Oncogene activation induces metabolic transformation resulting in insulin-independence in human breast cancer cells.

Normal breast epithelial cells require insulin and EGF for growth in serum-free media. We previously demonstrated that over expression of breast cancer oncogenes transforms MCF10A cells to an insulin-independent phenotype. Additionally, most breast cancer cell lines are insulin-independent for growth. In this study, we investigated the mechanism by which oncogene over expression transforms MCF10A cells to an insulin-independent phenotype. Analysis of the effects of various concentrations of insulin and/or IGF-I on proliferation of MCF10A cells demonstrated that some of the effects of insulin were independent from those of IGF-I, suggesting that oncogene over expression drives a true insulin-independent proliferative phenotype. To test this hypothesis, we examined metabolic functions of insulin signaling in insulin-dependent and insulin-independent cells. HER2 over expression in MCF10A cells resulted in glucose uptake in the absence of insulin at a rate equal to insulin-induced glucose uptake in non-transduced cells. We found that a diverse set of oncogenes induced the same result. To gain insight into how HER2 oncogene signaling affected increased insulin-independent glucose uptake we compared HER2-regulated gene expression signatures in MCF10A and HER2 over expressing MCF10A cells by differential analysis of time series gene expression data from cells treated with a HER2 inhibitor. This analysis identified genes specifically regulated by the HER2 oncogene, including VAMP8 and PHGDH, which have known functions in glucose uptake and processing of glycolytic intermediates, respectively. Moreover, these genes specifically implicated in HER2 oncogene-driven transformation are commonly altered in human breast cancer cells. These results highlight the diversity of oncogene effects on cell regulatory pathways and the importance of oncogene-driven metabolic transformation in breast cancer.

Activation of a nuclear factor kappaB/interleukin-1 positive feedback loop by amphiregulin in human breast cancer cells.

We have recently shown that an amphiregulin-mediated autocrine loop is responsible for growth factor-independent proliferation, motility, and invasive capacity of some aggressive breast cancer cells, such as the SUM149 breast cancer cell line. In the present study, we investigated the mechanisms by which amphiregulin activation of the epidermal growth factor receptor (EGFR) regulates these altered phenotypes. Bioinformatic analysis of gene expression networks regulated by amphiregulin implicated interleukin-1alpha (IL-1alpha) and IL-1beta as key mediators of amphiregulin's biological effects. The bioinformatic data were validated in experiments which showed that amphiregulin, but not epidermal growth factor, results in transcriptional up-regulation of IL-1alpha and IL-1beta. Both IL-1alpha and IL-1beta are synthesized and secreted by SUM149 breast cancer cells, as well as MCF10A cells engineered to express amphiregulin or MCF10A cells cultured in the presence of amphiregulin. Furthermore, EGFR, activated by amphiregulin but not epidermal growth factor, results in the prompt activation of the transcription factor nuclear factor-kappaB (NF-kappaB), which is required for transcriptional activation of IL-1. Once synthesized and secreted from the cells, IL-1 further activates NF-kappaB, and inhibition of IL-1 with the IL-1 receptor antagonist results in loss of NF-kappaB DNA binding activity and inhibition of cell proliferation. However, SUM149 cells can proliferate in the presence of IL-1 when EGFR activity is inhibited. Thus, in aggressive breast cancer cells, such as the SUM149 cells, or in normal human mammary epithelial cells growing in the presence of amphiregulin, EGFR signaling is integrated with NF-kappaB activation and IL-1 synthesis, which cooperate to regulate the growth and invasive capacity of the cells.

Structure alignment based on coding of local geometric measures.

