PubMed

18 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/02/05PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Mild inborn errors of metabolism in commonly used inbred mouse strains. Mol Genet Metab. 2019 Jan 24;: Authors: Leandro J, Violante S, Argmann CA, Hagen J, Dodatko T, Bender A, Zhang W, Williams EG, Bachmann AM, Auwerx J, Yu C, Houten SM Abstract Inbred mouse strains are a cornerstone of translational research but paradoxically many strains carry mild inborn errors of metabolism. For example, α-aminoadipic acidemia and branched-chain ketoacid dehydrogenase deficiency are known in C57BL/6J mice. Using RNA sequencing, we now reveal the causal variants in Dhtkd1 and Bckdhb, and the molecular mechanism underlying these metabolic defects. C57BL/6J mice have decreased Dhtkd1 mRNA expression due to a solitary long terminal repeat (LTR) in intron 4 of Dhtkd1. This LTR harbors an alternate splice donor site leading to a partial splicing defect and as a consequence decreased total and functional Dhtkd1 mRNA, decreased DHTKD1 protein and α-aminoadipic acidemia. Similarly, C57BL/6J mice have decreased Bckdhb mRNA expression due to an LTR retrotransposon in intron 1 of Bckdhb. This transposable element encodes an alternative exon 1 causing aberrant splicing, decreased total and functional Bckdhb mRNA and decreased BCKDHB protein. Using a targeted metabolomics screen, we also reveal elevated plasma C5-carnitine in 129 substrains. This biochemical phenotype resembles isovaleric acidemia and is caused by an exonic splice mutation in Ivd leading to partial skipping of exon 10 and IVD protein deficiency. In summary, this study identifies three causal variants underlying mild inborn errors of metabolism in commonly used inbred mouse strains. PMID: 30709776 [PubMed - as supplied by publisher]

Metabolomics assessment reveals oxidative stress and altered energy production in the heart after ischemic acute kidney injury in mice. Kidney Int. 2019 Jan 29;: Authors: Fox BM, Gil HW, Kirkbride-Romeo L, Bagchi RA, Wennersten SA, Haefner KR, Skrypnyk NI, Brown CN, Soranno DE, Gist KM, Griffin BR, Jovanovich A, Reisz JA, Wither MJ, D'Alessandro A, Edelstein CL, Clendenen N, McKinsey TA, Altmann C, Faubel S Abstract Acute kidney injury (AKI) is a systemic disease associated with widespread effects on distant organs, including the heart. Normal cardiac function is dependent on constant ATP generation, and the preferred method of energy production is via oxidative phosphorylation. Following direct ischemic cardiac injury, the cardiac metabolome is characterized by inadequate oxidative phosphorylation, increased oxidative stress, and increased alternate energy utilization. We assessed the impact of ischemic AKI on the metabolomics profile in the heart. Ischemic AKI was induced by 22 minutes of renal pedicle clamping, and 124 metabolites were measured in the heart at 4 hours, 24 hours, and 7 days post-procedure. Forty-one percent of measured metabolites were affected, with the most prominent changes observed 24 hours post-AKI. The post-AKI cardiac metabolome was characterized by amino acid depletion, increased oxidative stress, and evidence of alternative energy production, including a shift to anaerobic forms of energy production. These metabolomic effects were associated with significant cardiac ATP depletion and with echocardiographic evidence of diastolic dysfunction. In the kidney, metabolomics analysis revealed shifts suggestive of energy depletion and oxidative stress, which were reflected systemically in the plasma. This is the first study to examine the cardiac metabolome after AKI, and demonstrates that effects of ischemic AKI on the heart are akin to the effects of direct ischemic cardiac injury. PMID: 30709662 [PubMed - as supplied by publisher]

