PubMed

Related Articles Development and application of a comparative fatty acid analysis method to investigate voriconazole-induced hepatotoxicity. Clin Chim Acta. 2015 Jan 1;438:126-34 Authors: Chen GY, Chiu HH, Lin SW, Tseng YJ, Tsai SJ, Kuo CH Abstract BACKGROUND: As fatty acids play an important role in biological regulation, the profiling of fatty acid expression has been used to discover various disease markers and to understand disease mechanisms. This study developed an effective and accurate comparative fatty acid analysis method using differential labeling to speed up the metabolic profiling of fatty acids. METHODS: Fatty acids were derivatized with unlabeled (D0) or deuterated (D3) methanol, followed by GC-MS analysis. The comparative fatty acid analysis method was validated using a series of samples with different ratios of D0/D3-labeled fatty acid standards and with mouse liver extracts. RESULTS: Using a lipopolysaccharide (LPS)-treated mouse model, we found that the fatty acid profiles after LPS treatment were similar between the conventional single-sample analysis approach and the proposed comparative approach, with a Pearson's correlation coefficient of approximately 0.96. We applied the comparative method to investigate voriconazole-induced hepatotoxicity and revealed the toxicity mechanism as well as the potential of using fatty acids as toxicity markers. CONCLUSIONS: In conclusion, the comparative fatty acid profiling technique was determined to be fast and accurate and allowed the discovery of potential fatty acid biomarkers in a more economical and efficient manner. PMID: 25150729 [PubMed - indexed for MEDLINE]

Related Articles Salmonella Typhi and Salmonella Paratyphi A elaborate distinct systemic metabolite signatures during enteric fever. Elife. 2014;3 Authors: Näsström E, Vu Thieu NT, Dongol S, Karkey A, Voong Vinh P, Ha Thanh T, Johansson A, Arjyal A, Thwaites G, Dolecek C, Basnyat B, Baker S, Antti H Abstract The host-pathogen interactions induced by Salmonella Typhi and Salmonella Paratyphi A during enteric fever are poorly understood. This knowledge gap, and the human restricted nature of these bacteria, limit our understanding of the disease and impede the development of new diagnostic approaches. To investigate metabolite signals associated with enteric fever we performed two dimensional gas chromatography with time-of-flight mass spectrometry (GCxGC/TOFMS) on plasma from patients with S. Typhi and S. Paratyphi A infections and asymptomatic controls, identifying 695 individual metabolite peaks. Applying supervised pattern recognition, we found highly significant and reproducible metabolite profiles separating S. Typhi cases, S. Paratyphi A cases, and controls, calculating that a combination of six metabolites could accurately define the etiological agent. For the first time we show that reproducible and serovar specific systemic biomarkers can be detected during enteric fever. Our work defines several biologically plausible metabolites that can be used to detect enteric fever, and unlocks the potential of this method in diagnosing other systemic bacterial infections. PMID: 24902583 [PubMed - indexed for MEDLINE]

Related Articles Analytical strategies for studying stem cell metabolism. Front Biol (Beijing). 2015 Apr;10(2):141-153 Authors: Arnold JM, Choi WT, Sreekumar A, Maletić-Savatić M Abstract Owing to their capacity for self-renewal and pluripotency, stem cells possess untold potential for revolutionizing the field of regenerative medicine through the development of novel therapeutic strategies for treating cancer, diabetes, cardiovascular and neurodegenerative diseases. Central to developing these strategies is improving our understanding of biological mechanisms responsible for governing stem cell fate and self-renewal. Increasing attention is being given to the significance of metabolism, through the production of energy and generation of small molecules, as a critical regulator of stem cell functioning. Rapid advances in the field of metabolomics now allow for in-depth profiling of stem cells both in vitro and in vivo, providing a systems perspective on key metabolic and molecular pathways which influence stem cell biology. Understanding the analytical platforms and techniques that are currently used to study stem cell metabolomics, as well as how new insights can be derived from this knowledge, will accelerate new research in the field and improve future efforts to expand our understanding of the interplay between metabolism and stem cell biology. PMID: 26213533 [PubMed - as supplied by publisher]

