Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell-lines

N-glycoproteins exhibit a positive expression level-evolutionary rate correlation

The different proteins of any proteome evolve at enormously different rates. One of the primary factors influencing rates of protein evolution is expression level, with highly expressed proteins tending to evolve at slow rates. This phenomenon, known as the expression level-evolutionary rate (E-R) anticorrelation, has been attributed to the abundance-dependent deleterious effects of misfolding or misinteraction. We have recently shown that secreted proteins either lack an E-R anticorrelation or exhibit a significantly reduced E-R anticorrelation. This effect may be due to the strict quality control to which secreted proteins are subject in the endoplasmic reticulum (which is expected to reduce the rate of misfolding and its deleterious effects) or to their extracellular location (expected to reduce the rate of misinteraction and its deleterious effects). Among secreted proteins, N-glycosylated ones are under particularly strong quality control. Here, we investigate how N-linked glycosylation affects the E-R anticorrelation. Strikingly, we observe a positive E-R correlation among N-glycosylated proteins. That is, N-glycoproteins that are highly expressed evolve at faster rates than lowly expressed N-glycoproteins, in contrast to what is observed among intracellular proteins.

Cationic Intrinsically Disordered Antimicrobial Peptides (CIDAMPs) Represent a New Paradigm of Innate Defense with a Potential for Novel Anti-Infectives

In the search for potential mechanisms underlying the remarkable resistance of healthy skin against infection by soil bacteria like Pseudomonas (P.) aeruginosa we identified fragments of the intrinsically disordered protein hornerin as potent microbicidal agents in the stratum corneum. We found that, independent of the amino acid (AA)-sequence, any tested linear cationic peptide containing a high percentage of disorder-promoting AA and a low percentage of order-promoting AA is a potent microbicidal antimicrobial. We further show that the antimicrobial activity of these cationic intrinsically disordered antimicrobial peptides (CIDAMPs) depends on the peptide chain length, its net charge, lipidation and environmental conditions. The ubiquitous presence of latent CIDAMP sources in nature suggests a common and yet overlooked adapted innate disinfection system of body surfaces. The simple structure and virtually any imaginable sequence or composition of disorder-promoting AA allow the generation of a plethora of CIDAMPs. These are potential novel microbicidal anti-infectives for various bacterial pathogens, including P. aeruginosa, methicillin-resistant Staphylococcus aureus (MRSA) and fungal pathogens like Candida albicans and Cryptococcus neoformans.

Thermophilic Adaptation in Prokaryotes Is Constrained by Metabolic Costs of Proteostasis

Prokaryotes evolved to thrive in an extremely diverse set of habitats, and their proteomes bear signatures of environmental conditions. Although correlations between amino acid usage and environmental temperature are well-documented, understanding of the mechanisms of thermal adaptation remains incomplete. Here, we couple the energetic costs of protein folding and protein homeostasis to build a microscopic model explaining both the overall amino acid composition and its temperature trends. Low biosynthesis costs lead to low diversity of physical interactions between amino acid residues, which in turn makes proteins less stable and drives up chaperone activity to maintain appropriate levels of folded, functional proteins. Assuming that the cost of chaperone activity is proportional to the fraction of unfolded client proteins, we simulated thermal adaptation of model proteins subject to minimization of the total cost of amino acid synthesis and chaperone activity. For the first time, we predicted both the proteome-average amino acid abundances and their temperature trends simultaneously, and found strong correlations between model predictions and 402 genomes of bacteria and archaea. The energetic constraint on protein evolution is more apparent in highly expressed proteins, selected by codon adaptation index. We found that in bacteria, highly expressed proteins are similar in composition to thermophilic ones, whereas in archaea no correlation between predicted expression level and thermostability was observed. At the same time, thermal adaptations of highly expressed proteins in bacteria and archaea are nearly identical, suggesting that universal energetic constraints prevail over the phylogenetic differences between these domains of life.

