PubMed

Related Articles Metabolic profiling identifies trehalose as an abundant and diurnally fluctuating metabolite in the microalga Ostreococcus tauri. Metabolomics. 2017;13(6):68 Authors: Hirth M, Liverani S, Mahlow S, Bouget FY, Pohnert G, Sasso S Abstract INTRODUCTION: The picoeukaryotic alga Ostreococcus tauri (Chlorophyta) belongs to the widespread group of marine prasinophytes. Despite its ecological importance, little is known about the metabolism of this alga. OBJECTIVES: In this work, changes in the metabolome were quantified when O. tauri was grown under alternating cycles of 12 h light and 12 h darkness. METHODS: Algal metabolism was analyzed by gas chromatography-mass spectrometry. Using fluorescence-activated cell sorting, the bacteria associated with O. tauri were depleted to below 0.1% of total cells at the time of metabolic profiling. RESULTS: Of 111 metabolites quantified over light-dark cycles, 20 (18%) showed clear diurnal variations. The strongest fluctuations were found for trehalose. With an intracellular concentration of 1.6 mM in the dark, this disaccharide was six times more abundant at night than during the day. This fluctuation pattern of trehalose may be a consequence of starch degradation or of the synchronized cell cycle. On the other hand, maltose (and also sucrose) was below the detection limit (~10 μM). Accumulation of glycine in the light is in agreement with the presence of a classical glycolate pathway of photorespiration. We also provide evidence for the presence of fatty acid methyl and ethyl esters in O. tauri. CONCLUSIONS: This study shows how the metabolism of O. tauri adapts to day and night and gives new insights into the configuration of the carbon metabolism. In addition, several less common metabolites were identified. PMID: 28473745 [PubMed - in process]

Related Articles Untargeted metabolomics reveals a mild impact of remote ischemic conditioning on the plasma metabolome and α-hydroxybutyrate as a possible cardioprotective factor and biomarker of tissue ischemia. Metabolomics. 2017;13(6):67 Authors: Laursen MR, Hansen J, Elkjær C, Stavnager N, Nielsen CB, Pryds K, Johnsen J, Nielsen JM, Bøtker HE, Johannsen M Abstract INTRODUCTION: Remote ischemic conditioning (RIC) is a maneuver by which short non-lethal ischemic events are applied on distant organs or limbs to reduce ischemia and reperfusion injuries caused by e.g. myocardial infarct. Although intensively investigated, the specific mechanism of this protective phenomenon remains incompletely understood and in particular, knowledge on the role of small metabolites is scarce. OBJECTIVES: In this study, we aimed to study perturbations in the plasma metabolome following RIC and gain insight into metabolic changes by the intervention as well as to identify potential novel cardio-protective metabolites. METHODS: Blood plasma samples from ten healthy males were collected prior to and after RIC and tested for bioactivity in a HL-1 based cellular model of ischemia-reperfusion damage. Following this, the plasma was analyzed using untargeted LC-qTOF-MS and regulated metabolites were identified using univariate and multivariate statistical analysis. Results were finally verified in a second plasma study from the same group of volunteers and by testing a metabolite ester in the HL-1 cell model. RESULTS: The analysis revealed a moderate impact on the plasma metabolome following RIC. One metabolite, α-hydroxybutyrate (AHB) however, stood out as highly significantly upregulated after RIC. AHB might be a novel and more sensitive plasma-biomarker of transient tissue ischemia than lactate. Importantly, it was also found that a cell permeable AHB precursor protects cardiomyocytes from ischemia-reperfusion damage. CONCLUSION: Untargeted metabolomics analysis of plasma following RIC has led to insight into metabolism during RIC and revealed a possible novel metabolite of relevance to ischemic-reperfusion damage. PMID: 28473744 [PubMed - in process]