Related Articles Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics. Trends Analyt Chem. 2015 Jun 1;69:52-61 Authors: Vaniya A, Fiehn O Abstract Identification of unknown metabolites is the bottleneck in advancing metabolomics, leaving interpretation of metabolomics results ambiguous. The chemical diversity of metabolism is vast, making structure identification arduous and time consuming. Currently, comprehensive analysis of mass spectra in metabolomics is limited to library matching, but tandem mass spectral libraries are small compared to the large number of compounds found in the biosphere, including xenobiotics. Resolving this bottleneck requires richer data acquisition and better computational tools. Multi-stage mass spectrometry (MSn) trees show promise to aid in this regard. Fragmentation trees explore the fragmentation process, generate fragmentation rules and aid in sub-structure identification, while mass spectral trees delineate the dependencies in multi-stage MS of collision-induced dissociations. This review covers advancements over the past 10 years as a tool for metabolite identification, including algorithms, software and databases used to build and to implement fragmentation trees and mass spectral annotations. PMID: 26213431 [PubMed - as supplied by publisher]

Related Articles Metabolic profiling of a range of peach fruit varieties reveals high metabolic diversity and commonalities and differences during ripening. Food Chem. 2016 Jan 1;190:879-88 Authors: Monti LL, Bustamante CA, Osorio S, Gabilondo J, Borsani J, Lauxmann MA, Maulión E, Valentini G, Budde CO, Fernie AR, Lara MV, Drincovich MF Abstract Peach (Prunus persica) fruits from different varieties display differential organoleptic and nutritional properties, characteristics related to their chemical composition. Here, chemical biodiversity of peach fruits from fifteen varieties, at harvest and after post-harvest ripening, was explored by gas chromatography-mass spectrometry. Metabolic profiling revealed that metabolites involved in organoleptic properties (sugars, organic and amino acids), stress tolerance (raffinose, galactinol, maltitol), and with nutritional properties (amino, caffeoylquinic and dehydroascorbic acids) displayed variety-dependent levels. Peach varieties clustered into four groups: two groups of early-harvest varieties with higher amino acid levels; two groups of mid- and late-harvest varieties with higher maltose levels. Further separation was mostly dependent on organic acids/raffinose levels. Variety-dependent and independent metabolic changes associated with ripening were detected; which contribute to chemical diversity or can be used as ripening markers, respectively. The great variety-dependent diversity in the content of metabolites that define fruit quality reinforces metabolomics usage as a tool to assist fruit quality improvement in peach. PMID: 26213052 [PubMed - in process]