An improved estimation of tRNA expression to better elucidate the coevolution between tRNA abundance and codon usage in bacteria

The degree to which codon usage can be explained by tRNA abundance in bacterial species is often inadequate, partly because differential tRNA abundance is often approximated by tRNA copy numbers. To better understand the coevolution between tRNA abundance and codon usage, we provide a better estimate of tRNA abundance by profiling tRNA mapped reads (tRNA tpm) using publicly available RNA Sequencing data. To emphasize the feasibility of our approach, we demonstrate that tRNA tpm is consistent with tRNA abundances derived from RNA fingerprinting experiments in Escherichia coli, Bacillus subtilis, and Salmonella enterica. Furthermore, we do not observe an appreciable reduction in tRNA sequencing efficiency due to post-transcriptional methylations in the seven bacteria studied. To determine optimal codons, we calculate codon usage in highly and lowly expressed genes determined by protein per transcript. We found that tRNA tpm is sensitive to identify more translationally optimal codons than gene copy number and early tRNA fingerprinting abundances. Additionally, tRNA tpm improves the predictive power of tRNA adaptation index over codon preference. Our results suggest that dependence of codon usage on tRNA availability is not always associated with species growth-rate. Conversely, tRNA availability is better optimized to codon usage in fast-growing than slow-growing species.

3-Phosphoglycerate Transhydrogenation Instead of Dehydrogenation Alleviates the Redox State Dependency of Yeast de Novo l-Serine Synthesis

The enzymatic mechanism of 3-phosphoglycerate to 3-phosphohydroxypyruvate oxidation, which forms the first step of the main conserved de novo serine synthesis pathway, has been revisited recently in certain microorganisms. While this step is classically considered to be catalyzed by an NAD-dependent dehydrogenase (e.g., PHGDH in mammals), evidence has shown that in Pseudomonas, Escherichia coli, and Saccharomyces cerevisiae, the PHGDH homologues act as transhydrogenases. As such, they use α-ketoglutarate, rather than NAD⁺, as the final electron acceptor, thereby producing D-2-hydroxyglutarate in addition to 3-phosphohydroxypyruvate during 3-phosphoglycerate oxidation. Here, we provide a detailed biochemical and sequence-structure relationship characterization of the yeast PHGDH homologues, encoded by the paralogous SER3 and SER33 genes, in comparison to the human and other PHGDH enzymes. Using in vitro assays with purified recombinant enzymes as well as in vivo growth phenotyping and metabolome analyses of yeast strains engineered to depend on either Ser3, Ser33, or human PHGDH for serine synthesis, we confirmed that both yeast enzymes act as transhydrogenases, while the human enzyme is a dehydrogenase. In addition, we show that the yeast paralogs differ from the human enzyme in their sensitivity to inhibition by serine as well as hydrated NADH derivatives. Importantly, our in vivo data support the idea that a 3PGA transhydrogenase instead of dehydrogenase activity confers a growth advantage under conditions where the NAD⁺:NADH ratio is low. The results will help to elucidate why different species evolved different reaction mechanisms to carry out a widely conserved metabolic step in central carbon metabolism.

Proteomic Characterization of Epithelial-Like Extracellular Vesicles in Advanced Endometrial Cancer

Endometrial cancer (EC) is the most frequent gynecological cancer. Tumor dissemination affecting ∼20% of EC patients is characterized at the primary carcinoma by epithelial-to-mesenchymal transition (EMT) associated with myometrial infiltration. At distant sites, the interaction of circulating tumor cells (CTCs) with the microenvironment is crucial for metastatic colonization, with a participation of the extracellular vesicles (EVs). We comprehensively approached these primary and secondary sites to study the impact of tumor EVs on the metastatic efficiency of CTCs in EC. Tumor EVs in circulation reproduce the epithelial phenotype predominant in the primary carcinoma, whereas CTCs are characterized by an EMT phenotype. We modeled this EMT-related clinical scenario in the Hec1A endometrial cell line and characterized the epithelial-like EVs in circulation by SILAC proteome analysis. The identification of proteins involved in cell-cell and cell-matrix interaction and binding, together with in vitro evidence of an improved adhesion of CTC to a functionalized endothelium, suggests a contribution of the epithelial-like EVs in the homing of CTCs at metastatic sites. Accordingly, adhesion protein LGALS3BP was found to be significantly enriched in circulating EVs from a cohort of EC patients with a high risk of recurrence by targeted proteomics (multiple reaction monitoring), highlighting its potential in liquid biopsy in EC.

