A comprehensive assessment of N-terminal signal peptides prediction methods

Protein Sorting Prediction

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global property-based, and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches are described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

Proteomic and Transcriptomic Analyses to Decipher the Chitinolytic Response of Jeongeupia spp

In nature, chitin, the most abundant marine biopolymer, does not accumulate due to the action of chitinolytic organisms, whose saccharification systems provide instructional blueprints for effective chitin conversion. Therefore, discovery and deconstruction of chitinolytic machineries and associated enzyme systems are essential for the advancement of biotechnological chitin valorization. Through combined investigation of the chitin-induced secretome with differential proteomic and transcriptomic analyses, a holistic system biology approach has been applied to unravel the chitin response mechanisms in the Gram-negative Jeongeupia wiesaeckerbachi. Hereby, the majority of the genome-encoded chitinolytic machinery, consisting of various glycoside hydrolases and a lytic polysaccharide monooxygenase, could be detected extracellularly. Intracellular proteomics revealed a distinct translation pattern with significant upregulation of glucosamine transport, metabolism, and chemotaxis-associated proteins. While the differential transcriptomic results suggested the overall recruitment of more genes during chitin metabolism compared to that of glucose, the detected protein-mRNA correlation was low. As one of the first studies of its kind, the involvement of over 350 unique enzymes and 570 unique genes in the catabolic chitin response of a Gram-negative bacterium could be identified through a three-way systems biology approach. Based on the cumulative data, a holistic model for the chitinolytic machinery of Jeongeupia spp. is proposed.

Thousands of human non-AUG extended proteoforms lack evidence of evolutionary selection among mammals

The synthesis of most proteins begins at AUG codons, yet a small number of non-AUG initiated proteoforms are also known. Here we analyse a large number of publicly available Ribo-seq datasets to identify novel, previously uncharacterised non-AUG proteoforms using Trips-Viz implementation of a novel algorithm for detecting translated ORFs. In parallel we analyse genomic alignment of 120 mammals to identify evidence of protein coding evolution in sequences encoding potential extensions. Unexpectedly we find that the number of non-AUG proteoforms identified with ribosome profiling data greatly exceeds those with strong phylogenetic support suggesting their recent evolution. Our study argues that the protein coding potential of human genome greatly exceeds that detectable through comparative genomics and exposes the existence of multiple proteins encoded by the same genomic loci.

Comprehensive Transcriptome Analysis Reveals Genome-Wide Changes Associated with Endoplasmic Reticulum (ER) Stress in Potato (Solanum tuberosum L.)

We treated potato (Solanum tuberosum L.) plantlets with TM and performed gene expression studies to identify genome-wide changes associated with endoplasmic reticulum (ER) stress and the unfolded protein response (UPR). An extensive network of responses was identified, including chromatin remodeling, transcriptional reprogramming, as well as changes in the structural components of the endomembrane network system. Limited genome-wide changes in alternative RNA splicing patterns of protein-coding transcripts were also discovered. Significant changes in RNA metabolism, components of the translation machinery, as well as factors involved in protein folding and maturation occurred, which included a broader set of genes than expected based on Arabidopsis research. Antioxidant defenses and oxygen metabolic enzymes are differentially regulated, which is expected of cells that may be experiencing oxidative stress or adapting to protect proteins from oxidation. Surges in protein kinase expression indicated early signal transduction events. This study shows early genomic responses including an array of differentially expressed genes that have not been reported in Arabidopsis. These data describe novel ER stress responses in a solanaceous host.

The First Report of Polymorphisms and Genetic Characteristics of the Shadow of Prion Protein (SPRN) in Prion Disease-Resistant Animal, Chickens

Prion diseases are irreversible neurodegenerative disorders caused by the aggregated form of prion protein (PrPSc) derived from the normal form of prion protein (PrPC). Previous studies have reported that shadow of prion protein (Sho) interacts with prion protein (PrP) and accelerates the conversion of PrPC to PrPSc. In addition, genetic polymorphisms of the shadow of the prion protein gene (SPRN) are related to the vulnerability of prion diseases in various hosts. However, to date, polymorphisms and genetic features of the SPRN gene have not been investigated in chickens, which are prion disease-resistant animals. We investigated genetic polymorphisms of the SPRN gene in 2 breeds of chickens, i.e., Dekalb White and Ross, using amplicon sequencing. We analyzed genotype, allele and haplotype frequencies and linkage disequilibrium (LD) among the genetic polymorphisms. In addition, we compared the amino acid sequences of Sho among several prion-related species to identify the unique genetic features of chicken Sho using ClustalW. Furthermore, we evaluated the N-terminal signal peptide and glycosylphosphatidylinositol (GPI)-anchor using SignalP and PredGPI, respectively. Finally, we compared the number of SPRN polymorphisms between prion disease-resistant and prion disease-susceptible animals. We identified 7 novel single nucleotide polymorphisms (SNPs), including 1 synonymous SNP in the open reading frame (ORF) of the chicken SPRN gene. We also found significantly different genotypes, allele frequencies and haplotypes between the 2 chicken breeds. In addition, we found that the interaction regions between Sho and PrP and the NXT glycosylation motif were conserved among all species. Notably, sequence similarity was extremely low in the N-terminal and C-terminal regions between mammals and chickens. Furthermore, we found that chicken Sho was the longest N-terminal signal peptide, and the amino acids of the cutting site of chicken are different from those of mammals. Last, unlike other species investigated, omega-site and signal sequences of the GPI-anchor were not found in chickens. To the best of our knowledge, this is the first report of genetic polymorphisms of the SPRN gene in chickens.

Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences

At the time of translation, nascent proteins are thought to be sorted into their final subcellular localization sites, based on the part of their amino acid sequences (i.e., sorting or targeting signals). Thus, it is interesting to computationally recognize these signals from the amino acid sequences of any given proteins and to predict their final subcellular localization with such information, supplemented with additional information (e.g., k-mer frequency). This field has a long history and many prediction tools have been released. Even in this era of proteomic atlas at the single-cell level, researchers continue to develop new algorithms, aiming at accessing the impact of disease-causing mutations/cell type-specific alternative splicing, for example. In this article, we overview the entire field and discuss its future direction.

The luminescent HiBiT peptide enables selective quantitation of G protein-coupled receptor ligand engagement and internalization in living cells

G protein-coupled receptors (GPCRs) are prominent targets to new therapeutics for a range of diseases. Comprehensive assessments of their cellular interactions with bioactive compounds, particularly in a kinetic format, are imperative to the development of drugs with improved efficacy. Hence, we developed complementary cellular assays that enable equilibrium and real-time analyses of GPCR ligand engagement and consequent activation, measured as receptor internalization. These assays utilize GPCRs genetically fused to an N-terminal HiBiT peptide (1.3 kDa), which produces bright luminescence upon high-affinity complementation with LgBiT, an 18-kDa subunit derived from NanoLuc. The cell impermeability of LgBiT limits signal detection to the cell surface and enables measurements of ligand-induced internalization through changes in cell-surface receptor density. In addition, bioluminescent resonance energy transfer is used to quantify dynamic interactions between ligands and their cognate HiBiT-tagged GPCRs through competitive binding with fluorescent tracers. The sensitivity and dynamic range of these assays benefit from the specificity of bioluminescent resonance energy transfer and the high signal intensity of HiBiT/LgBiT without background luminescence from receptors present in intracellular compartments. These features allow analyses of challenging interactions having low selectivity or affinity and enable studies using endogenously tagged receptors. Using the β-adrenergic receptor family as a model, we demonstrate the versatility of these assays by utilizing the same HiBiT construct in analyses of multiple aspects of GPCR pharmacology. We anticipate that this combination of target engagement and proximal functional readout will prove useful to the study of other GPCR families and the development of new therapeutics.

Isolation of plastids and mitochondria from Chromera velia

We present an easy and effective procedure to purify plastids and mitochondria from Chromera velia. Our method enables downstream analyses of protein and metabolite content of the organelles. Chromerids are alveolate algae that are the closest known phototrophic relatives to apicomplexan parasites such as Plasmodium or Toxoplasma. While genomic and transcriptomic resources for chromerids are in place, tools and experimental conditions for proteomic studies have not been developed yet. Here we describe a rapid and efficient protocol for simultaneous isolation of plastids and mitochondria from the chromerid alga Chromera velia. This procedure involves enzymatic treatment and breakage of cells, followed by differential centrifugation. While plastids sediment in the first centrifugation step, mitochondria remain in the supernatant. Subsequently, plastids can be purified from the crude pellet by centrifugation on a discontinuous 60%/70% sucrose density gradient, while mitochondria can be obtained by centrifugation on a discontinuous 33%/80% Percoll density gradient. Isolated plastids are autofluorescent, and their multi-membrane structure was confirmed by transmission electron microscopy. Fluorescent optical microscopy was used to identify isolated mitochondria stained with MitoTracker^TM green, while their intactness and membrane potential were confirmed by staining with MitoTracker^TM orange CMTMRos. Total proteins were extracted from isolated organellar fractions, and the purity of isolated organelles was confirmed using immunoblotting. Antibodies against the beta subunit of the mitochondrial ATP synthase and the plastid protochlorophyllide oxidoreductase did not cross-react on immunoblots, suggesting that each organellar fraction is free of the residues of the other. The presented protocol represents an essential step for further proteomic, organellar, and cell biological studies of C. velia and can be employed, with minor optimizations, in other thick-walled unicellular algae.

Characterization, evolution, and expression analysis of TLR7 gene subfamily members in Mastacembelus armatus (Synbranchiformes: Mastacembelidae)

TLR7 subfamily members are important pattern recognition receptors participating in the recognition of pathogen-associated molecular patterns. In this study, we successfully identified 3 members of TLR7 subfamily from the spiny eel Mastacembelus armatus (MaTLR7, MaTLR8 and MaTLR9). The amino acid sequence identities of MaTLR7 and MaTLR8 with Monopterus albus TLR7 were 87.2% and 76.5%, respectively and the identity of MaTLR9 with Seriola lalandi TLR9 was 74.7%. The phylogenetic analysis revealed MaTLRs showed close relationship to other species in Synbranchiformes or Perciformes. Quantitative real-time PCR analysis revealed that they were expressed in all tested tissues and higher expression was found in spleen or gill. After infection with Aeromonas veronii, expression of MaTLR7, MaTLR8 and MaTLR9 were all significantly downregulated in spleen and kidney. Evolutionary analysis suggested that the ancestral lineages of teleost TLR8 and TLR9 had been subject to positive selection pressures and multiple Maximum likelihood methods recovered 3 positively selected sites in teleost TLR7, 4 in TLR8 and 8 in TLR9. Domain distribution revealed most positively selected sites were located in leucine-rich repeat domain. Our results will contribute to better understanding the antibacterial mechanism of TLRs and their co-evolution with pathogens.

Bacterial Signal Peptidases

Signal peptidases are the membrane bound enzymes that cleave off the amino-terminal signal peptide from secretory preproteins . There are two types of bacterial signal peptidases . Type I signal peptidase utilizes a serine/lysine catalytic dyad mechanism and is the major signal peptidase in most bacteria. Type II signal peptidase is an aspartic protease specific for prolipoproteins. This chapter will review what is known about the structure, function and mechanism of these unique enzymes.

