Optimal translational termination requires C4 lysyl hydroxylation of eRF1

Efficient stop codon recognition and peptidyl-tRNA hydrolysis are essential in order to terminate translational elongation and maintain protein sequence fidelity. Eukaryotic translational termination is mediated by a release factor complex that includes eukaryotic release factor 1 (eRF1) and eRF3. The N terminus of eRF1 contains highly conserved sequence motifs that couple stop codon recognition at the ribosomal A site to peptidyl-tRNA hydrolysis. We reveal that Jumonji domain-containing 4 (Jmjd4), a 2-oxoglutarate- and Fe(II)-dependent oxygenase, catalyzes carbon 4 (C4) lysyl hydroxylation of eRF1. This posttranslational modification takes place at an invariant lysine within the eRF1 NIKS motif and is required for optimal translational termination efficiency. These findings further highlight the role of 2-oxoglutarate/Fe(II) oxygenases in fundamental cellular processes and provide additional evidence that ensuring fidelity of protein translation is a major role of hydroxylation

Achillea grandiflora

We have extended the functionality of the Central Proteomics Facilities Pipeline (CPFP) to allow use of remote cloud and high performance computing (HPC) resources for shotgun proteomics data processing. CPFP has been modified to include modular local and remote scheduling for data processing jobs. The pipeline can now be run on a single PC or server, a local cluster, a remote HPC cluster, and/or the Amazon Web Services (AWS) cloud. We provide public images that allow easy deployment of CPFP in its entirety in the AWS cloud. This significantly reduces the effort necessary to use the software, and allows proteomics laboratories to pay for compute time ad hoc, rather than obtaining and maintaining expensive local server clusters. Alternatively the Amazon cloud can be used to increase the throughput of a local installation of CPFP as necessary. We demonstrate that cloud CPFP allows users to process data at higher speed than local installations but with similar cost and lower staff requirements. In addition to the computational improvements, the web interface to CPFP is simplified, and other functionalities are enhanced. The software is under active development at two leading institutions and continues to be released under an open-source license at http://cpfp.sourceforge.net

Type VI Secretion System Toxins Horizontally Shared between Marine Bacteria.

The type VI secretion system (T6SS) is a widespread protein secretion apparatus used by Gram-negative bacteria to deliver toxic effector proteins into adjacent bacterial or host cells. Here, we uncovered a role in interbacterial competition for the two T6SSs encoded by the marine pathogen Vibrio alginolyticus. Using comparative proteomics and genetics, we identified their effector repertoires. In addition to the previously described effector V12G01_02265, we identified three new effectors secreted by T6SS1, indicating that the T6SS1 secretes at least four antibacterial effectors, of which three are members of the MIX-effector class. We also showed that the T6SS2 secretes at least three antibacterial effectors. Our findings revealed that many MIX-effectors belonging to clan V are "orphan" effectors that neighbor mobile elements and are shared between marine bacteria via horizontal gene transfer. We demonstrated that a MIX V-effector from V. alginolyticus is a functional T6SS effector when ectopically expressed in another Vibrio species. We propose that mobile MIX V-effectors serve as an environmental reservoir of T6SS effectors that are shared and used to diversify antibacterial toxin repertoires in marine bacteria, resulting in enhanced competitive fitness

Erratum: Conserved and host-specific features of influenza virion architecture

Viruses use virions to spread between hosts, and virion composition is therefore the primary determinant of viral transmissibility and immunogenicity. However, the virions of many viruses are complex and pleomorphic, making them difficult to analyse in detail. Here we address this by identifying and quantifying virion proteins with mass spectrometry, producing a complete and quantified model of the hundreds of host-encoded and viral proteins that make up the pleomorphic virions of influenza viruses. We show that a conserved influenza virion architecture is maintained across diverse combinations of virus and host. This ‘core’ architecture, which includes substantial quantities of host proteins as well as the viral protein ​NS1, is elaborated with abundant host-dependent features. As a result, influenza virions produced by mammalian and avian hosts have distinct protein compositions. Finally, we note that influenza virions share an underlying protein composition with exosomes, suggesting that influenza virions form by subverting microvesicle production

Conserved and host-specific features of influenza virion architecture

Viruses use virions to spread between hosts, and virion composition is therefore the primary determinant of viral transmissibility and immunogenicity. However, the virions of many viruses are complex and pleomorphic, making them difficult to analyse in detail. Here we address this by identifying and quantifying virion proteins with mass spectrometry, producing a complete and quantified model of the hundreds of host-encoded and viral proteins that make up the pleomorphic virions of influenza viruses. We show that a conserved influenza virion architecture is maintained across diverse combinations of virus and host. This ‘core’ architecture, which includes substantial quantities of host proteins as well as the viral protein ​NS1, is elaborated with abundant host-dependent features. As a result, influenza virions produced by mammalian and avian hosts have distinct protein compositions. Finally, we note that influenza virions share an underlying protein composition with exosomes, suggesting that influenza virions form by subverting microvesicle production

T Cell Receptor (TCR)-induced Tyrosine Phosphorylation Dynamics Identifies THEMIS as a New TCR Signalosome Component*

Stimulation of the T cell antigen receptor (TCR) induces formation of a phosphorylation-dependent signaling network via multiprotein complexes, whose compositions and dynamics are incompletely understood. Using stable isotope labeling by amino acids in cell culture (SILAC)-based quantitative proteomics, we investigated the kinetics of signal propagation after TCR-induced protein tyrosine phosphorylation. We confidently assigned 77 proteins (of 758 identified) as a direct or indirect consequence of tyrosine phosphorylation that proceeds in successive “signaling waves” revealing the temporal pace at which tyrosine kinases activate cellular functions. The first wave includes thymocyte-expressed molecule involved in selection (THEMIS), a protein recently implicated in thymocyte development but whose signaling role is unclear. We found that tyrosine phosphorylation of THEMIS depends on the presence of the scaffold proteins Linker for activation of T cells (LAT) and SH2 domain-containing lymphocyte protein of 76 kDa (SLP-76). THEMIS associates with LAT, presumably via the adapter growth factor receptor-bound protein 2 (Grb2) and with phospholipase Cγ1 (PLC-γ1). RNAi-mediated THEMIS knock-down inhibited TCR-induced IL-2 gene expression due to reduced ERK and nuclear factor of activated T cells (NFAT)/activator protein 1 (AP-1) signaling, whereas JNK, p38, or nuclear factor κB (NF-κB) activation were unaffected. Our study reveals the dynamics of TCR-dependent signaling networks and suggests a specific role for THEMIS in early TCR signalosome function

ProteoSign: an end-user online differential proteomics statistical analysis platform.

Profiling of proteome dynamics is crucial for understanding cellular behavior in response to intrinsic and extrinsic stimuli and maintenance of homeostasis. Over the last 20 years, mass spectrometry (MS) has emerged as the most powerful tool for large-scale identification and characterization of proteins. Bottom-up proteomics, the most common MS-based proteomics approach, has always been challenging in terms of data management, processing, analysis and visualization, with modern instruments capable of producing several gigabytes of data out of a single experiment. Here, we present ProteoSign, a freely available web application, dedicated in allowing users to perform proteomics differential expression/abundance analysis in a user-friendly and self-explanatory way. Although several non-commercial standalone tools have been developed for post-quantification statistical analysis of proteomics data, most of them are not end-user appealing as they often require very stringent installation of programming environments, third-party software packages and sometimes further scripting or computer programming. To avoid this bottleneck, we have developed a user-friendly software platform accessible via a web interface in order to enable proteomics laboratories and core facilities to statistically analyse quantitative proteomics data sets in a resource-efficient manner. ProteoSign is available at http://bioinformatics.med.uoc.gr/ProteoSign and the source code at https://github.com/yorgodillo/ProteoSign

ProteoSign: an end-user online differential proteomics statistical analysis platform.

Profiling of proteome dynamics is crucial for understanding cellular behavior in response to intrinsic and extrinsic stimuli and maintenance of homeostasis. Over the last 20 years, mass spectrometry (MS) has emerged as the most powerful tool for large-scale identification and characterization of proteins. Bottom-up proteomics, the most common MS-based proteomics approach, has always been challenging in terms of data management, processing, analysis and visualization, with modern instruments capable of producing several gigabytes of data out of a single experiment. Here, we present ProteoSign, a freely available web application, dedicated in allowing users to perform proteomics differential expression/abundance analysis in a user-friendly and self-explanatory way. Although several non-commercial standalone tools have been developed for post-quantification statistical analysis of proteomics data, most of them are not end-user appealing as they often require very stringent installation of programming environments, third-party software packages and sometimes further scripting or computer programming. To avoid this bottleneck, we have developed a user-friendly software platform accessible via a web interface in order to enable proteomics laboratories and core facilities to statistically analyse quantitative proteomics data sets in a resource-efficient manner. ProteoSign is available at http://bioinformatics.med.uoc.gr/ProteoSign and the source code at https://github.com/yorgodillo/ProteoSign