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

Comprehensive identification of RNA-protein interactions in any organism using orthogonal organic phase separation (OOPS)

Existing high-throughput methods to identify RNA-binding proteins (RBPs) are based on capture of polyadenylated RNAs and cannot recover proteins that interact with nonadenylated RNAs, including long noncoding RNA, pre-mRNAs and bacterial RNAs. We present orthogonal organic phase separation (OOPS), which does not require molecular tagging or capture of polyadenylated RNA, and apply it to recover cross-linked protein-RNA and free protein, or protein-bound RNA and free RNA, in an unbiased way. We validated OOPS in HEK293, U2OS and MCF10A human cell lines, and show that 96% of proteins recovered were bound to RNA. We show that all long RNAs can be cross-linked to proteins, and recovered 1,838 RBPs, including 926 putative novel RBPs. OOPS is approximately 100-fold more efficient than existing methods and can enable analyses of dynamic RNA-protein interactions. We also characterize dynamic changes in RNA-protein interactions in mammalian cells following nocodazole arrest, and present a bacterial RNA-interactome for Escherichia coli. OOPS is compatible with downstream proteomics and RNA sequencing, and can be applied in any organism.

Dynamic Regulation of Long-Chain Fatty Acid Oxidation by a Noncanonical Interaction between the MCL-1 BH3 Helix and VLCAD

MCL-1 is a BCL-2 family protein implicated in the development and chemoresistance of human cancer. Unlike its anti-apoptotic homologs, Mcl-1 deletion has profound physiologic consequences, indicative of a broader role in homeostasis. We report that the BCL-2 homology 3 (BH3) α helix of MCL-1 can directly engage very long-chain acyl-CoA dehydrogenase (VLCAD), a key enzyme of the mitochondrial fatty acid β-oxidation (FAO) pathway. Proteomic analysis confirmed that the mitochondrial matrix isoform of MCL-1 (MCL-1^Matrix) interacts with VLCAD. Mcl-1 deletion, or eliminating MCL-1^Matrix alone, selectively deregulated long-chain FAO, causing increased flux through the pathway in response to nutrient deprivation. Transient elevation in MCL-1 upon serum withdrawal, a striking increase in MCL-1 BH3/VLCAD interaction upon palmitic acid titration, and direct modulation of enzymatic activity by the MCL-1 BH3 α helix are consistent with dynamic regulation. Thus, the MCL-1 BH3 interaction with VLCAD revealed a separable, gain-of-function role for MCL-1 in the regulation of lipid metabolism.

The nitrogen cost of photosynthesis

Global food security depends on three main cereal crops (wheat, rice and maize) achieving and maintaining high yields, as well as increasing their future yields. Fundamental to the production of this biomass is photosynthesis. The process of photosynthesis involves a large number of proteins that together account for the majority of the nitrogen in leaves. As large amounts of nitrogen are removed in the harvested grain, this needs to be replaced either from synthetic fertilizer or biological nitrogen fixation. Knowledge about photosynthetic properties of leaves in natural ecosystems is also important, particularly when we consider the potential impacts of climate change. While the relationship between nitrogen and photosynthetic capacity of a leaf differs between species, leaf nitrogen content provides a useful way to incorporate photosynthesis into models of ecosystems and the terrestrial biosphere. This review provides a generalized nitrogen budget for a C3 leaf cell and discusses the potential for improving photosynthesis from a nitrogen perspective.

