scholarly journals Biokinetic Modeling of Cd Bioaccumulation from Water, Diet and Sediment in a Marine Benthic Goby: A Triple Stable Isotope Tracing Technique

2018 ◽  
Vol 52 (15) ◽  
pp. 8429-8437 ◽  
Author(s):  
Zhiqiang Guo ◽  
Hengzhen Ye ◽  
Juan Xiao ◽  
Christer Hogstrand ◽  
Li Zhang
Keyword(s):  
Stable Isotope ◽  
Isotope Tracing ◽  
Biokinetic Modeling

scholarly journals Physiological impact of in vivo stable isotope tracing on cancer metabolism

2021 ◽  
pp. 101294
Author(s):  
Manuel Grima-Reyes ◽  
Adriana Martinez-Turtos ◽  
Ifat Abramovich ◽  
Eyal Gottlieb ◽  
Johanna Chiche ◽  
...  
Keyword(s):  
Stable Isotope ◽  
Cancer Metabolism ◽  
Isotope Tracing ◽  
Physiological Impact

Stable isotope tracing to assess tumor metabolism in vivo

2021 ◽  
Author(s):  
Brandon Faubert ◽  
Alpaslan Tasdogan ◽  
Sean J. Morrison ◽  
Thomas P. Mathews ◽  
Ralph J. DeBerardinis
Keyword(s):  
Stable Isotope ◽  
Tumor Metabolism ◽  
Isotope Tracing

scholarly journals Absorption and transport of dietary long-chain fatty acids in cirrhosis: a stable-isotope-tracing study

2005 ◽  
Vol 81 (3) ◽  
pp. 692-701 ◽  
Author(s):  
Eduard Cabré ◽  
José M Hernández-Pérez ◽  
Lourdes Fluvià ◽  
Cruz Pastor ◽  
August Corominas ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Stable Isotope ◽  
Long Chain Fatty Acids ◽  
Isotope Tracing ◽  
Long Chain ◽  
Chain Fatty Acids

scholarly journals Application of Stable Isotope Tracing to Elucidate Metabolic Dynamics During Yarrowia lipolytica α-Ionone Fermentation

iScience ◽  
2020 ◽  
Vol 23 (2) ◽  
pp. 100854 ◽  
Author(s):  
Jeffrey J. Czajka ◽  
Shrikaar Kambhampati ◽  
Yinjie J. Tang ◽  
Yechun Wang ◽  
Doug K. Allen
Keyword(s):  
Stable Isotope ◽  
Yarrowia Lipolytica ◽  
Isotope Tracing

scholarly journals Metabolite AutoPlotter - an application to automatically process and visualise metabolite data in the web-browser

2020 ◽  
Author(s):  
Matthias Pietzke ◽  
Alexei Vazquez
Keyword(s):  
Stable Isotope ◽  
Data Processing ◽  
Ease Of Use ◽  
Complex Data ◽  
Isotope Tracing ◽  
Wide Audience ◽  
Web Browser ◽  
Seamless Integration ◽  
Metabolomics Data ◽  
Copy And Paste

Abstract Background Metabolomics is gaining popularity as a standard tool for the investigation of biological systems. Yet, parsing metabolomics data in the absence of in-house computational scientists can be overwhelming and time consuming. As a consequence of manual data processing the results are often not processed in full depth, so potential novel findings might get lost. Methods To tackle this problem we developed Metabolite AutoPlotter, a tool to process and visualise metabolite data. It reads as input pre-processed compound-intensity tables and accepts different experimental designs, with respect to number of compounds, conditions and replicates. The code was written in R and wrapped into a shiny-application that can be run online in a web-browser on https://mpietzke.shinyapps.io/autoplotter. Results We demonstrate the main features and the ease of use with two different metabolite datasets, for quantitative experiments and for stable isotope tracing experiments. We show how the plots generated by the tool can be interactively modified with respect to plot type, colours, text labels and the shown statistics. We also demonstrate the application towards 13-C-tracing experiments and the seamless integration of natural abundance correction, which facilitates the better interpretation of stable isotope tracing experiments. The output of the tool is a zip-file containing one single plot for each compound as well as sorted and restructured tables that can be used for further analysis. Conclusion With the help of Metabolite AutoPlotter it is now possible to automate data processing and visualisation for a wide audience. High quality plots from complex data can be generated in a short time with pressing a few buttons. This offers dramatic improvements over manual processing. It is significantly faster and allows researchers to spend more time interpreting the results or to perform follow-up experiments. Further this eliminates potential copy-and paste errors or tedious repetitions when things need to be changed. We are sure that this tool will help to improve and speed up scientific discoveries.

scholarly journals Subcellular metabolic pathway kinetics are revealed by correcting for artifactual post harvest metabolism

2019 ◽  
Author(s):  
Sophie Trefely ◽  
Joyce Liu ◽  
Katharina Huber ◽  
Mary T. Doan ◽  
Helen Jiang ◽  
...  
Keyword(s):  
Stable Isotope ◽  
Metabolic Activity ◽  
Metabolic Pathways ◽  
Subcellular Fractionation ◽  
Clear Understanding ◽  
Isotope Tracing ◽  
Dynamic Regulation ◽  
Whole Cells ◽  
Post Harvest ◽  
Biochemical Fractionation

AbstractOBJECTIVEThe dynamic regulation of metabolic pathways can be monitored by stable isotope tracing. Yet, many metabolites are part of distinct processes within different subcellular compartments. Standard isotope tracing experiments relying on analyses in whole cells may not accurately reflect compartmentalized metabolic processes. Analysis of compartmentalized metabolism and the dynamic interplay between compartments can potentially be achieved by stable isotope tracing followed by subcellular fractionation. Although it is recognized that metabolism can take place during biochemical fractionation of cells, a clear understanding of how such post-harvest metabolism impacts the interpretation of subcellular isotope tracing data and methods to correct for this are lacking. We set out to directly assess artifactual metabolism, enabling us to develop and test strategies to correct for it. We apply these techniques to examine the compartment-specific metabolic kinetics of 13C-labeled substrates targeting central metabolic pathways.METHODSWe designed a stable isotope tracing strategy to interrogate post-harvest metabolic activity during subcellular fractionation using liquid chromatography-mass spectrometry (LC-MS).RESULTSWe show that post-harvest metabolic activity occurs rapidly (within seconds) upon cell harvest. With further characterization we reveal that this post-harvest metabolism is enzymatic, and reflects the metabolic capacity of the sub-cellular compartment analyzed; but is limited in the extent of its propagation into downstream metabolites in metabolic pathways. We also propose and test a post-labeling strategy to assess the amount of post-harvest metabolism occurring in an experiment and then to adjust data to account for this. We validate this approach for both mitochondrial and cytosolic metabolic analyses.CONCLUSIONSOur data indicate that isotope tracing coupled with sub-cellular fractionation can reveal distinct and dynamic metabolic features of cellular compartments, and that confidence in such data can be improved by applying a post-labeling correction strategy. We examine compartmentalized metabolism of acetate and glutamine and show that acetyl-CoA is turned over rapidly in the cytosol and acts as a pacemaker of anabolic metabolism in this compartment.

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