Transcriptional Coregulators: Fine-Tuning Metabolism

Metabolic homeostasis requires that cellular energy levels are adapted to environmental cues. This adaptation is largely regulated at the transcriptional level, through the interaction between transcription factors, coregulators, and the basal transcriptional machinery. Coregulators, which function as both metabolic sensors and transcriptional effectors, are ideally positioned to synchronize metabolic pathways to environmental stimuli. The balance between inhibitory actions of corepressors and stimulatory effects of coactivators enables the fine-tuning of metabolic processes. This tight regulation opens therapeutic opportunities to manage metabolic dysfunction by directing the activity of cofactors toward specific transcription factors, pathways, or cells/tissues, thereby restoring whole-body metabolic homeostasis

Molecular and Genetic Crosstalks between mTOR and ERRα Are Key Determinants of Rapamycin-Induced Nonalcoholic Fatty Liver

SummarymTOR and ERRα are key regulators of common metabolic processes, including lipid homeostasis. However, it is currently unknown whether these factors cooperate in the control of metabolism. ChIP-sequencing analyses of mouse liver reveal that mTOR occupies regulatory regions of genes on a genome-wide scale including enrichment at genes shared with ERRα that are involved in the TCA cycle and lipid biosynthesis. Genetic ablation of ERRα and rapamycin treatment, alone or in combination, alter the expression of these genes and induce the accumulation of TCA metabolites. As a consequence, both genetic and pharmacological inhibition of ERRα activity exacerbates hepatic hyperlipidemia observed in rapamycin-treated mice. We further show that mTOR regulates ERRα activity through ubiquitin-mediated degradation via transcriptional control of the ubiquitin-proteasome pathway. Our work expands the role of mTOR action in metabolism and highlights the existence of a potent mTOR/ERRα regulatory axis with significant clinical impact

Inhibition of acetyl-CoA carboxylase suppresses fatty acid synthesis and tumor growth of non-small-cell lung cancer in preclinical models.

Continuous de novo fatty acid synthesis is a common feature of cancer that is required to meet the biosynthetic demands of a growing tumor. This process is controlled by the rate-limiting enzyme acetyl-CoA carboxylase (ACC), an attractive but traditionally intractable drug target. Here we provide genetic and pharmacological evidence that in preclinical models ACC is required to maintain the de novo fatty acid synthesis needed for growth and viability of non-small-cell lung cancer (NSCLC) cells. We describe the ability of ND-646-an allosteric inhibitor of the ACC enzymes ACC1 and ACC2 that prevents ACC subunit dimerization-to suppress fatty acid synthesis in vitro and in vivo. Chronic ND-646 treatment of xenograft and genetically engineered mouse models of NSCLC inhibited tumor growth. When administered as a single agent or in combination with the standard-of-care drug carboplatin, ND-646 markedly suppressed lung tumor growth in the Kras;Trp53-/- (also known as KRAS p53) and Kras;Stk11-/- (also known as KRAS Lkb1) mouse models of NSCLC. These findings demonstrate that ACC mediates a metabolic liability of NSCLC and that ACC inhibition by ND-646 is detrimental to NSCLC growth, supporting further examination of the use of ACC inhibitors in oncology

Correlation of mRNA and protein levels: Cell type-specific gene expression of cluster designation antigens in the prostate

Background: Expression levels of mRNA and protein by cell types exhibit a range of correlations for different genes. In this study, we compared levels of mRNA abundance for several cluster designation (CD) genes determined by gene arrays using magnetic sorted and laser-capture microdissected human prostate cells with levels of expression of the respective CD proteins determined by immunohistochemical staining in the major cell types of the prostate - basal epithelial, luminal epithelial, stromal fibromuscular, and endothelial - and for prostate precursor/stem cells and prostate carcinoma cells. Immunohistochemical stains of prostate tissues from more than 50 patients were scored for informative CD antigen expression and compared with cell-type specific transcriptomes. Results: Concordance between gene and protein expression findings based on 'present' vs. 'absent' calls ranged from 46 to 68%. Correlation of expression levels was poor to moderate (Pearson correlations ranged from 0 to 0.63). Divergence between the two data types was most frequently seen for genes whose array signals exceeded background (> 50) but lacked immunoreactivity by immunostaining. This could be due to multiple factors, e.g. low levels of protein expression, technological sensitivities, sample processing, probe set definition or anatomical origin of tissue and actual biological differences between transcript and protein abundance. Conclusion: Agreement between these two very different methodologies has great implications for their respective use in both molecular studies and clinical trials employing molecular biomarkers.This work was supported by grant DK63630 and DK069690 from NIDDK. Additional funding came from grants CA85859, CA98699 and CA111244 from NCI, and PM50 GMO76547/Center for Systems Biology

Application of affymetrix array and massively parallel signature sequencing for identification of genes involved in prostate cancer progression