Related Articles Metabolic mapping of A3 adenosine receptor agonist MRS5980. Biochem Pharmacol. 2015 Jul 23; Authors: Fang ZZ, Tosh DK, Tanaka N, Wang H, Krausz KW, O'Connor R, Jacobson KA, Gonzalez FJ Abstract (1S,2R,3S,4R,5S)-4-(2-((5-Chlorothiophen-2-yl) ethynyl)-6-(methylamino)-9H-purin-9-yl)-2,3-dihydroxy-N-methylbicyclo[3.1.0]hexane-1-carboxamide (MRS5980) is an A3AR selective agonist containing multiple receptor affinity- and selectivity-enhancing modifications and a therapeutic candidate drug for many metabolic diseases. Metabolism-related poor pharmacokinetic behavior and toxicities are a major reason of drug R&D failure. Metabolomics with UPLC-MS was employed to profile the metabolism of MRS5980 and MRS5980-induced disruption of endogenous compounds. Recombinant drug-metabolizing enzymes screening experiment were used to determine the enzymes involved in MRS5980 metabolism. Analysis of lipid metabolism-related genes was performed to investigate the reason for MRS5980-induced lipid metabolic disorders. Unsupervised principal components analysis separated the control and MRS5980 treatment group in feces, urine, and liver samples, but not in bile and serum. The major ions mainly contributing to the separation for feces and urine were oxidized MRS5980, glutathione (GSH) conjugates and cysteine conjugate (degradation product of the GSH conjugates) of MRS5980. The major ions contributing to the group separation of liver samples were phosphatidylcholines. In vitro incubation experiments showed the major involvement of CYP3A enzymes in the oxidative metabolism of MRS5980 and direct GSH reactivity of MRS5980. The electrophilic attack by MRS5980 is a minor pathway and did not alter GSH levels in liver or liver histology, and thus may be of minor clinical consequence. Gene expression analysis further showed decreased expression of PC biosynthetic genes choline kinase a and b, which further accelerated conversion of lysophosphatidylcholine to phosphatidylcholines through increasing the expression of lysophosphatidylcholine acyltransferase 3. These data will be useful to guide rational design of drugs targeting A3AR, considering efficacy, metabolic elimination, and electrophilic reactivity. PMID: 26212548 [PubMed - as supplied by publisher]

Related Articles Metabolomics reveals the formation of aldehydes and iminium in gefitinib metabolism. Biochem Pharmacol. 2015 Jul 23; Authors: Liu X, Lu Y, Guan X, Dong B, Chavan H, Wang J, Zhang Y, Krishnamurthy P, Li F Abstract Gefitinib (GEF), an inhibitor of epidermal growth factor receptor (EGFR) tyrosine kinase, is widely used for the treatment of cancers, particularly non-small cell lung cancer. However, its clinical use is limited by multiple adverse effects associated with GEF, such as liver and lung injuries, severe nausea, and diarrhea. Although, the exact mechanism of GEF adverse effects are still unknown, xenobiotic-induced bioactivation is thought to play a significant role in GEF induced toxicity. Using a metabolomic approach, we investigated the metabolic pathways of GEF in human and mouse liver microsomes. Thirty four GEF metabolites and adducts were identified and half of them are novel. The potential reactive metabolites, two aldehydes and one iminium, were identified for the first time. The previously reported GSH adducts and primary amines were observed as well. The aldehyde and iminium pathways were further confirmed by using methoxylamine and potassium cyanide as trapping reagents. Using recombinant CYP450 isoforms, CYP3A4 inhibitor, and S9 from Cyp3a-null mice, we confirmed CYP3A is the major enzyme contributing to the formation of aldehydes, GSH adducts, and primary amines in liver. Multiple enzymes contribute to the formation of iminium. This study provided us more knowledge of GEF bioactivation and enzymes involved in metabolic pathways, which can be utilized for understanding the mechanism of adverse effects associated with GEF and predicting possible drug-drug interactions. Further studies are suggested to determine the roles of these bioactivation pathways in GEF toxicity. PMID: 26212543 [PubMed - as supplied by publisher]