Related Articles The effects of thawing on the plasma metabolome: evaluating differences between thawed plasma and multi-organ samples. Metabolomics. 2017;13(6):66 Authors: Torell F, Bennett K, Rännar S, Lundstedt-Enkel K, Lundstedt T, Trygg J Abstract INTRODUCTION: Post-collection handling, storage and transportation can affect the quality of blood samples. Pre-analytical biases can easily be introduced and can jeopardize accurate profiling of the plasma metabolome. Consequently, a mouse study must be carefully planned in order to avoid any kind of bias that can be introduced, in order not to compromise the outcome of the study. The storage and shipment of the samples should be made in such a way that the freeze-thaw cycles are kept to a minimum. In order to keep the latent effects on the stability of the blood metabolome to a minimum it is essential to study the effect that the post-collection and pre-analytical error have on the metabolome. OBJECTIVES: The aim of this study was to investigate the effects of thawing on the metabolic profiles of different sample types. METHODS: In the present study, a metabolomics approach was utilized to obtain a thawing profile of plasma samples obtained on three different days of experiment. The plasma samples were collected from the tail on day 1 and 3, while retro-orbital sampling was used on day 5. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS). RESULTS: The thawed plasma samples were found to be characterized by higher levels of amino acids, fatty acids, glycerol metabolites and purine and pyrimidine metabolites as a result of protein degradation, cell degradation and increased phospholipase activity. The consensus profile was thereafter compared to the previously published study comparing thawing profiles of tissue samples from gut, kidney, liver, muscle and pancreas. CONCLUSIONS: The comparison between thawed organ samples and thawed plasma samples indicate that the organ samples are more sensitive to thawing, however thawing still affected all investigated sample types. PMID: 28473743 [PubMed - in process]

Related Articles Omics Fusion - A Platform for Integrative Analysis of Omics Data. J Integr Bioinform. 2016 Dec 18;13(4):296 Authors: Brink BG, Seidel A, Kleinbölting N, Nattkemper TW, Albaum SP Abstract We present Omics Fusion, a new web-based platform for integrative analysis of omics data. Omics Fusion provides a collection of new and established tools and visualization methods to support researchers in exploring omics data, validating results or understanding how to adjust experiments in order to make new discoveries. It is easily extendible and new visualization methods are added continuously. It is available for free under: https://fusion.cebitec.uni-bielefeld.de/. PMID: 28187412 [PubMed - indexed for MEDLINE]

Related Articles Comprehensive metabolomics identified lipid peroxidation as a prominent feature in human plasma of patients with coronary heart diseases. Redox Biol. 2017 Apr 26;12:899-907 Authors: Lu J, Chen B, Chen T, Guo S, Xue X, Chen Q, Zhao M, Xia L, Zhu Z, Zheng L, Yin H Abstract Coronary heart disease (CHD) is a complex human disease associated with inflammation and oxidative stress. The underlying mechanisms and diagnostic biomarkers for the different types of CHD remain poorly defined. Metabolomics has been increasingly recognized as an enabling technique with the potential to identify key metabolomic features in an attempt to understand the pathophysiology and differentiate different stages of CHD. We performed comprehensive metabolomic analysis in human plasma from 28 human subjects with stable angina (SA), myocardial infarction (MI), and healthy control (HC). Subsequent analysis demonstrated a uniquely altered metabolic profile in these CHD: a total of 18, 37 and 36 differential metabolites were identified to distinguish SA from HC, MI from SA, and MI from HC groups respectively. Among these metabolites, glycerophospholipid (GPL) metabolism emerged as the most significantly disturbed pathway. Next, we used a targeted metabolomic approach to systematically analyze GPL, oxidized phospholipid (oxPL), and downstream metabolites derived from polyunsaturated fatty acids (PUFAs), such as arachidonic acid and linoleic acid. Surprisingly, lipids associated with lipid peroxidation (LPO) pathways including oxidized PL and isoprostanes, isomers of prostaglandins, were significantly elevated in plasma of MI patients comparing to HC and SA, consistent with the notion that oxidative stress-induced LPO is a prominent feature in CHD. Our studies using the state-of-the-art metabolomics help to understand the underlying biological mechanisms involved in the pathogenesis of CHD; LPO metabolites may serve as potential biomarkers to differentiation MI from SA and HC. PMID: 28472752 [PubMed - as supplied by publisher]

