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Metabolic requirements for cancer cell proliferation

Overview of attention for article published in Cancer & Metabolism, August 2016
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Title
Metabolic requirements for cancer cell proliferation
Published in
Cancer & Metabolism, August 2016
DOI 10.1186/s40170-016-0156-6
Pubmed ID
Authors

Mark A. Keibler, Thomas M. Wasylenko, Joanne K. Kelleher, Othon Iliopoulos, Matthew G. Vander Heiden, Gregory Stephanopoulos

Abstract

The study of cancer metabolism has been largely dedicated to exploring the hypothesis that oncogenic transformation rewires cellular metabolism to sustain elevated rates of growth and division. Intense examination of tumors and cancer cell lines has confirmed that many cancer-associated metabolic phenotypes allow robust growth and survival; however, little attention has been given to explicitly identifying the biochemical requirements for cell proliferation in a rigorous manner in the context of cancer metabolism. Using a well-studied hybridoma line as a model, we comprehensively and quantitatively enumerate the metabolic requirements for generating new biomass in mammalian cells; this indicated a large biosynthetic requirement for ATP, NADPH, NAD(+), acetyl-CoA, and amino acids. Extension of this approach to serine/glycine and glutamine metabolic pathways suggested lower limits on serine and glycine catabolism to supply one-carbon unit synthesis and significant availability of glutamine-derived carbon for biosynthesis resulting from nitrogen demands alone, respectively. We integrated our biomass composition results into a flux balance analysis model, placing upper bounds on mitochondrial NADH oxidation to simulate metformin treatment; these simulations reproduced several empirically observed metabolic phenotypes, including increased reductive isocitrate dehydrogenase flux. Our analysis clarifies the differential needs for central carbon metabolism precursors, glutamine-derived nitrogen, and cofactors such as ATP, NADPH, and NAD(+), while also providing justification for various extracellular nutrient uptake behaviors observed in tumors. Collectively, these results demonstrate how stoichiometric considerations alone can successfully predict empirically observed phenotypes and provide insight into biochemical dynamics that underlie responses to metabolic perturbations.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 218 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Unknown 217 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 61 28%
Researcher 36 17%
Student > Master 29 13%
Student > Bachelor 24 11%
Student > Doctoral Student 11 5%
Other 23 11%
Unknown 34 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 56 26%
Agricultural and Biological Sciences 49 22%
Medicine and Dentistry 14 6%
Engineering 13 6%
Immunology and Microbiology 9 4%
Other 34 16%
Unknown 43 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 22 September 2017.
All research outputs
#14,858,374
of 22,883,326 outputs
Outputs from Cancer & Metabolism
#131
of 204 outputs
Outputs of similar age
#208,607
of 343,111 outputs
Outputs of similar age from Cancer & Metabolism
#3
of 6 outputs
Altmetric has tracked 22,883,326 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% 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 29th percentile – i.e., 29% 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 343,111 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.