A systems analysis of the chemosensitivity of breast cancer cells to the polyamine analogue PG-11047.

BACKGROUND: Polyamines regulate important cellular functions and polyamine dysregulation frequently occurs in cancer. The objective of this study was to use a systems approach to study the relative effects of PG-11047, a polyamine analogue, across breast cancer cells derived from different patients and to identify genetic markers associated with differential cytotoxicity. METHODS: A panel of 48 breast cell lines that mirror many transcriptional and genomic features present in primary human breast tumours were used to study the antiproliferative activity of PG-11047. Sensitive cell lines were further examined for cell cycle distribution and apoptotic response. Cell line responses, quantified by the GI50 (dose required for 50% relative growth inhibition) were correlated with the omic profiles of the cell lines to identify markers that predict response and cellular functions associated with drug sensitivity. RESULTS: The concentrations of PG-11047 needed to inhibit growth of members of the panel of breast cell lines varied over a wide range, with basal-like cell lines being inhibited at lower concentrations than the luminal cell lines. Sensitive cell lines showed a significant decrease in S phase fraction at doses that produced little apoptosis. Correlation of the GI50 values with the omic profiles of the cell lines identified genomic, transcriptional and proteomic variables associated with response. CONCLUSIONS: A 13-gene transcriptional marker set was developed as a predictor of response to PG-11047 that warrants clinical evaluation. Analyses of the pathways, networks and genes associated with response to PG-11047 suggest that response may be influenced by interferon signalling and differential inhibition of aspects of motility and epithelial to mesenchymal transition.

A correlation with exon expression approach to identify cis-regulatory elements for tissue-specific alternative splicing.

Correlation of motif occurrences with gene expression intensity is an effective strategy for elucidating transcriptional cis-regulatory logic. Here we demonstrate that this approach can also identify cis-regulatory elements for alternative pre-mRNA splicing. Using data from a human exon microarray, we identified 56 cassette exons that exhibited higher transcript-normalized expression in muscle than in other normal adult tissues. Intron sequences flanking these exons were then analyzed to identify candidate regulatory motifs for muscle-specific alternative splicing. Correlation of motif parameters with gene-normalized exon expression levels was examined using linear regression and linear splines on RNA words and degenerate weight matrices, respectively. Our unbiased analysis uncovered multiple candidate regulatory motifs for muscle-specific splicing, many of which are phylogenetically conserved among vertebrate genomes. The most prominent downstream motifs were binding sites for Fox1- and CELF-related splicing factors, and a branchpoint-like element acuaac; pyrimidine-rich elements resembling PTB-binding sites were most significant in upstream introns. Intriguingly, our systematic study indicates a paucity of novel muscle-specific elements that are dominant in short proximal intronic regions. We propose that Fox and CELF proteins play major roles in enforcing the muscle-specific alternative splicing program, facilitating expression of unique isoforms of cytoskeletal proteins critical to muscle cell function.

Adaptively inferring human transcriptional subnetworks.

Although the human genome has been sequenced, progress in understanding gene regulation in humans has been particularly slow. Many computational approaches developed for lower eukaryotes to identify cis-regulatory elements and their associated target genes often do not generalize to mammals, largely due to the degenerate and interactive nature of such elements. Motivated by the switch-like behavior of transcriptional responses, we present a systematic approach that allows adaptive determination of active transcriptional subnetworks (cis-motif combinations, the direct target genes and physiological processes regulated by the corresponding transcription factors) from microarray data in mammals, with accuracy similar to that achieved in lower eukaryotes. Our analysis uncovered several new subnetworks active in human liver and in cell-cycle regulation, with similar functional characteristics as the known ones. We present biochemical evidence for our predictions, and show that the recently discovered G2/M-specific E2F pathway is wider than previously thought; in particular, E2F directly activates certain mitotic genes involved in hepatocellular carcinomas. Additionally, we demonstrate that this method can predict subnetworks in a condition-specific manner, as well as regulatory crosstalk across multiple tissues. Our approach allows systematic understanding of how phenotypic complexity is regulated at the transcription level in mammals and offers marked advantage in systems where little or no prior knowledge of transcriptional regulation is available.

Predictive models of gene regulation: application of regression methods to microarray data.

