Journal of Proteome Research (version 2.2.2) was used to assemble the set of peptides identiﬁed into a minimal list of proteins that could explain the observed spectral data set.21,22 A minimum of two peptides per protein were required for valid protein identiﬁcation with a peptide false discovery rate (FDR) of 5%. Proteins that could not be distin- guished due to fully shared peptide sequences were grouped together (protein groups). Statistically signiﬁcant diﬀerences in protein spectral counts between diﬀerent groups (i.e., SW480Null versus SW480APC) were calculated using the QuasiTel algorithm.23 QuasiTel is a statistical analysis package which uses quasi-likelihood modeling to compare diﬀerences in spectral count data between two groups. The algorithm assigns a log-transformed rate ratio indicating the magnitude of the observed diﬀerence in spectral counts, and a probability of observing such diﬀerential levels for each protein corrected for multiple comparisons. Complete results from QuasiTel outputs and corresponding summaries from IDpicker reports are provided as Supplementary Table S1, Supporting Information. LC MRM-MS Cell line samples for LC MRM-MS were prepared as out- lined above for LC MS/MS proteomics, except peptide extracts were not subjected to further fractionation by IEF. Peptide samples from each cell line were resuspended in 0.1% formic acid at 0.25 μg/μL and analyzed in triplicate (2 μL injection volume) on a TSQ Vantage triple quadrupole mass spectrometer (Thermo-Fisher, San Jose, CA) equipped with an Eksigent nanoLC solvent delivery system, autosampler and a nanospray source. The mobile phase consists of 0.1% formic acid in either HPLC grade water (A) or 90% acetonitrile (B). A 80-min gradient was carried out with a 15-min washing period (100% A). Following the washing period, the gradient was increased to 60% B by 43 min, followed by an increase to 95% B by 49 min and held for 11 min before returning 97% A. Four optimized tran- sitions for each peptide of corresponding proteins were selected using the Skyline software package24 (see Table S2 in Supporting Information for a list of peptides and corresponding precursor and product m/z values). Instrument parameters include Q2 gas 1.5 mTorr, scan width 0.004 m/z, scan time10 ms, and both Q1 and Q3 resolution fwhm 0.7. A stable isotope labeled version of the β-actin peptide GYSFTTTAER was used as an internal standard (60 fmol/injection) for relative quantiﬁcation of target proteins by the Labeled Reference Peptide (LRP) method.25 The integrated chromatographic peak areas for the transitions of each targeted peptide were obtained from Skyline,24 and summed, normalized to summed peak areas for the β-actin internal stan- dard, and multiplied by a factor of 106, as we described in detail elsewhere.25 An unpaired t test was used to test for signiﬁcant diﬀerences between samples (n = 3). Microarray Transcriptomic Analysis For total RNA isolation cells were washed once in 1X PBS, lysed in TRIzol, and homogenized. Nucleic acids were isolated by chloroform extraction, and RNA was precipitated with isopro- panol and puriﬁed by cesium chloride (CsCl) (5.7M) gradient centrifugation. Digoxigenin-UTP labeled cRNA was generated and linearly ampliﬁed from 1 μg of total RNA using Applied Biosystems Chemiluminescent RT-IVT Labeling Kit. cRNA was fragmented and hybridized to the Applied Biosystems Human Genome Survey Microarray V2.0 (29,098 genes) and imaged on an AB1700 chemilumiescent microarray analyzer (Applied Biosys- tems, Foster City, CA). We compared the two groups to identify ARTICLE genes that were diﬀerentially expressed between them. First, expression data were normalized using quartile normalization and then log transformed. Before log transformation, we increased all expression value by a base number to make the lowest expression level equals 1. The normalized and log transformed data was analyzed using the Limma package in Bioconductor.26 Speciﬁcally, we used Limma to ﬁt a linear model to the data using an empirical bayes method to moderate standard errors. Expression data were normalized using quartile normalization and then log transformed. Before log transformation, we increased all expression value by a base number to make the lowest expression level equal to 1. Hierarchical Clustering Analysis We performed a coclustering analysis integrating both mRNA and protein measurements for proteins that showed an adjusted p-value of less than 0.1 (QuasiTel analysis) and also had mRNA expression data. Before integration, protein and mRNA expres- sion data sets were standardized protein/gene-wise separately so that each protein/gene had a mean expression value of 0 and standard deviation of 1 across samples in the data set. On the basis of the integrated data, pairwise similarity was calculated using the Pearson’s correlation coeﬃcient. Average linkage was used for hierarchical clustering. Genes and samples were clus- tered separately. Webgestalt Enrichment Analysis of Differentially Expressed Proteins Enrichment analysis of proteomic data was carried out using Webgestalt.27,28 Webgestalt identiﬁes enriched classes of genes/ proteins in large scale data sets by searching from several public resources: NCBI Gene, NCBI Gene Expression Omnibus (GEO) Ensembl, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Pathway Commons, Wikipathways, Molecular Signatures Database (MSigDB). A set of 155 dif- ferentially expressed proteins (<0.1 adjusted quasi p-value) or 26 discordant proteins/transcripts (proteins with <0.1 adjusted p-value showing opposite expression trends with microarray data) were analyzed by Webgestalt using the entire set proteins observed (5002 proteins) as a reference set. Statistical signi- ﬁcance was calculated using the Fisher test with Benjamini- Hochberg correction for multiple testing. Each enriched category is assigned an adjusted p-value (listed in Results along with each enriched class) and an enrichment factor (ratio of observed to expected for given enrichment class) (see Table 2). ’ RESULTS Description of LC MS/MS Shotgun Proteomics Very few large scale global proteomic studies have examined the consequences of a single genetic mutation on the proteome and it is unclear if such studies are capable of detecting the eﬀects of such small perturbations of the cellular system.29,30 Previous studies relied on 2-D gel electrophoresis for identiﬁcation of diﬀerentially expressed proteins, thus restricting proteome cover- age to less than 1000 proteins.29,30 Label free shotgun proteomics may provide more a comprehensive approach to detection of the subtle eﬀects of a single gene change. Indeed, proteomic eﬀects may be limited to a discrete number of signaling networks related to the gene of interest, or there may be more widespread changes as a result of secondary events such as transcription factor activation, altered metabolism or growth rates. Furthermore, the proteomic consequences of a single gene mutation may diﬀer from changes in the gene expression proﬁle. Due to diﬀerences in 1186 dx.doi.org/10.1021/pr2009109 |J. Proteome Res. 2012, 11, 1184–1195