and converted to mzXML using IP Framework (www. proteomecommons.org). The mzXML files were searched using SEQUEST against the Human IPI database version 3.24 appended with an equal number of decoy sequences (reversed sequences from the original database). The following search parameters were selected: 0.5 da precursor mass tolerance, monoisotopic mass, semi-tryptic search with two or fewer missed cleavages, oxidized methionine, deamidation (Gln, Asn), iTRAQ label on Tyr, iTRAQ label on Lys and at the peptide N-terminus, and thiomethyl cysteine were specified as variable modifications. In total, 8580 SEQUEST search results (all from singly-charged spectra) were obtained and further processed using PeptideProphet and ProteinProphet, leading to the identification of 3686 peptides mapping to 904 proteins. Of these, 3550 peptides were quantified with post-data normalization, which mapped to 879 proteins. The estimated FDR using target-decoy strategy was below 0.5% [13]. Statistical analysis of differential protein expression iTRAQ analysis. Double-duplex iTRAQ experiment consisting of duplicates of vehicle- and R1881-treated samples resulted in the identification of 3686 peptides as shown above. Quantitative information for each peptide identified was extracted from the MS/MS spectrum using Libra, available as a part of the Trans-Proteomic Pipeline. Based on the distribution pattern of peak areas of tags 114, 115, 116 and 117 for all the peptides, a median centered normalization was applied at the peptide level to achieve a mean close to unity. Peptides with no value for peak area (peak intensity below a default threshold in Libra) in any of the tagged channels were removed. Outlier peptides that had iTRAQ values three standard deviations (SD) away from the population median were considered abnormal and were removed from controls (114 vs. 116) or treatments (115 vs. 117). This procedure resulted in a final set of 3550 peptides mapping to 879 proteins. The relative protein expression ratios were then determined with respect to the first control sample (tag 114). Intensities of peaks at 115, 116 and 117 were divided by the 114 peak intensity, and the ratios of all peptides corresponding to the same proteins were averaged. The resulting protein ratios were again normalized by their population median. The standard deviation of the control replicate (tag 116, SD = 0.16) was used as a scale to set the threshold ratio for differentially expressed proteins. A minimum threshold of 1.25 SD (ratio of 1.2 for up-regulated and 0.83 for down-regulated) from the global mean in both androgen-treated samples and not less than 1.5 SD in one of the androgen treated samples (1.25 and 0.8 for up- and down-regulated, respectively) were considered as androgen regulated. MudPIT analysis. Proteins identified in the two separate mass spectrometry runs (control vs. androgen treated) were combined into a single list, and ambiguities resulting from protein isoforms and multiple accession numbers [34] were resolved using in-house software. For each protein in the combined list (or protein group with multiple accession numbers), the total number of MS/MS spectra, which were assigned to peptides was calculated. The spectrum count measures for each protein were normalized. The normalization was done to account for small differences in the number of identified peptides in each analysis. Normalization factor was taken as the total number of spectra observed in each experiment that were assigned a peptide, from a protein identified in both experiments with high probability. For proteins that were identified in both experiments, the ratio of normalized spectrum counts was calculated and then log-transformed. The resulting distribution was fitted using a robust Gaussian distribution fitting procedure with outlier removal (10% of the data), and the mean and standard deviation (SD) were Proteomics of Prostate Cancer determined. Two-SD threshold was applied to derive the list of differentially expressed proteins. Consequently, the final established requirement was that, if the protein were identified by a single MS/MS spectrum in one of the experiments, it should be identified by four or more spectra in the other experiment. Similarly, a threshold of four or more spectra was applied to proteins identified in one of the runs only. Western. Proteins for immunoblotting were resolved by 4– 12% NUPAGE gels (Invitrogen) and transferred to PVDF membranes (GE Healthcare Bio-Sciences Corp., Piscataway, NJ). Membranes were then blocked with 5% skimmed milk in TBS-T (20 mM Tris.Cl, pH 7.4, 150 mM NaCl, 0.1 % Tween 20) over-night. Antibodies (indicated in ‘Materials’) were added in TBS-T containing 2.5% skimmed milk and the blots were washed with TBST (TBS +0.1% Tween 20). Immunoblot signals were developed using enhanced chemiluminescence reagent (ECL plus, GE Healthcare Bio-Sciences Corp., Piscataway, NJ). Western band intensity ratios were calculated using the ‘ImageJ’ software version 1.38 (rsb.info.nih.gov/ij/) Immunofluorescence and confocal microscopy. Control and R1881 treated cells were fixed on glass slides. Slides were heated in citrate buffer (pH 6.0) for 15 minutes in a pressure cooker. The slides were then blocked in PBS-T with 5% normal donkey serum for 1 hour. A mixture of rabbit anti-AR antibody (Labvision, Fremont, CA) and mouse anti-FASN antibody (BD Transduction Labs, Franklin Lakes, NJ) was added to the slides and incubated overnight at 4uC. Slides were then incubated with secondary antibodies (anti-rabbit Alexa 555 and anti-mouse Alexa-486 (Invitrogen) at 1:1000 dilution for 1 hour. They were washed and then mounted using Vectashield mounting medium. Confocal images were taken with a Ziess LSM510 META imaging system using Argon and Helium Neon 1 and Helium Neon 2 light sources (Carl Zeiss, Thornwood, NY). The wavelengths used were 486 nm and 555 nm for Alexa-486 and Alexa-555, respectively. Color images were exported as TIFF images. Oncomine and Molecular Concepts Mapping analyses. Methods used to identify gene signatures in the Molecular Concepts Mapping (MCM) tool are described elsewhere [23]. A‘‘molecular concept’’ is defined as a set of genes or proteins specific to a biological process or a regulatory mechanism or a particular disease represented by previously well characterized data sets. Currently, MCM has a total of 13364 such concepts consisting of 17 types that are derived from 13 sources including the prostate cancer gene expression signatures from Oncomine database [26]. For our analysis we used all concept types except the ‘Connectivity map’, and ‘Oncomine clusters’ [26]. All of our protein data sets were loaded into Oncomine, and analyzed through the Oncomine linked MCM [23,26]. MCM computes Fisher’s exact test for each pair of molecular concepts for association. All results that had an odds ratio greater than 1.25 and p-value of less than 0.01 were stored. Network maps were obtained between the selected enriched concepts in our protein sets and visualized using the MCM. From Oncomine ‘Cancer Signature’ type concepts, only the top 1%, 5% or 10% gene set concepts were included in our networks. For network analysis, we selected the most significant concept, at least one most significant concept from each concept type that was associated, and any concept that showed relevance to prostate cancer or androgen treatment. Meta-analysis of our proteomics data and the prostate cancer tissue microarray data was performed using MCM [26], and the heat map was visualized with the genes ranked based on their P- values. Three data sets used for the heat map were: 1) Lappointe (normal prostate, 41 samples vs. prostate cancer, 62 samples); 2) Welsh (normal prostate, 9 samples vs. prostate cancer, 25 samples); 3) LaTulippe (non-neoplastic prostate, 3 samples vs. prostate carconima, 23 samples). PLoS ONE | www.plosone.org 10 September 2009 | Volume 4 | Issue 9 | e7075