Related Articles Annotation of metabolites from gas chromatography/atmospheric pressure chemical ionization tandem mass spectrometry data using an in silico generated compound database and MetFrag. Rapid Commun Mass Spectrom. 2015 Aug 30;29(16):1521-9 Authors: Ruttkies C, Strehmel N, Scheel D, Neumann S Abstract RATIONALE: Gas chromatography (GC) coupled to atmospheric pressure chemical ionization quadrupole time-of-flight mass spectrometry (APCI-QTOFMS) is an emerging technology in metabolomics. Reference spectra for GC/APCI-MS/MS barely exist; therefore, in silico fragmentation approaches and structure databases are prerequisites for annotation. To expand the limited coverage of derivatised structures in structure databases, in silico derivatisation procedures are required. METHODS: A cheminformatics workflow has been developed for in silico derivatisation of compounds found in KEGG and PubChem, and validated on the Golm Metabolome Database (GMD). To demonstrate this workflow, these in silico generated databases were applied together with MetFrag to APCI-MS/MS spectra acquired from GC/APCI-MS/MS profiles of Arabidopsis thaliana and Solanum tuberosum. The Metabolite-Likeness of the original candidate structure was included as additional scoring term aiming at candidate structures of natural origin. RESULTS: The validation of our in silico derivatisation workflow on the GMD showed a true positive rate of 94%. MetFrag was applied to two datasets. In silico derivatisation of the KEGG and PubChem database served as a candidate source. For both datasets the Metabolite-Likeness score improved the identification performance. The derivatised data sources have been included into the MetFrag web application for the annotation of GC/APCI-MS/MS spectra. CONCLUSIONS: We demonstrated that MetFrag can support the identification of components from GC/APCI-MS/MS profiles, especially in the (common) case where reference spectra are not available. This workflow can be easily adapted to other types of derivatisation and is freely accessible together with the generated structure databases. Copyright © 2015 John Wiley & Sons, Ltd. PMID: 26212167 [PubMed - in process]

Related Articles High-throughput profiling of protein N-glycosylation by MALDI-TOF-MS employing linkage-specific sialic acid esterification. Anal Chem. 2014 Jun 17;86(12):5784-93 Authors: Reiding KR, Blank D, Kuijper DM, Deelder AM, Wuhrer M Abstract Protein glycosylation is an important post-translational modification associated, among others, with diseases and the efficacy of biopharmaceuticals. Matrix-assisted laser desorption/ionization (MALDI) time-of-fight (TOF) mass spectrometry (MS) can be performed to study glycosylation in a high-throughput manner, but is hampered by the instability and ionization bias experienced by sialylated glycan species. Stabilization and neutralization of these sialic acids can be achieved by permethylation or by specific carboxyl group derivatization with the possibility of discrimination between α2,3- and α2,6-linked sialic acids. However, these methods typically require relatively pure glycan samples, show sensitivity to side reactions, and need harsh conditions or long reaction times. We established a rapid, robust and linkage-specific high-throughput method for sialic acid stabilization and MALDI-TOF-MS analysis, to allow direct modification of impure glycan-containing mixtures such as PNGase F-released human plasma N-glycome. Using a combination of carboxylic acid activators in ethanol achieved near-complete ethyl esterification of α2,6-linked sialic acids and lactonization of α2,3-linked variants, in short time using mild conditions. Glycans were recovered by hydrophilic interaction liquid chromatography solid phase extraction and analyzed by MALDI-TOF-MS in reflectron positive mode with 2,5-dihydroxybenzoic acid as the matrix substance. Analysis of the human plasma N-glycome allowed high-throughput detection and relative quantitation of more than 100 distinct N-glycan compositions with varying sialic acid linkages. PMID: 24831253 [PubMed - indexed for MEDLINE]

Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation. J Chromatogr A. 2015 Jul 15; Authors: Domingo-Almenara X, Perera A, Ramírez N, Cañellas N, Correig X, Brezmes J Abstract Metabolomics GC-MS samples involve high complexity data that must be effectively resolved to produce chemically meaningful results. Multivariate curve resolution-alternating least squares (MCR-ALS) is the most frequently reported technique for that purpose. More recently, independent component analysis (ICA) has been reported as an alternative to MCR. Those algorithms attempt to infer a model describing the observed data and, therefore, the least squares regression used in MCR assumes that the data is a linear combination of that model. However, due to the high complexity of real data, the construction of a model to describe optimally the observed data is a critical step and these algorithms should prevent the influence from outlier data. This study proves independent component regression (ICR) as an alternative for GC-MS compound identification. Both ICR and MCR though require least squares regression to correctly resolve the mixtures. In this paper, a novel orthogonal signal deconvolution (OSD) approach is introduced, which uses principal component analysis to determine the compound spectra. The study includes a compound identification comparison between the results by ICA-OSD, MCR-OSD, ICR and MCR-ALS using pure standards and human serum samples. Results shows that ICR may be used as an alternative to multivariate curve methods, as ICR efficiency is comparable to MCR-ALS. Also, the study demonstrates that the proposed OSD approach achieves greater spectral resolution accuracy than the traditional least squares approach when compounds elute under undue interference of biological matrices. PMID: 26210114 [PubMed - as supplied by publisher]

