Genome-Wide Identification and Analysis of Genes Encoding Proteolytic Enzymes in Pineapple

Pineapple, Ananas comosus, is an economically important fruit crop. Recently its genome was completely sequenced and a total of 27,024 protein coding genes were predicted. Using a set of well evaluated bioinformatics tools we have predicted the protein subcellular locations and comparatively analyzed the protein conserved domains of the predicted proteomes in pineapple, Oryza sativa (rice), Sorghum bicolor (sorghum), and Brachypodium distachyson. Our analysis revealed that ~24–26 % of proteins were located in nucleus, 17–21 % in cytosol, 9–11 % in chloroplast, and 8–11 % proteins were secreted in these monocot plants. The secretomes in the four species were analyzed comparatively and a large number of secreted glycosyl hydrolases were identified. As pineapple proteolytic enzymes, knowns as bromelains, have been used for medical treatments, we focused on genome-wide identification and analysis of pineapple genes encoding proteases. A total of 512 pineapple genes encoding putative proteolytic enzymes were identified, with 152 secreted, 74 localized in cytosol, 67 in nucleus, 60 in chloroplast, 18 in mitochondria, and the remaining in other subcellular locations. The top large protease families in pineapple were papain family cysteine protease (62 genes), peptidase S8 family (56 genes), aspartyl protease family (38 genes), and serine carboxypeptidase (33 genes). Gene expression analysis revealed that among 512 protease genes 432 were expressed in various tissues and 72 genes were differentially expressed. The highly expressed protease genes were identified including 7 papain family cysteine proteases. The protease genes with the predicted protein subcellular locations will facilitate the efforts for examining their biological roles in pineapple growth and development and for expressing the recombinant proteases for medical use. The information of protein subcellular location of all plant species can be accessed at the PlantSecKB website (http://proteomics.ysu.edu/secretomes/plant.php).     

Secretomes of medically important fungi reflect morphological and phylogenetic diversity

Secretome represents a main target for understanding the mechanisms of fungal adaptation. In the present study, we focus on the secretomes of fungi associated with infections in humans and other mammals in order to explore relationships between the diverse morphological and phylogenetic groups. Almost all the mammalian pathogenic fungi analyzed have secretome sizes smaller than 1000 proteins and, secreted proteins comprise between 5% and 10% of the total proteome. As expected, the correlation pattern between the secretome size and the total proteome was similar to that described in previous secretome studies of fungi. With regard to the morphological groups, minimum secretome sizes of less than 250 secreted proteins and low values for the fraction of secreted proteins are shown in mammalian pathogenic fungi with reduced proteomes such as microsporidia, atypical fungi and some species of yeasts and yeast-like fungi (Malassezia). On the other hand, filamentous fungi have significantly more secreted proteins and the highest numbers are present in species of filamentous fungi that also are plant or insect pathogens (Fusarium verticilloides, Fusarium oxysporum and Basidiobolus meristosporus). With respect to phylogeny, there are also variations in secretome size across fungal subphyla: Microsporidia, Taphrinomycotina, Ustilagomycotina and Saccharomycotina contain small secretomes; whereas larger secretomes are found in Agaricomycotina, Pezizomycotina, Mucoromycotina and Entomophthoromycotina. Finally, principal component analysis (PCA) was conducted on the complete secretomes. The PCA results revealed that, in general, secretomes of fungi belonging to the same morphological group or subphyla cluster together. In conclusion, our results point out that in medically important fungi there is a relationship between the secretome and the morphological group or phylogenetic classification.     

SubChlo: Predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm

The chloroplast is a type of plant specific subcellular organelle. It is of central importance in several biological processes like photosynthesis and amino acid biosynthesis. Thus, understanding the function of chloroplast proteins is of significant value. Since the function of chloroplast proteins correlates with their subchloroplast locations, the knowledge of their subchloroplast locations can be very helpful in understanding their role in the biological processes. In the current paper, by introducing the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm, we developed a method for predicting the protein subchloroplast locations. This is the first algorithm for predicting the protein subchloroplast locations. We have implemented our algorithm as an online service, SubChlo (http://bioinfo.au.tsinghua.edu.cn/subchlo). This service may be useful to the chloroplast proteome research.     

植物病原真菌分泌蛋白质组学研究进展

分泌蛋白质组是指在特定时间和特定条件下,由组织或细胞等分泌的全部蛋白质。在病原真菌与植物的相互作用过程中,病原真菌会分泌大量的蛋白质和代谢产物,在病原真菌对植物的侵入、定殖和扩展等致病过程中起着重要作用。本文主要介绍了分泌蛋白质在植物病原真菌致病性中的作用、重要植物病原真菌分泌蛋白质组的研究进展、及植物病原真菌分泌蛋白质组的生物信息学预测分析等,对于全面了解植物病原真菌的致病机理具有重要意义。     

Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization

One of the fundamental goals in proteomics and cell biology is to identify the functions of proteins in various cellular organelles and pathways. Information of subcellular locations of proteins can provide useful insights for revealing their functions and understanding how they interact with each other in cellular network systems. Most of the existing methods in predicting plant protein subcellular localization can only cover three or four location sites, and none of them can be used to deal with multiplex plant proteins that can simultaneously exist at two, or move between, two or more different location sites. Actually, such multiplex proteins might have special biological functions worthy of particular notice. The present study was devoted to improve the existing plant protein subcellular location predictors from the aforementioned two aspects. A new predictor called “Plant-mPLoc” is developed by integrating the gene ontology information, functional domain information, and sequential evolutionary information through three different modes of pseudo amino acid composition. It can be used to identify plant proteins among the following 12 location sites: (1) cell membrane, (2) cell wall, (3) chloroplast, (4) cytoplasm, (5) endoplasmic reticulum, (6) extracellular, (7) Golgi apparatus, (8) mitochondrion, (9) nucleus, (10) peroxisome, (11) plastid, and (12) vacuole. Compared with the existing methods for predicting plant protein subcellular localization, the new predictor is much more powerful and flexible. Particularly, it also has the capacity to deal with multiple-location proteins, which is beyond the reach of any existing predictors specialized for identifying plant protein subcellular localization. As a user-friendly web-server, Plant-mPLoc is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results. It is anticipated that the Plant-mPLoc predictor as presented in this paper will become a very useful tool in plant science as well as all the relevant areas.     

