ABC transporters as cancer drivers: Potential functions in cancer development

Background

ABC transporters have attracted considerable attention for their function as drug transporters in a broad range of tumours and are therefore considered as major players in cancer chemoresistance. However, less attention has been focused on their potential role as active players in cancer development and progression.

Protein kinase signaling networks in cancer

Protein kinases orchestrate the activation of signaling cascades in response to extracellular and intracellular stimuli to control cell growth, proliferation, and survival. The complexity of numerous intracellular signaling pathways is highlighted by the number of kinases encoded by the human genome (539) and the plethora of phosphorylation sites identified in phosphoproteomic studies. Perturbation of these signaling networks by mutations or abnormal protein expression underlies the cause of many diseases including cancer. Recent RNAi screens and cancer genomic sequencing studies have revealed that many more kinases than anticipated contribute to tumorigenesis and are potential targets for inhibitor drug development intervention. This review will highlight recent insights into known pathways essential for tumorigenesis and discuss exciting new pathways for therapeutic intervention.     

Integrated gene networks in breast cancer development

Breast cancer is a complex and heterogenous disease. Classical molecular medical approaches cannot fully understand and comprehend its pathogenesis. In this review, the development of new biological markers for the early detection and creation of guided and specific therapy of breast cancer are discussed in light of the rapid advances in the “omics”. Results of cancer research in combination with large-scale methods that examine the expression status of genes and proteins have identified a large number of new biomarkers as well as confirmed the human growth hormone as an important player in the pathogenesis of this disease through its autocrine regulation where it influences the activation of Pax5 and HOXA1 gene networks.     

A new method for the re-implementation of threshold logic functions with cellular neural networks

A new strategy is presented for the implementation of threshold logic functions with binary-output Cellular Neural Networks (CNNs). The objective is to optimize the CNNs weights to develop a robust implementation. Hence, the concept of generative set is introduced as a convenient representation of any linearly separable Boolean function. Our analysis of threshold logic functions leads to a complete algorithm that automatically provides an optimized generative set. New weights are deduced and a more robust CNN template assuming the same function can thus be implemented. The strategy is illustrated by a detailed example.     

Protein mislocalization: Mechanisms,functions and clinical applications in cancer

The changes from normal cells to cancer cells are primarily regulated by genome instability, which foster hallmark functions of cancer through multiple mechanisms including protein mislocalization. Mislocalization of these proteins, including oncoproteins, tumor suppressors, and other cancer-related proteins, can interfere with normal cellular function and cooperatively drive tumor development and metastasis. This review describes the cancer-related effects of protein subcellular mislocalization, the related mislocalization mechanisms, and the potential application of this knowledge to cancer diagnosis, prognosis, and therapy.     

Chain functions and scoring functions in genetic networks

One of the grand challenges of system biology is to reconstruct the network of regulatory control among genes and proteins. High throughput data, particularly from expression experiments, may gradually make this possible in the future. Here we address two key ingredients in any such 'reverse engineering' effort: The choice of a biologically relevant, yet restricted, set of potential regulation functions, and the appropriate score to evaluate candidate regulatory relations. We propose a set of regulation functions which we call chain functions, and argue for their ubiquity in biological networks. We analyze their complexity and show that their number is exponentially smaller than all boolean functions of the same dimension. We define two new scores: one evaluating the fitness of a candidate set of regulators of a particular gene, and the other evaluating a candidate function. Both scores use established statistical methods. Finally, we test our methods on experimental gene expression data from the yeast galactose pathway. We show the utility of using chain functions and the improved inference using our scores in comparison to several extant scores. We demonstrate that the combined use of the two scores gives an extra advantage. We expect both chain functions and the new scores to be helpful in future attempts to infer regulatory networks.     

The Yin and Yang functions of the Myc oncoprotein in cancer development and as targets for therapy

The Myc proto-oncoprotein coordinates a number of normal physiological processes necessary for growth and expansion of somatic cells by controlling the expression of numerous target genes. Deregulation of MYC as a consequence of carciogenic events enforces cells to undergo a transition to a hyperproliferative state. This increases the risk of additional oncogenic mutations that in turn can result in further tumor progression. However, Myc activation also provokes intrinsic tumor suppressor mechanisms including apoptosis, cellular senescence and DNA damage responses that act as barriers for tumor development and therefore needs to be overcome during tumorigenesis. Myc thus possesses two seemingly contradictory “faces” here referred to as “Yin and Yang”. Observations that many tumor suppressor pathways remain intact but are latent in tumor cells opens the possibility that pharmacological inhibition of the Yin or activation of the Yang functions can prevail and offer new attractive approaches for treating diverse types of cancer.     

Tumor suppressor functions of FBW7 in cancer development and progression

FBW7 (F-box and WD repeat domain-containing 7) has been characterized as an onco-suppressor protein in human cancers. Recent studies have also shown that FBW7 exerts its anti-tumor function primarily by promoting the degradation of various oncoproteins, through which FBW7 regulates cellular proliferation, differentiation and causes genetic instability. In this review, we will discuss the role of FBW7 downstream substrates and how dysregulation of Fbw7-mediated proteolysis of these substrates contributes to tumorigenesis. Additionally, we will also summarize the currently available various Fbw7-knockout mouse models that support Fbw7 as a tumor suppressor gene in the development and progression of human malignancies.     

