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Mark Viant   Professor  Senior Scientist or Principal Investigator 
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Mark Viant published an article in February 2019.
Top co-authors See all
Royston Goodacre

452 shared publications

Manchester Institute of Biotechnology, School of Chemistry, The University of Manchester, Princess Street, Manchester, U.K., M1 7DN

Kazuki Saito

383 shared publications

Metabolomics Research Group, RIKEN Center for Sustainable Resource Science, Japan

Philip Marriott

366 shared publications

Australian Centre for Research on Separation Science, School of Chemistry, Monash University, Clayton, Australia

Oliver Fiehn

271 shared publications

West Coast Metabolomics Center, University of California, Davis, One Shields Avenue, Davis, California 95616, United States

Joachim Kopka

205 shared publications

Max Planck Institute of Molecular Plant Physiology

206
Publications
12
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623
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Publication Record
Distribution of Articles published per year 
(1996 - 2019)
Total number of journals
published in
 
36
 
Publications See all
Article 0 Reads 0 Citations PhenoMeNal: processing and analysis of metabolomics data in the cloud. Kristian Peters, James Bradbury, Sven Bergmann, Marco Capucc... Published: 01 February 2019
GigaScience, doi: 10.1093/gigascience/giy149
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and 'omics research domains.
Article 0 Reads 0 Citations Comparison of modified Matyash method to conventional solvent systems for polar metabolite and lipid extractions Jelena Sostare, Riccardo Di Guida, Jennifer Kirwan, Karnpree... Published: 01 December 2018
Analytica Chimica Acta, doi: 10.1016/j.aca.2018.03.019
DOI See at publisher website ABS Show/hide abstract
In the last decade, metabolomics has experienced significant advances in the throughput and robustness of analytical methodologies. Yet the preparation of biofluids and low-mass tissue samples remains a laborious and potentially inconsistent manual process, and a significant bottleneck for high-throughput metabolomics. To address this, we have compared three different sample extraction solvent systems in three diverse sample types with the purpose of selecting an optimum protocol for subsequent automation of sample preparation. We have investigated and re-optimised the solvent ratios in the recently introduced methyl tert-butyl ether (MTBE)/methanol/water solvent system (here termed modified Matyash; 2.6/2.0/2.4, v/v/v) and compared it to the original Matyash method (10/3/2.5, v/v/v) and the conventional chloroform/methanol/water (stepwise Bligh and Dyer, 2.0/2.0/1.8, v/v/v) using two biofluids (human serum and urine) and one tissue (whole Daphnia magna). This is the first report of the use of the Matyash method for extracting metabolites from the US National Institutes of Health (NIH) model organism D. magna. Extracted samples were analysed by non-targeted direct infusion mass spectrometry metabolomics or LC-MS metabolomics. Overall, the modified Matyash method yielded a higher number of peaks and putatively annotated metabolites compared to the original Matyash method (1–29% more peaks and 1–30% more metabolites) and the Bligh and Dyer method (4–20% more peaks and 1–41% more metabolites). Additionally the modified Matyash method was superior when considering metabolite intensities. The reproducibility of the modified Matyash method was higher than other methods (in 10 out of 12 datasets, compared to the original Matyash method; and in 8 out of 12 datasets, compared to the Bligh and Dyer method), based upon the observation of a lower mRSD of peak intensities. In conclusion, the modified Matyash method tended to provide a higher yield and reproducibility for most sample types in this study compared to two widely used methods.
Article 0 Reads 2 Citations Quantitative Lipoprotein Subclass and Low Molecular Weight Metabolite Analysis in Human Serum and Plasma by 1H NMR Spect... Beatriz Jimenez, Elaine Holmes, Clement Heude, Rose Farzaneh... Published: 13 September 2018
Analytical Chemistry, doi: 10.1021/acs.analchem.8b02412
DOI See at publisher website
Article 0 Reads 0 Citations Terahertz VRT Spectroscopy of the Water Hexamer-h12 Cage: Dramatic Libration-Induced Enhancement of Hydrogen Bond Tunnel... William T. S. Cole, Özlem Yonder, Akber A. Sheikh, Raymond S... Published: 27 August 2018
The Journal of Physical Chemistry A, doi: 10.1021/acs.jpca.8b05777
DOI See at publisher website
Article 0 Reads 0 Citations Metabolomics Discovers Early-Response Metabolic Biomarkers that Can Predict Chronic Reproductive Fitness in Individual D... Nadine S. Taylor, Alex Gavin, Mark R. Viant Published: 23 July 2018
Metabolites, doi: 10.3390/metabo8030042
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Chemical risk assessment remains entrenched in chronic toxicity tests that set safety thresholds based on animal pathology or fitness. Chronic tests are resource expensive and lack mechanistic insight. Discovering a chemical’s mode-of-action can in principle provide predictive molecular biomarkers for a toxicity endpoint. Furthermore, since molecular perturbations precede pathology, early-response molecular biomarkers may enable shorter, more resource efficient testing that can predict chronic animal fitness. This study applied untargeted metabolomics to attempt to discover early-response metabolic biomarkers that can predict reproductive fitness of Daphnia magna, an internationally-recognized test species. First, we measured the reproductive toxicities of cadmium, 2,4-dinitrophenol and propranolol to individual Daphnia in 21-day OECD toxicity tests, then measured the metabolic profiles of these animals using mass spectrometry. Multivariate regression successfully discovered putative metabolic biomarkers that strongly predict reproductive impairment by each chemical, and for all chemicals combined. The non-chemical-specific metabolic biomarkers were then applied to metabolite data from Daphnia 24-h acute toxicity tests and correctly predicted that significant decreases in reproductive fitness would occur if these animals were exposed to cadmium, 2,4-dinitrophenol or propranolol for 21 days. While the applicability of these findings is limited to three chemicals, they provide proof-of-principle that early-response metabolic biomarkers of chronic animal fitness can be discovered for regulatory toxicity testing.
Article 0 Reads 0 Citations Use of 5-azacytidine in a proof-of-concept study to evaluate the impact of pre-natal and post-natal exposures, as well a... Camila Gonçalves Athanasio, Ulf Sommer, Mark R. Viant, James... Published: 05 April 2018
Ecotoxicology, doi: 10.1007/s10646-018-1927-3
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Short-term exposures at critical stages of development can lead to delayed adverse effects long after the initial stressor has been removed, a concept referred to as developmental origin of adult disease. This indicates that organisms’ phenotypes may epigenetically reflect their past exposure history as well as reflecting chemicals currently present in their environment. This concept has significant implications for environmental monitoring. However, there is as yet little or no implementation of epigenetics in environmental risk assessment. In a proof-of-principle study we exposed Daphnia magna to 5-azacytidine, a known DNA de-methylating agent. Exposures covered combinations of prenatal and postnatal exposures as well as different exposure durations and recovery stages. Growth, the transcription of genes and levels of metabolites involved in regulating DNA methylation, and methylation levels of several genes were measured. Our data shows that prenatal exposures caused significant changes in the methylome of target genes, indicating that prenatal stages of Daphnia are also susceptible to same level of change as post-natal stages of Daphnia. While the combination of pre- and postnatal exposures caused the most extreme reduction in DNA methylation compared to the control group. Furthermore, some of the changes in the methylation patterns were persistent even after the initial stressor was removed. Our results suggest that epigenetic biomarkers have the potential to be used as indicators of past chemical exposure history of organisms and provide strong support for implementing changes to the current regimes for chemical risk assessment to mimic realistic environmental scenarios.
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