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1.
N. Cesbron A.-L. Royer Y. Guitton A. Sydor B. Le Bizec G. Dervilly-Pinel 《Metabolomics : Official journal of the Metabolomic Society》2017,13(8):99
Introduction
Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.Objectives
In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.Methods
The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.Results
A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.Conclusion
The workflow generated repeatable and informative fingerprints for robust metabolome characterization.2.
Background
Identification of phosphorylation sites by computational methods is becoming increasingly important because it reduces labor-intensive and costly experiments and can improve our understanding of the common properties and underlying mechanisms of protein phosphorylation.Methods
A multitask learning framework for learning four kinase families simultaneously, instead of studying each kinase family of phosphorylation sites separately, is presented in the study. The framework includes two multitask classification methods: the Multi-Task Least Squares Support Vector Machines (MTLS-SVMs) and the Multi-Task Feature Selection (MT-Feat3).Results
Using the multitask learning framework, we successfully identify 18 common features shared by four kinase families of phosphorylation sites. The reliability of selected features is demonstrated by the consistent performance in two multi-task learning methods.Conclusions
The selected features can be used to build efficient multitask classifiers with good performance, suggesting they are important to protein phosphorylation across 4 kinase families.3.
Objectives
To characterize biomarkers that underlie osteosarcoma (OS) metastasis based on an ego-network.Results
From the microarray data, we obtained 13,326 genes. By combining PPI data and microarray data, 10,520 shared genes were found and constructed into ego-networks. 17 significant ego-networks were identified with p < 0.05. In the pathway enrichment analysis, seven ego-networks were identified with the most significant pathway.Conclusions
These significant ego-modules were potential biomarkers that reveal the potential mechanisms in OS metastasis, which may contribute to understanding cancer prognoses and providing new perspectives in the treatment of cancer.4.
Background
During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.Methods
We combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations.Results
We apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data.Conclusions
We clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation.5.
Rachel A. Spicer Christoph Steinbeck 《Metabolomics : Official journal of the Metabolomic Society》2018,14(1):16
Introduction
Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.Objectives
(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.Methods
A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.Results
Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.Conclusion
Further efforts are required to improve data sharing in metabolomics.6.
Background
In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.Methods
The physical basis of these intuitive maps is clarified by means of analytically solvable problems.Results
Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.Conclusion
Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.7.
Introduction
Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.Objectives
Assessment of the quality of raw data processing in untargeted metabolomics.Methods
Five published untargeted metabolomics studies, were reanalyzed.Results
Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.Conclusion
Incomplete raw data processing shows unexplored potential of current and legacy data.8.
Jamie V. de Seymour Stephanie Tu Xiaoling He Hua Zhang Ting-Li Han Philip N. Baker Karolina Sulek 《Metabolomics : Official journal of the Metabolomic Society》2018,14(6):79
Introduction
Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.Objective
Investigate the maternal hair metabolome for predictive biomarkers of ICP.Methods
The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.Results
Of 105 metabolites detected in hair, none were significantly associated with ICP.Conclusion
Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.9.
Renato de Souza Pinto Lemgruber Kaspar Valgepea Mark P. Hodson Ryan Tappel Sean D. Simpson Michael Köpke Lars K. Nielsen Esteban Marcellin 《Metabolomics : Official journal of the Metabolomic Society》2018,14(3):35
Introduction
Quantification of tetrahydrofolates (THFs), important metabolites in the Wood–Ljungdahl pathway (WLP) of acetogens, is challenging given their sensitivity to oxygen.Objective
To develop a simple anaerobic protocol to enable reliable THFs quantification from bioreactors.Methods
Anaerobic cultures were mixed with anaerobic acetonitrile for extraction. Targeted LC–MS/MS was used for quantification.Results
Tetrahydrofolates can only be quantified if sampled anaerobically. THF levels showed a strong correlation to acetyl-CoA, the end product of the WLP.Conclusion
Our method is useful for relative quantification of THFs across different growth conditions. Absolute quantification of THFs requires the use of labelled standards.10.
