Patrick J. Halvey

IDPicker 2.0: Improved protein assembly with high discrimination peptide identification filtering.

Tandem mass spectrometry-based shotgun proteomics has become a widespread technology for analyzing complex protein mixtures. A number of database searching algorithms have been developed to assign peptide sequences to tandem mass spectra. Assembling the peptide identifications to proteins, however, is a challenging issue because many peptides are shared among multiple proteins. IDPicker is an open-source protein assembly tool that derives a minimum protein list from peptide identifications filtered to a specified False Discovery Rate. Here, we update IDPicker to increase confident peptide identifications by combining multiple scores produced by database search tools. By segregating peptide identifications for thresholding using both the precursor charge state and the number of tryptic termini, IDPicker retrieves more peptides for protein assembly. The new version is more robust against false positive proteins, especially in searches using multispecies databases, by requiring additional novel peptides in the parsimony process. IDPicker has been designed for incorporation in many identification workflows by the addition of a graphical user interface and the ability to read identifications from the pepXML format. These advances position IDPicker for high peptide discrimination and reliable protein assembly in large-scale proteomics studies. The source code and binaries for the latest version of IDPicker are available from http://fenchurch.mc.vanderbilt.edu/ .

Proteomic analysis of exosomes from mutant KRAS colon cancer cells identifies intercellular transfer of mutant KRAS

Activating mutations in KRAS occur in 30% to 40% of colorectal cancers. How mutant KRAS alters cancer cell behavior has been studied intensively, but non-cell autonomous effects of mutant KRAS are less understood. We recently reported that exosomes isolated from mutant KRAS-expressing colon cancer cells enhanced the invasiveness of recipient cells relative to exosomes purified from wild-type KRAS-expressing cells, leading us to hypothesize mutant KRAS might affect neighboring and distant cells by regulating exosome composition and behavior. Herein, we show the results of a comprehensive proteomic analysis of exosomes from parental DLD-1 cells that contain both wild-type and G13D mutant KRAS alleles and isogenically matched derivative cell lines, DKO-1 (mutant KRAS allele only) and DKs-8 (wild-type KRAS allele only). Mutant KRAS status dramatically affects the composition of the exosome proteome. Exosomes from mutant KRAS cells contain many tumor-promoting proteins, including KRAS, EGFR, SRC family kinases, and integrins. DKs-8 cells internalize DKO-1 exosomes, and, notably, DKO-1 exosomes transfer mutant KRAS to DKs-8 cells, leading to enhanced three-dimensional growth of these wild-type KRAS-expressing non-transformed cells. These results have important implications for non-cell autonomous effects of mutant KRAS, such as field effect and tumor progression.

Protein identification using customized protein sequence databases derived from RNA-Seq data

The standard shotgun proteomics data analysis strategy relies on searching MS/MS spectra against a context-independent protein sequence database derived from the complete genome sequence of an organism. Because transcriptome sequence analysis (RNA-Seq) promises an unbiased and comprehensive picture of the transcriptome, we reason that a sample-specific protein database derived from RNA-Seq data can better approximate the real protein pool in the sample and thus improve protein identification. In this study, we have developed a two-step strategy for building sample-specific protein databases from RNA-Seq data. First, the database size is reduced by eliminating unexpressed or lowly expressed genes according to transcript quantification. Second, high-quality nonsynonymous coding single nucleotide variations (SNVs) are identified based on RNA-Seq data, and corresponding protein variants are added to the database. Using RNA-Seq and shotgun proteomics data from two colorectal cancer cell lines SW480 and RKO, we demonstrated that customized protein sequence databases could significantly increase the sensitivity of peptide identification, reduce ambiguity in protein assembly, and enable the detection of known and novel peptide variants. Thus, sample-specific databases from RNA-Seq data can enable more sensitive and comprehensive protein discovery in shotgun proteomics studies.

A bioinformatics workflow for variant peptide detection in shotgun proteomics

Shotgun proteomics data analysis usually relies on database search. However, commonly used protein sequence databases do not contain information on protein variants and thus prevent variant peptides and proteins from been identified. Including known coding variations into protein sequence databases could help alleviate this problem. Based on our recently published human Cancer Proteome Variation Database, we have created a protein sequence database that comprehensively annotates thousands of cancer-related coding variants collected in the Cancer Proteome Variation Database as well as noncancer-specific ones from the Single Nucleotide Polymorphism Database (dbSNP). Using this database, we then developed a data analysis workflow for variant peptide identification in shotgun proteomics. The high risk of false positive variant identifications was addressed by a modified false discovery rate estimation method. Analysis of colorectal cancer cell lines SW480, RKO, and HCT-116 revealed a total of 81 peptides that contain either noncancer-specific or cancer-related variations. Twenty-three out of 26 variants randomly selected from the 81 were confirmed by genomic sequencing. We further applied the workflow on data sets from three individual colorectal tumor specimens. A total of 204 distinct variant peptides were detected, and five carried known cancer-related mutations. Each individual showed a specific pattern of cancer-related mutations, suggesting potential use of this type of information for personalized medicine. Compatibility of the workflow has been tested with four popular database search engines including Sequest, Mascot, X!Tandem, and MyriMatch. In summary, we have developed a workflow that effectively uses existing genomic data to enable variant peptide detection in proteomics.