Eukaryotic transcription is a complex process. A myriad of biochemical signals cause activators and repressors to bind specific cis-elements on the promoter DNA, which help to recruit the basal transcription machinery that ultimately initiates transcription. In this chapter, we discuss how regression techniques can be effectively used to infer the functional cis-regulatory elements and their cooperativity from microarray data. Examples from yeast cell cycle are drawn to demonstrate the power of these techniques. Periodic regulation of the cell cycle, connection with underlying energetics, and the inference of combinatorial logic are also discussed. An implementation based on regression splines is discussed in detail.

Restriction of receptor movement alters cellular response: physical force sensing by EphA2.

Activation of the EphA2 receptor tyrosine kinase by ephrin-A1 ligands presented on apposed cell surfaces plays important roles in development and exhibits poorly understood functional alterations in cancer. We reconstituted this intermembrane signaling geometry between live EphA2-expressing human breast cancer cells and supported membranes displaying laterally mobile ephrin-A1. Receptor-ligand binding, clustering, and subsequent lateral transport within this junction were observed. EphA2 transport can be blocked by physical barriers nanofabricated onto the underlying substrate. This physical reorganization of EphA2 alters the cellular response to ephrin-A1, as observed by changes in cytoskeleton morphology and recruitment of a disintegrin and metalloprotease 10. Quantitative analysis of receptor-ligand spatial organization across a library of 26 mammary epithelial cell lines reveals characteristic differences that strongly correlate with invasion potential. These observations reveal a mechanism for spatio-mechanical regulation of EphA2 signaling pathways.

Basal subtype and MAPK/ERK kinase (MEK)-phosphoinositide 3-kinase feedback signaling determine susceptibility of breast cancer cells to MEK inhibition.

Specific inhibitors of mitogen-activated protein kinase/extracellular signal-regulated kinase (ERK) kinase (MEK) have been developed that efficiently inhibit the oncogenic RAF-MEK-ERK pathway. We used a systems-based approach to identify breast cancer subtypes particularly susceptible to MEK inhibitors and to understand molecular mechanisms conferring resistance to such compounds. Basal-type breast cancer cells were found to be particularly susceptible to growth inhibition by small-molecule MEK inhibitors. Activation of the phosphatidylinositol 3-kinase (PI3K) pathway in response to MEK inhibition through a negative MEK-epidermal growth factor receptor-PI3K feedback loop was found to limit efficacy. Interruption of this feedback mechanism by targeting MEK and PI3K produced synergistic effects, including induction of apoptosis and, in some cell lines, cell cycle arrest and protection from apoptosis induced by proapoptotic agents. These findings enhance our understanding of the interconnectivity of oncogenic signal transduction circuits and have implications for the design of future clinical trials of MEK inhibitors in breast cancer by guiding patient selection and suggesting rational combination therapies.

Regulation of the PDK4 isozyme by the Rb-E2F1 complex.

Loss of the transcription factor E2F1 elicits a complex metabolic phenotype in mice underscored by reduced adiposity and protection from high fat diet-induced diabetes. Here, we demonstrate that E2F1 directly regulates the gene encoding PDK4 (pyruvate dehydrogenase kinase 4), a key nutrient sensor and modulator of glucose homeostasis that is chronically elevated in obesity and diabetes and acutely induced under the metabolic stress of starvation or fasting. We show that loss of E2F1 in vivo blunts PDK4 expression and improves myocardial glucose oxidation. The absence of E2F1 also corresponds to lower blood glucose levels, improved plasma lipid profile, and increased sensitivity to insulin stimulation. Consistently, enforced E2F1 expression up-regulates PDK4 levels and suppresses glucose oxidation in C(2)C(12) myoblasts. Furthermore, inactivation of Rb, the repressor of E2F-dependent transcription, markedly induces PDK4 and triggers the enrichment of E2F1 occupancy onto the PDK4 promoter as detected by chromatin immunoprecipitation analysis. Two overlapping E2F binding sites were identified on this promoter. Transactivation assays later verified E2F1 responsiveness of this promoter element in C(2)C(12) myoblasts and IMR90 fibroblasts, an effect that was completely abrogated following mutation of the E2F sites. Taken together, our data illustrate how the E2F1 mitogen directly regulates PDK4 levels and influences cellular bioenergetics, namely mitochondrial glucose oxidation. These results are relevant to the pathophysiology of chronic diseases like obesity and diabetes, where PDK4 is dysregulated and could have implications pertinent to the etiology of tumor metabolism, especially in cancers with Rb pathway defects.