Quantitative Proteomics Data in the Public Domain: Challenges and Opportunities

Mass spectrometry based proteomics is no longer only a qualitative discipline, and can be successfully employed to obtain a truly multidimensional view of the proteome. In particular, systematic protein expression profiling is now a routine part of many studies in the field and beyond. The large growth in the number of quantitative studies is accompanied by a trend to share publicly the associated analysis results and the underlying raw data. This trend, established and strongly supported by public repositories such as the PRIDE database at the European Bioinformatics Institute, opens up enormous possibilities to explore the data beyond the original publications, for instance by reusing, reanalyzing, and performing different flavors of meta-analysis studies. To help researchers and scientists realize about this potential, here we describe the mainstream public proteomics resources containing quantitative proteomics data, including the processed analysis results and/or the underlying raw data. We then present and discuss the most important points to consider when attempting to (re)use proteomics data in the public domain. We conclude by highlighting potential pitfalls of (re)using quantitative data and discuss some of our own experiences in this context.

HDAC1 Substrate Profiling Using Proteomics-Based Substrate Trapping

Histone deacetylase (HDAC) proteins are overexpressed in multiple diseases, including cancer, and have emerged as anticancer drug targets. HDAC proteins regulate cellular processes, such as the cell cycle, apoptosis, and cell proliferation, by deacetylating histone and non-histone substrates. Although a plethora of acetylated proteins have been identified using large-scale proteomic approaches, the HDAC proteins responsible for their dynamic deacetylation have been poorly studied. For example, few substrates of HDAC1 have been identified, which is mainly due to the scarcity of substrate identification tools. We recently developed a mutant trapping strategy to identify novel substrates of HDAC1. Herein, we introduce an improved version of the trapping method that uses mass spectrometry (MS)-based proteomics to identify multiple substrates simultaneously. Among the substrate hits, CDK1, AIFM1, MSH6, and RuvB-like 1 were identified as likely HDAC1 substrates. These newly discovered HDAC1 substrates are involved in various biological processes, suggesting novel functions of HDAC1 apart from epigenetics. Substrate trapping combined with MS-based proteomics provides an efficient approach to HDAC1 substrate identification and contributes to the full characterization of HDAC function in normal and disease states.

RNA-Seq-Based Analysis Reveals Heterogeneity in Mature 16S rRNA 3' Termini and Extended Anti-Shine-Dalgarno Motifs in Bacterial Species

We present an RNA-Seq based approach to map 3′ end sequences of mature 16S rRNA (3′ TAIL) in bacteria with single-base specificity. Our results show that 3′ TAILs are heterogeneous among species; they contain the core CCUCC anti-Shine-Dalgarno motif, but vary in downstream lengths. Importantly, our findings rectify the mis-annotated 16S rRNAs in 11 out of 13 bacterial species studied herein (covering Cyanobacteria, Deinococcus-Thermus, Firmicutes, Proteobacteria, Tenericutes, and Spirochaetes). Furthermore, our results show that species-specific 3′ TAIL boundaries are retained due to their high complementarity with preferred Shine-Dalgarno sequences, suggesting that 3′ TAIL bases downstream of the canonical CCUCC motif play a more important role in translation initiation than previously reported.

General decapping activators target different subsets of inefficiently translated mRNAs

The Dcp1-Dcp2 decapping enzyme and the decapping activators Pat1, Dhh1, and Lsm1 regulate mRNA decapping, but their mechanistic integration is unknown. We analyzed the gene expression consequences of deleting PAT1, LSM1, or DHH1, or the DCP2 C-terminal domain, and found that: i) the Dcp2 C-terminal domain is an effector of both negative and positive regulation; ii) rather than being global activators of decapping, Pat1, Lsm1, and Dhh1 directly target specific subsets of yeast mRNAs and loss of the functions of each of these factors has substantial indirect consequences for genome-wide mRNA expression; and iii) transcripts targeted by Pat1, Lsm1, and Dhh1 exhibit only partial overlap, are generally translated inefficiently, and, as expected, are targeted to decapping-dependent decay. Our results define the roles of Pat1, Lsm1, and Dhh1 in decapping of general mRNAs and suggest that these factors may monitor mRNA translation and target unique features of individual mRNAs.