Bone Morphogenetic Protein functions as a context-dependent angiogenic cue in vertebrates

Bone Morphogenetic Protein (BMP) signaling has been implicated in diverse biological processes. Although how BMP signaling regulates behaviors of endothelial cells during angiogenesis are not fully understood, increasing evidence indicate functions of BMP signaling components are essential in developmental and pathological angiogenesis. Here we review recent advances in delineating the functions of BMP signaling during angiogenesis. In addition, we discuss downstream pathways that transduce BMP signaling in endothelial cells, and factors that modulate BMP signaling response in endothelial cells. Finally, we provide recent insight on how BMP signaling functions as a context dependent angiogenic cue.     

Neural networks as robust tools in drug lead discovery and development

Empirical methods for building predictive models of the relationships between molecular structure and useful properties are becoming increasingly important. This has arisen because drug discovery and development have become more complex. A large amount of biological target information is becoming available though molecular biology. Automation of chemical synthesis and pharmacological screening has also provided a vast amount of experimental data. Tools for designing libraries and extracting information from molecular databases and high-throughput screening (HTS) experiments robustly and quickly enable leads to be discovered more effectively. As drug leads progress down the development pipeline, the ability to predict physicochemical, pharmacokinetic, and toxicological properties of these leads is becoming increasingly important in reducing the number of expensive, late-development failures. Neural network methods have much to offer in these areas. This review introduces the concepts behind neural networks applied to quantitative structure-activity relationships (QSARs), points out problems that may be encountered, suggests ways of avoiding the pitfalls, and introduces several exciting new neural network methods discovered during the last decade.     

Protein interaction networks

The study of protein interactions is playing an ever increasing role in our attempts to understand cells and diseases on a system-wide level. This article reviews several experimental approaches that are currently being used to measure protein-protein, protein-DNA and gene-gene interactions. These techniques have now been scaled up to produce extensive genome-wide data sets that are providing us with a first glimpse of global interaction networks. Complementing these experimental approaches, several computational methodologies to predict protein interactions are also reviewed. Existing databases that serve as repositories for protein interaction information and how such databases are used to analyze high-throughput data from a pathway perspective is also addressed. Finally, current efforts to combine multiple data types to obtain more accurate and comprehensive models of protein interactions are discussed. It is clear that the evolution of these experimental and computational approaches is rapidly changing our view of biology, and promises to provide us with an unprecedented ability to model cells and organisms at a system-wide level.     

Protein kinases and ovarian functions

The present focus survey represents a short review of current knowledge concerning involvement of protein kinases in control of basic ovarian functions. Ovarian cells produce a number of protein kinases, whose expression depends on type of cells, their state and action of hormones and other protein kinases. A number of protein kinases are involved in control of ovarian cell proliferation, apoptosis, oocyte maturation, hormone release, reception and response to hormones, as well as in mediating action of hormones on these ovarian functions. Complexity of interrelationships between different protein kinase‐dependent signaling pathways occurs. Protein kinases and their regulators could be used for characterization, prediction and control of ovarian folliculogenesis and atresia, Corpus luteum functions, oocyte maturation, fertility, release of hormones, response of ovarian structures to hormonal regulators, as well as for treatment of some reproductive disorders. The present data demonstrate importance of protein kinases in control of basic ovarian function and potential usage of protein kinases for characterization, prediction and control of these functions. J. Cell. Physiol. 226: 37–45, 2010. © 2010 Wiley‐Liss, Inc.     

Protein interaction networks in plants

Protein–protein interactions are fundamental to virtually every aspect of cellular functions. With the development of high-throughput technologies of both the yeast two-hybrid system and tandem mass spectrometry, genome-wide protein-linkage mapping has become a major objective in post-genomic research. While at least partial “interactome” networks of several model organisms are already available, in the plant field, progress in this respect is slow. However, even with comprehensive protein interaction data still missing, substantial recent advance in the graph-theoretical functional interpretation of complex network architectures might pave the way for novel approaches in plant research. This article reviews current progress and discussions in network biology. Emphasis is put on the question of what can be learned about protein functions and cellular processes by studying the topology of complex protein interaction networks and the evolutionary mechanisms underlying their development. Particularly the intermediate and local levels of network organization—the modules, motifs and cliques—are increasingly recognized as the operational units of biological functions. As demonstrated by some recent results from systematic analyses of plant protein families, protein interaction networks promise to be a valuable tool for a molecular understanding of functional specificities and for identifying novel regulatory components and pathways.     

Protein interaction networks in bacteria

The complement of expressed cellular proteins - the proteome - is organized into functional, structured networks of protein interactions that mediate assembly of molecular machines and dynamic cellular pathways. Recent studies reveal the biological roles of protein interactions in bacteriophage T7 and Helicobacter pylori, and new methods allow to compare and to predict interaction networks in other species. Smaller scale networks provide biological insights into DNA replication and chromosome dynamics in Bacillus subtilis and Archeoglobus fulgidus, and into the assembly of multiprotein complexes such as the type IV secretion system of Agrobacterium tumefaciens, and the cell division machinery of Escherichia coli. Genome-wide interaction networks in several species are needed to obtain a biologically meaningful view of the higher order organization of the proteome in bacteria.