass spectrometry -based methodologies, was used to capture metabolic alterations in the metabolome and lipidome of plasma from NOD mice progressing or not progressing to T1D. Using this multi-platform approach, we identified >1000 circulating lipids and metabolites in male and female progressor and non-progressor animals (n=71). Statistical and multivariate analyses were used to identify age- and sex-independent metabolic markers, which best differentiated metabolic profiles of progressors and non-progressors. Key T1D-associated perturbations were related with 1) increases in oxidation products glucono delta lactone and galactonic acid, and reductions in cysteine methionine and threonic acid, suggesting increased oxidative stress; 2) reductions in circulating polyunsaturated fatty acids and lipid signaling mediators, most notably arachidonic acid (AA) and AA-derived eicosanoids, implying impaired states of systemic inflammation; 3) elevations in circulating triacylglyercides reflective of hypertriglyceremia; and 4) reductions in major structural lipids, most notably lysophosphatidylcholines and phosphatidylcholines. Taken together, our results highlight the systemic perturbations that accompany a loss of glycemic control and development of overt T1D. PMID: 25852003 [PubMed - as supplied by publisher]

Neurochemical Metabolomics Reveals Disruption to Sphingolipid Metabolism Following Chronic Haloperidol Administration. J Neuroimmune Pharmacol. 2015 Apr 8; Authors: McClay JL, Vunck SA, Batman AM, Crowley JJ, Vann RE, Beardsley PM, van den Oord EJ Abstract Haloperidol is an effective antipsychotic drug for treatment of schizophrenia, but prolonged use can lead to debilitating side effects. To better understand the effects of long-term administration, we measured global metabolic changes in mouse brain following 3 mg/kg/day haloperidol for 28 days. These conditions lead to movement-related side effects in mice akin to those observed in patients after prolonged use. Brain tissue was collected following microwave tissue fixation to arrest metabolism and extracted metabolites were assessed using both liquid and gas chromatography mass spectrometry (MS). Over 300 unique compounds were identified across MS platforms. Haloperidol was found to be present in all test samples and not in controls, indicating experimental validity. Twenty-one compounds differed significantly between test and control groups at the p < 0.05 level. Top compounds were robust to analytical method, also being identified via partial least squares discriminant analysis. Four compounds (sphinganine, N-acetylornithine, leucine and adenosine diphosphate) survived correction for multiple testing in a non-parametric analysis using false discovery rate threshold < 0.1. Pathway analysis of nominally significant compounds (p < 0.05) revealed significant findings for sphingolipid metabolism (p = 0.015) and protein biosynthesis (p = 0.024). Altered sphingolipid metabolism is suggestive of disruptions to myelin. This interpretation is supported by our observation of elevated N-acetyl-aspartyl-glutamate in the haloperidol-treated mice (p = 0.004), a marker previously associated with demyelination. This study further demonstrates the utility of murine neurochemical metabolomics as a method to advance understanding of CNS drug effects. PMID: 25850894 [PubMed - as supplied by publisher]

Related Articles Intestinal microbiota-derived metabolomic blood plasma markers for prior radiation injury. Int J Radiat Oncol Biol Phys. 2015 Feb 1;91(2):360-7 Authors: Ó Broin P, Vaitheesvaran B, Saha S, Hartil K, Chen EI, Goldman D, Fleming WH, Kurland IJ, Guha C, Golden A Abstract PURPOSE: Assessing whole-body radiation injury and absorbed dose is essential for remediation efforts following accidental or deliberate exposure in medical, industrial, military, or terrorist incidents. We hypothesize that variations in specific metabolite concentrations extracted from blood plasma would correlate with whole-body radiation injury and dose. METHODS AND MATERIALS: Groups of C57BL/6 mice (n=12 per group) were exposed to 0, 2, 4, 8, and 10.4 Gy of whole-body gamma radiation. At 24 hours after treatment, all animals were euthanized, and both plasma and liver biopsy samples were obtained, the latter being used to identify a distinct hepatic radiation injury response within plasma. A semiquantitative, untargeted metabolite/lipid profile was developed using gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry, which identified 354 biochemical compounds. A second set of C57BL/6 mice (n=6 per group) were used to assess a subset of identified plasma markers beyond 24 hours. RESULTS: We identified a cohort of 37 biochemical compounds in plasma that yielded the optimal separation of the irradiated sample groups, with the most correlated metabolites associated with pyrimidine (positively correlated) and tryptophan (negatively correlated) metabolism. The latter were predominantly associated with indole compounds, and there was evidence that these were also correlated between liver and plasma. No evidence of saturation as a function of dose was observed, as has been noted for studies involving metabolite analysis of urine. CONCLUSIONS: Plasma profiling of specific metabolites related to pyrimidine and tryptophan pathways can be used to differentiate whole-body radiation injury and dose response. As the tryptophan-associated indole compounds have their origin in the intestinal microbiome and subsequently the liver, these metabolites particularly represent an attractive marker for radiation injury within blood plasma. PMID: 25636760 [PubMed - indexed for MEDLINE]