Both Intrinsically Disordered Regions and Structural Domains Evolve Rapidly in Immune-Related Mammalian Proteins

Eukaryotic proteins consist of structural domains (SDs) and intrinsically disordered regions (IDRs), i.e., regions that by themselves do not assume unique three-dimensional structures. IDRs are generally subject to less constraint and evolve more rapidly than SDs. Proteins with a lower number of protein-to-protein interactions (PPIs) are also less constrained and tend to evolve fast. Extracellular proteins of mammals, especially immune-related extracellular proteins, on average have relatively high evolution rates. This article aims to examine if a high evolution rate in IDRs or that in SDs accounts for the rapid evolution of extracellular proteins. To this end, we classified eukaryotic proteins based on their cellular localizations and analyzed them. Moreover, we divided proteins into SDs and IDRs and calculated the respective evolution rate. Fractional IDR content is positively correlated with evolution rate. For their fractional IDR content, immune-related extracellular proteins show an aberrantly high evolution rate. IDRs evolve more rapidly than SDs in most subcellular localizations. In extracellular proteins, however, the difference is diminished. For immune-related proteins in mammals in particular, the evolution rates in SDs come close to those in IDRs. Thus high evolution rates in both IDRs and SDs account for the rapid evolution of immune-related proteins.

In-Cell NMR within Budding Yeast Reveals Cytoplasmic Masking of Hydrophobic Residues of FG Repeats

In-cell NMR spectroscopy is a powerful tool to determine the properties of proteins and nucleic acids within living cells. In-cell NMR can give site-specific measurements of interactions, modifications, and dynamics as well as their modulation by the cellular environment. In-cell NMR requires selective incorporation of heavy isotopes into a protein of interest, either through the introduction of exogenously produced protein to a cell's interior or the selective overexpression of a protein. We developed conditions to allow the use of Saccharomyces cerevisiae, which was chosen because of its genetic tractability, as a eukaryotic expression system for in-cell NMR. We demonstrate this technique using a fragment of S. cerevisiae Nsp1, an FG Nup. FG Nups are intrinsically disordered proteins containing phenylalanine (F)-glycine (G) repeats and form the selective barrier within the nuclear pore complex. Yeast FG Nups have previously been shown to be maintained in a highly dynamic state within living bacteria as measured by in-cell NMR. Interactions thought to stabilize this dynamic state are also present in the protein's native organism, although site specificity of interaction is different between the two cytosols.

A Quantitative Model for BicD2/Cargo Interactions

Dynein adaptor proteins such as Bicaudal D2 (BicD2) are integral components of the dynein transport machinery, as they recognize cargoes for cell cycle-specific transport and link them to the motor complex. Human BicD2 switches from selecting secretory and Golgi-derived vesicles for transport in G1 and S phase (by recognizing Rab6^GTP), to selecting the nucleus for transport in G2 phase (by recognizing nuclear pore protein Nup358), but the molecular mechanisms governing this switch are elusive. Here, we have developed a quantitative model for BicD2/cargo interactions that integrates affinities, oligomeric states, and cellular concentrations of the reactants. BicD2 and cargo form predominantly 2:2 complexes. Furthermore, the affinity of BicD2 toward its cargo Nup358 is higher than that toward Rab6^GTP. Based on our calculations, an estimated 1000 BicD2 molecules per cell would be recruited to the nucleus through Nup358 in the absence of regulation. Notably, RanGTP is a negative regulator of the Nup358/BicD2 interaction that weakens the affinity by a factor of 10 and may play a role in averting dynein recruitment to the nucleus outside of the G2 phase. However, our quantitative model predicts that an additional negative regulator remains to be identified. In the absence of negative regulation, the affinity of Nup358 would likely be sufficient to recruit BicD2 to the nucleus in G2 phase. Our quantitative model makes testable predictions of how cellular transport events are orchestrated. These transport processes are important for brain development, cell cycle control, signaling, and neurotransmission at synapses.

