Background: Relapse due to chemoresistant residual disease is a major cause of death in acute myelogenous leukemia (AML). The present study was undertaken to elucidate the molecular mechanisms of chemoresistance by comparing differential gene expression in blasts from patients with resistant relapsing AML and chemosensitive AML.

The 14-3-3 family of proteins was originally identified in 1967 as simply an abundant brain protein. However it took almost 25 years before the ubiquitous role of 14-3-3 in cell biology was recognized when it was found to interact with several signalling and proto-oncogene proteins. Subsequently 14-3-3 proteins were the first protein recognized to bind a discrete phosphoserine/threonine-binding motifs. In mammals the 14-3-3 protein family is comprised of seven homologous isoforms. The 14-3-3 family members are expressed in all eukaryotes and although no single conserved function of the 14-3-3s is apparent, their ability to bind other proteins seems a crucial characteristic. To date more than 300 binding partners have been identified, of which most are phosphoproteins. Consequently, it has become clear that 14-3-3 proteins are involved in the regulation of most cellular processes, including several metabolic pathways, redox-regulation, transcription, RNA processing, protein synthesis, protein folding and degradation, cell cycle, cytoskeletal organization and cellular trafficking. In this review we include recent reports on the regulation of 14-3-3 by phosphorylation, and discuss the possible functional significance of the existence of distinct 14-3-3 isoforms in light of recent proteomics studies. In addition we discuss 14-3-3 interaction as a possible drug target.

Cellular signaling lies at the core of cellular behavior, and is central for the understanding of many pathologic conditions. To comprehend how signal transduction is orchestrated at the molecular level remains the ultimate challenge for cell biology. In the last years there has been a revolution in the development of high-throughput methodologies in proteomics and genomics, which have provided extensive knowledge about expression profiles and molecular interaction-networks. However, these methods have typically provided qualitative and static information. This is about to turn, and several high-throughput methods are now available that provide quantitative and temporal information. These data are well suited for analysis by computational methods and bioinformatics, which are becoming increasingly valuable tools to grasp the complexity of cellular networks. At present, several cellular pathways have been modeled in silico and the analysis provides new understanding of the underlying properties that contribute to their dynamic features. Here, we review methodologies that are used for in silico modeling as well as methods to obtain large-scale quantitative data, and discuss how they can be integrated to generate powerful and predictive models of cellular processes. We argue that the generation of such models provide powerful tools to understand how systems properties emerges in healthy and pathologic states, and to generate efficient strategies for pharmacological intervention.