(1)H NMR based metabolomics approach to study the toxic effects of dichlorvos on goldfish (Carassius auratus). Chemosphere. 2015 Jul 22;138:537-545 Authors: Liu Y, Chen T, Li MH, Xu HD, Jia AQ, Zhang JF, Wang JS Abstract Dichlorvos (DDVP), one of the most widely used organophosphorus pesticides (OPs), has caused serious pollution in environment. In this study, (1)H nuclear magnetic resonance (NMR) based metabolomics approach combined with histopathological and immunohistochemical examination, and biochemical assays were used to investigate toxicities of DDVP on goldfish (Carassius auratus). After 10days' exposure of DDVP at three dosages of 5.18, 2.59 and 1.73mg/L, goldfish tissues (gill, brain, liver and kidney) and serum were collected. Histopathology revealed severe impairment of gills, livers and kidneys, and immunohistochemistry disclosed glial fibrillary acidic protein (GFAP) positive reactive astrocytes in brains. Orthogonal signal correction-partial least squares-discriminant analysis (OSC-PLS-DA) of NMR profiles disclosed that DDVP influenced many metabolites (glutamate, aspartate, acetylcholine, 4-aminobutyrate, glutathione, AMP and lactate in brain; glutathione, glucose, histamine in liver; BCAAs, AMP, aspartate, glutamate, riboflavin in kidney) dose-dependently, involved with imbalance of neurotransmitters, oxidative stress, and disorders of energy and amino acid metabolism. Several self-protection mechanisms concerning glutamate degradation and glutathione (GSH) redox system were found in DDVP intoxicated goldfish. PMID: 26210017 [PubMed - as supplied by publisher]

Related Articles Prognostic clinical and molecular biomarkers of renal disease in type 2 diabetes. Nephrol Dial Transplant. 2015 Aug;30(suppl 4):iv86-iv95 Authors: Pena MJ, de Zeeuw D, Mischak H, Jankowski J, Oberbauer R, Woloszczuk W, Benner J, Dallmann G, Mayer B, Mayer G, Rossing P, Lambers Heerspink HJ Abstract Diabetic kidney disease occurs in ∼25-40% of patients with type 2 diabetes. Given the high risk of progressive renal function loss and end-stage renal disease, early identification of patients with a renal risk is important. Novel biomarkers may aid in improving renal risk stratification. In this review, we first focus on the classical panel of albuminuria and estimated glomerular filtration rate as the primary clinical predictors of renal disease and then move our attention to novel biomarkers, primarily concentrating on assay-based multiple/panel biomarkers, proteomics biomarkers and metabolomics biomarkers. We focus on multiple biomarker panels since the molecular processes of renal disease progression in type 2 diabetes are heterogeneous, rendering it unlikely that a single biomarker significantly adds to clinical risk prediction. A limited number of prospective studies of multiple biomarkers address the predictive performance of novel biomarker panels in addition to the classical panel in type 2 diabetes. However, the prospective studies conducted so far have small sample sizes, are insufficiently powered and lack external validation. Adequately sized validation studies of multiple biomarker panels are thus required. There is also a paucity of studies that assess the effect of treatments on novel biomarker panels and determine whether initial treatment-induced changes in novel biomarkers predict changes in long-term renal outcomes. Such studies can not only improve our healthcare but also our understanding of the mechanisms of actions of existing and novel drugs and may yield biomarkers that can be used to monitor drug response. We conclude that this will be an area to focus research on in the future. PMID: 26209743 [PubMed - as supplied by publisher]