Related Articles Saccharin induced liver inflammation in mice by altering the gut microbiota and its metabolic functions. Food Chem Toxicol. 2017 May 01;: Authors: Bian X, Tu P, Chi L, Gao B, Ru H, Lu K Abstract Maintaining the balance of the gut microbiota and its metabolic functions is vital for human health, however, this balance can be disrupted by various external factors including food additives. A range of food and beverages are sweetened by saccharin, which is generally considered to be safe despite controversial debates. However, recent studies indicated that saccharin perturbed the gut microbiota. Inflammation is frequently associated with disruptions of the gut microbiota. The aim of this study is to investigate the relationship between host inflammation and perturbed gut microbiome by saccharin. C57BL/6J male mice were treated with saccharin in drinking water for six months. Q-PCR was used to detect inflammatory markers in mouse liver, while 16S rRNA gene sequencing and metabolomics were used to reveal changes of the gut microbiota and its metabolomic profiles. Elevated expression of pro-inflammatory iNOS and TNF-α in liver indicated that saccharin induced inflammation in mice. The altered gut bacterial genera, enriched orthologs of pathogen-associated molecular patterns, such as LPS and bacterial toxins, in concert with increased pro-inflammatory metabolites suggested that the saccharin-induced liver inflammation could be associated with the perturbation of the gut microbiota and its metabolic functions. PMID: 28472674 [PubMed - as supplied by publisher]

Related Articles Oxidative profiling of the failing right heart in rats with pulmonary hypertension. PLoS One. 2017;12(5):e0176887 Authors: Wang X, Shults NV, Suzuki YJ Abstract Right heart failure is the major cause of death among patients with pulmonary arterial hypertension (PAH). Understanding the biology of the right ventricle (RV) should help developing new therapeutic strategies. Rats subjected to the injection of Sugen5416 (an inhibitors of vascular endothelial growth factor receptor) plus the ovalbumin immunization had increased pulmonary arterial pressure and severe vascular remodeling. RVs of these rats were hypertrophied and had severe cardiac fibrosis. No apoptosis was, however, detected. Metabolomics analysis revealed that oxidized glutathione, xanthine and uric acid had increased in PAH RVs, suggesting the production of reactive oxygen species by xanthine oxidase. PAH RVs were also found to have a 30-fold lower level of α-tocopherol nicotinate, consistent with oxidative stress decreasing antioxidants and also demonstrating for the first time that the nicotinate ester of vitamin E is endogenously expressed. Oxidative/nitrosative protein modifications including S-glutathionylation, S-nitrosylation and nitrotyrosine formation, but not protein carbonylation, were found to be increased in RVs of rats with PAH. Mass spectrometry identified that S-nitrosylated proteins include heat shock protein 90 and sarcoplasmic reticulum Ca2+-ATPase. These results demonstrate that RV failure is associated with the promotion of specific oxidative and nitrosative stress. PMID: 28472095 [PubMed - in process]

Related Articles Metabolite Spectral Accuracy on Orbitraps. Anal Chem. 2017 May 04;: Authors: Su X, Lu W, Rabinowitz JD Abstract Orbitraps are high-resolution ion-trap mass spectrometers that are widely used in metabolomics. While the mass accuracy and resolving power of orbitraps have been extensively documented, their spectral accuracy-i.e. accuracy in measuring the abundances of isotopic peaks-remains less studied. In analyzing spectra of unlabeled metabolites, we discovered a systematic under representation of heavier natural isotopic species, especially for high molecular weight metabolites (~20% underestimation of [M+1]/[M+0] ratio at m/z 600). We hypothesize that these discrepancies arise for metabolites far from lower limit of the mass scan range, due to the weaker containment in the C-trap that results in suboptimal trajectories inside the Orbitrap analyzer. Consistent with this, spectral fidelity was restored by dividing the mass scan range (initially 75 m/z to 1000 m/z) into two scan events, one for lower molecular weight and the other for higher molecular weight metabolites. Having thus obtained accurate mass spectra at high resolution, we found that natural isotope correction for high-resolution labeling data requires more sophisticated algorithms than typically employed: the correction algorithm must take into account whether isotopologues with the same nominal mass are resolved. We present an algorithm and associated open-source code, named AccuCor, for this purpose. Together, these improvements in instrument parameters and natural isotope correction enable more accurate measurement of metabolite labeling and thus metabolic flux. PMID: 28471646 [PubMed - as supplied by publisher]