Qian Xiao Andriy Derkach Steven C. Moore Wei Zheng Xiao-Ou Shu Fangyi Gu Neil E. Caporaso Joshua N. Sampson Charles E. Matthews 《Metabolomics : Official journal of the Metabolomic Society》2017,13(5):63
Introduction
Sleep plays an important role in cardiometabolic health. The sleep-wake cycle is partially driven by the endogenous circadian clock, which governs a range of metabolic pathways. The association between sleep and cardiometabolic health may be mediated by alterations of the human metabolome.Objectives
To better understand the biological mechanism underlying the association between sleep and health, we examined human plasma metabolites in relation to sleep duration and sleep timing.Methods
Using an untargeted approach, 329 fasting plasma metabolites were measured in 277 Chinese participants. We measured sleep timing (midpoint between bedtime and wake up time) using repeated time-use surveys (4 weeks during 1 year) and previous night sleep duration from questionnaires completed before sample donation.Results
We found 64 metabolites that were associated with sleep timing with a false discovery rate of 0.2 or lower, after adjusting for potential confounders. Notably, we found that later sleep timing was associated with higher levels of multiple metabolites in amino acid metabolism, including branched chain amino acids and their gamma-glutamyl dipeptides. We also found widespread associations between sleep timing and numerous metabolites in lipid metabolism, including bile acids, carnitines and fatty acids. In contrast, previous night sleep duration was not associated with plasma metabolites in our study.Conclusion
Sleep timing was associated with a large number of metabolites across a variety of biochemical pathways. Some metabolite associations are consistent with a relationship between late chronotype and adverse effects on cardiometabolic health.11.
Sonia Liggi Christine Hinz Zoe Hall Maria Laura Santoru Simone Poddighe John Fjeldsted Luigi Atzori Julian L. Griffin 《Metabolomics : Official journal of the Metabolomic Society》2018,14(4):52
Introduction
Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.Objectives
Merge in the same platform the steps required for metabolomics data processing.Methods
KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.Results
The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.Conclusion
KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.12.
Ferran Casbas Pinto Srinivarao Ravipati David A. Barrett T. Charles Hodgman 《Metabolomics : Official journal of the Metabolomic Society》2017,13(7):81
Introduction
It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.Objectives
This work simplifies this process.Methods
A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.Results
The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.Conclusion
This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.13.
Thijs Welle Anna T. Hoekstra Ineke A. J. J. M. Daemen Celia R. Berkers Matheus O. Costa 《Metabolomics : Official journal of the Metabolomic Society》2017,13(7):83
Introduction
Swine dysentery caused by Brachyspira hyodysenteriae is a production limiting disease in pig farming. Currently antimicrobial therapy is the only treatment and control method available.Objective
The aim of this study was to characterize the metabolic response of porcine colon explants to infection by B. hyodysenteriae.Methods
Porcine colon explants exposed to B. hyodysenteriae were analyzed for histopathological, metabolic and pro-inflammatory gene expression changes.Results
Significant epithelial necrosis, increased levels of l-citrulline and IL-1α were observed on explants infected with B. hyodysenteriae.Conclusions
The spirochete induces necrosis in vitro likely through an inflammatory process mediated by IL-1α and NO.14.
Nadine Strehmel David Strunk Veronika Strehmel 《Metabolomics : Official journal of the Metabolomic Society》2017,13(11):135
Introduction
Aqueous–methanol mixtures have successfully been applied to extract a broad range of metabolites from plant tissue. However, a certain amount of material remains insoluble.Objectives
To enlarge the metabolic compendium, two ionic liquids were selected to extract the methanol insoluble part of trunk from Betula pendula.Methods
The extracted compounds were analyzed by LC/MS and GC/MS.Results
The results show that 1-butyl-3-methylimidazolium acetate (IL-Ac) predominantly resulted in fatty acids, whereas 1-ethyl-3-methylimidazolium tosylate (IL-Tos) mostly yielded phenolic structures. Interestingly, bark yielded more ionic liquid soluble metabolites compared to interior wood.Conclusion
From this one can conclude that the application of ionic liquids may expand the metabolic snapshot.15.