CD36-dependent regulation of muscle FoxO1 and PDK4 in the PPAR delta/beta-mediated adaptation to metabolic stress.

The transcription factor FoxO1 contributes to the metabolic adaptation to fasting by suppressing muscle oxidation of glucose, sparing it for glucose-dependent tissues. Previously, we reported that FoxO1 activation in C(2)C(12) muscle cells recruits the fatty acid translocase CD36 to the plasma membrane and increases fatty acid uptake and oxidation. This, together with FoxO1 induction of lipoprotein lipase, would promote the reliance on fatty acid utilization characteristic of the fasted muscle. Here, we show that CD36-mediated fatty acid uptake, in turn, up-regulates protein levels and activity of FoxO1 as well as its target PDK4, the negative regulator of glucose oxidation. Increased fatty acid flux or enforced CD36 expression in C(2)C(12) cells is sufficient to induce FoxO1 and PDK4, whereas CD36 knockdown has opposite effects. In vivo, CD36 loss blunts fasting induction of FoxO1 and PDK4 and the associated suppression of glucose oxidation. Importantly, CD36-dependent regulation of FoxO1 is mediated by the nuclear receptor PPARdelta/beta. Loss of PPARdelta/beta phenocopies CD36 deficiency in blunting fasting induction of muscle FoxO1 and PDK4 in vivo. Expression of PPARdelta/beta in C(2)C(12) cells, like that of CD36, robustly induces FoxO1 and suppresses glucose oxidation, whereas co-expression of a dominant negative PPARdelta/beta compromises FoxO1 induction. Finally, several PPRE sites were identified in the FoxO1 promoter, which was responsive to PPARdelta/beta. Agonists of PPARdelta/beta were sufficient to confer responsiveness and transactivate the heterologous FoxO1 promoter but not in the presence of dominant negative PPARdelta/beta. Taken together, our findings suggest that CD36-dependent FA activation of PPARdelta/beta results in the transcriptional regulation of FoxO1 as well as PDK4, recently shown to be a direct PPARdelta/beta target. FoxO1 in turn can regulate CD36, lipoprotein lipase, and PDK4, reinforcing the action of PPARdelta/beta to increase muscle reliance on FA. The findings could have implications in the chronic abnormalities of fatty acid metabolism associated with obesity and diabetes.

Mining ChIP-chip data for transcription factor and cofactor binding sites.

MOTIVATION: Identification of single motifs and motif pairs that can be used to predict transcription factor localization in ChIP-chip data, and gene expression in tissue-specific microarray data. RESULTS: We describe methodology to identify de novo individual and interacting pairs of binding site motifs from ChIP-chip data, using an algorithm that integrates localization data directly into the motif discovery process. We combine matrix-enumeration based motif discovery with multivariate regression to evaluate candidate motifs and identify motif interactions. When applied to the HNF localization data in liver and pancreatic islets, our methods produce motifs that are either novel or improved known motifs. All motif pairs identified to predict localization are further evaluated according to how well they predict expression in liver and islets and according to how conserved are the relative positions of their occurrences. We find that interaction models of HNF1 and CDP motifs provide excellent prediction of both HNF1 localization and gene expression in liver. Our results demonstrate that ChIP-chip data can be used to identify interacting binding site motifs. AVAILABILITY: Motif discovery programs and analysis tools are available on request from the authors.

Interacting models of cooperative gene regulation.

Cooperativity between transcription factors is critical to gene regulation. Current computational methods do not take adequate account of this salient aspect. To address this issue, we present a computational method based on multivariate adaptive regression splines to correlate the occurrences of transcription factor binding motifs in the promoter DNA and their interactions to the logarithm of the ratio of gene expression levels. This allows us to discover both the individual motifs and synergistic pairs of motifs that are most likely to be functional, and enumerate their relative contributions at any arbitrary time point for which mRNA expression data are available. We present results of simulations and focus specifically on the yeast cell-cycle data. Inclusion of synergistic interactions can increase the prediction accuracy over linear regression to as much as 1.5- to 3.5-fold. Significant motifs and combinations of motifs are appropriately predicted at each stage of the cell cycle. We believe our multivariate adaptive regression splines-based approach will become more significant when applied to higher eukaryotes, especially mammals, where cooperative control of gene regulation is absolutely essential.