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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

Mendeley readers

The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 91 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 23%
Student > Ph. D. Student 20 22%
Student > Master 12 13%
Other 7 8%
Student > Bachelor 6 7%
Other 15 16%
Unknown 10 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 30%
Biochemistry, Genetics and Molecular Biology 22 24%
Chemistry 4 4%
Engineering 4 4%
Pharmacology, Toxicology and Pharmaceutical Science 3 3%
Other 16 18%
Unknown 15 16%
Attention Score in Context

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
#20,322,106
of 22,865,319 outputs
Outputs from Cancer & Metabolism
#183
of 204 outputs
Outputs of similar age
#253,435
of 299,155 outputs
Outputs of similar age from Cancer & Metabolism
#6
of 6 outputs
Altmetric has tracked 22,865,319 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 204 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 299,155 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
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