BACKGROUND: A structure alignment method based on a local geometric property is presented and its performance is tested in pairwise and multiple structure alignments. In this approach, the writhing number, a quantity originating from integral formulas of Vassiliev knot invariants, is used as a local geometric measure. This measure is used in a sliding window to calculate the local writhe down the length of the protein chain. By encoding the distribution of writhing numbers across all the structures in the protein databank (PDB), protein geometries are represented in a 20-letter alphabet. This encoding transforms the structure alignment problem into a sequence alignment problem and allows the well-established algorithms of sequence alignment to be employed. Such geometric alignments offer distinct advantages over structural alignments in Cartesian coordinates as it better handles structural subtleties associated with slight twists and bends that distort one structure relative to another. RESULTS: The performance of programs for pairwise local alignment (TLOCAL) and multiple alignment (TCLUSTALW) are readily adapted from existing code for Smith-Waterman pairwise alignment and for multiple sequence alignment using CLUSTALW. The alignment algorithms employed a blocked scoring matrix (TBLOSUM) generated using the frequency of changes in the geometric alphabet of a block of protein structures. TLOCAL was tested on a set of 10 difficult proteins and found to give high quality alignments that compare favorably to those generated by existing pairwise alignment programs. A set of protein comparison involving hinged structures was also analyzed and TLOCAL was seen to compare favorably to other alignment methods. TCLUSTALW was tested on a family of protein kinases and reveal conserved regions similar to those previously identified by a hand alignment. CONCLUSION: These results show that the encoding of the writhing number as a geometric measure allow high quality structure alignments to be generated using standard algorithms of sequence alignment. This approach provides computationally efficient algorithms that allow fast database searching and multiple structure alignment. Because the geometric measure can employ different window sizes, the method allows the exploration of alignments on different, well-defined length scales.

From microarray to biological networks: Analysis of gene expression profiles.

Powerful new methods, such as expression profiles using cDNA arrays, have been used to monitor changes in gene expression levels as a result of a variety of metabolic, xenobiotic, or pathogenic challenges. This potentially vast quantity of data enables, in principle, the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Here we present a general approach to developing dynamic models for analyzing time series of whole-genome expression. The parameters in the model show the influence of one gene expression level on another and are calculated using singular value decomposition as a means of inverting noisy and near-singular matrices. Correlative networks can then be generated based on these parameters with a simple threshold approach. We also demonstrate how dynamic models can be used in conjunction with cluster analysis to analyze microarray time series. Using the parameters from the dynamic model as a metric, two-way hierarchical clustering could be performed to visualize how influencing genes affect the expression levels of responding genes. Application of these approaches is demonstrated using gene expression data in yeast cell cycle.

A random polymer model of the statistical significance of structure alignment.

A theory for assessing the statistical significance of structure alignment is developed using a random or Gaussian chain model. In this model, we consider the statistical distribution of the root mean square distance (rmsd) of the alignment between two random chains of equal length and common center of mass (referred to as Case 1). We demonstrate that the rmsd2 is distributed as a sum of independent Gamma variables. Analytic results on the mean and variance of the rmsd2 are presented. Since rmsd is strongly dependent on the length, we define the dimensionless quantity, reduced rmsd, as the rmsd divided by the radius of gyration. We find that the reduced rmsd can be accurately approximated by an extreme value distribution (EVD) that is independent of chain length and of bond length. The parameters of the EVD can be calculated from the mean and the variance of the rmsd2. We also consider the case of two chains with a common center of mass that are then rotated to minimize the rmsd (Case 2). In this case, the distribution of reduced rmsd can again be accurately approximated by an EVD, which is independent of the chain length and expected bond length. This distribution is used to calculate the p-value for a given reduced rmsd. Performing an analogous comparison for proteins, we find that approximately M(nu) and nu = 0.28 and 0.32 for Case 1 and Case 2, respectively, where M is the chain length. This result for Case 2 exactly matches with previous scaling results and suggests that rmsd/M(nu)is an appropriate metric for protein structure alignment and will be independent of chain length. We also find that the new score roughly follows the EVD.

Stochastic fluctuations in gene expression far from equilibrium: Omega expansion and linear noise approximation.

The Omega expansion of the master equation is used to investigate the intrinsic noise in an autoregulatory gene expression system. This Omega expansion provides a mesoscale description of the system and is used to analyze the effect of feedback regulation on intrinsic noise when the system state is far from equilibrium. Using the linear noise approximation, analytic results are obtained for a single gene system with linear feedback that is far from equilibrium. Additionally, analytic expressions are obtained for nonlinear systems near equilibrium. Simulations of such autoregulatory reaction schemes with nonlinear feedback show that during the approach to equilibrium the noise is not always reduced by the strength of the feedback. This is contrary to results seen in the equilibrium limit which show decreased noise with feedback strength. These results demonstrate that the behavior of linearized systems near equilibrium cannot be readily applied to systems far from equilibrium and highlight the need to explore nonequilibrium domains in mesoscopic systems.

A new scoring function and associated statistical significance for structure alignment by CE.

A new scoring function for assessing the statistical significance of protein structure alignment has been developed. The new scores were tested empirically using the combinatorial extension (CE) algorithm. The significance of a given score was given a p-value by curve-fitting the distribution of the scores generated by a random comparison of proteins taken from the PDB_SELECT database and the structural classification of proteins (SCOP) database. Although the scoring function was developed based on the CE algorithm, it is portable to any other protein structure alignment algorithm. The new scoring function is examined by sensitivity, specificity, and ROC curves.

