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

Overview of attention for article published in Cancer & Metabolism, April 2016
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Title
Bridging the gap between non-targeted stable isotope labeling and metabolic flux analysis
Published in
Cancer & Metabolism, April 2016
DOI 10.1186/s40170-016-0150-z
Pubmed ID
Authors

Daniel Weindl, Thekla Cordes, Nadia Battello, Sean C. Sapcariu, Xiangyi Dong, Andre Wegner, Karsten Hiller

Abstract

Metabolism 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. We 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. Differential 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.

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 83 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 25%
Student > Ph. D. Student 16 19%
Student > Master 12 14%
Other 9 11%
Student > Bachelor 6 7%
Other 15 18%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 33%
Biochemistry, Genetics and Molecular Biology 24 29%
Psychology 4 5%
Chemistry 4 5%
Pharmacology, Toxicology and Pharmaceutical Science 3 4%
Other 13 16%
Unknown 8 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 April 2016.
All research outputs
#6,582,326
of 7,601,780 outputs
Outputs from Cancer & Metabolism
#67
of 75 outputs
Outputs of similar age
#223,861
of 267,036 outputs
Outputs of similar age from Cancer & Metabolism
#4
of 4 outputs
Altmetric has tracked 7,601,780 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 75 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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