Related Articles Deciphering molecular determinants of chemotherapy in gastrointestinal malignancy using systems biology approaches. Drug Discov Today. 2014 Sep;19(9):1402-9 Authors: Lin LL, Huang HC, Juan HF, 2013 Taida Cancer Systems Biology Study Group Abstract Gastrointestinal cancers are asymptomatic in early tumor development, leading to high mortality rates. Peri- or postoperative chemotherapy is a common strategy used to prolong the life expectancy of patients with these diseases. Understanding the molecular mechanisms by which anticancer drugs exert their effect is crucial to the development of anticancer therapies, especially when drug resistance occurs and an alternative drug is needed. By integrating high-throughput techniques and computational modeling to explore biological systems at different levels, from gene expressions to networks, systems biology approaches have been successfully applied in various fields of cancer research. In this review, we highlight chemotherapy studies that reveal potential signatures using microarray analysis, next-generation sequencing (NGS), proteomic and metabolomic approaches for the treatment of gastrointestinal cancers. PMID: 24793142 [PubMed - indexed for MEDLINE]

Related Articles [GC-MS determination of metabolites in rat kidneys]. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2013 Jul;38(7):661-9 Authors: Liu S, Wang F, Mei W, Tao L Abstract OBJECTIVE: To establish a method to determine the metabolites in rat kidney tissues by gas chromatography-mass spectrometry (GC-MS) combined with chemometric techniques. METHODS: Metabolites were separated and identified on HP-5MS column (30 m × 0.25 μm × 0.25 mm). The initial column temperature was 100 Celsius degree lasting 3 min, and then programmed at 8 Celsius degree/ min to 300 Celsius degree, maintaining at this temperature for 6 min. The internal standard was heptadecanoic acid. The grinded kidney tissue was exacted by methanol. The supernatant was dried by nitrogen. After the oximation and derivation, the supernatant was analyzed by GC-MS. The overlapped peaks were resolved into pure chromatogram and mass spectra with chemometric techniques. Qualitative analysis was performed by comparing the obtained pure mass spectra with those in NIST mass spectra database and certificated by the standards and the references. The internal method was used for semi-quantitation. RESULTS: A total of 53 compounds were identified. The main constitutions in the kidney tissue were amino acids, saccharides, fatty acids and urea. CONCLUSION: The combination of methods is rapid and accurate for the analysis of metabolites in the kidney tissue, which provides more information for further study of metabonomics in kidney tissues. PMID: 23908085 [PubMed - indexed for MEDLINE]

Profiles of 21-Carbon Steroids in 21-hydroxylase Deficiency. J Clin Endocrinol Metab. 2015 Apr 7;:jc20151023 Authors: Turcu AF, Rege J, Chomic R, Liu J, Nishimoto HK, Else T, Moraitis AG, Palapattu GS, Rainey WE, Auchus RJ Abstract CONTEXT: Marked elevations of 17-hydroxyprogesterone (17OHP) are characteristic of classic 21-hydroxylase deficiency (21OHD). Testing of 17OHP provides the basis for 21OHD diagnosis, although it suffers from several pitfalls. False-positive or false-negative results and poor discrimination of nonclassic 21OHD from carriers limit the utility of serum 17OHP and necessitate dynamic testing following cosyntropin stimulation when values are indeterminate. OBJECTIVE: To provide a detailed characterization of 21-carbon (C21) steroids in classic 21OHD, which might identify other candidate steroids that could be employed for the diagnosis of 21OHD. SETTING AND PARTICIPANTS: Patients (11 women, 10 men) with classic 21OHD and 21 sex- and age-matched controls seen in a tertiary referral center. METHODS: C21 steroids in the peripheral sera from all subjects, as well as in media from cultured testicular adrenal rest (TART) cells and normal adrenal (NA) cells were analyzed using liquid chromatography/tandem mass spectrometry (10 steroids). Additionally, the dynamics of C21 steroid metabolism in TART and NA cells were assessed with radiotracer studies. RESULTS: Five C21 steroids were significantly higher in 21OHD patients: 17OHP (67-fold, p<0.01); 21-deoxycortisol (21dF, 35-fold, p<0.01); 16α-hydroxyprogesterone (16OHP, 28-fold, p<0.01); progesterone (2 fold, p<0.01) and 11β-hydroxyprogesterone (11OHP, not detected in controls, p<0.01). The same steroids were the highest in media from TART cells relative to the NA cells: 11OHP, 58-65 fold; 21dF, 41-30 fold; 17OHP, 9-fold; progesterone, 9-12 fold and 16OHP, 7 fold. CONCLUSION: Measurement of 16OHP and 11OHP along with 17OHP and 21dF by LC-MS/MS might comprise a biomarker panel to accurately diagnose all forms of 21OHD. PMID: 25850025 [PubMed - as supplied by publisher]

