↓ Skip to main content

Distinct gene regulatory programs define the inhibitory effects of liver X receptors and PPARG on cancer cell proliferation

Overview of attention for article published in Genome Medicine, January 2016
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)

Mentioned by

11 tweeters


16 Dimensions

Readers on

34 Mendeley
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Distinct gene regulatory programs define the inhibitory effects of liver X receptors and PPARG on cancer cell proliferation
Published in
Genome Medicine, January 2016
DOI 10.1186/s13073-016-0328-6
Pubmed ID

Daniel Savic, Ryne C. Ramaker, Brian S. Roberts, Emma C. Dean, Todd C. Burwell, Sarah K. Meadows, Sara J. Cooper, Michael J. Garabedian, Jason Gertz, Richard M. Myers, Savic, Daniel, Ramaker, Ryne C, Roberts, Brian S, Dean, Emma C, Burwell, Todd C, Meadows, Sarah K, Cooper, Sara J, Garabedian, Michael J, Gertz, Jason, Myers, Richard M


The liver X receptors (LXRs, NR1H2 and NR1H3) and peroxisome proliferator-activated receptor gamma (PPARG, NR1C3) nuclear receptor transcription factors (TFs) are master regulators of energy homeostasis. Intriguingly, recent studies suggest that these metabolic regulators also impact tumor cell proliferation. However, a comprehensive temporal molecular characterization of the LXR and PPARG gene regulatory responses in tumor cells is still lacking. To better define the underlying molecular processes governing the genetic control of cellular growth in response to extracellular metabolic signals, we performed a comprehensive, genome-wide characterization of the temporal regulatory cascades mediated by LXR and PPARG signaling in HT29 colorectal cancer cells. For this analysis, we applied a multi-tiered approach that incorporated cellular phenotypic assays, gene expression profiles, chromatin state dynamics, and nuclear receptor binding patterns. Our results illustrate that the activation of both nuclear receptors inhibited cell proliferation and further decreased glutathione levels, consistent with increased cellular oxidative stress. Despite a common metabolic reprogramming, the gene regulatory network programs initiated by these nuclear receptors were widely distinct. PPARG generated a rapid and short-term response while maintaining a gene activator role. By contrast, LXR signaling was prolonged, with initial, predominantly activating functions that transitioned to repressive gene regulatory activities at late time points. Through the use of a multi-tiered strategy that integrated various genomic datasets, our data illustrate that distinct gene regulatory programs elicit common phenotypic effects, highlighting the complexity of the genome. These results further provide a detailed molecular map of metabolic reprogramming in cancer cells through LXR and PPARG activation. As ligand-inducible TFs, these nuclear receptors can potentially serve as attractive therapeutic targets for the treatment of various cancers.

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 6%
Unknown 32 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 24%
Student > Ph. D. Student 6 18%
Unspecified 5 15%
Student > Master 4 12%
Professor > Associate Professor 4 12%
Other 7 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 38%
Biochemistry, Genetics and Molecular Biology 11 32%
Unspecified 6 18%
Medicine and Dentistry 2 6%
Neuroscience 1 3%
Other 1 3%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 14 July 2016.
All research outputs
of 8,071,125 outputs
Outputs from Genome Medicine
of 722 outputs
Outputs of similar age
of 258,661 outputs
Outputs of similar age from Genome Medicine
of 30 outputs
Altmetric has tracked 8,071,125 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 722 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.6. This one is in the 32nd percentile – i.e., 32% 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 258,661 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.