Background
Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs.Methods
In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori.Results
We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis.Conclusions
The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.16.
Background
Fatigue can be triggered by previous perceived stress which may lead to impairment of performance and function. The purpose of the study was to investigate the relationship between fatigue and perceived stress.Method
Health determinants including sociodemographic factors for associations between fatigue and perceived stress in the general population (N = 2,483) are outlined. Fatigue and stress were assessed with the Chalder Fatigue Scale (CFS) and the Perceived Stress Questionnaire (PSQ).Results
Within the general population, 25.9% of male and 34.5% of female respondents reported moderate fatigue during the last six months; 9.7% of subjects reported substantial fatigue lasting six months or longer. An adjusted regression analysis (R2corr = .28, p < .001) showed that fatigue is highest associated with perceived stress and self-perceived health status. The following factors were correlated with increased rates of fatigue and perceived stress: female gender, divorce/separation, low social class and poor health status.Conclusion
We conclude that the two conditions overlap most in terms of socio-economic status and self-perceived health status.17.
Nicholas J. Bond Albert Koulman Julian L. Griffin Zoe Hall 《Metabolomics : Official journal of the Metabolomic Society》2017,13(11):128
Introduction
Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools.Objectives
We have developed massPix—an R package for analysing and interpreting data from MSI of lipids in tissue.Methods
massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries.Results
Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering.Conclusion
massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications.18.
Dorothea Lesche Roland Geyer Daniel Lienhard Christos T. Nakas Gaëlle Diserens Peter Vermathen Alexander B. Leichtle 《Metabolomics : Official journal of the Metabolomic Society》2016,12(10):159
Background
Centrifugation is an indispensable procedure for plasma sample preparation, but applied conditions can vary between labs.Aim
Determine whether routinely used plasma centrifugation protocols (1500×g 10 min; 3000×g 5 min) influence non-targeted metabolomic analyses.Methods
Nuclear magnetic resonance spectroscopy (NMR) and High Resolution Mass Spectrometry (HRMS) data were evaluated with sparse partial least squares discriminant analyses and compared with cell count measurements.Results
Besides significant differences in platelet count, we identified substantial alterations in NMR and HRMS data related to the different centrifugation protocols.Conclusion
Already minor differences in plasma centrifugation can significantly influence metabolomic patterns and potentially bias metabolomics studies.19.
Jatayev Satyvaldy Kurishbayev Akhylbek Zotova Lyudmila Khasanova Gulmira Serikbay Dauren Zhubatkanov Askar Botayeva Makpal Zhumalin Aibek Turbekova Arysgul Soole Kathleen Langridge Peter Shavrukov Yuri 《BMC plant biology》2017,17(2):254-93
Background
KASP (KBioscience Competitive Allele Specific PCR) and Amplifluor (Amplification with fluorescence) SNP markers are two prominent technologies based upon a shared identical Allele-specific PCR platform.Methods
Amplifluor-like SNP and KASP analysis was carried out using published and own design of Universal probes (UPs) and Gene-specific primers (GSPs).Results
Advantages of the Amplifluor-like system over KASP include the significantly lower costs and much greater flexibility in the adjustment and development of ‘self-designed’ dual fluorescently-labelled UPs and regular GSPs. The presented results include optimisation of ‘tail’ length in UPs and GSPs, protocol adjustment, and the use of various fluorophores in different qPCR instruments. The compatibility of the KASP Master-mix in both original and Amplifluor-like systems has been demonstrated in the presented results, proving their similar principles. Results of SNP scoring with rare alleles in addition to more frequently occurring alleles are shown.Conclusions
The Amplifluor-like system produces SNP genotyping results with a level of sensitivity and accuracy comparable to KASP but at a significantly cheaper cost and with much greater flexibility for UPs with self-designed GSPs.20.
Korey J. Brownstein Mahmoud Gargouri William R. Folk David R. Gang 《Metabolomics : Official journal of the Metabolomic Society》2017,13(11):133