Metabolomic responses of mussel Mytilus galloprovincialis to fluoranthene exposure under different nutritive conditions. Mar Environ Res. 2019 Jan 25;: Authors: Campillo JA, Sevilla A, González-Fernández C, Bellas J, Bernal C, Cánovas M, Albentosa M Abstract Biomarkers are useful tools to assess biological effects of pollutants that are extensively used in monitoring programs to assess ecosystem health. However, they are strongly affected by mussel physiological state, especially nutritive status, which has led to the search of new biological indicators of chemical pollutants exposition. Environmental metabolomics is an approach for examining the metabolic responses (measurement of low molecular weight endogenous metabolites) of an organism to both natural and anthropogenic stressors that can occur in its environment. The aim of the present work was to assess the effect of the polycyclic aromatic hydrocarbon fluoranthene (FLU) exposure on the metabolomic profiles of mussel digestive glands under different nutritive conditions. To achieve this objective, mussels were reared, for a period of 56 days, under three different food rations in order to obtain a gradient of nutritive status (negative, zero and positive energy balance), and after that, they were exposed, during 3 weeks, to a nominal concentration of 3 μg FLU L-1. A total of 43 metabolites, including aminoacids (Ala, Val, Leu, Ile, etc.), energy metabolism related metabolites (ATP, AMP, etc.), organic osmolytes (taurine, etc.), redox metabolism (GSH, NADP+) and nucleotides, were identified and quantified in the digestive glands of the mussels. Principal Component Analysis (PCA) defined two principal components (PC1 and PC2) that explained 55.6% of the total variance, although the first component explains more than 80% of this variance, this being related to the mussel nutritive condition. The effect of the toxicant, explained by the PC2, is similar to that produced under conditions of food restriction, which masks the effect of the toxicant under these conditions. As the feeding conditions are more favorable, the toxic effect becomes more apparent. Therefore, the great influence of nutritive condition on mussel metabolome implies a handicap for the use of metabolomic biomarkers, as previously demonstrated for biochemical and other molecular biomarkers, in large-scale monitoring programs in which several food conditions coexist with pollution levels. PMID: 30709639 [PubMed - as supplied by publisher]

Heterologous phosphoketolase expression redirects flux towards acetate, perturbs sugar phosphate pools and increases respiratory demand in Saccharomyces cerevisiae. Microb Cell Fact. 2019 Feb 01;18(1):25 Authors: Bergman A, Hellgren J, Moritz T, Siewers V, Nielsen J, Chen Y Abstract INTRODUCTION: Phosphoketolases (Xfpk) are a non-native group of enzymes in yeast, which can be expressed in combination with other metabolic enzymes to positively influence the yield of acetyl-CoA derived products by reducing carbon losses in the form of CO2. In this study, a yeast strain expressing Xfpk from Bifidobacterium breve, which was previously found to have a growth defect and to increase acetate production, was characterized. RESULTS: Xfpk-expression was found to increase respiration and reduce biomass yield during glucose consumption in batch and chemostat cultivations. By cultivating yeast with or without Xfpk in bioreactors at different pHs, we show that certain aspects of the negative growth effects coupled with Xfpk-expression are likely to be explained by proton decoupling. At low pH, this manifests as a reduction in biomass yield and growth rate in the ethanol phase. Secondly, we show that intracellular sugar phosphate pools are significantly altered in the Xfpk-expressing strain. In particular a decrease of the substrates xylulose-5-phosphate and fructose-6-phosphate was detected (26% and 74% of control levels) together with an increase of the products glyceraldehyde-3-phosphate and erythrose-4-phosphate (208% and 542% of control levels), clearly verifying in vivo Xfpk enzymatic activity. Lastly, RNAseq analysis shows that Xfpk expression increases transcription of genes related to the glyoxylate cycle, the TCA cycle and respiration, while expression of genes related to ethanol and acetate formation is reduced. The physiological and transcriptional changes clearly demonstrate that a heterologous phosphoketolase flux in combination with endogenous hydrolysis of acetyl-phosphate to acetate increases the cellular demand for acetate assimilation and respiratory ATP-generation, leading to carbon losses. CONCLUSION: Our study shows that expression of Xfpk in yeast diverts a relatively small part of its glycolytic flux towards acetate formation, which has a significant impact on intracellular sugar phosphate levels and on cell energetics. The elevated acetate flux increases the ATP-requirement for ion homeostasis and need for respiratory assimilation, which leads to an increased production of CO2. A majority of the negative growth effects coupled to Xfpk expression could likely be counteracted by preventing acetate accumulation via direct channeling of acetyl-phosphate towards acetyl-CoA. PMID: 30709397 [PubMed - in process]

