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

Savic D, Ramaker RC, Roberts BS, Dean EC, Burwell TC, Meadows SK, Cooper SJ, Garabedian MJ, Gertz J, Myers RM - Genome Med (2016)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus

Chromatin dynamics and gene expression profiles from nuclear receptor activation. a The number of differentially regulated genes (up- and down-regulated) after 24 and 48 h of drug treatment (adjusted p < 0.01 and fold change cutoff ±2). b Gene Ontology connectivity map for GW3965 at 24 h for up-regulated (red) and down-regulated (red) pathways. c Gene Ontology connectivity map for GW3965 at 48 h for up-regulated (red) and down-regulated (red) pathways. d The number of H3K27ac sites gained (red) and lost (blue) after 24 and 48 h of GW3965 and rosiglitazone drug treatment
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4940857&req=5

Fig2: Chromatin dynamics and gene expression profiles from nuclear receptor activation. a The number of differentially regulated genes (up- and down-regulated) after 24 and 48 h of drug treatment (adjusted p < 0.01 and fold change cutoff ±2). b Gene Ontology connectivity map for GW3965 at 24 h for up-regulated (red) and down-regulated (red) pathways. c Gene Ontology connectivity map for GW3965 at 48 h for up-regulated (red) and down-regulated (red) pathways. d The number of H3K27ac sites gained (red) and lost (blue) after 24 and 48 h of GW3965 and rosiglitazone drug treatment

Mentions: We next evaluated changes to the cellular transcriptome after GW3965 and rosiglitazone drug treatments. We performed RNA-seq under control culture conditions (DMSO), as well as after 24 and 48 h of drug treatment. For each time point, we calculated the number of differentially regulated genes (adjusted p < 0.01, fold change cutoff ±2) in response to each drug (Table 1 and Fig. 2a). The PPARG response generated a near linear curve in the number of responsive genes. By contrast, the GW3965 treatment was delayed at 24 h and the vast majority (80.6 %) of these early genes were up-regulated. To validate this slow response, we performed RNA-seq by using a different LXR agonist, T0901317 (Fig. 2a); the resulting transcriptional response also produced a stalled expression profile. Notably, both GW3965 and T0901317 gene sets were highly correlated (Additional file 1: Figure S2a). By further integrating these gene sets, we generated a list of high-confidence, LXR target genes (GW3965 + T0901317). Supporting our observations, GW3965 + T0901317 gene targets also produced a stalled response (Additional file 1: Figure S2b). The use of a lower gene expression fold change cutoff (±1.5) generated an identical transcriptional pattern for all drug treatments (Additional file 1: Figure S3). Importantly, these gene expression changes are also in agreement with changes in cellular metabolites we described above.Table 1


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

Savic D, Ramaker RC, Roberts BS, Dean EC, Burwell TC, Meadows SK, Cooper SJ, Garabedian MJ, Gertz J, Myers RM - Genome Med (2016)

Chromatin dynamics and gene expression profiles from nuclear receptor activation. a The number of differentially regulated genes (up- and down-regulated) after 24 and 48 h of drug treatment (adjusted p < 0.01 and fold change cutoff ±2). b Gene Ontology connectivity map for GW3965 at 24 h for up-regulated (red) and down-regulated (red) pathways. c Gene Ontology connectivity map for GW3965 at 48 h for up-regulated (red) and down-regulated (red) pathways. d The number of H3K27ac sites gained (red) and lost (blue) after 24 and 48 h of GW3965 and rosiglitazone drug treatment
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4940857&req=5

Fig2: Chromatin dynamics and gene expression profiles from nuclear receptor activation. a The number of differentially regulated genes (up- and down-regulated) after 24 and 48 h of drug treatment (adjusted p < 0.01 and fold change cutoff ±2). b Gene Ontology connectivity map for GW3965 at 24 h for up-regulated (red) and down-regulated (red) pathways. c Gene Ontology connectivity map for GW3965 at 48 h for up-regulated (red) and down-regulated (red) pathways. d The number of H3K27ac sites gained (red) and lost (blue) after 24 and 48 h of GW3965 and rosiglitazone drug treatment
Mentions: We next evaluated changes to the cellular transcriptome after GW3965 and rosiglitazone drug treatments. We performed RNA-seq under control culture conditions (DMSO), as well as after 24 and 48 h of drug treatment. For each time point, we calculated the number of differentially regulated genes (adjusted p < 0.01, fold change cutoff ±2) in response to each drug (Table 1 and Fig. 2a). The PPARG response generated a near linear curve in the number of responsive genes. By contrast, the GW3965 treatment was delayed at 24 h and the vast majority (80.6 %) of these early genes were up-regulated. To validate this slow response, we performed RNA-seq by using a different LXR agonist, T0901317 (Fig. 2a); the resulting transcriptional response also produced a stalled expression profile. Notably, both GW3965 and T0901317 gene sets were highly correlated (Additional file 1: Figure S2a). By further integrating these gene sets, we generated a list of high-confidence, LXR target genes (GW3965 + T0901317). Supporting our observations, GW3965 + T0901317 gene targets also produced a stalled response (Additional file 1: Figure S2b). The use of a lower gene expression fold change cutoff (±1.5) generated an identical transcriptional pattern for all drug treatments (Additional file 1: Figure S3). Importantly, these gene expression changes are also in agreement with changes in cellular metabolites we described above.Table 1

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus