A Multivariate Computational Method to Analyze High-Content RNAi Screening Data.

High-content screening (HCS) using RNA interference (RNAi) in combination with automated microscopy is a powerful investigative tool to explore complex biological processes. However, despite the plethora of data generated from these screens, little progress has been made in analyzing HC data using multivariate methods that exploit the full richness of multidimensional data. We developed a novel multivariate method for HCS, multivariate robust analysis method (M-RAM), integrating image feature selection with ranking of perturbations for hit identification, and applied this method to an HC RNAi screen to discover novel components of the DNA damage response in an osteosarcoma cell line. M-RAM automatically selects the most informative phenotypic readouts and time points to facilitate the more efficient design of follow-up experiments and enhance biological understanding. Our method outperforms univariate hit identification and identifies relevant genes that these approaches would have missed. We found that statistical cell-to-cell variation in phenotypic responses is an important predictor of hits in RNAi-directed image-based screens. Genes that we identified as modulators of DNA damage signaling in U2OS cells include B-Raf, a cancer driver gene in multiple tumor types, whose role in DNA damage signaling we confirm experimentally, and multiple subunits of protein kinase A.

Proteasome inhibitors block DNA repair and radiosensitize non-small cell lung cancer.

Despite optimal radiation therapy (RT), chemotherapy and/or surgery, a majority of patients with locally advanced non-small cell lung cancer (NSCLC) fail treatment. To identify novel gene targets for improved tumor control, we performed whole genome RNAi screens to identify knockdowns that most reproducibly increase NSCLC cytotoxicity. These screens identified several proteasome subunits among top hits, including the topmost hit PSMA1, a component of the core 20 S proteasome. Radiation and proteasome inhibition showed synergistic effects. Proteasome inhibition resulted in an 80-90% decrease in homologous recombination (HR), a 50% decrease in expression of NF-κB-inducible HR genes BRCA1 and FANCD2, and a reduction of BRCA1, FANCD2 and RAD51 ionizing radiation-induced foci. IκBα RNAi knockdown rescued NSCLC radioresistance. Irradiation of mice with NCI-H460 xenografts after inducible PSMA1 shRNA knockdown markedly increased murine survival compared to either treatment alone. Proteasome inhibition is a promising strategy for NSCLC radiosensitization via inhibition of NF-κB-mediated expression of Fanconi Anemia/HR DNA repair genes.

The bromodomain protein Brd4 insulates chromatin from DNA damage signalling.

DNA damage activates a signalling network that blocks cell-cycle progression, recruits DNA repair factors and/or triggers senescence or programmed cell death. Alterations in chromatin structure are implicated in the initiation and propagation of the DNA damage response. Here we further investigate the role of chromatin structure in the DNA damage response by monitoring ionizing-radiation-induced signalling and response events with a high-content multiplex RNA-mediated interference screen of chromatin-modifying and -interacting genes. We discover that an isoform of Brd4, a bromodomain and extra-terminal (BET) family member, functions as an endogenous inhibitor of DNA damage response signalling by recruiting the condensin II chromatin remodelling complex to acetylated histones through bromodomain interactions. Loss of this isoform results in relaxed chromatin structure, rapid cell-cycle checkpoint recovery and enhanced survival after irradiation, whereas functional gain of this isoform compacted chromatin, attenuated DNA damage response signalling and enhanced radiation-induced lethality. These data implicate Brd4, previously known for its role in transcriptional control, as an insulator of chromatin that can modulate the signalling response to DNA damage.

Pride-asap: automatic fragment ion annotation of identified PRIDE spectra.

We present an open source software application and library written in Java that provides a uniform annotation of identified spectra stored in the PRIDE database. Pride-asap can be ran in a command line mode for automated processing of multiple PRIDE experiments, but also has a graphical user interface that allows end users to annotate the spectra in PRIDE experiments and to inspect the results in detail. Pride-asap binaries, source code and additional information can be downloaded from http://pride-asa-pipeline.googlecode.com.This article is part of a Special Issue entitled: Standardization and Quality Control in Proteomics.

Systematic phosphorylation analysis of human mitotic protein complexes.

Progression through mitosis depends on a large number of protein complexes that regulate the major structural and physiological changes necessary for faithful chromosome segregation. Most, if not all, of the mitotic processes are regulated by a set of mitotic protein kinases that control protein activity by phosphorylation. Although many mitotic phosphorylation events have been identified in proteome-scale mass spectrometry studies, information on how these phosphorylation sites are distributed within mitotic protein complexes and which kinases generate these phosphorylation sites is largely lacking. We used systematic protein-affinity purification combined with mass spectrometry to identify 1818 phosphorylation sites in more than 100 mitotic protein complexes. In many complexes, the phosphorylation sites were concentrated on a few subunits, suggesting that these subunits serve as "switchboards" to relay the kinase-regulatory signals within the complexes. Consequent bioinformatic analyses identified potential kinase-substrate relationships for most of these sites. In a subsequent in-depth analysis of key mitotic regulatory complexes with the Aurora kinase B (AURKB) inhibitor Hesperadin and a new Polo-like kinase (PLK1) inhibitor, BI 4834, we determined the kinase dependency for 172 phosphorylation sites on 41 proteins. Combination of the results of the cellular studies with Scansite motif prediction enabled us to identify 14 sites on six proteins as direct candidate substrates of AURKB or PLK1.

Quantitative phospho-proteomics to investigate the polo-like kinase 1-dependent phospho-proteome.

