Large-scale mapping of human protein–protein interactions by mass spectrometry

Mapping protein–protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein–protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24 540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein–protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations

CLPM: A Cross-Linked Peptide Mapping Algorithm for Mass Spectrometric Analysis

BACKGROUND: Protein-protein, protein-DNA and protein-RNA interactions are of central importance in biological systems. Quadrapole Time-of-flight (Q-TOF) mass spectrometry is a sensitive, promising tool for studying these interactions. Combining this technique with chemical crosslinking, it is possible to identify the sites of interactions within these complexes. Due to the complexities of the mass spectrometric data of crosslinked proteins, new software is required to analyze the resulting products of these studies. RESULT: We designed a Cross-Linked Peptide Mapping (CLPM) algorithm which takes advantage of all of the information available in the experiment including the amino acid sequence from each protein, the identity of the crosslinker, the identity of the digesting enzyme, the level of missed cleavage, and possible chemical modifications. The algorithm does in silico digestion and crosslinking, calculates all possible mass values and matches the theoretical data to the actual experimental data provided by the mass spectrometry analysis to identify the crosslinked peptides. CONCLUSION: Identifying peptides by their masses can be an efficient starting point for direct sequence confirmation. The CLPM algorithm provides a powerful tool in identifying these potential interaction sites in combination with chemical crosslinking and mass spectrometry. Through this cost-effective approach, subsequent efforts can quickly focus attention on investigating these specific interaction sites

Bio::Homology::InterologWalk - A Perl module to build putative protein-protein interaction networks through interolog mapping

<p>Abstract</p> <p>Background</p> <p>Protein-protein interaction (PPI) data are widely used to generate network models that aim to describe the relationships between proteins in biological systems. The fidelity and completeness of such networks is primarily limited by the paucity of protein interaction information and by the restriction of most of these data to just a few widely studied experimental organisms. In order to extend the utility of existing PPIs, computational methods can be used that exploit functional conservation between orthologous proteins across taxa to predict putative PPIs or 'interologs'. To date most interolog prediction efforts have been restricted to specific biological domains with fixed underlying data sources and there are no software tools available that provide a generalised framework for 'on-the-fly' interolog prediction.</p> <p>Results</p> <p>We introduce <monospace>Bio::Homology::InterologWalk</monospace>, a Perl module to retrieve, prioritise and visualise putative protein-protein interactions through an orthology-walk method. The module uses orthology and experimental interaction data to generate putative PPIs and optionally collates meta-data into an Interaction Prioritisation Index that can be used to help prioritise interologs for further analysis. We show the application of our interolog prediction method to the genomic interactome of the fruit fly, <it>Drosophila melanogaster</it>. We analyse the resulting interaction networks and show that the method proposes new interactome members and interactions that are candidates for future experimental investigation.</p> <p>Conclusions</p> <p>Our interolog prediction tool employs the Ensembl Perl API and PSICQUIC enabled protein interaction data sources to generate up to date interologs 'on-the-fly'. This represents a significant advance on previous methods for interolog prediction as it allows the use of the latest orthology and protein interaction data for all of the genomes in Ensembl. The module outputs simple text files, making it easy to customise the results by post-processing, allowing the putative PPI datasets to be easily integrated into existing analysis workflows. The <monospace>Bio::Homology::InterologWalk</monospace> module, sample scripts and full documentation are freely available from the Comprehensive Perl Archive Network (CPAN) under the GNU Public license.</p

Srf1 Is a Novel Regulator of Phospholipase D Activity and Is Essential to Buffer the Toxic Effects of C16:0 Platelet Activating Factor