Related Articles A novel approach to the simultaneous extraction and non-targeted analysis of the small molecules metabolome and lipidome using 96-well solid phase extraction plates with column-switching technology. J Chromatogr A. 2015 Jul 16; Authors: Li Y, Zhang Z, Liu X, Li A, Hou Z, Wang Y, Zhang Y Abstract This study combines solid phase extraction (SPE) using 96-well plates with column-switching technology to construct a rapid and high-throughput method for the simultaneous extraction and non-targeted analysis of small molecules metabolome and lipidome based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry. This study first investigated the columns and analytical conditions for small molecules metabolome and lipidome, separated by an HSS T3 and BEH C18 columns, respectively. Next, the loading capacity and actuation duration of SPE were further optimized. Subsequently, SPE and column switching were used together to rapidly and comprehensively analyze the biological samples. The experimental results showed that the new analytical procedure had good precision and maintained sample stability (RSD<15%). The method was then satisfactorily applied to more widely analyze the small molecules metabolome and lipidome to test the throughput. The resulting method represents a new analytical approach for biological samples, and a highly useful tool for researches in metabolomics and lipidomics. PMID: 26209193 [PubMed - as supplied by publisher]

Comparison of Bile Acids and Acetaminophen Protein Adducts in Children and Adolescents with Acetaminophen Toxicity. PLoS One. 2015;10(7):e0131010 Authors: James L, Yan K, Pence L, Simpson P, Bhattacharyya S, Gill P, Letzig L, Kearns G, Beger R Abstract Metabolomics approaches have enabled the study of new mechanisms of liver injury in experimental models of drug toxicity. Disruption of bile acid homeostasis is a known mechanism of drug induced liver injury. The relationship of individual bile acids to indicators of oxidative drug metabolism (acetaminophen protein adducts) and liver injury was examined in children with acetaminophen overdose, hospitalized children with low dose exposure to acetaminophen, and children with no recent exposure to acetaminophen. Nine bile acids were quantified through targeted metabolomic analysis in the serum samples of the three groups. Bile acids were compared to serum levels of acetaminophen protein adducts and alanine aminotransferase. Glycodeoxycholic acid, taurodeoxycholic acid, and glycochenodeoxycholic acid were significantly increased in children with acetaminophen overdose compared to healthy controls. Among patients with acetaminophen overdose, bile acids were higher in subjects with acetaminophen protein adduct values > 1.0 nmol/mL and modest correlations were noted for three bile acids and acetaminophen protein adducts as follows: taurodeoxycholic acid (R=0.604; p<0.001), glycodeoxycholic acid (R=0.581; p<0.001), and glycochenodeoxycholic acid (R=0.571; p<0.001). Variability in bile acids was greater among hospitalized children receiving low doses of acetaminophen than in healthy children with no recent acetaminophen exposure. Compared to bile acids, acetaminophen protein adducts more accurately discriminated among children with acetaminophen overdose, children with low dose exposure to acetaminophen, and healthy control subjects. In children with acetaminophen overdose, elevations of conjugated bile acids were associated with specific indicators of acetaminophen metabolism and non-specific indicators of liver injury. PMID: 26208104 [PubMed - as supplied by publisher]