Polo-like kinase 1 (PLK1) is a key regulator of mitotic progression and cell division, and small molecule inhibitors of PLK1 are undergoing clinical trials to evaluate their utility in cancer therapy. Despite this importance, current knowledge about the identity of PLK1 substrates is limited. Here we present the results of a proteome-wide analysis of PLK1-regulated phosphorylation sites in mitotic human cells. We compared phosphorylation sites in HeLa cells that were or were not treated with the PLK1-inhibitor BI 4834, by labeling peptides via methyl esterification, fractionation of peptides by strong cation exchange chromatography, and phosphopeptide enrichment via immobilized metal affinity chromatography. Analysis by quantitative mass spectrometry identified 4070 unique mitotic phosphorylation sites on 2069 proteins. Of these, 401 proteins contained one or multiple phosphorylation sites whose abundance was decreased by PLK1 inhibition. These include proteins implicated in PLK1-regulated processes such as DNA damage, mitotic spindle formation, spindle assembly checkpoint signaling, and chromosome segregation, but also numerous proteins that were not suspected to be regulated by PLK1. Analysis of amino acid sequence motifs among phosphorylation sites down-regulated under PLK1 inhibition in this data set identified two potential novel variants of the PLK1 consensus motif.

The mTOR-regulated phosphoproteome reveals a mechanism of mTORC1-mediated inhibition of growth factor signaling.

The mammalian target of rapamycin (mTOR) protein kinase is a master growth promoter that nucleates two complexes, mTORC1 and mTORC2. Despite the diverse processes controlled by mTOR, few substrates are known. We defined the mTOR-regulated phosphoproteome by quantitative mass spectrometry and characterized the primary sequence motif specificity of mTOR using positional scanning peptide libraries. We found that the phosphorylation response to insulin is largely mTOR dependent and that mTOR exhibits a unique preference for proline, hydrophobic, and aromatic residues at the +1 position. The adaptor protein Grb10 was identified as an mTORC1 substrate that mediates the inhibition of phosphoinositide 3-kinase typical of cells lacking tuberous sclerosis complex 2 (TSC2), a tumor suppressor and negative regulator of mTORC1. Our work clarifies how mTORC1 inhibits growth factor signaling and opens new areas of investigation in mTOR biology.

The Proteomics Identifications database: 2010 update.

The Proteomics Identifications database (PRIDE, http://www.ebi.ac.uk/pride) at the European Bioinformatics Institute has become one of the main repositories of mass spectrometry-derived proteomics data. For the last 2 years, PRIDE data holdings have grown substantially, comprising 60 different species, more than 2.5 million protein identifications, 11.5 million peptides and over 50 million spectra by September 2009. We here describe several new and improved features in PRIDE, including the revised submission process, which now includes direct submission of fragment ion annotations. Correspondingly, it is now possible to visualize spectrum fragmentation annotations on tandem mass spectra, a key feature for compliance with journal data submission requirements. We also describe recent developments in the PRIDE BioMart interface, which now allows integrative queries that can join PRIDE data to a growing number of biological resources such as Reactome, Ensembl, InterPro and UniProt. This ability to perform extremely powerful across-domain queries will certainly be a cornerstone of future bioinformatics analyses. Finally, we highlight the importance of data sharing in the proteomics field, and the corresponding integration of PRIDE with other databases in the ProteomExchange consortium.

A guide to the Proteomics Identifications Database proteomics data repository.

The Proteomics Identifications Database (PRIDE, www.ebi.ac.uk/pride) is one of the main repositories of MS derived proteomics data. Here, we point out the main functionalities of PRIDE both as a submission repository and as a source for proteomics data. We describe the main features for data retrieval and visualization available through the PRIDE web and BioMart interfaces. We also highlight the mechanism by which tailored queries in the BioMart can join PRIDE to other resources such as Reactome, Ensembl or UniProt to execute extremely powerful across-domain queries. We then present the latest improvements in the PRIDE submission process, using the new easy-to-use, platform-independent graphical user interface submission tool PRIDE Converter. Finally, we speak about future plans and the role of PRIDE in the ProteomExchange consortium.

Targeted quantitative analysis of Streptococcus pyogenes virulence factors by multiple reaction monitoring.

In many studies, particularly in the field of systems biology, it is essential that identical protein sets are precisely quantified in multiple samples such as those representing differentially perturbed cell states. The high degree of reproducibility required for such experiments has not been achieved by classical mass spectrometry-based proteomics methods. In this study we describe the implementation of a targeted quantitative approach by which predetermined protein sets are first identified and subsequently quantified at high sensitivity reliably in multiple samples. This approach consists of three steps. First, the proteome is extensively mapped out by multidimensional fractionation and tandem mass spectrometry, and the data generated are assembled in the PeptideAtlas database. Second, based on this proteome map, peptides uniquely identifying the proteins of interest, proteotypic peptides, are selected, and multiple reaction monitoring (MRM) transitions are established and validated by MS2 spectrum acquisition. This process of peptide selection, transition selection, and validation is supported by a suite of software tools, TIQAM (Targeted Identification for Quantitative Analysis by MRM), described in this study. Third, the selected target protein set is quantified in multiple samples by MRM. Applying this approach we were able to reliably quantify low abundance virulence factors from cultures of the human pathogen Streptococcus pyogenes exposed to increasing amounts of plasma. The resulting quantitative protein patterns enabled us to clearly define the subset of virulence proteins that is regulated upon plasma exposure.