MetazSecKB: the human and animal secretome and subcellular proteome knowledgebase

Proteins of generalist and specialist pathogens differ in their amino acid composition

Pathogens differ in their host specificities, with species infecting a unique host (specialist pathogens) and others having a wide host range (generalists). Molecular determinants of pathogen's host range remain poorly understood. Secreted proteins of generalist pathogens are expected to have a broader range of intermolecular interactions (i.e., higher promiscuity) compared with their specialist counterparts. We hypothesize that this increased promiscuity of generalist secretomes may be based on an elevated content of primitive amino acids and intrinsically disordered regions, as these features are known to increase protein flexibility and interactivity. Here, we measure the proportion of primitive amino acids and percentage of intrinsically disordered residues in secreted, membrane, and cytoplasmic proteins from pathogens with different host specificity. Supporting our prediction, there is a significant general enrichment for primitive amino acids and intrinsically disordered regions in proteins from generalists compared to specialists, particularly among secreted proteins in prokaryotes. Our findings support our hypothesis that secreted proteins' amino acid composition and disordered content influence the pathogens' host range.

Disulfide Bonds Enable Accelerated Protein Evolution

The different proteins of any proteome evolve at enormously different rates. What factors contribute to this variability, and to what extent, is still a largely open question. We hypothesized that disulfide bonds, by increasing protein stability, should make proteins' structures relatively independent of their amino acid sequences, thus acting as buffers of deleterious mutations and enabling accelerated sequence evolution. In agreement with this hypothesis, we observed that membrane proteins with disulfide bonds evolved 88% faster than those without disulfide bonds, and that extracellular proteins with disulfide bonds evolved 49% faster than those without disulfide bonds. In addition, genes encoding proteins with disulfide bonds exhibit an increased likelihood of showing signatures of positive selection. Multivariate analyses indicate that the trend is independent of a number of potentially confounding factors. The effect, however, is not observed among the longest proteins, which can become stabilized by mechanisms other than disulfide bonds.

VerSeDa: vertebrate secretome database

Based on the current tools, de novo secretome (full set of proteins secreted by an organism) prediction is a time consuming bioinformatic task that requires a multifactorial analysis in order to obtain reliable in silico predictions. Hence, to accelerate this process and offer researchers a reliable repository where secretome information can be obtained for vertebrates and model organisms, we have developed VerSeDa (Vertebrate Secretome Database). This freely available database stores information about proteins that are predicted to be secreted through the classical and non-classical mechanisms, for the wide range of vertebrate species deposited at the NCBI, UCSC and ENSEMBL sites. To our knowledge, VerSeDa is the only state-of-the-art database designed to store secretome data from multiple vertebrate genomes, thus, saving an important amount of time spent in the prediction of protein features that can be retrieved from this repository directly.

Database URL: VerSeDa is freely available at http://genomics.cicbiogune.es/VerSeDa/index.php

Gaining insights into cancer biology through exploration of the cancer secretome using proteomic and bioinformatic tools

INTRODUCTION

Tumor-associated proteins released by cancer cells and by tumor stroma cells, referred as 'cancer secretome', represent a valuable resource for discovery of potential cancer biomarkers. The last decade was marked by a great increase in number of studies focused on various aspects of cancer secretome including, composition and identification of components externalized by malignant cells and by the components of tumor microenvironment. Areas covered: Here, we provide an overview of achievements in the proteomic analysis of the cancer secretome, elicited through the tumor-associated interstitial fluid recovered from malignant tissues ex vivo or the protein component of conditioned media obtained from cultured cancer cells in vitro. We summarize various bioinformatic tools and approaches and critically appraise their outcomes, focusing on problems and challenges that arise when applied for the analysis of cancer secretomic databases. Expert commentary: Recent achievements in the omics- analysis of structural and metabolic aspects of altered cancer secretome contribute greatly to the various hallmarks of cancer including the identification of clinically significant biomarkers and potential targets for therapeutic intervention.