Related Articles The Warburg effect: a balance of flux analysis. Metabolomics. 2015 Aug;11(4):787-796 Authors: Vaitheesvaran B, Xu J, Yee J, Q-Y L, Go VL, Xiao GG, Lee WN Abstract Cancer metabolism is characterized by increased macromolecular syntheses through coordinated increases in energy and substrate metabolism. The observation that cancer cells produce lactate in an environment of oxygen sufficiency (aerobic glycolysis) is a central theme of cancer metabolism known as the Warburg effect. Aerobic glycolysis in cancer metabolism is accompanied by increased pentose cycle and anaplerotic activities producing energy and substrates for macromolecular synthesis. How these processes are coordinated is poorly understood. Recent advances have focused on molecular regulation of cancer metabolism by oncogenes and tumor suppressor genes which regulate numerous enzymatic steps of central glucose metabolism. In the past decade, new insights in cancer metabolism have emerged through the application of stable isotopes particularly from (13)C carbon tracing. Such studies have provided new evidence for system-wide changes in cancer metabolism in response to chemotherapy. Interestingly, experiments using metabolic inhibitors on individual biochemical pathways all demonstrate similar system-wide effects on cancer metabolism as in targeted therapies. Since biochemical reactions in the Warburg effect place competing demands on available precursors, high energy phosphates and reducing equivalents, the cancer metabolic system must fulfill the condition of balance of flux (homeostasis). In this review, the functions of the pentose cycle and of the tricarboxylic acid (TCA) cycle in cancer metabolism are analyzed from the balance of flux point of view. Anticancer treatments that target molecular signaling pathways or inhibit metabolism alter the invasive or proliferative behavior of the cancer cells by their effects on the balance of flux (homeostasis) of the cancer metabolic phenotype. PMID: 26207106 [PubMed - as supplied by publisher]

Related Articles Cellular and Molecular Mechanisms of Phenotypic Switch in Gastrointestinal Smooth Muscle. J Cell Physiol. 2015 Jul 23; Authors: Scirocco A, Matarrese P, Carabotti M, Ascione B, Malorni W, Severi C Abstract As a general rule, smooth muscle cells (SMC) are able to switch from a contractile phenotype to a less mature synthetic phenotype. This switch is accompanied by a loss of differentiation with decreased expression of contractile markers, increased proliferation as well as the synthesis and the release of several signaling molecules such as pro-inflammatory cytokines, chemotaxis-associated molecules and growth factors. This SMC phenotypic plasticity has extensively been investigated in vascular diseases but interest is also emerging in the field of gastroenterology. It has in fact been postulated that altered micro-environmental conditions, including the composition of microbiota, could trigger the remodeling of the enteric SMC, with phenotype changes and consequent alterations of contraction and impairment of gut motility. Several molecular actors participate in this phenotype remodeling. These include extracellular molecules such as cytokines and extracellular matrix proteins, as well as intracellular proteins, e.g. transcription factors. Epigenetic control mechanisms and miRNA have also been suggested to participate. In this review key roles and actors of smooth muscle phenotypic switch, mainly in GI tissue, are described and discussed in the light of literature data available so far. This article is protected by copyright. All rights reserved. PMID: 26206426 [PubMed - as supplied by publisher]

Related Articles Dynamic metabolic change is indicative of inflammation-induced transformation of hepatic cells. Int J Biochem Cell Biol. 2015 Jul 20; Authors: Peng B, Liu F, Han R, Luo G, Cathopoulis T, Lu K, Li X, Yang L, Liu GY, Cai JC, Sh SL Abstract 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. PMID: 26205150 [PubMed - as supplied by publisher]

