Non-targeted Tracer Fate Detection

Weindl, Daniel; Wegner, André; Hiller, Karsten

doi:10.1016/bs.mie.2015.04.003

Cited by 17 publications

(22 citation statements)

References 43 publications

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“…Non-targeted detection of stable isotope labeling and mass isotopolome analysis were performed using an in-house software based on the NTFD algorithm [ 13 , 14 , 27 ]. For data analysis we considered all compounds for which the NTFD algorithm detected at least under one experimental condition two mass spectrometric fragments with a coefficient of MID determination of R 2 >0.98, , M 0 abundance of 0.45<M 0 <1 and minimal enrichment 1−M 0 ≥0.05.…”

Section: Methodsmentioning

confidence: 99%

Bridging the gap between non-targeted stable isotope labeling and metabolic flux analysis

et al. 2016

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BackgroundMetabolism gained increasing interest for the understanding of diseases and to pinpoint therapeutic intervention points. However, classical metabolomics techniques only provide a very static view on metabolism. Metabolic flux analysis methods, on the other hand, are highly targeted and require detailed knowledge on metabolism beforehand.ResultsWe present a novel workflow to analyze non-targeted metabolome-wide stable isotope labeling data to detect metabolic flux changes in a non-targeted manner. Furthermore, we show how similarity-analysis of isotopic enrichment patterns can be used for pathway contextualization of unidentified compounds. We illustrate our approach with the analysis of changes in cellular metabolism of human adenocarcinoma cells in response to decreased oxygen availability. Starting without a priori knowledge, we detect metabolic flux changes, leading to an increased glutamine contribution to acetyl-CoA production, reveal biosynthesis of N-acetylaspartate by N-acetyltransferase 8-like (NAT8L) in lung cancer cells and show that NAT8L silencing inhibits proliferation of A549, JHH-4, PH5CH8, and BEAS-2B cells.ConclusionsDifferential stable isotope labeling analysis provides qualitative metabolic flux information in a non-targeted manner. Furthermore, similarity analysis of enrichment patterns provides information on metabolically closely related compounds. N-acetylaspartate and NAT8L are important players in cancer cell metabolism, a context in which they have not received much attention yet.Electronic supplementary materialThe online version of this article (doi:10.1186/s40170-016-0150-z) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Bridging the gap between non-targeted stable isotope labeling and metabolic flux analysis

et al. 2016

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“…The mass isotopomer distribution (MID) (i.e. M þ 0 2 M þ 10) for every detected and labelled fragment ion was calculated using the strictest detection settings in NTFD, in order to minimize the generation of false positives [37,38]. Compounds were considered enriched if at least two spectral fragments were detected under at least one experimental treatment, R 2 ¼ 0.98, minimal enrichment ¼ 5, and maximal fragment deviation ¼ 0.02.…”

Section: (G) Data Analysis and Validationmentioning

confidence: 99%

Partner switching and metabolic flux in a model cnidarian–dinoflagellate symbiosis

et al. 2018

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Metabolite exchange is fundamental to the viability of the cnidarian-Symbiodiniaceae symbiosis and survival of coral reefs. Coral holobiont tolerance to environmental change might be achieved through changes in Symbiodiniaceae species composition, but differences in the metabolites supplied by different Symbiodiniaceae species could influence holobiont fitness. Using 13 C stable-isotope labelling coupled to gas chromatography-mass spectrometry, we characterized newly fixed carbon fate in the model cnidarian Exaiptasia pallida (Aiptasia) when experimentally colonized with either native Breviolum minutum or non-native Durusdinium trenchii. Relative to anemones containing B. minutum, D. trenchii-colonized hosts exhibited a 4.5-fold reduction in 13 C-labelled glucose and reduced abundance and diversity of 13 C-labelled carbohydrates and lipogenesis precursors, indicating symbiont species-specific modifications to carbohydrate availability and lipid storage. Mapping carbon fate also revealed significant alterations to host molecular signalling pathways. In particular, D. trenchii-colonized hosts exhibited a 40-fold reduction in 13 C-labelled scyllo-inositol, a potential interpartner signalling molecule in symbiosis specificity. 13 C-labelling also highlighted differential antioxidant-and ammoniumproducing pathway activities, suggesting physiological responses to different symbiont species. Such differences in symbiont metabolite contribution and host utilization may limit the proliferation of stress-driven symbioses; this contributes valuable information towards future scenarios that select in favour of less-competent symbionts in response to environmental change.

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“…All detected isotopically enriched mass spectrometric fragments can be analyzed. If replicate measurements are provided, confidence intervals and quality measures of MID determination such as coefficient of determination are provided (Weindl et al , 2015b). …”

Section: Featuresmentioning

confidence: 99%

“…MIDs are determined from the difference between mass spectra of an isotopically enriched compound and those of the non-enriched compound (Weindl et al , 2015b). Thus, two metabolite extracts need to be measured: One from a stable isotope labeling experiment and one from a label-free experiment.…”

Section: Experimental Requirementsmentioning

confidence: 99%

MIA: non-targeted mass isotopolome analysis

2016

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Summary: MIA detects and visualizes isotopic enrichment in gas chromatography electron ionization mass spectrometry (GC–EI-MS) datasets in a non-targeted manner. It provides an easy-to-use graphical user interface that allows for visual mass isotopomer distribution analysis across multiple datasets. MIA helps to reveal changes in metabolic fluxes, visualizes metabolic proximity of isotopically enriched compounds and shows the fate of the applied stable isotope labeled tracer.Availability and Implementation: Linux and Windows binaries, documentation, and sample data are freely available for download at http://massisotopolomeanalyzer.lu. MIA is a stand-alone application implemented in C ++ and based on Qt5, NTFD and the MetaboliteDetector framework.Contact: karsten.hiller@uni.lu

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Non-targeted Tracer Fate Detection

Cited by 17 publications

References 43 publications

Bridging the gap between non-targeted stable isotope labeling and metabolic flux analysis

Bridging the gap between non-targeted stable isotope labeling and metabolic flux analysis

Partner switching and metabolic flux in a model cnidarian–dinoflagellate symbiosis

MIA: non-targeted mass isotopolome analysis

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