Related Articles Hydroxyeicosapentaenoic Acids and Epoxyeicosatetraenoic Acids Attenuate Early Occurrence of Nonalcoholic Fatty Liver Disease. Br J Pharmacol. 2017 May 04;: Authors: Wang C, Liu W, Yao L, Zhang X, Zhang X, Ye C, Jiang H, He J, Zhu Y, Ai D Abstract BACKGROUND AND PURPOSE: The ω-3 polyunsaturated fatty acids (PUFAs) mediate protective effects on several metabolic disorders. However, the functions of their metabolites in the early stage of nonalcoholic fatty liver disease (NAFLD) are largely unknown. EXPERIMENTAL APPROACH: Mice were fed a control diet, high-fat diet (HFD) or ω-3 PUFA-enriched HFD (ω3HFD) for 4 days and phenotypes were analyzed. LC-MS/MS was used to determine the eicosanoid profiles. Primary hepatocytes and peritoneal macrophages were used for the mechanism study. KEY RESULTS: In short-term HFD-fed mice, the significantly induced lipid accumulation in liver was reversed by ω-3 PUFA supplementation. Metabolomics showed the plasma content of hydroxyeicosapentaenoic acids (HEPEs) and epoxyeicosatetraenoic acids (EEQs) was reduced by a short-term HFD and markedly increased by the ω3HFD. However, HEPE/EEQ treatment had no direct protective effect on hepatocytes. ω3HFD also significantly attenuated HFD-induced adipose tissue inflammation. Furthermore, the expression of proinflammatory cytokines and activation of JNK pathway induced by palmitate were suppressed by HEPEs and EEQs in macrophages. 17,18-EEQ, 5-HEPE and 9-HEPE were identified as the efficient components among these metabolites, as evidenced by their greater suppression of the palmitate-induced expression of inflammatory factors, chemotaxis and JNK activation as compared with other metabolites in macrophages. A mixture of 17,18-EEQ, 5-HEPE and 9-HEPE significantly ameliorated the short-term HFD-induced accumulation of macrophages in adipose tissue and hepatic steatosis. CONCLUSION AND IMPLICATIONS: 17,18-EEQ, 5-HEPE and 9-HEPE may be potential approaches to prevent NAFLD in the early stage by inhibiting the inflammatory response in adipose tissue macrophages via JNK signaling. PMID: 28471490 [PubMed - as supplied by publisher]

Related Articles Metabolite Profiling of Mammalian Cell Culture Processes to Evaluate Cellular Viability. Methods Mol Biol. 2017;1601:137-152 Authors: Evie IM, Dickson AJ, Elvin M Abstract Metabolite profiling allows for the identification of metabolites that become limiting during cell culture and/or for finding bottlenecks in metabolic pathways that limit culture growth and proliferation. Here we describe one protocol with two different sampling methodologies for GC-MS-based metabolite profiling. We also highlight an example of the types of datasets that are attainable and how such datasets can be evaluated to identify factors related to cell viability. We also demonstrate, via the same methodology, the accurate quantification of a number of metabolites of interest. PMID: 28470524 [PubMed - in process]

Related Articles Synthesis of cyclooxygenase metabolites of 8,9-epoxyeicosatrienoic acid (EET): 11- and 15-hydroxy 8,9-EETs. Org Biomol Chem. 2017 May 04;: Authors: Barnych B, Rand AA, Cajka T, Lee KSS, Hammock BD Abstract COX metabolites of 8,9-EET, previously observed as potent mitogenic lipid mediators, were synthesized for the first time by using two synthetic approaches. These synthetic materials allow for structural confirmation of COX metabolites of 8,9-EET and further study of their biological roles. PMID: 28470279 [PubMed - as supplied by publisher]

