Quantitative assessment of the p53-Mdm2 feedback loop using protein lysate microarrays.

Mathematical simulations of the p53-Mdm2 feedback loop suggest that both proteins will exhibit impulsive expression characteristics in response to high cellular stress levels. However, little quantitative experimental evaluation has been done, particularly of the phosphorylated forms. To evaluate the mathematical models experimentally, we used lysate microarrays from an isogenic pair of gamma-ray-irradiated cell lysates from HCT116 (p53(+/+) and p53(-/-)). Both p53 and Mdm2 proteins showed expected pulses in the wild type, whereas no pulses were seen in the knockout. Based on experimental observations, we determined model parameters and generated an in silico "knockout," reflecting the experimental data, including phosphorylated proteins.

Protein and lysate array technologies in cancer research.

Capturing quantitative proteomic information provides new insights for enhancing the understanding of cancer biology. There have been several protein microarray formats, and each has an advantage depending on what is being detected. However, in contrast to nucleotide printing, the production of protein arrays generally requires the capability of handling viscous solutions, and the mishandling of various factors, such as temperature and humidity, adversely affect protein status. The requirement for such specifications is critical when increasing the throughput for monitoring a large number of samples for rigorous quantitation. In particular, a new solid pin arrayer has been extremely powerful when highly viscous cell lysates printed for high-density, "reverse-phase" protein arrays, and acquired data allows for theoretical models of protein signaling networks to be constructed. In this review, applications of currently available protein microarray technology to cancer research are discussed including the advantages of the new solid pin architecture for opening up powerful proteomic applications.

[Application of quantitative proteomic analysis for cancer therapy using "reverse-phase" protein lysate microarrays].

Proteomic analysis using quantitative high-throughput technology can provide new insights in cancer therapeutics. It can reveal how cancer cells respond to given therapies by measuring multiple dimensions of information, from which dynamic proteomic responses can be observed. A lack of high throughput proteomic technologies has previously limited such multi-dimensional approaches. We have developed a high-throughput, "reverse-phase" protein microarray system which can handle more than 20,000 lysate features on a single glass slide. Subsequent immunochemical detection methods allow us to monitor protein expression in a quantitative manner as a function of both time and drug dosage. The data generated using this RPA technology has proved to be an excellent reference for theoretical protein network modeling in vitro. Clinical evaluation of drug efficacy based on the data generated by this technology may provide a means to accurately predict the effectiveness of cancer therapies.

Quantitative protein network monitoring in response to DNA damage.

Conventional molecular biology techniques have identified a large number of cell signaling pathways; however, the importance of these pathways often varies, depending on factors such as treatment type, dose, time after treatment, and cell type. Here, we describe a technique using "reverse-phase" protein lysate microarrays (RPAs) to acquire multiple dimensions of information on protein dynamics in response to DNA damage. Whole-cell lysates from three cellular stress treatments (IR, UV, and ADR) were collected at four doses per treatment, and each, in turn, at 10 time points, resulting in a single-slide RPA consisting of 10,240 features, including replicates. The dynamic molecular profile of 18 unique protein species was compared to phenotypic fate by FACS analysis for corresponding stress conditions. Our initial quantitative results in this new platform confirmed that (1) there is clear stress dose-response effect in p53 protein and (2) a comparison of the rates of increase of p21 and Cyclin D3/p53-Ser15 in response to DNA damage may be associated with the pattern of DNA content. This method, offering a quantitative time-course monitoring of protein expression levels, can provide an experimental reference for developing mathematical models of cell signaling dynamics. Although the present study focuses on the DNA damage-repair pathway, the technique is generally useful to the study of protein signaling.