Proteomics of breast cancer for marker discovery and signal pathway profiling

Breast cancer is the most common form of cancer among women and the identification of markers to discriminate tumorigenic from normal cells, as well as the different stages of this pathology, is of critical importance. Two-dimensional electrophoresis has been used before for studying breast cancer, but the progressive completion of human genomic sequencing and the introduction of mass spectrometry, combined with advanced bioinformatics for protein identification, have considerably increased the possibilities for characterizing new markers and therapeutic targets. Breast cancer proteomics has already identified markers of potential clinical interest (such as the molecular chaperone 14-3-3 sigma) and technological innovations such as large scale and high throughput analysis are now driving the field. Methods in functional proteomics have also been developed to study the intracellular signaling pathways that underlie the development of breast cancer. As illustrated with fibroblast growth factor-2, a mitogen and motogen factor for breast cancer cells, proteomics is a powerful approach to identify signaling proteins and to decipher the complex signaling circuitry involved in tumor growth. Together with genomics, proteomics is well on the way to molecularly characterizing the different types of breast tumor, and thus defining new therapeutic targets for future treatment.

GLUT4 - At the cross roads between membrane trafficking and signal transduction

GLUT4 is a mammalian facilitative glucose transporter that is highly expressed in adipose tissue and striated muscle. In response to insulin, GLUT4 moves from intracellular storage areas to the plasma membrane, thus increasing cellular glucose uptake. While the verification of this 'translocation hypothesis' (Cushman SW. Wardzala LJ. J Biol Chem 1980;255: 4758-4762 and Suzuki K, Kono T. Proc Natl Acad Sci 1980;77: 2542-2545) has increased our understanding of insulin-regulated glucose transport, a number of fundamental questions remain unanswered. Where is GLUT4 stored within the basal cell? How does GLUT4 move to the cell surface and what mechanism does insulin employ to accelerate this process) Ultimately we require a convergence of trafficking studies with research in signal transduction. However, despite more than 30 years of intensive research we have still not reached this point. The problem is complex, involving at least two separate signal transduction pathways which feed into what appears to be a very dynamic sorting process. Below we discuss some of these complexities and highlight new data that are bringing us closer to the resolution of these questions.

Role of peripheral afference during acquisition of a complex coordination task

It has long been supposed that the interference observed in certain patterns of coordination is mediated, at least in part, by peripheral afference from the moving limbs. We manipulated the level of afferent input, arising from movement of the opposite limb, during the acquisition of a complex coordination task. Participants learned to generate flexion and extension movements of the right wrist, of 75degrees amplitude, that were a quarter cycle out of phase with a 1-Hz sinusoidal visual reference signal. On separate trials, the left wrist either was at rest, or was moved passively by a torque motor through 50degrees, 75degrees or 100degrees, in synchrony with the reference signal. Five acquisition sessions were conducted on successive days. A retention session was conducted I week later. Performance was initially superior when the opposite limb was moved passively than when it was static. The amplitude and frequency of active movement were lower in the static condition than in the driven conditions and the variation in the relative phase relation across trials was greater than in the driven conditions. In addition, the variability of amplitude, frequency and the relative phase relation during each trial was greater when the opposite limb was static than when driven. Similar effects were expressed in electromyograms. The most marked and consistent differences in the accuracy and consistency of performance (defined in terms of relative phase) were between the static condition and the condition in which the left wrist was moved through 50degrees. These outcomes were exhibited most prominently during initial exposure to the task. Increases in task performance during the acquisition period, as assessed by a number of kinematic variables, were generally well described by power functions. In addition, the recruitment of extensor carpi radialis (ECR), and the degree of co-contraction of flexor carpi radialis and ECR, decreased during acquisition. Our results indicate that, in an appropriate task context, afferent feedback from the opposite limb, even when out of phase with the focal movement, may have a positive influence upon the stability of coordination.

Prediction of Golgi Type II membrane proteins based on their transmembrane domains

Motivation: A major issue in cell biology today is how distinct intracellular regions of the cell, like the Golgi Apparatus, maintain their unique composition of proteins and lipids. The cell differentially separates Golgi resident proteins from proteins that move through the organelle to other subcellular destinations. We set out to determine if we could distinguish these two types of transmembrane proteins using computational approaches. Results: A new method has been developed to predict Golgi membrane proteins based on their transmembrane domains. To establish the prediction procedure, we took the hydrophobicity values and frequencies of different residues within the transmembrane domains into consideration. A simple linear discriminant function was developed with a small number of parameters derived from a dataset of Type II transmembrane proteins of known localization. This can discriminate between proteins destined for Golgi apparatus or other locations (post-Golgi) with a success rate of 89.3% or 85.2%, respectively on our redundancy-reduced data sets.

Computational differentiation of N-terminal signal peptides and transmembrane helices

Signal peptides and transmembrane helices both contain a stretch of hydrophobic amino acids. This common feature makes it difficult for signal peptide and transmembrane helix predictors to correctly assign identity to stretches of hydrophobic residues near the N-terminal methionine of a protein sequence. The inability to reliably distinguish between N-terminal transmembrane helix and signal peptide is an error with serious consequences for the prediction of protein secretory status or transmembrane topology. In this study, we report a new method for differentiating protein N-terminal signal peptides and transmembrane helices. Based on the sequence features extracted from hydrophobic regions (amino acid frequency, hydrophobicity, and the start position), we set up discriminant functions and examined them on non-redundant datasets with jackknife tests. This method can incorporate other signal peptide prediction methods and achieve higher prediction accuracy. For Gram-negative bacterial proteins, 95.7% of N-terminal signal peptides and transmembrane helices can be correctly predicted (coefficient 0.90). Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 99% (coefficient 0.92). For eukaryotic proteins, 94.2% of N-terminal signal peptides and transmembrane helices can be correctly predicted with coefficient 0.83. Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 87% (coefficient 0.85). The method can be used to complement current transmembrane protein prediction and signal peptide prediction methods to improve their prediction accuracies. (C) 2003 Elsevier Inc. All rights reserved.