GeLC-MRM quantitation of mutant KRAS oncoprotein in complex biological samples

Tumor-derived mutant KRAS (v-Ki-ras-2 Kirsten rat sarcoma viral oncogene) oncoprotein is a critical driver of cancer phenotypes and a potential biomarker for many epithelial cancers. Targeted mass spectrometry analysis by multiple reaction monitoring (MRM) enables selective detection and quantitation of wild-type and mutant KRAS proteins in complex biological samples. A recently described immunoprecipitation approach (Proc. Nat. Acad. Sci.2011, 108, 2444-2449) can be used to enrich KRAS for MRM analysis, but requires large protein inputs (2-4 mg). Here, we describe sodium dodecyl sulfate-polyacrylamide gel electrophoresis-based enrichment of KRAS in a low molecular weight (20-25 kDa) protein fraction prior to MRM analysis (GeLC-MRM). This approach reduces background proteome complexity, thus, allowing mutant KRAS to be reliably quantified in low protein inputs (5-50 μg). GeLC-MRM detected KRAS mutant variants (G12D, G13D, G12V, G12S) in a panel of cancer cell lines. GeLC-MRM analysis of wild-type and mutant was linear with respect to protein input and showed low variability across process replicates (CV = 14%). Concomitant analysis of a peptide from the highly similar HRAS and NRAS proteins enabled correction of KRAS-targeted measurements for contributions from these other proteins. KRAS peptides were also quantified in fluid from benign pancreatic cysts and pancreatic cancers at concentrations from 0.08 to 1.1 fmol/μg protein. GeLC-MRM provides a robust, sensitive approach to quantitation of mutant proteins in complex biological samples.

Proteomic Consequences of a Single Gene Mutation in a Colorectal Cancer Model

The proteomic effects of specific cancer-related mutations have not been well characterized. In colorectal cancer (CRC), a relatively small number of mutations in key signaling pathways appear to drive tumorigenesis. Mutations in adenomatous polyposis coli (APC), a negative regulator of Wnt signaling, occur in up to 60% of CRC tumors. Here we examine the proteomic consequences of a single gene mutation by using an isogenic CRC cell culture model in which wildtype APC expression has been ectopically restored. Using LC–MS/MS label free shotgun proteomics, over 5000 proteins were identified in SW480Null (mutant APC) and SW480APC (APC restored). We observed 155 significantly differentially expressed proteins between the two cell lines, with 26 proteins showing opposite expression trends relative to gene expression measurements. Protein changes corresponded to previously characterized features of the APCNull phenotype: loss of cell adhesion proteins, increase in cell cycle regulators, alteration in Wnt signaling related proteins, and redistribution of β-catenin. Increased expression of RNA processing and isoprenoid biosynthetic proteins occurred in SW480Null cells. Therefore, shotgun proteomics reveals proteomic differences associated with a single gene change, including many novel differences that fall outside known target pathways.

Integrative omics analysis reveals the importance and scope of translational repression in microRNA-mediated regulation

MicroRNAs (miRNAs) are key post-transcriptional regulators that inhibit gene expression by promoting mRNA decay and/or suppressing translation. However, the relative contributions of these two mechanisms to gene repression remain controversial. Early studies favor a translational repression-centric scenario, whereas recent large-scale studies suggest a dominant role of mRNA decay in miRNA regulation. Here we generated proteomics data for nine colorectal cancer cell lines and integrated them with matched miRNA and mRNA expression data to infer and characterize miRNA-mediated regulation. Consistent with previous reports, we found that 8mer site, site positioning within 3′UTR, local AU-rich context, and additional 3′ pairing could all help boost miRNA-mediated mRNA decay. However, these sequence features were generally not correlated with increased translational repression, except for local AU-rich context. Thus the contribution of translational repression might be underestimated in recent studies in which the analyses were based primarily on the response of genes with canonical 7–8 mer sites in 3′UTRs. Indeed, we found that translational repression was involved in more than half, and played a major role in one-third of all predicted miRNA-target interactions. It was even the predominant contributor to miR-138 mediated regulation, which was further supported by the observation that differential expression of miR-138 in two genetically matched cell lines corresponded to altered protein but not mRNA abundance of most target genes. In addition, our study also provided interesting insights into colon cancer biology such as the possible contributions of miR-138 and miR-141/miR-200c in inducing specific phenotypes of SW480 and RKO cell lines, respectively.