Related Articles Metabolomics approach reveals annual metabolic variation in roots of Cyathula officinalis Kuan based on gas chromatography-mass spectrum. Chin Med. 2017;12:12 Authors: Tong K, Li ZL, Sun X, Yan S, Jiang MJ, Deng MS, Chen J, Li JW, Tian ML Abstract BACKGROUND: Herbal quality is strongly influenced by harvest time. It is therefore one of crucial factors that should be well respected by herbal producers when optimizing cultivation techniques, so that to obtain herbal products of high quality. In this work, we paid attention on one of common used Chinese herbals, Cyathula officinalis Kuan. According to previous studies, its quality may be related with growth years because of the variation of several main bioactive components in different growth years. However, information about the whole chemical composition is still scarce, which may jointly determine the herbal quality. METHODS: Cyathula officinalis samples were collected in 1-4 growth years after sowing. To obtain a global insight on chemical profile of herbs, we applied a metabolomics approach based on gas chromatography-mass spectrum. Analysis of variance, principal component analysis, partial least squares discriminant analysis and hierarchical cluster analysis were combined to explore the significant difference in different growth years. RESULTS: 166 metabolites were identified by using gas chromatography-mass spectrum method. 63 metabolites showed significant change in different growth years in terms of analysis of variance. Those metabolites then were grouped into 4 classes by hierarchical cluster analysis, characterizing the samples of different growth ages. Samples harvested in the earliest years (1-2) were obviously differ with the latest years (3-4) as reported by principal component analysis. Further, partial least squares discriminant analysis revealed the detail difference in each growth year. Gluconic acid, xylitol, glutaric acid, pipecolinic acid, ribonic acid, mannose, oxalic acid, digalacturonic acid, lactic acid, 2-deoxyerythritol, acetol, 3-hydroxybutyric acid, citramalic acid, N-carbamylglutamate, and cellobiose are the main 15 discrimination metabolites between different growth years. CONCLUSION: Harvest time should be well considered when producing C. officinalis. In order to boost the consistency of herbal quality, C. officinalis is recommended to harvest in 4th growth year. The method of GC-MS combined with multivariate analysis was a powerful tool to evaluate the herbal quality. PMID: 28469699 [PubMed - in process]

Related Articles PII Protein-Derived FRET Sensors for Quantification and Live-Cell Imaging of 2-Oxoglutarate. Sci Rep. 2017 May 03;7(1):1437 Authors: Lüddecke J, Francois L, Spät P, Watzer B, Chilczuk T, Poschet G, Hell R, Radlwimmer B, Forchhammer K Abstract The citric acid cycle intermediate 2-oxoglutarate (2-OG, a.k.a. alpha-ketoglutarate) links the carbon and nitrogen metabolic pathways and can provide information on the metabolic status of cells. In recent years, it has become exceedingly clear that 2-OG also acts as a master regulator of diverse biologic processes in all domains of life. Consequently, there is a great demand for time-resolved data on 2-OG fluctuations that can't be adequately addressed using established methods like mass spectrometry-based metabolomics analysis. Therefore, we set out to develop a novel intramolecular 2-OG FRET sensor based on the signal transduction protein PII from Synechococcus elongatus PCC 7942. We created two variants of the sensor, with a dynamic range for 2-OG from 0.1 µM to 0.1 mM or from 10 µM to 10 mM. As proof of concept, we applied the sensors to determine in situ glutamine:2-oxoglutarate aminotransferase (GOGAT) activity in Synechococcus elongatus PCC 7942 cells and measured 2-OG concentrations in cell extracts from Escherichia coli in vitro. Finally, we could show the sensors' functionality in living human cell lines, demonstrating their potential in the context of mechanistic studies and drug screening. PMID: 28469248 [PubMed - in process]