Protein Sorting Prediction

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

Characterization, expression, and evolutionary analysis of new TLR3 and TLR5M genes cloned from the spiny eel Mastacembelus armatus

Toll-like receptors (TLRs) play an important role in innate and adaptive immunity. Here, we identify two new TLRs from the spiny eel Mastacembelus armatus (TLR3 and membrane TLR5M). Both MaTLR3 and MaTLR5M were expressed in all tested tissues; expression was highest in liver and spleen, respectively. After infection with Vibrio parahaemolyticus, expression of both TLRs fluctuated and differed significantly from controls at several time points. The predicted three-dimensional model of the MaTLR3 and MaTLR5M proteins indicates that most sites under positive selection were located in the extracellular domains of TLRs. Evolutionary analysis detected positively selected sites in the ancestral lineages of vertebrates, amphibians and reptiles. Multiple ML methods recovered 10 positively selected sites in teleost TLR3 and 24 in TLR5M, and most sites were located in leucine-rich repeat domain, possibly related to an "arms-race" co-evolution with pathogens.

Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms

When predicting the subcellular localization of proteins from their amino acid sequences, there are basically three approaches: signal-based, global property-based, and homology-based. Each of these has its advantages and drawbacks, and it is important when comparing methods to know which approach was used. Various statistical and machine learning algorithms are used with all three approaches, and various measures and standards are employed when reporting the performances of the developed methods. This chapter presents a number of available methods for prediction of sorting signals and subcellular localization, but rather than providing a checklist of which predictors to use, it aims to function as a guide for critical assessment of prediction methods.

Predicting Secretory Proteins with SignalP

SignalP is the currently most widely used program for prediction of signal peptides from amino acid sequences. Proteins with signal peptides are targeted to the secretory pathway, but are not necessarily secreted. After a brief introduction to the biology of signal peptides and the history of signal peptide prediction, this chapter will describe all the options of the current version of SignalP and the details of the output from the program. The chapter includes a case study where the scores of SignalP were used in a novel way to predict the functional effects of amino acid substitutions in signal peptides.

SEC-Translocon Dependent Extracytoplasmic Proteins of Candidatus Liberibacter asiaticus

Citrus Huanglongbing (HLB) is the most destructive citrus disease worldwide. HLB is associated with three species of the phloem-limited, gram-negative, fastidious α-proteobacteria: Candidatus Liberibacter asiaticus (Las), Ca. L. americanus (Lam), and Ca. L. africanus (Laf) with Las being the most widespread species. Las has not been cultured in artificial media, which has greatly hampered our efforts to understand its virulence mechanisms. Las contains a complete Sec-translocon, which has been suggested to transport Las proteins including virulence factors into the extracytoplasmic milieu. In this study, we characterized the Sec-translocon dependent, signal peptide containing extracytoplasmic proteins of Las. A total of 166 proteins of Las-psy62 strain were predicted to contain signal peptides targeting them out of the cell cytoplasm via the Sec-translocon using LipoP, SigalP 3.0, SignalP 4.1, and Phobius. We also predicated SP containing extracytoplasmic proteins for Las-gxpsy and Las-Ishi-1, Lam, Laf, Ca. L. solanacearum (Lso), and L. crescens (Lcr). For experimental validation of the predicted extracytoplasmic proteins, Escherichia coli based alkaline phosphatase (PhoA) gene fusion assays were conducted. A total of 86 out of the 166 predicted Las proteins were experimentally validated to contain signal peptides. Additionally, Las-psy62 lepB (CLIBASIA_04190), the gene encodes signal peptidase I, was able to partially complement the amber mutant of lepB of E. coli. This work will contribute to the identification of Sec-translocon dependent effector proteins of Las, which might be involved in virulence of Las.

Exploring the Secretomes of Microbes and Microbial Communities Using Filamentous Phage Display

Microbial surface and secreted proteins (the secretome) contain a large number of proteins that interact with other microbes, host and/or environment. These proteins are exported by the coordinated activities of the protein secretion machinery present in the cell. A group of bacteriophage, called filamentous phage, have the ability to hijack bacterial protein secretion machinery in order to amplify and assemble via a secretion-like process. This ability has been harnessed in the use of filamentous phage of Escherichia coli in biotechnology applications, including screening large libraries of variants for binding to “bait” of interest, from tissues in vivo to pure proteins or even inorganic substrates. In this review we discuss the roles of secretome proteins in pathogenic and non-pathogenic bacteria and corresponding secretion pathways. We describe the basics of phage display technology and its variants applied to discovery of bacterial proteins that are implicated in colonization of host tissues and pathogenesis, as well as vaccine candidates through filamentous phage display library screening. Secretome selection aided by next-generation sequence analysis was successfully applied for selective display of the secretome at a microbial community scale, the latter revealing the richness of secretome functions of interest and surprising versatility in filamentous phage display of secretome proteins from large number of Gram-negative as well as Gram-positive bacteria and archaea.