Dynamic Changes in Amino Acid Concentration Profiles in Patients with Sepsis. PLoS One. 2015;10(4):e0121933 Authors: Su L, Li H, Xie A, Liu D, Rao W, Lan L, Li X, Li F, Xiao K, Wang H, Yan P, Li X, Xie L Abstract OBJECTIVES: The goal of this work was to explore the dynamic concentration profiles of 42 amino acids and the significance of these profiles in relation to sepsis, with the aim of providing guidance for clinical therapies. METHODS: Thirty-five critically ill patients with sepsis were included. These patients were further divided into sepsis (12 cases) and severe sepsis (23 cases) groups or survivor (20 cases) and non-survivor (15 cases) groups. Serum samples from the patients were collected on days 1, 3, 5, 7, 10, and 14 following intensive care unit (ICU) admission, and the serum concentrations of 42 amino acids were measured. RESULTS: The metabolic spectrum of the amino acids changed dramatically in patients with sepsis. As the disease progressed further or with poor prognosis, the levels of the different amino acids gradually increased, decreased, or fluctuated over time. The concentrations of sulfur-containing amino acids (SAAs), especially taurine, decreased significantly as the severity of sepsis worsened or with poor prognosis of the patient. The serum concentrations of SAAs, especially taurine, exhibited weak negative correlations with the Sequential Organ Failure Assessment (SOFA) (r=-0.319) and Acute Physiology and Chronic Health Evaluation (APACHE) II (r=-0.325) scores. The areas under the receiver operating characteristic curves of cystine, taurine, and SAA levels and the SOFA and APACHE II scores, which denoted disease prognosis, were 0.623, 0.674, 0.678, 0.86, and 0.857, respectively. CONCLUSIONS: Critically ill patients with disorders of amino acid metabolism, especially of SAAs such as cystine and taurine, may provide an indicator of the need for the nutritional support of sepsis in the clinic. TRIAL REGISTRATION: ClinicalTrial.gov identifier NCT01818830. PMID: 25849571 [PubMed - as supplied by publisher]

1H NMR-Based Metabolomics Investigation of Copper-Laden Rat: A Model of Wilson's Disease. PLoS One. 2015;10(4):e0119654 Authors: Xu J, Jiang H, Li J, Cheng KK, Dong J, Chen Z Abstract BACKGROUND AND PURPOSE: Wilson's disease (WD), also known as hepatoleticular degeneration (HLD), is a rare autosomal recessive genetic disorder of copper metabolism, which causes copper to accumulate in body tissues. In this study, rats fed with copper-laden diet are used to render the clinical manifestations of WD, and their copper toxicity-induced organ lesions are studied. To investigate metabolic behaviors of 'decoppering' process, penicillamine (PA) was used for treating copper-laden rats as this chelating agent could eliminate excess copper through the urine. To date, there has been limited metabolomics study on WD, while metabolic impacts of copper accumulation and PA administration have yet to be established. MATERIALS AND METHODS: A combination of 1HNMR spectroscopy and multivariate statistical analysis was applied to examine the metabolic profiles of the urine and blood serum samples collected from the copper-laden rat model of WD with PA treatment. RESULTS: Copper accumulation in the copper-laden rats is associated with increased lactate, creatinine, valine and leucine, as well as decreased levels of glucose and taurine in the blood serum. There were also significant changes in p-hydroxyphenylacetate (p-HPA), creatinine, alpha-ketoglutarate (α-KG), dimethylamine, N-acetylglutamate (NAG), N-acetylglycoprotein (NAC) in the urine of these rats. Notably, the changes in p-HPA, glucose, lactate, taurine, valine, leucine, and NAG were found reversed following PA treatment. Nevertheless, there were no changes for dimethylamine, α-KG, and NAC as a result of the treatment. Compared with the controls, the concentrations of hippurate, formate, alanine, and lactate were changed when PA was applied and this is probably due to its side effect. A tool named SMPDB (Small Molecule Pathway Database) is introduced to identify the metabolic pathway influenced by the copper-laden diet. CONCLUSION: The study has shown the potential application of NMR-based metabolomic analysis in providing further insights into the molecular mechanism underlying disorder due to WD. PMID: 25849323 [PubMed - as supplied by publisher]