BACKGROUND: Affymetrix GeneChip Array and Massively Parallel Signature Sequencing (MPSS) are two high throughput methodologies used to profile transcriptomes. Each method has certain strengths and weaknesses; however, no comparison has been made between the data derived from Affymetrix arrays and MPSS. In this study, two lineage-related prostate cancer cell lines, LNCaP and C4-2, were used for transcriptome analysis with the aim of identifying genes associated with prostate cancer progression. METHODS: Affymetrix GeneChip array and MPSS analyses were performed. Data was analyzed with GeneSpring 6.2 and in-house perl scripts. Expression array results were verified with RT-PCR. RESULTS: Comparison of the data revealed that both technologies detected genes the other did not. In LNCaP, 3,180 genes were only detected by Affymetrix and 1,169 genes were only detected by MPSS. Similarly, in C4-2, 4,121 genes were only detected by Affymetrix and 1,014 genes were only detected by MPSS. Analysis of the combined transcriptomes identified 66 genes unique to LNCaP cells and 33 genes unique to C4-2 cells. Expression analysis of these genes in prostate cancer specimens showed CA1 to be highly expressed in bone metastasis but not expressed in primary tumor and EPHA7 to be expressed in normal prostate and primary tumor but not bone metastasis. CONCLUSION: Our data indicates that transcriptome profiling with a single methodology will not fully assess the expression of all genes in a cell line. A combination of transcription profiling technologies such as DNA array and MPSS provides a more robust means to assess the expression profile of an RNA sample. Finally, genes that were differentially expressed in cell lines were also differentially expressed in primary prostate cancer and its metastases

Genomic Convergence among ERRα, PROX1, and BMAL1 in the Control of Metabolic Clock Outputs

Metabolic homeostasis and circadian rhythms are closely intertwined biological processes. Nuclear receptors, as sensors of hormonal and nutrient status, are actively implicated in maintaining this physiological relationship. Although the orphan nuclear receptor estrogen-related receptor α (ERRα, NR3B1) plays a central role in the control of energy metabolism and its expression is known to be cyclic in the liver, its role in temporal control of metabolic networks is unknown. Here we report that ERRα directly regulates all major components of the molecular clock. ERRα-null mice also display deregulated locomotor activity rhythms and circadian period lengths under free-running conditions, as well as altered circulating diurnal bile acid and lipid profiles. In addition, the ERRα-null mice exhibit time-dependent hypoglycemia and hypoinsulinemia, suggesting a role for ERRα in modulating insulin sensitivity and glucose handling during the 24-hour light/dark cycle. We also provide evidence that the newly identified ERRα corepressor PROX1 is implicated in rhythmic control of metabolic outputs. To help uncover the molecular basis of these phenotypes, we performed genome-wide location analyses of binding events by ERRα, PROX1, and BMAL1, an integral component of the molecular clock. These studies revealed the existence of transcriptional regulatory loops among ERRα, PROX1, and BMAL1, as well as extensive overlaps in their target genes, implicating these three factors in the control of clock and metabolic gene networks in the liver. Genomic convergence of ERRα, PROX1, and BMAL1 transcriptional activity thus identified a novel node in the molecular circuitry controlling the daily timing of metabolic processes

Application of affymetrix array and massively parallel signature sequencing for identification of genes involved in prostate cancer progression

Abstract Background Affymetrix GeneChip Array and Massively Parallel Signature Sequencing (MPSS) are two high throughput methodologies used to profile transcriptomes. Each method has certain strengths and weaknesses; however, no comparison has been made between the data derived from Affymetrix arrays and MPSS. In this study, two lineage-related prostate cancer cell lines, LNCaP and C4-2, were used for transcriptome analysis with the aim of identifying genes associated with prostate cancer progression. Methods Affymetrix GeneChip array and MPSS analyses were performed. Data was analyzed with GeneSpring 6.2 and in-house perl scripts. Expression array results were verified with RT-PCR. Results Comparison of the data revealed that both technologies detected genes the other did not. In LNCaP, 3,180 genes were only detected by Affymetrix and 1,169 genes were only detected by MPSS. Similarly, in C4-2, 4,121 genes were only detected by Affymetrix and 1,014 genes were only detected by MPSS. Analysis of the combined transcriptomes identified 66 genes unique to LNCaP cells and 33 genes unique to C4-2 cells. Expression analysis of these genes in prostate cancer specimens showed CA1 to be highly expressed in bone metastasis but not expressed in primary tumor and EPHA7 to be expressed in normal prostate and primary tumor but not bone metastasis. Conclusion Our data indicates that transcriptome profiling with a single methodology will not fully assess the expression of all genes in a cell line. A combination of transcription profiling technologies such as DNA array and MPSS provides a more robust means to assess the expression profile of an RNA sample. Finally, genes that were differentially expressed in cell lines were also differentially expressed in primary prostate cancer and its metastases.</p

The homeobox protein Prox1 is a negative modulator of ERRα/PGC-1α bioenergetic functions

Estrogen-related receptor α (ERRα) and proliferator-activated receptor γ coactivator-1α (PGC-1α) play central roles in the transcriptional control of energy homeostasis, but little is known about factors regulating their activity. Here we identified the homeobox protein prospero-related homeobox 1 (Prox1) as one such factor. Prox1 interacts with ERRα and PGC-1α, occupies promoters of metabolic genes on a genome-wide scale, and inhibits the activity of the ERRα/PGC-1α complex. DNA motif analysis suggests that Prox1 interacts with the genome through tethering to ERRα and other factors. Importantly, ablation of Prox1 and ERRα have opposite effects on the respiratory capacity of liver cells, revealing an unexpected role for Prox1 in the control of energy homeostasis