Hornerin contains a Linked Series of Ribosome-Targeting Peptide Antibiotics

Cationic intrinsically disordered antimicrobial peptides (CIDAMPs) belong to a novel class of epithelial peptide antibiotics with microbicidal activity against various pathogens, including Pseudomonas aeruginosa, Escherichia coli, Staphylococcus aureus and Candida albicans. Here we show that treatment of distinct bacteria with different hornerin (HRNR)-derived CIDAMPs cause formation of unique cytoplasmic protein aggregates, suggesting a common intracellular mode of action. We further found that, unlike most amphipathic antimicrobial peptides, HRNR traverses bacterial membranes energy-dependently and accumulates within the cytoplasm. Strikingly, certain structurally different, HRNR-based CIDAMPs were found to bind to an identical panel of distinct bacterial ribosomal proteins, thereby manifesting features of several known classes of antibiotics. This may cause the formation of aberrant proteins and toxic protein aggregates in HRNR-treated pathogens which eventually may induce its death. Our study reveals evidence that structurally distinct CIDAMPs of an abundant body surface protein simultaneously target multiple sites of the bacterial protein synthesis machinery.

In vivo 3'-to-5' exoribonuclease targetomes of Streptococcus pyogenes

To cope with harsh environments and cause infection, bacteria need to constantly adjust gene expression. Ribonucleases (RNases) control the abundance of regulatory and protein-coding RNA through degradation and maturation. The current characterization of 3′-to-5′ exoribonucleases (exoRNases), processing RNAs from their 3′ end, is solely based on the description of a limited number of targets processed by these RNases. Here, we characterized bacterial 3′-to-5′ exoRNase targetomes. We show that YhaM, polynucleotide phosphorylase (PNPase), and RNase R have exoribonucleolytic activities in the human pathogen Streptococcus pyogenes. We demonstrate that PNPase is the main 3′-to-5′ exoRNase participating in RNA decay, we show that RNase R has a limited processing activity, and we describe an intriguing RNA processing behavior for YhaM.

mRNA decay plays an essential role in the control of gene expression in bacteria. Exoribonucleases (exoRNases), which trim transcripts starting from the 5′ or 3′ end, are particularly important to fully degrade unwanted transcripts and renew the pool of nucleotides available in the cell. While recent techniques have allowed genome-wide identification of ribonuclease (RNase) targets in bacteria in vivo, none of the 3′-to-5′ exoRNase targetomes (i.e., global processing sites) have been studied so far. Here, we report the targetomes of YhaM, polynucleotide phosphorylase (PNPase), and RNase R of the human pathogen Streptococcus pyogenes. We determined that YhaM is an unspecific enzyme that trims a few nucleotides and targets the majority of transcript ends, generated either by transcription termination or by endonucleolytic activity. The molecular determinants for YhaM-limited processivity are yet to be deciphered. We showed that PNPase clears the cell from mRNA decay fragments produced by endoribonucleases (endoRNases) and is the major 3′-to-5′ exoRNase for RNA turnover in S. pyogenes. In particular, PNPase is responsible for the degradation of regulatory elements from 5′ untranslated regions. However, we observed little RNase R activity in standard culture conditions. Overall, our study sheds light on the very distinct features of S. pyogenes 3′-to-5′ exoRNases.

Qualitative Differences in Capsidless L-Particles Released as a By-Product of Bovine Herpesvirus 1 and Herpes Simplex Virus 1 Infections

The alphaherpesvirus family includes viruses that infect humans and animals. Hence, not only do they have a significant impact on human health, but they also have a substantial economic impact on the farming industry. While the pathogenic manifestations of the individual viruses differ from host to host, their relative genetic compositions suggest similarity at the molecular level. This study provides a side-by-side comparison of the particle outputs from the major human pathogen HSV-1 and the veterinary pathogen BoHV-1. Ultrastructural and proteomic analyses have revealed that both viruses have broadly similar morphogenesis profiles and infectious virus compositions. However, the demonstration that BoHV-1 has the capacity to generate vast numbers of capsidless enveloped particles that differ from those produced by HSV-1 in composition implies a divergence in the cell biology of these viruses that impacts our general understanding of alphaherpesvirus morphogenesis.