Analysis of the Secretome of Apoptotic Peripheral Blood Mononuclear Cells: Impact of Released Proteins and Exosomes for Tissue Regeneration

We previously showed that, when peripheral blood mononuclear cells (PBMCs) were stressed with ionizing radiation, they released paracrine factors that showed regenerative capacity in vitro and in vivo. This study aimed to characterize the secretome of PBMCs and to investigate its biologically active components in vitro and vivo. Bioinformatics analysis revealed that irradiated PBMCs differentially expressed genes that encoded secreted proteins. These genes were primarily involved in (a) pro-angiogenic and regenerative pathways and (b) the generation of oxidized phospholipids with known pro-angiogenic and inflammation-modulating properties. Subsequently, in vitro assays showed that the exosome and protein fractions of irradiated and non-irradiated PBMC secretome were the major biological components that enhanced cell mobility; conversely, secreted lipids and microparticles had no effects. We tested a viral-cleared PBMC secretome, prepared according to good manufacturing practice (GMP), in a porcine model of closed chest, acute myocardial infarction. We found that the potency for preventing ventricular remodeling was similar with the GMP-compliant and experimentally-prepared PBMC secretomes. Our results indicate that irradiation modulates the release of proteins, lipid-mediators and extracellular vesicles from human PBMCs. In addition our findings implicate the use of secretome fractions as valuable material for the development of cell-free therapies in regenerative medicine.

Evaluation of Secretion Prediction Highlights Differing Approaches Needed for Oomycete and Fungal Effectors

The steadily increasing number of sequenced fungal and oomycete genomes has enabled detailed studies of how these eukaryotic microbes infect plants and cause devastating losses in food crops. During infection, fungal and oomycete pathogens secrete effector molecules which manipulate host plant cell processes to the pathogen's advantage. Proteinaceous effectors are synthesized intracellularly and must be externalized to interact with host cells. Computational prediction of secreted proteins from genomic sequences is an important technique to narrow down the candidate effector repertoire for subsequent experimental validation. In this study, we benchmark secretion prediction tools on experimentally validated fungal and oomycete effectors. We observe that for a set of fungal SwissProt protein sequences, SignalP 4 and the neural network predictors of SignalP 3 (D-score) and SignalP 2 perform best. For effector prediction in particular, the use of a sensitive method can be desirable to obtain the most complete candidate effector set. We show that the neural network predictors of SignalP 2 and 3, as well as TargetP were the most sensitive tools for fungal effector secretion prediction, whereas the hidden Markov model predictors of SignalP 2 and 3 were the most sensitive tools for oomycete effectors. Thus, previous versions of SignalP retain value for oomycete effector prediction, as the current version, SignalP 4, was unable to reliably predict the signal peptide of the oomycete Crinkler effectors in the test set. Our assessment of subcellular localization predictors shows that cytoplasmic effectors are often predicted as not extracellular. This limits the reliability of secretion predictions that depend on these tools. We present our assessment with a view to informing future pathogenomics studies and suggest revised pipelines for secretion prediction to obtain optimal effector predictions in fungi and oomycetes.

In silico analysis of Acinetobacter baumannii phospholipase D as a subunit vaccine candidate

The rate of human health care-associated infections caused by Acinetobacter baumannii has increased significantly in recent years for its remarkable resistance to desiccation and most antibiotics. Phospholipases, capable of destroying a phospholipid substrate, are heterologous group of enzymes which are believed to be the bacterial virulence determinants. There is a need for in silico studies to identify potential vaccine candidates. A. baumannii phospholipase D (PLD) role has been proved in increasing organism's resistance to human serum, destruction of host epithelial cell and pathogenesis in murine model. In this in silico study high potentials of A. baumannii PLD in elicitation of humoral and cellular immunities were elucidated. Thermal stability, long half-life, non-similarity to human and gut flora proteome and non-allergenicity were in a list of A. baumannii PLD positive properties. Potential epitopic sequences were also identified that could be used as peptide vaccines against A. baumannii and various other human bacterial pathogens.

Hidden Markov models for evolution and comparative genomics analysis

The problem of reconstruction of ancestral states given a phylogeny and data from extant species arises in a wide range of biological studies. The continuous-time Markov model for the discrete states evolution is generally used for the reconstruction of ancestral states. We modify this model to account for a case when the states of the extant species are uncertain. This situation appears, for example, if the states for extant species are predicted by some program and thus are known only with some level of reliability; it is common for bioinformatics field. The main idea is formulation of the problem as a hidden Markov model on a tree (tree HMM, tHMM), where the basic continuous-time Markov model is expanded with the introduction of emission probabilities of observed data (e.g. prediction scores) for each underlying discrete state. Our tHMM decoding algorithm allows us to predict states at the ancestral nodes as well as to refine states at the leaves on the basis of quantitative comparative genomics. The test on the simulated data shows that the tHMM approach applied to the continuous variable reflecting the probabilities of the states (i.e. prediction score) appears to be more accurate then the reconstruction from the discrete states assignment defined by the best score threshold. We provide examples of applying our model to the evolutionary analysis of N-terminal signal peptides and transcription factor binding sites in bacteria. The program is freely available at http://bioinf.fbb.msu.ru/~nadya/tHMM and via web-service at http://bioinf.fbb.msu.ru/treehmmweb.

SCL-Epred: a generalised de novo eukaryotic protein subcellular localisation predictor

Knowledge of the subcellular location of a protein provides valuable information about its function, possible interaction with other proteins and drug targetability, among other things. The experimental determination of a protein's location in the cell is expensive, time consuming and open to human error. Fast and accurate predictors of subcellular location have an important role to play if the abundance of sequence data which is now available is to be fully exploited. In the post-genomic era, genomes in many diverse organisms are available. Many of these organisms are important in human and veterinary disease and fall outside of the well-studied plant, animal and fungi groups. We have developed a general eukaryotic subcellular localisation predictor (SCL-Epred) which predicts the location of eukaryotic proteins into three classes which are important, in particular, for determining the drug targetability of a protein-secreted proteins, membrane proteins and proteins that are neither secreted nor membrane. The algorithm powering SCL-Epred is a N-to-1 neural network and is trained on very large non-redundant sets of protein sequences. SCL-Epred performs well on training data achieving a Q of 86 % and a generalised correlation of 0.75 when tested in tenfold cross-validation on a set of 15,202 redundancy reduced protein sequences. The three class accuracy of SCL-Epred and LocTree2, and in particular a consensus predictor comprising both methods, surpasses that of other widely used predictors when benchmarked using a large redundancy reduced independent test set of 562 proteins. SCL-Epred is publicly available at http://distillf.ucd.ie/distill/ .