Secreted Proteins Defy the Expression Level-Evolutionary Rate Anticorrelation

The rates of evolution of the proteins of any organism vary across orders of magnitude. A primary factor influencing rates of protein evolution is expression. A strong negative correlation between expression levels and evolutionary rates (the so-called E-R anticorrelation) has been observed in virtually all studied organisms. This effect is currently attributed to the abundance-dependent fitness costs of misfolding and unspecific protein-protein interactions, among other factors. Secreted proteins are folded in the endoplasmic reticulum, a compartment where chaperones, folding catalysts, and stringent quality control mechanisms promote their correct folding and may reduce the fitness costs of misfolding. In addition, confinement of secreted proteins to the extracellular space may reduce misinteractions and their deleterious effects. We hypothesize that each of these factors (the secretory pathway quality control and extracellular location) may reduce the strength of the E-R anticorrelation. Indeed, here we show that among human proteins that are secreted to the extracellular space, rates of evolution do not correlate with protein abundances. This trend is robust to controlling for several potentially confounding factors and is also observed when analyzing protein abundance data for 6 human tissues. In addition, analysis of mRNA abundance data for 32 human tissues shows that the E-R correlation is always less negative, and sometimes nonsignificant, in secreted proteins. Similar observations were made in Caenorhabditis elegans and in Escherichia coli, and to a lesser extent in Drosophila melanogaster, Saccharomyces cerevisiae and Arabidopsis thaliana. Our observations contribute to understand the causes of the E-R anticorrelation.

Genome-wide computational analysis of the secretome of brown algae (Phaeophyceae)

Brown algae have evolved complex multicellularity in the heterokont lineage. They are phylogenetically distant to land plants, fungi and animals. Especially, the members of Laminariales (so-called kelps) have developed highly differentiated tissues. Extracellular matrix (ECM) plays pivotal roles in a number of essential processes in multicellular organisms, such as cell adhesion, cell and tissue differentiations, cell-to-cell communication, and responses to environmental stimuli. In these processes, a set of extracellular secreted proteins called the secretome operates remodeling of the physicochemical nature of ECM and signal transduction by interacting with cell surface proteins and signaling molecules. Characterization of the secretome is a critical step to clarify the contributions of ECM to the multicellularity of brown algae. However, the identity of the brown algal secretome has been poorly understood. In order to reveal the repertory of the brown algal secretome and its involvement in the evolution of Laminariales, we conducted a genome-wide analysis of the brown algal secretome utilizing the published complete genome data of Ectocarpus siliculosus and Saccharina japonica as well as newly obtained RNA-seq data of seven laminarialean species (Agarum clathratum, Alaria crassifolia, Aureophycus aleuticus, Costaria costata, Pseudochorda nagaii, Saccharina angustata and Undaria pinnatifida) largely covering the laminarialean families. We established the in silico pipeline to systematically and accurately detect the secretome by combining multiple prediction algorithms for the N-terminal signal peptide and transmembrane domain within the protein sequence. From 16,189 proteins of E. siliculosus and 18,733 proteins of S. japonica, 552 and 964 proteins respectively were predicted to be classified as the secretome. Conserved domain analysis showed that the domain repertory were very similar to each other, and that of the brown algal secretome was partially common with that of the secretome of other multicellular organisms (land plants, fungi and animals). In the laminarialean species, it was estimated that the gene abundance and the domain architecture of putative ECM remodeling-related proteins were altered compared with those of E. siliculosus, and that the alteration started from the basal group of Laminariales. These results suggested that brown algae have developed their own secretome, and its functions became more elaborated in the more derived members in Laminariales.

Global mRNA sequencing of human skeletal muscle: Search for novel exercise-regulated myokines

Objective

Skeletal muscle is an important secretory organ, producing and releasing numerous myokines, which may be involved in mediating beneficial health effects of physical activity. More than 100 myokines have been identified by different proteomics approaches, but these techniques may not detect all myokines. We used mRNA sequencing as an untargeted approach to study gene expression of secreted proteins in skeletal muscle upon acute as well as long-term exercise.