Despite differences in the pathogenesis and host range of alphaherpesviruses, many stages of their morphogenesis are thought to be conserved. Here, an ultrastructural study of bovine herpesvirus 1 (BoHV-1) envelopment revealed profiles similar to those previously found for herpes simplex virus 1 (HSV-1), with BoHV-1 capsids associating with endocytic tubules. Consistent with the similarity of their genomes and envelopment strategies, the proteomic compositions of BoHV-1 and HSV-1 virions were also comparable. However, BoHV-1 morphogenesis exhibited a diversity in envelopment events. First, heterogeneous primary envelopment profiles were readily detectable at the inner nuclear membrane of BoHV-1-infected cells. Second, the BoHV-1 progeny comprised not just full virions but also an abundance of capsidless, noninfectious light particles (L-particles) that were released from the infected cells in numbers similar to those of virions and in the absence of DNA replication. Proteomic analysis of BoHV-1 L-particles and the much less abundant HSV-1 L-particles revealed that they contained the same complement of envelope proteins as virions but showed variations in tegument content. In the case of HSV-1, the U_L46 tegument protein was reproducibly found to be >6-fold enriched in HSV-1 L-particles. More strikingly, the tegument proteins U_L36, U_L37, U_L21, and U_L16 were depleted in BoHV-1 but not HSV-1 L-particles. We propose that these combined differences reflect the presence of truly segregated “inner” and “outer” teguments in BoHV-1, making it a critical system for studying the structure and process of tegumentation and envelopment.

IMPORTANCE The alphaherpesvirus family includes viruses that infect humans and animals. Hence, not only do they have a significant impact on human health, but they also have a substantial economic impact on the farming industry. While the pathogenic manifestations of the individual viruses differ from host to host, their relative genetic compositions suggest similarity at the molecular level. This study provides a side-by-side comparison of the particle outputs from the major human pathogen HSV-1 and the veterinary pathogen BoHV-1. Ultrastructural and proteomic analyses have revealed that both viruses have broadly similar morphogenesis profiles and infectious virus compositions. However, the demonstration that BoHV-1 has the capacity to generate vast numbers of capsidless enveloped particles that differ from those produced by HSV-1 in composition implies a divergence in the cell biology of these viruses that impacts our general understanding of alphaherpesvirus morphogenesis.

TISSUES 2.0: an integrative web resource on mammalian tissue expression

Physiological and molecular similarities between organisms make it possible to translate findings from simpler experimental systems—model organisms—into more complex ones, such as human. This translation facilitates the understanding of biological processes under normal or disease conditions. Researchers aiming to identify the similarities and differences between organisms at the molecular level need resources collecting multi-organism tissue expression data. We have developed a database of gene–tissue associations in human, mouse, rat and pig by integrating multiple sources of evidence: transcriptomics covering all four species and proteomics (human only), manually curated and mined from the scientific literature. Through a scoring scheme, these associations are made comparable across all sources of evidence and across organisms. Furthermore, the scoring produces a confidence score assigned to each of the associations. The TISSUES database (version 2.0) is publicly accessible through a user-friendly web interface and as part of the STRING app for Cytoscape. In addition, we analyzed the agreement between datasets, across and within organisms, and identified that the agreement is mainly affected by the quality of the datasets rather than by the technologies used or organisms compared.

Database URL: http://tissues.jensenlab.org/

Iron in Friedreich Ataxia: A Central Role in the Pathophysiology or an Epiphenomenon?

Friedreich ataxia is a neurodegenerative disease with an autosomal recessive inheritance. In most patients, the disease is caused by the presence of trinucleotide GAA expansions in the first intron of the frataxin gene. These expansions cause the decreased expression of this mitochondrial protein. Many evidences indicate that frataxin deficiency causes the deregulation of cellular iron homeostasis. In this review, we will discuss several hypotheses proposed for frataxin function, their caveats, and how they could provide an explanation for the deregulation of iron homeostasis found in frataxin-deficient cells. We will also focus on the potential mechanisms causing cellular dysfunction in Friedreich Ataxia and on the potential use of the iron chelator deferiprone as a therapeutic agent for this disease.