Related Articles Exhaled breath condensate to discriminate individuals with different smoking habits by GC-TOF/MS. Sci Rep. 2017 May 03;7(1):1421 Authors: Peralbo-Molina A, Calderón-Santiago M, Jurado-Gámez B, Luque de Castro MD, Priego-Capote F Abstract Smoking is a crucial factor in respiratory diseases and lung inflammation, which are the reasons for high mortality worldwide. Despite the negative impact that tobacco consumption causes on health, few metabolomics studies have compared the composition of biofluids from smoker and non-smoker individuals. Exhaled breath condensate (EBC) is one of the biofluids less employed for clinical studies despite its non-invasive sampling and the foreseeable relationship between its composition and respiratory diseases. EBC was used in this research as clinical sample to compare three groups of individuals: current smokers (CS), former smokers (FS) and never smokers (NS). Special attention was paid to the cumulative consumption expressed as smoked pack-year. The levels of 12 metabolites found statistically significant among the three groups of individuals were discussed to find an explanation to their altered levels. Significant compounds included monoacylglycerol derivatives, terpenes and other compounds, the presence of which could be associated to the influence of smoking on the qualitative and quantitative composition of the microbiome. PMID: 28469199 [PubMed - in process]

Related Articles Simultaneous Quantification of Amino Metabolites in Multiple Metabolic Pathways Using Ultra-High Performance Liquid Chromatography with Tandem-mass Spectrometry. Sci Rep. 2017 May 03;7(1):1423 Authors: Wang J, Zhou L, Lei H, Hao F, Liu X, Wang Y, Tang H Abstract Metabolites containing amino groups cover multiple pathways and play important roles in redox homeostasis and biosyntheses of proteins, nucleotides and neurotransmitters. Here, we report a new method for simultaneous quantification of 124 such metabolites. This is achieved by derivatization-assisted sensitivity enhancement with 5-aminoisoquinolyl-N-hydroxysuccinimidyl carbamate (5-AIQC) followed with comprehensive analysis using ultra-high performance liquid chromatography and electrospray ionization tandem mass spectrometry (UHPLC-MS/MS). In an one-pot manner, this quantification method enables simultaneous coverage of 20 important metabolic pathways including protein biosynthesis/degradation, biosyntheses of catecholamines, arginine and glutathione, metabolisms of homocysteine, taurine-hypotaurine etc. Compared with the reported ones, this method is capable of simultaneously quantifying thiols, disulfides and other oxidation-prone analytes in a single run and suitable for quantifying aromatic amino metabolites. This method is also much more sensitive for all tested metabolites with LODs well below 50 fmol (at sub-fmol for most tested analytes) and shows good precision for retention time and quantitation with inter-day and intra-day relative standard deviations (RSDs) below 15% and good recovery from renal cancer tissue, rat urine and plasma. The method was further applied to quantify the amino metabolites in silkworm hemolymph from multiple developmental stages showing its applicability in metabolomics and perhaps some clinical chemistry studies. PMID: 28469184 [PubMed - in process]

Related Articles Serum Metabolomics Investigation of Humanized Mouse Model with Dengue Infection. J Virol. 2017 May 03;: Authors: Cui L, Hou J, Fang J, Lee YH, Costa VV, Wong LH, Chen Q, Ooi EE, Tannenbaum SR, Chen J, Ong CN Abstract Dengue is an acute febrile illness caused by dengue virus (DENV) and a major cause of morbidity and mortality in tropical and subtropical regions of the world. The lack of an appropriate small-animal model of dengue infection has greatly hindered the study of dengue pathogenesis and the development of therapeutics. In this study, we conducted a mass spectrometry-based serum metabolic profiling from a humanized mice (humice) model with DENV serotype 2 infection at 0, 3, 7, 14 and 28 days post infection (dpi). Forty-eight differential metabolites were identified, including fatty acids, purines and pyrimidines, acylcarnitines, acylglycines, phospholipids, sphingolipids, amino acid and derivatives, free fatty acids, bile acid, etc. These metabolites showed a reversible change trend - most were significantly perturbed at 3 or 7 dpi and returned to control levels at 14 or 28 dpi, indicating that these metabolites might serve as prognostic markers of the disease in humice. Major perturbed metabolic pathways included purine and pyrimidine metabolism, fatty acid β-oxidation, phospholipid catabolism, arachidonic acid and linoleic acid metabolism, sphingolipid metabolism, tryptophan metabolism, phenylalanine metabolism, lysine biosynthesis and degradation, bile acid biosynthesis, etc. Most of these disturbed pathways are similar to our previous metabolomics findings in a longitudinal cohort of adult human dengue patients across different infection stages. Our analyses revealed the commonalities of host responses between humice and human to DENV infections and suggested that humice could be a useful small-animal model for the study of dengue pathogenesis and the development of dengue therapeutics.Importance: Dengue virus is the most widespread arbovirus, causing an estimated 390 million dengue infections worldwide every year. As yet, there is currently no effective treatment against the disease, and the lack of an appropriate small-animal model of dengue infection has greatly increased the challenges in the study of dengue pathogenesis and the development of therapeutics. Metabolomics provides global views of small molecule metabolites and is a useful tool of finding metabolic pathways related to disease processes. Here we conducted serum metabolomics study on a humanized mice model, which has significant levels of human platelets, monocytes/macrophages, and hepatocytes, with dengue infection. Forty-eight differential metabolites were identified, and the underlying perturbed metabolic pathways are quite similar to the pathways altered in dengue patients in previous metabolomics studies, indicating that humanized mice could be a highly relevant small-animal model for the study of dengue pathogenesis and the development of dengue therapeutics. PMID: 28468882 [PubMed - as supplied by publisher]

