Type III secreted effectors (T3SEs), such as Pseudomonas syringae HopZ1, are essential bacterial virulence proteins injected into the host cytosol to facilitate infection. However, few direct targets of T3SEs are known. Investigating the target(s) of HopZ1 in soybean, a natural P. syringae host, we find that HopZ1 physically interacts with the isoflavone biosynthesis enzyme, 2-hydroxyisoflavanone dehydratase (GmHID1). P. syringae infection induces gmhid1 expression and production of daidzein, a major soybean isoflavone. Silencing gmhid1 increases susceptibility to P. syringae infection, supporting a role for GmHID1 in innate immunity. P. syringae expressing active but not the catalytic mutant of HopZ1 inhibits daidzein induction and promotes bacterial multiplication in soybean. HopZ1-enhanced P. syringae multiplication is at least partially dependent on GmHID1. Thus, GmHID1 is a virulence target of HopZ1 to promote P. syringae infection of soybean. This work highlights the isoflavonoid biosynthesis pathway as an antibacterial defense mechanism and a direct T3SE target.

SummarySignaling networks downstream of receptor tyrosine kinases are among the most extensively studied biological networks, but new approaches are needed to elucidate causal relationships between network components and understand how such relationships are influenced by biological context and disease. Here, we investigate the context specificity of signaling networks within a causal conceptual framework using reverse-phase protein array time-course assays and network analysis approaches. We focus on a well-defined set of signaling proteins profiled under inhibition with five kinase inhibitors in 32 contexts: four breast cancer cell lines (MCF7, UACC812, BT20, and BT549) under eight stimulus conditions. The data, spanning multiple pathways and comprising ∼70,000 phosphoprotein and ∼260,000 protein measurements, provide a wealth of testable, context-specific hypotheses, several of which we experimentally validate. Furthermore, the data provide a unique resource for computational methods development, permitting empirical assessment of causal network learning in a complex, mammalian setting.

Breast cancer subtype-specific molecular variations can dramatically affect patient responses to existing therapies. It is thought that differentially phosphorylated protein isoforms might be a useful prognostic biomarker of drug response in the clinic. However, the accurate detection and quantitative analysis of cancer-related protein isoforms and phospho-isoforms in tumors are limited by current technologies. Using a novel, fully automated nanocapillary electrophoresis immunoassay (NanoPro TM 1000) designed to separate protein molecules based on their isoelectric point, we developed a reliable and highly sensitive assay for the detection and quantitation of AKT isoforms and phosphoforms in breast cancer. This assay enabled the measurement of activated AKT1/2/3 in breast cancer cells using protein produced from as few as 56 cells. Importantly, we were able to assign an identity for the phosphorylated S473 phosphoform of AKT1, the major form of activated AKT involved in multiple cancers, including breast, and a current focus in clinical trials for targeted intervention. The ability of our AKT assay to detect and measure AKT phosphorylation from very low amounts of total protein will allow the accurate evaluation of patient response to drugs targeting activated PI3K-AKT using scarce clinical specimens. Moreover, the capacity of this assay to detect and measure all three AKT isoforms using one single pan-specific antibody enables the study of the multiple and variable roles that these isoforms play in AKT tumorigenesis. Molecular & Cellular Proteomics

SummarySignaling networks downstream of receptor tyrosine kinases are among the most extensively studied biological networks. However, it remains unclear whether signaling networks depend on biological context. Signaling networks encode causal influences -and not just correlations -between network components. Here, using a causal framework and systematic time-course assays of signaling proteins, we investigate the context-specificity of signaling networks in a cell line system. We focus on a well-defined set of signaling proteins profiled in four breast cancer cell lines under eight stimulus conditions and inhibition of specific kinases. The data, spanning multiple pathways and comprising approximately 70,000 phosphoprotein and 260,000 protein measurements, provide a wealth of testable, context-specific hypotheses, several of which we validate in independent experiments. Furthermore, the data provide a resource for computational methods development, permitting empirical assessment of causal network learning in a complex, mammalian setting.

Our research efforts focus on the identification and detection of fundamental molecular differences between normal and tumor cells in breast, as well as differences among distinct breast cancer subtypes, especially in terms of signal transduction pathways that control cell cycle, apoptosis and cell growth. Cancer subtype specific molecular variations dramatically affect patient responses to already existing treatments. For example, the phosphorylation status of many proteins that are involved in signal transduction pathways perturbed in cancer cells is extremely important in determining whether these cells are susceptible to killing by available cancer therapeutics. Therefore, differentially phosphorylated protein isoforms can be a particularly useful prognostic biomarker of drug response in the clinic. However, accurate detection and quantitative analysis of cancer-related phosphoproteins in tumors is limited by current technologies.Using a novel, fully automated nanocapillary electrophoresis technology (FireFlyTM) designed to separate protein molecules based on their isoelectric point (pI), we are currently developing highly sensitive assays for reliable assessment of the phosphorylation status of cancer-related phosphoproteins in tumors, before and during drug treatment. We have already developed and optimized assays measuring AKT1, AKT2, AKT3, ERK1 and ERK2, and their respective phosphoisoforms. Using these assays, we were able to measure levels of activated ERK1/2 and AKT1/2/3 in a breast cancer cell line panel developed in our lab, using protein extracted from as few as 125 cells. Based on RNA expression data, cell lines in this panel have previously been categorized in two distinct subtypes (Basal and Luminal) and their molecular phenotypes closely resemble the respective profiles of tumors obtained from breast cancer patients (CITATION). This cell line panel is extensively used to measure cellular responses to breast cancer therapeutics, including drugs that target MEK, ERK, PI3K and AKT. Using FireFly assays, we are currently measuring changes in the phosphorylation states of these targets during drug treatment, in order to completely characterize pharmacodynamic changes in these cells during treatment, and develop molecular profiles that predict response in breast cancer tumors. Since this technology enables accurate detection and quantification of protein isoforms and post-translational modifications from only very small amounts of tumor samples or serum, it promises to propel cancer biomarker discovery and enable the development of clinically useful prognostic and diagnostic assays that predict responses to drugs targeting cancer-specific molecular networks. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3172.