Related Articles Modern bioinformatics meets traditional Chinese medicine. Brief Bioinform. 2014 Nov;15(6):984-1003 Authors: Gu P, Chen H Abstract MOTIVATION: Traditional Chinese medicine (TCM) is gaining increasing attention with the emergence of integrative medicine and personalized medicine, characterized by pattern differentiation on individual variance and treatments based on natural herbal synergism. Investigating the effectiveness and safety of the potential mechanisms of TCM and the combination principles of drug therapies will bridge the cultural gap with Western medicine and improve the development of integrative medicine. Dealing with rapidly growing amounts of biomedical data and their heterogeneous nature are two important tasks among modern biomedical communities. Bioinformatics, as an emerging interdisciplinary field of computer science and biology, has become a useful tool for easing the data deluge pressure by automating the computation processes with informatics methods. Using these methods to retrieve, store and analyze the biomedical data can effectively reveal the associated knowledge hidden in the data, and thus promote the discovery of integrated information. Recently, these techniques of bioinformatics have been used for facilitating the interactional effects of both Western medicine and TCM. The analysis of TCM data using computational technologies provides biological evidence for the basic understanding of TCM mechanisms, safety and efficacy of TCM treatments. At the same time, the carrier and targets associated with TCM remedies can inspire the rethinking of modern drug development. This review summarizes the significant achievements of applying bioinformatics techniques to many aspects of the research in TCM, such as analysis of TCM-related '-omics' data and techniques for analyzing biological processes and pharmaceutical mechanisms of TCM, which have shown certain potential of bringing new thoughts to both sides. PMID: 24067932 [PubMed - indexed for MEDLINE]

Related Articles (1)H NMR-based metabolic profiling of liver in chronic unpredictable mild stress rats with genipin treatment. J Pharm Biomed Anal. 2015 Jul 9;115:150-158 Authors: Chen JL, Shi BY, Xiang H, Hou WJ, Qin XM, Tian JS, Du GH Abstract Genipin, a hydrolyzed metabolite of geniposide extracted from the fruit of Gardenia jasminoides Ellis, has shown promise in alleviating depressive symptoms, however, the antidepressant mechanism of genipin remains unclear and incomprehensive. In this study, the metabolic profiles of aqueous and lipophilic extracts in liver of the chronic unpredictable mild stress (CUMS)-induced rat with genipin treatment were investigated using proton nuclear magnetic resonance ((1)H NMR) spectroscopy coupled with multivariate data analysis. Significant differences in the metabolic profiles of rats in the CUMS model group (MS) and the control group (NS) were observed with metabolic effects including decreasing in choline, glycerol and glycogen, increasing in lactate, alanine and succinate, and a disordered lipid metabolism, while the moderate dose (50mg/kg) of genipin could significantly regulate the concentrations of glycerol, lactate, alanine, succinate and the lipid to their normal levels. These biomakers were involved in metabolism pathways such as glycolysis/gluconeogensis, tricarboxylic acid (TCA) cycle and lipid metabolism, which may be helpful for understanding of antidepressant mechanism of genipin. PMID: 26204246 [PubMed - as supplied by publisher]

Related Articles Quantitative profiling of polar primary metabolites of two chickpea cultivars with contrasting responses to salinity. J Chromatogr B Analyt Technol Biomed Life Sci. 2015 Jul 13;1000:1-13 Authors: Dias DA, Hill CB, Jayasinghe NS, Atieno J, Sutton T, Roessner U Abstract This study reports a GC-QqQ-MS method for the quantification of forty-eight primary metabolites from four major classes (sugars, sugar acids, sugar phosphates, and organic acids) which can be applied to a number of biological systems. The method was validated in terms of linearity, reproducibility and recovery, using both calibration standards and real samples. Additionally, twenty-eight biogenic amines and amino acids were quantified using an established LC-QqQ-MS method. Both GC-QqQ-MS and LC-QqQ-MS quantitative methods were applied to plant extracts from flower and pod tissue of two chickpea (Cicer arietinum L.) cultivars differing in their ability to tolerate salinity, which were grown under control and salt-treated conditions. Statistical analysis was applied to the data sets using the absolute concentrations of metabolites to investigate the differences in metabolite profiles between the different cultivars, plant tissues, and treatments. The method is a significant improvement of present methodology for quantitative GC-MS metabolite profiling of organic acids and sugars, and provides new insights of chickpea metabolic responses to salinity stress. It is applicable to the analysis of dynamic changes in endogenous concentrations of polar primary metabolites to study metabolic responses to environmental stresses in complex biological tissues. PMID: 26204234 [PubMed - as supplied by publisher]