Evaluation of the Hepatotoxicity of the Zhi-Zi-Hou-Po Decoction by Combining UPLC-Q-Exactive-MS-Based Metabolomics and HPLC-MS/MS-Based Geniposide Tissue Distribution. Molecules. 2019 Jan 31;24(3): Authors: Wang Y, Feng F Abstract With traditional Chinese medicine (TCM) becoming widespread globally, its safety has increasingly become a concern, especially its hepatoxicity. For example, Gardenia jasminoides Ellis is a key ingredient in the Zhi-Zi-Hou-Po decoction (ZZHPD), which is a commonly-used clinically combined prescription of TCM that may induce hepatoxicity. However, the underlying toxicity mechanism of ZZHPD is not fully understood. In this study, a plasma metabolomics strategy was used to investigate the mechanism of ZZHPD-induced hepatotoxicity through profiling entire endogenous metabolites. Twenty-four Sprague-Dawley rats were randomly assigned into four groups, which were orally administered with 0.9% saline, as well as 2.7 g/kg/day, 8.1 g/kg/day, or 27 g/kg/day of ZZHPD for 30 consecutive days, respectively. Biochemical assay and metabolomics assay were used to detect serum and plasma samples, whilst histopathological assay was used for detecting liver tissues, and the geniposide distribution in tissues was simultaneously measured. The results showed that the concentration of 20 metabolites linked to amino acid, lipid, and bile acid metabolism had significant changes in the ZZHPD-treated rats. Moreover, toxic effects were aggravated with serum biochemical and histopathological examines in liver tissues as the dosage increased, which may be associated with the accumulation of geniposide in the liver as the dosage increased. Notably, our findings also demonstrated that the combined metabolomics strategy with tissue distribution had significant potential for elucidating the mechanistic complexity of the toxicity of TCM. PMID: 30708983 [PubMed - in process]

16 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/02/02PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Related Articles Interaction of Clopidogrel and Fufang Danshen Dripping Pills Assay in Coronary Heart Disease based on Non-target Metabolomics. J Ethnopharmacol. 2019 Jan 28;: Authors: Mengzhe G, Tianyun W, Jian Y, Chang H, Ji S, Daoquan T Abstract ETHNOPHARMACOLOGICAL RELEVANCE: Clopidogrel is the recommended treatment by current clinical practice guidelines to prevent adverse cardiovascular events in patients with coronary heart disease (CHD), but this treatment regimen still fails and 5%-40% patients display inadequate antiplatelet responses. Fufang Danshen Dripping Pill (FDDP) was used as the combination with clopidogrel to improve the therapeutic effect. However, the mechanism of the interaction between clopidogrel and FDDP has not been elucidated. MATERIALS AND METHODS: We have used non-targeted metabolism method based on GC-MS and LC-MS for the investigation of drug interactions between clopidogrel and FDDP. 63 patients were divided into four groups with different dosage regimen and the serum samples were collected for the analysis. RESULTS: We have found 5 and 55 differential metabolites between health volunteer group and CHD patients group, respectively. The contents of these differential metabolites had diverse changes in clopidogrel group, FDDP group, and drug combination group, indicating that the clopidogrel and FDDP combination can adjust the glycometabolism, lipid metabolism, and phospholipid metabolism, sequentially made the content of downstream related metabolites towards to the health volunteer group. CONCLUSION: This work has explained the mechanism of the interaction between clopidogrel and FDDP from the point of view of metabolic product change, and revealed the potential metabolic pathways it affects, which provided the new ideas for clinical medication. PMID: 30703494 [PubMed - as supplied by publisher]

Related Articles Comprehensive and empirical evaluation of machine learning algorithms for small molecule LC retention time prediction. Anal Chem. 2019 Jan 31;: Authors: Bouwmeester R, Martens L, Degroeve S Abstract Liquid chromatography is a core component of almost all mass spectrometric analyses of (bio)molecules. Because of the high-throughput nature of mass spectrometric analyses, the interpretation of these chromatographic data increasingly relies on informatics solutions that attempt to predict an analyte's retention time. The key components of such predictive algorithms are the features these are supplies with, and the actual machine learning algorithm used to fit the model parameters. Therefore, we have evaluated the performance of seven machine learning algorithms on 36 distinct metabolomics data sets, using two distinct feature sets. Interestingly, the results show that no single learning algorithm performs optimally for all data sets, with different types of algorithms achieving top performance for different types of analytes or different protocols. Our results thus show that an evaluation of machine learning algorithms for retention time prediction is needed to find a suitable algorithm for specific analytes or protocols. Importantly, however, our results also show that blending different types of models together decreases the error on outliers, indicating that the combination of several approaches holds substantial promise for the development of more generic, high-performing algorithms. PMID: 30702864 [PubMed - as supplied by publisher]