A plasmodesmal glycosyltransferase-like protein

Plasmodesmata (Pd) are plant intercellular connections that represent cytoplasmic conduits for a wide spectrum of cellular transport cargoes, from ions to house-keeping proteins to transcription factors and RNA silencing signals; furthermore, Pd are also utilized by most plant viruses for their spread between host cells. Despite this central role of Pd in the plant life cycle, their structural and functional composition remains poorly characterized. In this study, we used a known Pd-associated calreticulin protein AtCRT1 as bait to isolate other Pd associated proteins in Arabidopsis thaliana. These experiments identified a beta-1,6-N-acetylglucosaminyl transferase-like enzyme (AtGnTL). Subcellular localization studies using confocal microscopy observed AtGnTL at Pd within living plant cells and demonstrated colocalization with a Pd callose-binding protein (AtPDCB1). That AtGnTL is resident in Pd was consistent with its localization within the plant cell wall following plasmolysis. Initial characterization of an Arabidopsis T-DNA insertional mutant in the AtGnTL gene revealed defects in seed germination and delayed plant growth.

Bioinformatics tools for secretome analysis

Over recent years, analyses of secretomes (complete sets of secreted proteins) have been reported in various organisms, cell types, and pathologies and such studies are quickly gaining popularity. Fungi secrete enzymes can break down potential food sources; plant secreted proteins are primarily parts of the cell wall proteome; and human secreted proteins are involved in cellular immunity and communication, and provide useful information for the discovery of novel biomarkers, such as for cancer diagnosis. Continuous development of methodologies supports the wide identification and quantification of secreted proteins in a given cellular state. The role of secreted factors is also investigated in the context of the regulation of major signaling events, and connectivity maps are built to describe the differential expression and dynamic changes of secretomes. Bioinformatics has become the bridge between secretome data and computational tasks for managing, mining, and retrieving information. Predictions can be made based on this information, contributing to the elucidation of a given organism's physiological state and the determination of the specific malfunction in disease states. Here we provide an overview of the available bioinformatics databases and software that are used to analyze the biological meaning of secretome data, including descriptions of the main functions and limitations of these tools. The important challenges of data analysis are mainly related to the integration of biological information from dissimilar sources. Improvements in databases and developments in software will likely substantially contribute to the usefulness and reliability of secretome studies. This article is part of a Special Issue entitled: An Updated Secretome.

Identification of Candidate Vaccine Antigens In Silico

The identification of immunogenic whole-protein antigens is fundamental to the successful discovery of candidate subunit vaccines and their rapid, effective, and efficient transformation into clinically useful, commercially successful vaccine formulations. In the wider context of the experimental discovery of vaccine antigens, with particular reference to reverse vaccinology, this chapter adumbrates the principal computational approaches currently deployed in the hunt for novel antigens: genome-level prediction of antigens, antigen identification through the use of protein sequence alignment-based approaches, antigen detection through the use of subcellular location prediction, and the use of alignment-independent approaches to antigen discovery. Reference is also made to the recent emergence of various expert systems for protein antigen identification.

Functional characterization and immune recognition of the extracellular superoxide dismutase from the human pathogenic parasite Onchocerca volvulus (OvEC-SOD)

Onchocerca volvulus is a human pathogenic filarial nematode causing chronic onchocerciasis, a disease characterized by chronic skin and eye lesions. Despite attempts to control this infection from many perspectives, it still remains a threat to public health because of adverse effects of available drugs and recent reports of drug resistance. Under control of an intact immune system, O. volvulus survives for a long time in the host by employing a variety of strategies including the utility of antioxidant enzymes. In the present study, we focus on the extracellular superoxide dismutase from O. volvulus (OvEC-SOD) found in the excretory/secretory products of adult worms. Contrary to previous studies, the OvEC-SOD was found to have a 19 amino acid long signal peptide that is cleaved off during the process of maturation. To validate this result, we designed a novel method based on Caenorhabditis elegans cup5(ar465) mutants to specifically evaluate signal peptide-mediated secretion of nematodal proteins. Following purification, the recombinant OvEC-SOD was active as a dimer. Site-directed mutagenesis of the three cysteines present in the OvEC-SOD shows that enzyme activity is markedly reduced in the Cys-192 mutant. A homology model of the OvEC-SOD underlines the importance of Cys-192 for the stabilization of the adjacent active site channel. The generation of a humoral immune response to secretory OvEC-SOD was indicated by demonstrating IgG reactivity in sera from patients infected with O. volvulus while the cross-reactivity of IgG in plasma samples from cows, infected with the most closely related parasite Onchocerca ochengi, occurred only marginally. High IgG1 and IgM titres were recorded in sera from mice infected with the filaria Litomosoides sigmodontis, however, low or no cellular proliferative responses were observed. Thus, the present data suggest that secretory OvEC-SOD is a target of the humoral immune response in human onchocerciasis and induced strongest IgG responses in hyperreactive onchocerciasis. Furthermore, humoral response during murine infection induced SOD-specific IgG that cross-reacted with OvEC-SOD.