Methods

Twenty-six middle-aged, sedentary men underwent combined endurance and strength training for 12 weeks. Skeletal muscle biopsies from m. vastus lateralis and blood samples were taken before and after an acute bicycle test, performed at baseline as well as after 12 weeks of training intervention. We identified transcripts encoding secretory proteins that were changed more than 1.5-fold in muscle after exercise. Secretory proteins were defined based on either curated UniProt annotations or predictions made by multiple bioinformatics methods.

Results

This approach led to the identification of 161 candidate secretory transcripts that were up-regulated after acute exercise and 99 that where increased after 12 weeks exercise training. Furthermore, 92 secretory transcripts were decreased after acute and/or long-term physical activity. From these responsive transcripts, we selected 17 candidate myokines sensitive to short- and/or long-term exercise that have not been described as myokines before. The expression of these transcripts was confirmed in primary human skeletal muscle cells during in vitro differentiation and electrical pulse stimulation (EPS). One of the candidates we identified was macrophage colony-stimulating factor-1 (CSF1), which influences macrophage homeostasis. CSF1 mRNA increased in skeletal muscle after acute and long-term exercise, which was accompanied by a rise in circulating CSF1 protein. In cultured muscle cells, EPS promoted a significant increase in the expression and secretion of CSF1.

Conclusion

We identified 17 new, exercise-responsive transcripts encoding secretory proteins. We further identified CSF1 as a novel myokine, which is secreted from cultured muscle cells and up-regulated in muscle and plasma after acute exercise.

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Numerous transcripts were identified that were regulated in human skeletal muscle after acute and/or long-term exercise.

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These transcripts encode potential myokines, which may play key roles in local and systemic adaptations to exercise.

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CSF1 was identified as a novel myokine. CSF1 was increased after acute exercise, and secreted from cultured human myotubes in response to EPS.

Equipping Physiologists with an Informatics Tool Chest: Toward an Integerated Mitochondrial Phenome

Understanding the complex involvement of mitochondrial biology in disease development often requires the acquisition, analysis, and integration of large-scale molecular and phenotypic data. An increasing number of bioinformatics tools are currently employed to aid in mitochondrial investigations, most notably in predicting or corroborating the spatial and temporal dynamics of mitochondrial molecules, in retrieving structural data of mitochondrial components, and in aggregating as well as transforming mitochondrial centric biomedical knowledge. With the increasing prevalence of complex Big Data from omics experiments and clinical cohorts, informatics tools have become indispensable in our quest to understand mitochondrial physiology and pathology. Here we present an overview of the various informatics resources that are helping researchers explore this vital organelle and gain insights into its form, function, and dynamics.

Data set of Aspergillus flavus induced alterations in tear proteome: Understanding the pathogen-induced host response to fungal infection

Fungal keratitis is one of the leading causes of blindness in the tropical countries affecting individuals in their most productive age. The host immune response during this infection is poorly understood. We carried out comparative tear proteome analysis of Aspergillus flavus keratitis patients and uninfected controls. Proteome was separated into glycosylated and non-glycosylated fractions using lectin column chromatography before mass spectrometry. The data revealed the major processes activated in the human host in response to fungal infection and reflected in the tear. Extended analysis of this dataset presented here complements the research article entitled "Aspergillus flavus induced alterations in tear protein profile reveal pathogen-induced host response to fungal infection [1]" (Jeyalakhsmi Kandhavelu, Naveen Luke Demonte, Venkatesh Prajna Namperumalsamy, Lalitha Prajna, Chitra Thangavel, Jeya Maheshwari Jayapal, Dharmalingam Kuppamuthu, 2016). The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PRIDE:PXD003825.

Integrated data mining of transcriptomic and proteomic datasets to predict the secretome of adipose tissue and muscle in ruminants

Adipose tissue and muscle are endocrine organs releasing signalling and mediator proteins termed adipokines and myokines. The identification of the complete set of proteins secreted by adipose tissue and muscle is a challenge to understand the molecular cross-talk between these tissues and to reveal potential targets to control body or muscle composition and metabolism.