Related Articles Metabolic Discrimination of Breast Cancer Subtypes at Single-Cell Level by Multiple Microextraction Coupled with Mass Spectrometry. Anal Chem. 2019 Jan 31;: Authors: Wang R, Zhao H, Zhang X, Zhao X, Song Z, Ouyang J Abstract Discrimination of cancer subtypes at the single-cell level is critical for the early diagnosis and accurate treatment of cancer. However, the discrimination of breast cancer subtypes based on their metabolite information, which could provide a new perspective of the cellular metabolomics, is still in its infancy. Herein, a high-coverage single cell metabolic analysis was carried out for the discrimination of breast cancer subtypes by combining multiple microextraction with mass spectrometry (MS). About 4300 ion signals were extracted from each cell and assigned to lipids, energy metabolites, etc. Based on the multivariate analysis of the metabolite information, four subtypes of breast cancer were successfully discriminated. Characteristic components of each subtype were also identified as potential biomarkers such as phosphatidylcholine (PC) (PC (32:1), PC (34:1)), UDP/UDP-HexNAc, and Hex-bis-P/Hex-P). Moreover, metabolomics correlation analysis at the single-cell level further revealed the co-regulation clusters of the identified components, which provided more metabolites data for bioinformatics studies. Over-all, our results on single cell metabolic analysis could give new insights to precision medicine, early diagnosis and cancer treatments. PMID: 30702862 [PubMed - as supplied by publisher]

Related Articles Plasma MicroRNA Signature Validation for Early Detection of Colorectal Cancer. Clin Transl Gastroenterol. 2019 Jan;10(1):e00003 Authors: Herreros-Villanueva M, Duran-Sanchon S, Martín AC, Pérez-Palacios R, Vila-Navarro E, Marcuello M, Diaz-Centeno M, Cubiella J, Diez MS, Bujanda L, Lanas A, Jover R, Hernández V, Quintero E, José Lozano J, García-Cougil M, Martínez-Arranz I, Castells A, Gironella M, Arroyo R Abstract OBJECTIVES: Specific microRNA (miRNA) signatures in biological fluids can facilitate earlier detection of the tumors being then minimally invasive diagnostic biomarkers. Circulating miRNAs have also emerged as promising diagnostic biomarkers for colorectal cancer (CRC) screening. In this study, we investigated the performance of a specific signature of miRNA in plasma samples to design a robust predictive model that can distinguish healthy individuals from those with CRC or advanced adenomas (AA) diseases. METHODS: Case control study of 297 patients from 8 Spanish centers including 100 healthy individuals, 101 diagnosed with AA, and 96 CRC cases. Quantitative real-time reverse transcription was used to quantify a signature of miRNA (miRNA19a, miRNA19b, miRNA15b, miRNA29a, miRNA335, and miRNA18a) in plasma samples. Binary classifiers (Support Vector Machine [SVM] linear, SVM radial, and SVM polynomial) were built for the best predictive model. RESULTS: Area under receiving operating characteristic curve of 0.92 (95% confidence interval 0.871-0.962) was obtained retrieving a model with a sensitivity of 0.85 and specificity of 0.90, positive predictive value of 0.94, and negative predictive value of 0.76 when advanced neoplasms (CRC and AA) were compared with healthy individuals. CONCLUSIONS: We identified and validated a signature of 6 miRNAs (miRNA19a, miRNA19b, miRNA15b, miRNA29a, miRNA335, and miRNA18a) as predictors that can differentiate significantly patients with CRC and AA from those who are healthy. However, large-scale validation studies in asymptomatic screening participants should be conducted. PMID: 30702491 [PubMed - in process]

Related Articles Flexible nitrogen utilisation by the metabolic generalist pathogen Mycobacterium tuberculosis. Elife. 2019 Jan 31;8: Authors: Agapova A, Serafini A, Petridis M, Hunt DM, Garza-Garcia A, Sohaskey CD, de Carvalho LPS Abstract Bacterial metabolism is fundamental to survival and pathogenesis. We explore how Mycobacterium tuberculosis utilises amino acids as nitrogen sources, using a combination of bacterial physiology and stable isotope tracing coupled to mass spectrometry metabolomics methods. Our results define core properties of the nitrogen metabolic network from M. tuberculosis, such as: (i) the lack of homeostatic control of certain amino acid pool sizes; (ii) similar rates of utilisation of different amino acids as sole nitrogen sources; (iii) improved nitrogen utilisation from amino acids compared to ammonium; and (iv) co-metabolism of nitrogen sources. Finally, we discover that alanine dehydrogenase, is involved in ammonium assimilation in M. tuberculosis, in addition to its essential role in alanine utilisation as a nitrogen source. This study represents the first in-depth analysis of nitrogen source utilisation by M. tuberculosis and reveals a flexible metabolic network with characteristics that are likely product of evolution in the human host. PMID: 30702426 [PubMed - as supplied by publisher]