Secretory signal peptide modification for optimized antibody-fragment expression-secretion in Leishmania tarentolae

BACKGROUND

Secretory signal peptides (SPs) are well-known sequence motifs targeting proteins for translocation across the endoplasmic reticulum membrane. After passing through the secretory pathway, most proteins are secreted to the environment. Here, we describe the modification of an expression vector containing the SP from secreted acid phosphatase 1 (SAP1) of Leishmania mexicana for optimized protein expression-secretion in the eukaryotic parasite Leishmania tarentolae with regard to recombinant antibody fragments. For experimental design the online tool SignalP was used, which predicts the presence and location of SPs and their cleavage sites in polypeptides. To evaluate the signal peptide cleavage site as well as changes of expression, SPs were N-terminally linked to single-chain Fragment variables (scFv's). The ability of L. tarentolae to express complex eukaryotic proteins with highly diverse post-translational modifications and its easy bacteria-like handling, makes the parasite a promising expression system for secretory proteins.

RESULTS

We generated four vectors with different SP-sequence modifications based on in-silico analyses with SignalP in respect to cleavage probability and location, named pLTEX-2 to pLTEX-5. To evaluate their functionality, we cloned four individual scFv-fragments into the vectors and transfected all 16 constructs into L. tarentolae. Independently from the expressed scFv, pLTEX-5 derived constructs showed the highest expression rate, followed by pLTEX-4 and pLTEX-2, whereas only low amounts of protein could be obtained from pLTEX-3 clones, indicating dysfunction of the SP. Next, we analysed the SP cleavage sites by Edman degradation. For pLTEX-2, -4, and -5 derived scFv's, the results corresponded to in-silico predictions, whereas pLTEX-3 derived scFv's contained one additional amino-acid (AA).

CONCLUSIONS

The obtained results demonstrate the importance of SP-sequence optimization for efficient expression-secretion of scFv's. We could successfully demonstrate that minor modifications in the AA-sequence in the c-region of the natural SP from SAP1, based on in-silico predictions following the (-3, -1) rule, resulted in different expression-secretion rates of the protein of interest. The yield of scFv production could be improved close to one order of magnitude. Therefore, SP-sequence optimization is a viable option to increase the overall yield of recombinant protein production.

Renal function at hospital admission and mortality due to acute kidney injury after myocardial infarction

BACKGROUND

The role of an impaired estimated glomerular filtration rate (eGFR) at hospital admission in the outcome of acute kidney injury (AKI) after acute myocardial infarction (AMI) has been underreported. The aim of this study was to assess the influence of an admission eGFR<60 mL/min/1.73 m(2) on the incidence and early and late mortality of AMI-associated AKI.

METHODS

A prospective study of 828 AMI patients was performed. AKI was defined as a serum creatinine increase of ≥ 50% from the time of admission (RIFLE criteria) in the first 7 days of hospitalization. Patients were divided into subgroups according to their eGFR upon hospital admission (MDRD formula, mL/min/1.73 m(2)) and the development of AKI: eGFR ≥ 60 without AKI, eGFR<60 without AKI, eGFR ≥ 60 with AKI and eGFR<60 with AKI.

RESULTS

Overall, 14.6% of the patients in this study developed AKI. The admission eGFR had no impact on the incidence of AKI. However, the admission eGFR was associated with the outcome of AMI-associated AKI. The adjusted hazard ratios (AHR, Cox multivariate analysis) for 30-day mortality were 2.00 (95% CI 1.11-3.61) for eGFR<60 without AKI, 4.76 (95% CI 2.45-9.26) for eGFR ≥ 60 with AKI and 6.27 (95% CI 3.20-12.29) for eGFR<60 with AKI. Only an admission eGFR of <60 with AKI was significantly associated with a 30-day to 1-year mortality hazard (AHR 3.05, 95% CI 1.50-6.19).

CONCLUSIONS

AKI development was associated with an increased early mortality hazard in AMI patients with either preserved or impaired admission eGFR. Only the association of impaired admission eGFR and AKI was associated with an increased hazard for late mortality among these patients.

Evidence for conserved function of γ-glutamyltranspeptidase in Helicobacter genus

The confounding consequences of Helicobacter bilis infection in experimental mice populations are well recognized, but the role of this bacterium in human diseases is less known. Limited data are available on virulence determinants of this species. In Helicobacter pylori, γ-glutamyltranspeptidase (γGT) contributes to the colonization of the gastric mucosa and to the pathogenesis of peptic ulcer. The role of γGT in H. bilis infections remains unknown. The annotated genome sequence of H. bilis revealed two putative ggt genes and our aim was to characterize these H. bilis γGT paralogues. We performed a phylogenetic analysis to understand the evolution of Helicobacter γGTs and to predict functional activities of these two genes. In addition, both copies of H. bilis γGTs were expressed as recombinant proteins and their biochemical characteristics were analysed. Functional complementation of Esherichia coli deficient in γGT activity and deletion of γGT in H. bilis were performed. Finally, the inhibitory effect of T-cell and gastric cell proliferation by H. bilis γGT was assessed. Our results indicated that one gene is responsible for γGT activity, while the other showed no γGT activity due to lack of autoprocessing. Although both H. bilis and H. pylori γGTs exhibited a similar affinity to L-Glutamine and γ-Glutamyl-p-nitroanilide, the H. bilis γGT was significantly less active. Nevertheless, H. bilis γGT inhibited T-cell proliferation at a similar level to that observed for H. pylori. Finally, we showed a similar suppressive influence of both H. bilis and H. pylori γGTs on AGS cell proliferation mediated by an apoptosis-independent mechanism. Our data suggest a conserved function of γGT in the Helicobacter genus. Since γGT is present only in a few enterohepatic Helicobacter species, its expression appears not to be essential for colonization of the lower gastrointestinal tract, but it could provide metabolic advantages in colonization capability of different niches.