Extending Single Molecule Imaging to Proteome Analysis by Quantitation of Fluorescent Labeling Homogeneity in Complex Protein Samples

Fluorescence-based electrophoresis has been widely used for proteome analysis in which every protein species in cells is labeled with a fluorescent dye, separated by electric migration, and quantified using fluorescence detection. The ultimate limit of sensitivity for this approach could be reached by single-molecule fluorescence imaging and counting individual proteins, requiring exhaustive fluorescent labeling of proteins across molecular populations and species. However, it remains unclear how homogeneous the fluorescence labeling of individual protein molecules of each species is across the proteome. To address this question, we developed a method to measure the labeling homogeneity based on a single-molecule fluorescence counting assay. Our results reveal that the proportion of proteins labeled with at least one dye, called labeling occupancy (LO), was 35% for fluorescently labeled BSA using existing protocols. We then found that the LO could be improved to 82% under high pH and surfactant-rich conditions. Furthermore, when a proteome sample from a human cell lysate was analyzed, the total LO was 71%, whereby the values varied between 50 and 90% for low and high molecular weight proteome fractions, respectively. The results support the possibility of sensitive detection of proteins using single-molecule counting with fluorescent labeling at the proteome scale.

A study on multi-omic oscillations in Escherichia coli metabolic networks

BACKGROUND

Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels.

RESULTS

Here we address the problem of integrating various types of molecular information (a large collection of gene expression and sequence data, codon usage and protein abundances) to analyse the E.coli metabolic response to treatments at the whole network level. Our algorithm, MORA (Multi-omic relations adjacency) is able to detect patterns which may represent metabolic network motifs at pathway and supra pathway levels which could hint at some functional role. We provide a description and insights on the algorithm by testing it on a large database of responses to antibiotics. Along with the algorithm MORA, a novel model for the analysis of oscillating multi-omics has been proposed. Interestingly, the resulting analysis suggests that some motifs reveal recurring oscillating or position variation patterns on multi-omics metabolic networks. Our framework, implemented in R, provides effective and friendly means to design intervention scenarios on real data. By analysing how multi-omics data build up multi-scale phenotypes, the software allows to compare and test metabolic models, design new pathways or redesign existing metabolic pathways and validate in silico metabolic models using nearby species.

CONCLUSIONS

The integration of multi-omic data reveals that E.coli multi-omic metabolic networks contain position dependent and recurring patterns which could provide clues of long range correlations in the bacterial genome.

Purifying and positive selection in the evolution of stop codons

Modes of evolution of stop codons in protein-coding genes, especially the conservation of UAA, have been debated for many years. We reconstructed the evolution of stop codons in 40 groups of closely related prokaryotic and eukaryotic genomes. The results indicate that the UAA codons are maintained by purifying selection in all domains of life. In contrast, positive selection appears to drive switches from UAG to other stop codons in prokaryotes but not in eukaryotes. Changes in stop codons are significantly associated with increased substitution frequency immediately downstream of the stop. These positions are otherwise more strongly conserved in evolution compared to sites farther downstream, suggesting that such substitutions are compensatory. Although GC content has a major impact on stop codon frequencies, its contribution to the decreased frequency of UAA differs between bacteria and archaea, presumably, due to differences in their translation termination mechanisms.

Protein-Sol: a web tool for predicting protein solubility from sequence

Motivation

Protein solubility is an important property in industrial and therapeutic applications. Prediction is a challenge, despite a growing understanding of the relevant physicochemical properties.

Results

Protein–Sol is a web server for predicting protein solubility. Using available data for Escherichia coli protein solubility in a cell-free expression system, 35 sequence-based properties are calculated. Feature weights are determined from separation of low and high solubility subsets. The model returns a predicted solubility and an indication of the features which deviate most from average values. Two other properties are profiled in windowed calculation along the sequence: fold propensity, and net segment charge. The utility of these additional features is demonstrated with the example of thioredoxin.

Availability and implementation

The Protein–Sol webserver is available at http://protein-sol.manchester.ac.uk.