Related Articles What is 'LDL cholesterol'? Nat Rev Cardiol. 2019 Jan 30;: Authors: Holmes MV, Ala-Korpela M Abstract PMID: 30700860 [PubMed - as supplied by publisher]

Related Articles Dissecting metabolism using zebrafish models of disease. Biochem Soc Trans. 2019 Jan 30;: Authors: Salmi TM, Tan VWT, Cox AG Abstract Zebrafish (Danio rerio) are becoming an increasingly powerful model organism to study the role of metabolism in disease. Since its inception, the zebrafish model has relied on unique attributes such as the transparency of embryos, high fecundity and conservation with higher vertebrates, to perform phenotype-driven chemical and genetic screens. In this review, we describe how zebrafish have been used to reveal novel mechanisms by which metabolism regulates embryonic development, obesity, fatty liver disease and cancer. In addition, we will highlight how new approaches in advanced microscopy, transcriptomics and metabolomics using zebrafish as a model system have yielded fundamental insights into the mechanistic underpinnings of disease. PMID: 30700500 [PubMed - as supplied by publisher]

Related Articles Low Molecular Mass Myocardial Hyaluronan in Human Hypertrophic Cardiomyopathy. Cells. 2019 Jan 29;8(2): Authors: Lorén CE, Dahl CP, Do L, Almaas VM, Geiran OR, Mörner S, Hellman U Abstract During the development of hypertrophic cardiomyopathy, the heart returns to fetal energy metabolism where cells utilize more glucose instead of fatty acids as a source of energy. Metabolism of glucose can increase synthesis of the extracellular glycosaminoglycan hyaluronan, which has been shown to be involved in the development of cardiac hypertrophy and fibrosis. The aim of this study was to investigate hyaluronan metabolism in cardiac tissue from patients with hypertrophic cardiomyopathy in relation to cardiac growth. NMR and qRT-PCR analysis of human cardiac tissue from hypertrophic cardiomyopathy patients and healthy control hearts showed dysregulated glucose and hyaluronan metabolism in the patients. Gas phase electrophoresis revealed a higher amount of low molecular mass hyaluronan and larger cardiomyocytes in cardiac tissue from patients with hypertrophic cardiomyopathy. Histochemistry showed high concentrations of hyaluronan around individual cardiomyocytes in hearts from hypertrophic cardiomyopathy patients. Experimentally, we could also observe accumulation of low molecular mass hyaluronan in cardiac hypertrophy in a rat model. In conclusion, the development of hypertrophic cardiomyopathy with increased glucose metabolism affected both hyaluronan molecular mass and amount. The process of regulating cardiomyocyte size seems to involve fragmentation of hyaluronan. PMID: 30699940 [PubMed]

Related Articles Metabolomic and Transcriptomic Analyses of Escherichia coli for Efficient Fermentation of L-Fucose. Mar Drugs. 2019 Jan 29;17(2): Authors: Kim J, Cheong YE, Jung I, Kim KH Abstract L-Fucose, one of the major monomeric sugars in brown algae, possesses high potential for use in the large-scale production of bio-based products. Although fucose catabolic pathways have been enzymatically evaluated, the effects of fucose as a carbon source on intracellular metabolism in industrial microorganisms such as Escherichia coli are still not identified. To elucidate the effects of fucose on cellular metabolism and to find clues for efficient conversion of fucose into bio-based products, comparative metabolomic and transcriptomic analyses were performed on E. coli on L-fucose and on D-glucose as a control. When fucose was the carbon source for E. coli, integration of the two omics analyses revealed that excess gluconeogenesis and quorum sensing led to severe depletion of ATP, resulting in accumulation and export of fucose extracellularly. Therefore, metabolic engineering and optimization are needed for E. coil to more efficiently ferment fucose. This is the first multi-omics study investigating the effects of fucose on cellular metabolism in E. coli. These omics data and their biological interpretation could be used to assist metabolic engineering of E. coli producing bio-based products using fucose-containing brown macroalgae. PMID: 30699916 [PubMed - in process]

20 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/01/31PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

20 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/01/31PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

18 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/01/30PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

18 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/01/30PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.