Signatures of positive selection in Toll-like receptor (TLR) genes in mammals

Background

Toll-like receptors (TLRs) are a major class of pattern recognition receptors (PRRs) expressed in the cell surface or membrane compartments of immune and non-immune cells. TLRs are encoded by a multigene family and represent the first line of defense against pathogens by detecting foreigner microbial molecular motifs, the pathogen-associated molecular patterns (PAMPs). TLRs are also important by triggering the adaptive immunity in vertebrates. They are characterized by the presence of leucine-rich repeats (LRRs) in the ectodomain, which are associated with the PAMPs recognition. The direct recognition of different pathogens by TLRs might result in different evolutionary adaptations important to understand the dynamics of the host-pathogen interplay. Ten mammal TLR genes, viral (TLR3, 7, 8, 9) and non-viral (TLR1-6, 10), were selected to identify signatures of positive selection that might have been imposed by interacting pathogens and to clarify if viral and non-viral TLRs might display different patterns of molecular evolution.

Results

By using Maximum Likelihood approaches, evidence of positive selection was found in all the TLRs studied. The number of positively selected codons (PSC) ranged between 2-26 codons (0.25%-2.65%) with the non-viral TLR4 as the receptor with higher percentage of positively selected codons (2.65%), followed by the viral TLR8 (2.50%). The results indicated that viral and non-viral TLRs are similarly under positive selection. Almost all TLRs have at least one PSC located in the LRR ectodomain which underlies the importance of the pathogen recognition by this region.

Conclusions

Our results are not in line with previous studies on primates and birds that identified more codons under positive selection in non-viral TLRs. This might be explained by the fact that both primates and birds are homogeneous groups probably being affected by only a restricted number of related viruses with equivalent motifs to be recognized. The analyses performed in this work encompassed a large number of species covering some of the most representative mammalian groups - Artiodactyla, Rodents, Carnivores, Lagomorphs and Primates - that are affected by different families of viruses. This might explain the role of adaptive evolution in shaping viral TLR genes.

ClubSub-P: Cluster-Based Subcellular Localization Prediction for Gram-Negative Bacteria and Archaea

The subcellular localization (SCL) of proteins provides important clues to their function in a cell. In our efforts to predict useful vaccine targets against Gram-negative bacteria, we noticed that misannotated start codons frequently lead to wrongly assigned SCLs. This and other problems in SCL prediction, such as the relatively high false-positive and false-negative rates of some tools, can be avoided by applying multiple prediction tools to groups of homologous proteins. Here we present ClubSub-P, an online database that combines existing SCL prediction tools into a consensus pipeline from more than 600 proteomes of fully sequenced microorganisms. On top of the consensus prediction at the level of single sequences, the tool uses clusters of homologous proteins from Gram-negative bacteria and from Archaea to eliminate false-positive and false-negative predictions. ClubSub-P can assign the SCL of proteins from Gram-negative bacteria and Archaea with high precision. The database is searchable, and can easily be expanded using either new bacterial genomes or new prediction tools as they become available. This will further improve the performance of the SCL prediction, as well as the detection of misannotated start codons and other annotation errors. ClubSub-P is available online at http://toolkit.tuebingen.mpg.de/clubsubp/

Characterization of Toxoplasma gondii 5' UTR with encyclopedic TSS information

The 5' UTR is widely involved in gene expression via post-transcriptional regulation. However, a detailed profile of the 5' UTR for Toxoplasma gondii has not yet been demonstrated. To investigate the issue, we compared the predicted open reading frames (ORFs) and transcription start sites (TSSs) of T. gondii obtained by TSS-seq, a method that enables analysis of encyclopedic TSSs with next-generation sequencers. As a result, it was demonstrated that the mode length of the 5' UTR is between 120 and 140 nucleotides (nts) when a subset of genes with predicted signal peptides was examined. However, when genes without the signal peptide were examined, the length was extended to approximately 600 nts. Because additional information on the predicted signal peptide generates increased reliability to the 5' end estimation of each ORF, we believe that the former value was more reliable as a representative of the 5' UTR length of T. gondii. The discrepancy suggests that current predictions of the 5' end of the ORF were less accurate and considerably more discordant with the natural status. The 5' untranslated region (5' UTR) is defined as that between the 5' end of the transcripts and just in front of a start codon of an ORF. Therefore, the 5' UTR does not contain any information for a protein sequence; however, it is involved in the control of protein expression via the modulation of translational efficiency (Kozak, 1991b; Hughes, 2006).

Experimental annotation of post-translational features and translated coding regions in the pathogen Salmonella Typhimurium

Background

Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. However, determining protein-coding genes for most new genomes is almost completely performed by inference using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function.

Results

We experimentally annotated the bacterial pathogen Salmonella Typhimurium 14028, using "shotgun" proteomics to accurately uncover the translational landscape and post-translational features. The data provide protein-level experimental validation for approximately half of the predicted protein-coding genes in Salmonella and suggest revisions to several genes that appear to have incorrectly assigned translational start sites, including a potential novel alternate start codon. Additionally, we uncovered 12 non-annotated genes missed by gene prediction programs, as well as evidence suggesting a role for one of these novel ORFs in Salmonella pathogenesis. We also characterized post-translational features in the Salmonella genome, including chemical modifications and proteolytic cleavages. We find that bacteria have a much larger and more complex repertoire of chemical modifications than previously thought including several novel modifications. Our in vivo proteolysis data identified more than 130 signal peptide and N-terminal methionine cleavage events critical for protein function.

Conclusion

This work highlights several ways in which application of proteomics data can improve the quality of genome annotations to facilitate novel biological insights and provides a comprehensive proteome map of Salmonella as a resource for systems analysis.