Duplication models for biological networks.

Are biological networks different from other large complex networks? Both large biological and nonbiological networks exhibit power-law graphs (number of nodes with degree k, N(k) approximately k(-beta)), yet the exponents, beta, fall into different ranges. This may be because duplication of the information in the genome is a dominant evolutionary force in shaping biological networks (like gene regulatory networks and protein-protein interaction networks) and is fundamentally different from the mechanisms thought to dominate the growth of most nonbiological networks (such as the Internet). The preferential choice models used for nonbiological networks like web graphs can only produce power-law graphs with exponents greater than 2. We use combinatorial probabilistic methods to examine the evolution of graphs by node duplication processes and derive exact analytical relationships between the exponent of the power law and the parameters of the model. Both full duplication of nodes (with all their connections) as well as partial duplication (with only some connections) are analyzed. We demonstrate that partial duplication can produce power-law graphs with exponents less than 2, consistent with current data on biological networks. The power-law exponent for large graphs depends only on the growth process, not on the starting graph.

Cluster analysis of dynamic parameters of gene expression.

Cluster analysis has proven to be a valuable statistical method for analyzing whole genome expression data. Although clustering methods have great utility, they do represent a lower level statistical analysis that is not directly tied to a specific model. To extend such methods and to allow for more sophisticated lines of inference, we use cluster analysis in conjunction with a specific model of gene expression dynamics. This model provides phenomenological dynamic parameters on both linear and non-linear responses of the system. This analysis determines the parameters of two different transition matrices (linear and nonlinear) that describe the influence of one gene expression level on another. Using yeast cell cycle microarray data as test set, we calculated the transition matrices and used these dynamic parameters as a metric for cluster analysis. Hierarchical cluster analysis of this transition matrix reveals how a set of genes influence the expression of other genes activated during different cell cycle phases. Most strikingly, genes in different stages of cell cycle preferentially activate or inactivate genes in other stages of cell cycle, and this relationship can be readily visualized in a two-way clustering image. The observation is prior to any knowledge of the chronological characteristics of the cell cycle process. This method shows the utility of using model parameters as a metric in cluster analysis.

A duplication growth model of gene expression networks.

MOTIVATION: There has been considerable interest in developing computational techniques for inferring genetic regulatory networks from whole-genome expression profiles. When expression time series data sets are available, dynamic models can, in principle, be used to infer correlative relationships between gene expression levels, which may be causal. However, because of the range of detectable expression levels and the current quality of the data, the predictive nature of such inferred, quantitative models is questionable. Network models derived from simple rate laws offer an intermediate level analysis, going beyond simple statistical analysis, but falling short of a fully quantitative description. This work shows how such network models can be constructed and describes the global properties of the networks derived from such a model. These global properties are statistically robust and provide insights into the design of the underlying network. RESULTS: Several whole-genome expression time series data sets from yeast microarray experiments were analyzed using a Markov-modeling method (Dewey and Galas, FUNC: Integr. Genomics, 1, 269-278, 2001) to infer an approximation to the underlying genetic network. We found that the global statistical properties of all the resulting networks are similar. The overall structure of these biological networks is distinctly different from that of other recently studied networks such as the Internet or social networks. These biological networks show hierarchical, hub-like structures that have some properties similar to a class of graphs known as small world graphs. Small world networks exhibit local cliquishness while exhibiting strong global connectivity. In addition to the small world properties, the biological networks show a power law or scale free distribution of connectivities. An inverse power law, N(k) approximately k(-3/2), for the number of vertices (genes) with k connections was observed for three different data sets from yeast. We propose network growth models based on gene duplication events. Simulations of these models yield networks with the same combination of global graphical properties that we inferred from the expression data.

From microarrays to networks: mining expression time series.

Over the past few years, powerful new methods have been devised that enable researchers to study the expression dynamics of many genes simultaneously (e.g. gene expression profiles using cDNA microarrays). In principle, this potentially vast quantity of data enables the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Finding the patterns in those data represents the next major phase in our understanding of the programming and functioning of the living cell. Simple dynamic models can be used to generate gene expression networks. These networks reveal the phenomenological link between the expression of different genes. This review discuss how these networks are generated and outlines several data-mining techniques for extracting relationships and hypotheses in gene expression. These emerging methods can be applied to a range of biological problems.