Related Articles Proceedings of 3(rd) Australian Lipid Meeting. Metabolites. 2017 May 01;7(2): Authors: Brown SH, Rupasinghe TWT Abstract n/a. PMID: 28468309 [PubMed - in process]

Related Articles The Undiagnosed Diseases Network: Accelerating Discovery about Health and Disease. Am J Hum Genet. 2017 Feb 02;100(2):185-192 Authors: Ramoni RB, Mulvihill JJ, Adams DR, Allard P, Ashley EA, Bernstein JA, Gahl WA, Hamid R, Loscalzo J, McCray AT, Shashi V, Tifft CJ, Undiagnosed Diseases Network, Wise AL Abstract Diagnosis at the edges of our knowledge calls upon clinicians to be data driven, cross-disciplinary, and collaborative in unprecedented ways. Exact disease recognition, an element of the concept of precision in medicine, requires new infrastructure that spans geography, institutional boundaries, and the divide between clinical care and research. The National Institutes of Health (NIH) Common Fund supports the Undiagnosed Diseases Network (UDN) as an exemplar of this model of precise diagnosis. Its goals are to forge a strategy to accelerate the diagnosis of rare or previously unrecognized diseases, to improve recommendations for clinical management, and to advance research, especially into disease mechanisms. The network will achieve these objectives by evaluating patients with undiagnosed diseases, fostering a breadth of expert collaborations, determining best practices for translating the strategy into medical centers nationwide, and sharing findings, data, specimens, and approaches with the scientific and medical communities. Building the UDN has already brought insights to human and medical geneticists. The initial focus has been on data sharing, establishing common protocols for institutional review boards and data sharing, creating protocols for referring and evaluating patients, and providing DNA sequencing, metabolomic analysis, and functional studies in model organisms. By extending this precision diagnostic model nationally, we strive to meld clinical and research objectives, improve patient outcomes, and contribute to medical science. PMID: 28157539 [PubMed - indexed for MEDLINE]

Related Articles Analyzing and interpreting genome data at the network level with ConsensusPathDB. Nat Protoc. 2016 Oct;11(10):1889-907 Authors: Herwig R, Hardt C, Lienhard M, Kamburov A Abstract ConsensusPathDB consists of a comprehensive collection of human (as well as mouse and yeast) molecular interaction data integrated from 32 different public repositories and a web interface featuring a set of computational methods and visualization tools to explore these data. This protocol describes the use of ConsensusPathDB (http://consensuspathdb.org) with respect to the functional and network-based characterization of biomolecules (genes, proteins and metabolites) that are submitted to the system either as a priority list or together with associated experimental data such as RNA-seq. The tool reports interaction network modules, biochemical pathways and functional information that are significantly enriched by the user's input, applying computational methods for statistical over-representation, enrichment and graph analysis. The results of this protocol can be observed within a few minutes, even with genome-wide data. The resulting network associations can be used to interpret high-throughput data mechanistically, to characterize and prioritize biomarkers, to integrate different omics levels, to design follow-up functional assay experiments and to generate topology for kinetic models at different scales. PMID: 27606777 [PubMed - indexed for MEDLINE]

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