A reporter system for translational readthrough of stop codons in human cells

Agents to induce readthrough of premature termination codons (PTCs) are useful research tools and potential therapeutics. Reporters used to detect PTC readthrough are gene‐specific and thus are not suited to for general assessment of readthrough activity or in cases where PTC‐inactivated genes are unknown. Here we describe a GFP‐based reporter construct pMHG‐W57∗ which is capable of detecting dose‐dependent drug‐induced PTC readthrough both by fluorescence microscopy and flow cytometry. pMHG‐W57∗ may be used as a general indicator of PTC readthrough in living cells and obviates the need for gene‐specific recoding sequences in reporter constructs.

Abstract 3964: In colorectal cancer cells, the beta-catenin/TCF complex regulates several growth suppressive microRNAs that target cancer promoting genes

Aberrant activation of the Wnt signaling pathway is causally involved in the formation of most colorectal cancers (CRCs). Whereas detailed knowledge exists regarding Wnt regulated protein coding genes, much less is known about the possible involvement of non-coding RNAs. Here we used TaqMan Array MicroRNA Cards, capable of detecting 664 unique human microRNAs (miRNAs), to describe changes of the miRNA transcriptome following disruption of beta-catenin/TCF4 activity in DLD1 CRC cells. Most miRNAs appeared to respond independently of host gene regulation and proximal TCF4 chromatin occupancy. A module of miRNAs suppressed by Wnt signaling in vitro was downregulated in a series of human primary CRCs (n = 46), and CRC cell lines (n = 7), relative to normal adjacent mucosa (n = 14). Several of these miRNAs (miR-145, miR-126, miR-30e-3p, and miR-139-5p) markedly inhibited CRC cell growth in vitro when ectopically expressed. By using an integrative approach of proteomics and microarrays, together interrogating thousands of proteins and genome-wide mRNA levels, we also provide detailed insight into the target gene network controlled by miR-30e-3p. Specifically HELZ and PIK3C2A were directly repressed via several functional seed matching binding motifs located within the transcripts. siRNA-mediated silencing of PIK3C2A partly phenocopied the growth suppressive phenotype of miR-30e-3p suggesting that miR-30e-3p restricts CRC cell growth in part by targeting PIK3C2A. Collectively, our study demonstrates that multiple miRNAs are upregulated as a consequence of forced attenuation of Wnt signaling in CRC cells, and some of these miRNAs inhibit cell growth, possibly through repression of several growth stimulatory cancer-related genes. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3964. doi:10.1158/1538-7445.AM2011-3964

Abstract 4951: In-depth analysis of gene and protein expression levels in the colorectal adenocarcinoma cell line Caco-2

Novel shotgun proteomic analysis platforms using liquid-chromatograpy tandem mass spectrometry (LC-MS/MS) allow in-depth surveys of thousands of proteins in a semi-quantitative manner. The colorectal carcinoma cell line Caco-2 was analyzed in triplicate by Affymetrix Hu-133 microarray and by multidimensional shotgun proteomic analysis using iso-electric focusing and analysis on an LTQ-Orbitrap instrument. MS-spectra were searched using Myrimatch against the NCBI database (version 36.51) in forward and reverse orientation. Proteins were included when identified by a minimum of 2 distinct peptides. The combined microarray and shotgun proteomics results were compared to a manual curation of protein immunohistochemistry (IHC) data from ProteinAtlas (www.proteinatlas.org). Microarray analysis yielded a total of 9861 expressed mRNAs which were observed in all 3 replicate analyses. Shotgun proteomic analysis identified 3896 protein groups (grouping eliminates redundancy from protein isoforms) while 2986 proteins were observed from ProteinAtlas. A comparison of microarray with shotgun proteome data showed that the proportion of proteins identified for each of 5 ranked mRNA expression categories increased with increasing mRNA levels. Of the top 20% most highly expressed mRNAs, 2600 (75%) were also identified by shotgun proteomics, while of the bottom 20% mRNAs only 384 (11%) could be identified by shotgun proteomics. When classified by GO categories for biological processes, molecular function or cellular location, the proportions of genes or proteins classified in each of the sub-categories did not differ substantially, suggesting that shotgun proteomics identifies a representative collection of proteins which is similar to mRNA distribution. A large majority of the proteins identified by shotgun analysis, 3695 (95%) of 3896, could be unambiguously mapped to genes from identified by microarray analysis. A much smaller overlap between mRNA and protein results was observed with ProteinAtlas data: only 2002 (67%) of 2986 proteins from ProteinAtlas were also found by microarray analysis. Only 37 proteins were found by both protein analysis methods and not by microarray, the large majority of which were immunoglobulins. In conclusion, our comparative analysis shows that shotgun proteomic analysis performed on a standardized platform provides an in-depth and unbiased protein expression profile, which largely follows the expression levels determined by gene expression analysis. Although it is unclear why almost 1,000 proteins identified by IHC are not detectable by either microarray or shotgun proteomic analysis, this apparent discrepancy may indicate a lack of antibody specificity. Future research will be needed to expand the coverage of shotgun proteomics to include all possible expressed proteins within a complex protein mixture. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4951.