RNAi-based validation of antibodies for reverse phase protein arrays

<p>Abstract</p> <p>Background</p> <p>Reverse phase protein arrays (RPPA) have been demonstrated to be a useful experimental platform for quantitative protein profiling in a high-throughput format. Target protein detection relies on the readout obtained from a single detection antibody. For this reason, antibody specificity is a key factor for RPPA. RNAi allows the specific knockdown of a target protein in complex samples and was therefore examined for its utility to assess antibody performance for RPPA applications.</p> <p>Results</p> <p>To proof the feasibility of our strategy, two different anti-EGFR antibodies were compared by RPPA. Both detected the knockdown of EGFR but at a different rate. Western blot data were used to identify the most reliable antibody. The RNAi approach was also used to characterize commercial anti-STAT3 antibodies. Out of ten tested anti-STAT3 antibodies, four antibodies detected the STAT3-knockdown at 80-85%, and the most sensitive anti-STAT3 antibody was identified by comparing detection limits. Thus, the use of RNAi for RPPA antibody validation was demonstrated to be a stringent approach to identify highly specific and highly sensitive antibodies. Furthermore, the RNAi/RPPA strategy is also useful for the validation of isoform-specific antibodies as shown for the identification of AKT1/AKT2 and CCND1/CCND3-specific antibodies.</p> <p>Conclusions</p> <p>RNAi is a valuable tool for the identification of very specific and highly sensitive antibodies, and is therefore especially useful for the validation of RPPA-suitable detection antibodies. On the other hand, when a set of well-characterized RPPA-antibodies is available, large-scale RNAi experiments analyzed by RPPA might deliver useful information for network reconstruction.</p

Evaluation of reverse phase protein array (RPPA)-based pathway-activation profiling in 84 non-small cell lung cancer (NSCLC) cell lines as platform for cancer proteomics and biomarker discovery

AbstractThe reverse phase protein array (RPPA) approach was employed for a quantitative analysis of 71 cancer-relevant proteins and phosphoproteins in 84 non-small cell lung cancer (NSCLC) cell lines and by monitoring the activation state of selected receptor tyrosine kinases, PI3K/AKT and MEK/ERK1/2 signaling, cell cycle control, apoptosis, and DNA damage. Additional information on NSCLC cell lines such as that of transcriptomic data, genomic aberrations, and drug sensitivity was analyzed in the context of proteomic data using supervised and non-supervised approaches for data analysis. First, the unsupervised analysis of proteomic data indicated that proteins clustering closely together reflect well-known signaling modules, e.g. PI3K/AKT- and RAS/RAF/ERK-signaling, cell cycle regulation, and apoptosis. However, mutations of EGFR, ERBB2, RAF, RAS, TP53, and PI3K were found dispersed across different signaling pathway clusters. Merely cell lines with an amplification of EGFR and/or ERBB2 clustered closely together on the proteomic, but not on the transcriptomic level. Secondly, supervised data analysis revealed that sensitivity towards anti-EGFR drugs generally correlated better with high level EGFR phosphorylation than with EGFR abundance itself. High level phosphorylation of RB and high abundance of AURKA were identified as candidates that can potentially predict sensitivity towards the aurora kinase inhibitor VX680. Examples shown demonstrate that the RPPA approach presents a useful platform for targeted proteomics with high potential for biomarker discovery. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge

TMPRSS2-ERG -specific transcriptional modulation is associated with prostate cancer biomarkers and TGF-β signaling

<p>Abstract</p> <p>Background</p> <p><it>TMPRSS2-ERG </it>gene fusions occur in about 50% of all prostate cancer cases and represent promising markers for molecular subtyping. Although <it>TMPRSS2-ERG </it>fusion seems to be a critical event in prostate cancer, the precise functional role in cancer development and progression is still unclear.</p> <p>Methods</p> <p>We studied large-scale gene expression profiles in 47 prostate tumor tissue samples and in 48 normal prostate tissue samples taken from the non-suspect area of clinical low-risk tumors using Affymetrix GeneChip Exon 1.0 ST microarrays.</p> <p>Results</p> <p>Comparison of gene expression levels among <it>TMPRSS2-ERG </it>fusion-positive and negative tumors as well as benign samples demonstrated a distinct transcriptional program induced by the gene fusion event. Well-known biomarkers for prostate cancer detection like <it>CRISP3 </it>were found to be associated with the gene fusion status. WNT and TGF-β/BMP signaling pathways were significantly associated with genes upregulated in <it>TMPRSS2-ERG </it>fusion-positive tumors.</p> <p>Conclusions</p> <p>The <it>TMPRSS2-ERG </it>gene fusion results in the modulation of transcriptional patterns and cellular pathways with potential consequences for prostate cancer progression. Well-known biomarkers for prostate cancer detection were found to be associated with the gene fusion. Our results suggest that the fusion status should be considered in retrospective and future studies to assess biomarkers for prostate cancer detection, progression and targeted therapy.</p

A pro-inflammatory signalome is constitutively activated by C33Y mutant TNF receptor 1 in TNF receptor-associated periodic syndrome (TRAPS)

