New Methodology for Known Metabolite Identification in Metabonomics/Metabolomics: Topological Metabolite Identification Carbon Efficiency (tMICE)

Scott

et al. 2018

Front. Mol. Biosci.

Self Cite

It was recently demonstrated in mice that knockout of the flavin-containing monooxygenase 5 gene, Fmo5, slows metabolic ageing via pleiotropic effects. We have now used an NMR-based metabonomics approach to study the effects of ageing directly on the metabolic profiles of urine and plasma from male, wild-type C57BL/6J and Fmo5−/− (FMO5 KO) mice back-crossed onto the C57BL/6J background. The aim of this study was to identify metabolic signatures that are associated with ageing in both these mouse lines and to characterize the age-related differences in the metabolite profiles between the FMO5 KO mice and their wild-type counterparts at equivalent time points. We identified a range of age-related biomarkers in both urine and plasma. Some metabolites, including urinary 6-hydroxy-6-methylheptan-3-one (6H6MH3O), a mouse sex pheromone, showed similar patterns of changes with age, regardless of genetic background. Others, however, were altered only in the FMO5 KO, or only in the wild-type mice, indicating the impact of genetic modifications on mouse ageing. Elevated concentrations of urinary taurine represent a distinctive, ageing-related change observed only in wild-type mice.

Section: Methodsmentioning

confidence: 99%

“…Thus, in this 8-carbon metabolite, the MICE (Everett, 2015 ) value is 24/8 = 3.0; and 6H6MH3O is, thus, confidently identified. A topological MICE analysis (Sanchon-Lopez and Everett, 2016 ) of the metabolite identification is not reported, as the two tautomers each have different molecular topologies.…”

Section: Methodsmentioning

confidence: 99%

Metabolic Biomarkers of Ageing in C57BL/6J Wild-Type and Flavin-Containing Monooxygenase 5 (FMO5)-Knockout Mice

Scott

et al. 2018

Front. Mol. Biosci.

Self Cite

“…Expanding on the metabolite identification carbon efficiency (MICE) concept, the topological metabolite identification carbon efficiency (tMICE) is shown to be a quantitative approach for assessing the confidence in NMR‐based identification of known metabolites in metabolomics experiments .…”

Section: Data Preprocessing Toolsmentioning

confidence: 99%

Review of emerging metabolomic tools and resources: 2015–2016

Misra

Fahrmann

2017

Electrophoresis

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

“…One important consideration in this arena is that different confidence levels can be associated with structural identification of metabolites 17 , and standardized annotation schemes for such evidence descriptions are currently emerging 18 . The highest confidence level can only be observed when instrumental data for an authentic chemical standard is matched to the data for the biological samples.…”

Section: Identification Of Challengesmentioning

confidence: 99%

The future of metabolomics in ELIXIR

Rijswijk

Beirnaert

Caron³

et al. 2017

F1000Res

Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the “Future of metabolomics in ELIXIR” was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.