During Alzheimer's Disease, sustained exposure to amyloid-β42 oligomers perturbs metabolism of ether-linked glycerophospholipids defined by a saturated 16 carbon chain at the sn-1 position. The intraneuronal accumulation of 1-O-hexadecyl-2-acetyl-sn-glycerophosphocholine (C16:0 PAF), but not its immediate precursor 1-O-hexadecyl-sn-glycerophosphocholine (C16:0 lyso-PAF), participates in signaling tau hyperphosphorylation and compromises neuronal viability. As C16:0 PAF is a naturally occurring lipid involved in cellular signaling, it is likely that mechanisms exist to protect cells against its toxic effects. Here, we utilized a chemical genomic approach to identify key processes specific for regulating the sensitivity of Saccharomyces cerevisiae to alkyacylglycerophosphocholines elevated in Alzheimer's Disease. We identified ten deletion mutants that were hypersensitive to C16:0 PAF and five deletion mutants that were hypersensitive to C16:0 lyso-PAF. Deletion of YDL133w, a previously uncharacterized gene which we have renamed SRF1 (Spo14 Regulatory Factor 1), resulted in the greatest differential sensitivity to C16:0 PAF over C16:0 lyso-PAF. We demonstrate that Srf1 physically interacts with Spo14, yeast phospholipase D (PLD), and is essential for PLD catalytic activity in mitotic cells. Though C16:0 PAF treatment does not impact hydrolysis of phosphatidylcholine in yeast, C16:0 PAF does promote delocalization of GFP-Spo14 and phosphatidic acid from the cell periphery. Furthermore, we demonstrate that, similar to yeast cells, PLD activity is required to protect mammalian neural cells from C16:0 PAF. Together, these findings implicate PLD as a potential neuroprotective target capable of ameliorating disruptions in lipid metabolism in response to accumulating oligomeric amyloid-β42

Regulation of Septin Dynamics by the Saccharomyces cerevisiae Lysine Acetyltransferase NuA4

In the budding yeast Saccharomyces cerevisiae, the lysine acetyltransferase NuA4 has been linked to a host of cellular processes through the acetylation of histone and non-histone targets. To discover proteins regulated by NuA4-dependent acetylation, we performed genome-wide synthetic dosage lethal screens to identify genes whose overexpression is toxic to non-essential NuA4 deletion mutants. The resulting genetic network identified a novel link between NuA4 and septin proteins, a group of highly conserved GTP-binding proteins that function in cytokinesis. We show that acetyltransferase-deficient NuA4 mutants have defects in septin collar formation resulting in the development of elongated buds through the Swe1-dependent morphogenesis checkpoint. We have discovered multiple sites of acetylation on four of the five yeast mitotic septins, Cdc3, Cdc10, Cdc12 and Shs1, and determined that NuA4 can acetylate three of the four in vitro. In vivo we find that acetylation levels of both Shs1 and Cdc10 are reduced in a catalytically inactive esa1 mutant. Finally, we determine that cells expressing a Shs1 protein with decreased acetylation in vivo have defects in septin localization that are similar to those observed in NuA4 mutants. These findings provide the first evidence that yeast septin proteins are acetylated and that NuA4 impacts septin dynamics

Synergism between particle-based multiplexing and microfluidics technologies may bring diagnostics closer to the patient

In the field of medical diagnostics there is a growing need for inexpensive, accurate, and quick high-throughput assays. On the one hand, recent progress in microfluidics technologies is expected to strongly support the development of miniaturized analytical devices, which will speed up (bio)analytical assays. On the other hand, a higher throughput can be obtained by the simultaneous screening of one sample for multiple targets (multiplexing) by means of encoded particle-based assays. Multiplexing at the macro level is now common in research labs and is expected to become part of clinical diagnostics. This review aims to debate on the “added value” we can expect from (bio)analysis with particles in microfluidic devices. Technologies to (a) decode, (b) analyze, and (c) manipulate the particles are described. Special emphasis is placed on the challenges of integrating currently existing detection platforms for encoded microparticles into microdevices and on promising microtechnologies that could be used to down-scale the detection units in order to obtain compact miniaturized particle-based multiplexing platforms