Mutations in TNFRSF1A encoding TNF receptor 1 (TNFR1) cause the autosomal dominant TNF receptor-associated periodic syndrome (TRAPS): a systemic autoinflammatory disorder. Misfolding, intracellular aggregation, and ligand-independent signaling by mutant TNFR1 are central to disease pathophysiology. Our aim was to understand the extent of signaling pathway perturbation in TRAPS. A prototypic mutant TNFR1 (C33Y), and wild-type TNFR1 (WT), were expressed at near physiological levels in an SK-Hep-1 cell model. TNFR1-associated signaling pathway intermediates were examined in this model, and in PBMCs from C33Y TRAPS patients and healthy controls. In C33Y-TNFR1-expressing SK-Hep-1 cells and TRAPS patients' PBMCs, a subtle, constitutive upregulation of a wide spectrum of signaling intermediates and their phosphorylated forms was observed; these were associated with a proinflammatory/antiapoptotic phenotype. In TRAPS patients' PBMCs, this upregulation of proinflammatory signaling pathways was observed irrespective of concurrent treatment with glucocorticoids, anakinra or etanercept, and the absence of overt clinical symptoms at the time that the blood samples were taken. This study reveals the pleiotropic effect of a TRAPS-associated mutant form of TNFR1 on inflammatory signaling pathways (a proinflammatory signalome), which is consistent with the variable and limited efficacy of cytokine-blocking therapies in TRAPS. It highlights new potential target pathways for therapeutic intervention

Characterizing Protein Interactions Employing a Genome-Wide siRNA Cellular Phenotyping Screen

<div><p>Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the effect of this interaction. Here, we present a novel computational development that employs mitotic phenotypes of a genome-wide RNAi knockdown screen and enables identifying the activating and inhibiting effects of PPIs. Exemplarily, we applied our technique to a knockdown screen of HeLa cells cultivated at standard conditions. Using a machine learning approach, we obtained high accuracy (82% AUC of the receiver operating characteristics) by cross-validation using 6,870 known activating and inhibiting PPIs as gold standard. We predicted <i>de novo</i> unknown activating and inhibiting effects for 1,954 PPIs in HeLa cells covering the ten major signaling pathways of the Kyoto Encyclopedia of Genes and Genomes, and made these predictions publicly available in a database. We finally demonstrate that the predicted effects can be used to cluster knockdown genes of similar biological processes in coherent subgroups. The characterization of the activating or inhibiting effect of individual PPIs opens up new perspectives for the interpretation of large datasets of PPIs and thus considerably increases the value of PPIs as an integrated resource for studying the detailed function of signaling pathways of the cellular system of interest.</p></div

Peroxiredoxins 3 and 4 Are Overexpressed in Prostate Cancer Tissue and Affect the Proliferation of Prostate Cancer Cells in Vitro

The present study aimed to investigate the proteome profiling of surgically treated prostate cancers. Hereto, 2D-DIGE and mass spectrometry were performed for protein identification, and data validation for peroxiredoxin 3 and 4 (PRDX3 and PRDX4) was accomplished by reverse phase protein arrays (RPPA). The Formal Concept Analysis (FCA) method was applied to assess whether the TMPRSS2-ERG gene fusion could influence the degree of overexpression of PRDX3 and PRDX4 in prostate cancer. Lastly, we performed an <i>in vitro</i> functional characterization of both PRDX3 and PRDX4 using the classical human prostate cancer cell lines DU145 and LNCaP. Reverse phase protein arrays verified that the overexpression of both PRDX3 and PRDX4 in tumor samples is negatively correlated with the presence of the TMPRSS2-ERG gene fusion. Functional characterization of PRDX3 and PRDX4 activity in PCa cell lines suggests a role of these members of the peroxiredoxin family in the pathophysiology of this tumor entity

Characterization of phenotypic similarity by linear discrimination.

<p>(<b>a-c</b>) Images of cells in which <i>sfrp1, dvl2</i> or <i>fzd7</i> were knocked down, respectively. (<b>d</b>) First two principal components (PC 1 and PC 2) of the features for cells with knockdown of <i>sfrp1</i> and <i>dvl2</i>. (<b>e</b>) First two principal components of the features for cells with knockdown of <i>dvl2</i> and <i>fzd7</i>. Dotted lines sketch a linear separation.</p

Pairs of Pfam domain sets showing significant^* enrichment of predicted interactions.

<p>^*p≤0.1 only; t≥1 for both pos/neg - no zeroes.</p><p>Pairs of Pfam domain sets showing significant^{<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003814#nt101" target="_blank">*</a>} enrichment of predicted interactions.</p

Workflow.

<p>Images of a genome-wide cellular RNAi knockdown screen (the screening data was derived from the Mitocheck project, <a href="http://www.mitocheck.org" target="_blank">www.mitocheck.org</a>) were segmented and their features extracted to compile pairwise phenotype descriptors for a large set of gene pairs. These descriptors were used to train a machine learning system to discriminate activating and inhibiting PPIs taken from a reference. The performance was evaluated using cross-validation. The trained SVM models were used to predict the effects of uncharacterized PPIs. In addition, the SVM models were used to estimate similarity of the effects of proteins for all combinations of protein pairs in the network. Subsequently, this Effect Similarity Rate (ESR) was exemplarily used for clustering of functionally related protein sub-networks.</p