Protein Secretion Systems in Pseudomonas aeruginosa: An Essay on Diversity, Evolution, and Function

Protein secretion systems are molecular nanomachines used by Gram-negative bacteria to thrive within their environment. They are used to release enzymes that hydrolyze complex carbon sources into usable compounds, or to release proteins that capture essential ions such as iron. They are also used to colonize and survive within eukaryotic hosts, causing acute or chronic infections, subverting the host cell response and escaping the immune system. In this article, the opportunistic human pathogen Pseudomonas aeruginosa is used as a model to review the diversity of secretion systems that bacteria have evolved to achieve these goals. This diversity may result from a progressive transformation of cell envelope complexes that initially may not have been dedicated to secretion. The striking similarities between secretion systems and type IV pili, flagella, bacteriophage tail, or efflux pumps is a nice illustration of this evolution. Differences are also needed since various secretion configurations call for diversity. For example, some proteins are released in the extracellular medium while others are directly injected into the cytosol of eukaryotic cells. Some proteins are folded before being released and transit into the periplasm. Other proteins cross the whole cell envelope at once in an unfolded state. However, the secretion system requires conserved basic elements or features. For example, there is a need for an energy source or for an outer membrane channel. The structure of this review is thus quite unconventional. Instead of listing secretion types one after each other, it presents a melting pot of concepts indicating that secretion types are in constant evolution and use basic principles. In other words, emergence of new secretion systems could be predicted the way Mendeleïev had anticipated characteristics of yet unknown elements.

Genome-wide protein localization prediction strategies for gram negative bacteria

BACKGROUND

Genome-wide prediction of protein subcellular localization is an important type of evidence used for inferring protein function. While a variety of computational tools have been developed for this purpose, errors in the gene models and use of protein sorting signals that are not recognized by the more commonly accepted tools can diminish the accuracy of their output.

RESULTS

As part of an effort to manually curate the annotations of 19 strains of Shewanella, numerous insights were gained regarding the use of computational tools and proteomics data to predict protein localization. Identification of the suite of secretion systems present in each strain at the start of the process made it possible to tailor-fit the subsequent localization prediction strategies to each strain for improved accuracy. Comparisons of the computational predictions among orthologous proteins revealed inconsistencies in the computational outputs, which could often be resolved by adjusting the gene models or ortholog group memberships. While proteomic data was useful for verifying start site predictions and post-translational proteolytic cleavage, care was needed to distinguish cellular versus sample processing-mediated cleavage events. Searches for lipoprotein signal peptides revealed that neither TatP nor LipoP are designed for identification of lipoprotein substrates of the twin arginine translocation system and that the +2 rule for lipoprotein sorting does not apply to this Genus. Analysis of the relationships between domain occurrence and protein localization prediction enabled identification of numerous location-informative domains which could then be used to refine or increase confidence in location predictions. This collective knowledge was used to develop a general strategy for predicting protein localization that could be adapted to other organisms.

CONCLUSION

Improved localization prediction accuracy is not simply a matter of developing better computational algorithms. It also entails gathering key knowledge regarding the host architecture and translocation machinery and associated substrate recognition via experimentation and integration of diverse computational analyses from many proteins and, where possible, that are derived from different species within the same genus.

Computer aided selection of candidate vaccine antigens

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.

In-depth analysis of exoproteomes from marine bacteria by shotgun liquid chromatography-tandem mass spectrometry: the Ruegeria pomeroyi DSS-3 case-study

Microorganisms secrete into their extracellular environment numerous compounds that are required for their survival. Many of these compounds could be of great interest for biotechnology applications and their genes used in synthetic biology design. The secreted proteins and the components of the translocation systems themselves can be scrutinized in-depth by the most recent proteomic tools. While the secretomes of pathogens are well-documented, those of non-pathogens remain largely to be established. Here, we present the analysis of the exoproteome from the marine bacterium Ruegeria pomeroyi DSS-3 grown in standard laboratory conditions. We used a shotgun approach consisting of trypsin digestion of the exoproteome, and identification of the resulting peptides by liquid chromatography coupled to tandem mass spectrometry. Three different proteins that have domains homologous to those observed in RTX toxins were uncovered and were semi-quantified as the most abundantly secreted proteins. One of these proteins clearly stands out from the catalogue, representing over half of the total exoproteome. We also listed many soluble proteins related to ABC and TRAP transporters implied in the uptake of nutrients. The Ruegeria pomeroyi DSS-3 case-study illustrates the power of the shotgun nano-LC-MS/MS strategy to decipher the exoproteome from marine bacteria and to contribute to environmental proteomics.

Towards a career in bioinformatics

The 2009 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation from 1998, was organized as the 8^th International Conference on Bioinformatics (InCoB), Sept. 9-11, 2009 at Biopolis, Singapore. InCoB has actively engaged researchers from the area of life sciences, systems biology and clinicians, to facilitate greater synergy between these groups. To encourage bioinformatics students and new researchers, tutorials and student symposium, the Singapore Symposium on Computational Biology (SYMBIO) were organized, along with the Workshop on Education in Bioinformatics and Computational Biology (WEBCB) and the Clinical Bioinformatics (CBAS) Symposium. However, to many students and young researchers, pursuing a career in a multi-disciplinary area such as bioinformatics poses a Himalayan challenge. A collection to tips is presented here to provide signposts on the road to a career in bioinformatics. An overview of the application of bioinformatics to traditional and emerging areas, published in this supplement, is also presented to provide possible future avenues of bioinformatics investigation. A case study on the application of e-learning tools in undergraduate bioinformatics curriculum provides information on how to go impart targeted education, to sustain bioinformatics in the Asia-Pacific region. The next InCoB is scheduled to be held in Tokyo, Japan, Sept. 26-28, 2010.