Muscle tissue as an endocrine organ: comparative secretome profiling of slow-oxidative and fast-glycolytic rat muscle explants and its variation with exercise

The notion that skeletal muscle is a secretory organ capable to release proteins that can act locally in an autocrine/paracrine manner or even in an endocrine manner to communicate with distant tissues has now been recognized. Under this context, a new paradigm has arisen implicating the muscle in metabolism regulation. Considering the evidences that give exercise a protective role against illnesses associated to physical inactivity, it becomes of especial relevance to characterize muscle secreted proteins. In the present study we show for the first time the secretome characterization and the comparative 2-DE secretome analysis among fast-glycolytic (gastrocnemius) and slow-oxidative (soleus) rat muscle explants and its variation after exercise intervention. We have identified 19 differently secreted proteins when comparing soleus and gastrocnemius secretomes, and 10 in gastrocnemius and 17 in soleus distinctive secreted proteins after 1 week of endurance exercise training. Among identified proteins, DJ-1 was found to be more abundant in fast-glycolytic fiber secretomes. On the contrary, FABP-3 was elevated in slow-oxidative fiber secretomes, although its secretion from gastrocnemius muscle increased in exercised animals. These and other secreted proteins identified in this work may be considered as potential myokines.     

Compositions of fungal secretomes indicate a greater impact of phylogenetic history than lifestyle adaptation

Background

Since the first fungal genome sequences became available, investigators have been employing comparative genomics to understand how fungi have evolved to occupy diverse ecological niches. The secretome, i.e. the entirety of all proteins secreted by an organism, is of particular importance, as by these proteins fungi acquire nutrients and communicate with their surroundings.

Results

It is generally assumed that fungi with similar nutritional lifestyles have similar secretome compositions. In this study, we test this hypothesis by annotating and comparing the soluble secretomes, defined as the sets of proteins containing classical signal peptides but lacking transmembrane domains of fungi representing a broad diversity of nutritional lifestyles. Secretome size correlates with phylogeny and to a lesser extent with lifestyle. Plant pathogens and saprophytes have larger secretomes than animal pathogens. Small secreted cysteine-rich proteins (SSCPs), which may comprise many effectors important for the interaction of plant pathogens with their hosts, are defined here to have a mature length of ≤ 300 aa residues, at least four cysteines, and a total cysteine content of ≥5%. SSCPs are found enriched in the secretomes of the Pezizomycotina and Basidiomycota in comparison to Saccharomycotina. Relative SSCP content is noticeably higher in plant pathogens than in animal pathogens, while saprophytes were in between and closer to plant pathogens. Expansions and contractions of gene families and in the number of occurrences of functional domains are largely lineage specific, e.g. contraction of glycoside hydrolases in Saccharomycotina, and are only weakly correlated with lifestyle. However, within a given lifestyle a few general trends exist, such as the expansion of secreted family M14 metallopeptidases and chitin-binding proteins in plant pathogenic Pezizomycotina.

Conclusions

While the secretomes of fungi with similar lifestyles share certain characteristics, the expansion and contraction of gene families is largely lineage specific, and not shared among all fungi of a given lifestyle.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-722) contains supplementary material, which is available to authorized users.     

Proteomic profiling of the cancer cell secretome: informing clinical research

Introduction: Cancer represents one of the major causes of human deaths. Identification of proteins as biomarkers for early detection of cancer and therapeutic targets for cancer treatment are important issues in precision medicine. Secretome of cancer cells represents the collection of proteins secreted or shed from cancer cells. Proteomic profiling of the cancer cell secretome has been proven to be a convenient and efficient way to discover cancer biomarker and/or therapeutic targets.

Areas covered: There have been numerous reviews describing the history and application of secretome analysis in cancer biomarker/therapeutic target research. The present review focuses on the technological advancement for profiling low-molecular-mass proteins in secretome, the latest information regarding the new candidate biomarkers and molecular mechanisms discovered on the basis of cancer cell secretome analysis, as well as the previously discovered candidate biomarkers that enter into clinical trials.

Expert commentary: Current technologies for protein sample preparation/separation and MS-based protein identification have allowed in-depth analysis of cancer cell secretome. Future efforts should focus on the comprehensiveness of cancer cell secretome, meta-analysis of different secretome datasets and integrated analysis via combining other omics datasets, as well as the incorporation of MS-based biomarker verification pipeline into both preclinical studies and clinical trials.     

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/.     

Proteomic investigation of the secretome of Cellvibrio japonicus during growth on chitin

Studies of the secretomes of microbes grown on insoluble substrates are important for the discovery of novel proteins involved in biomass conversion. However, data in literature and this study indicate that secretome samples tend to be contaminated with cytoplasmic proteins. We have examined the secretome of the Gram‐negative soil bacterium Cellvibrio japonicus using a simple plate‐based culturing technique that yields samples with high fractions (60–75%) of proteins that are predicted to be secreted. By combining this approach with label‐free quantification using the MaxLFQ algorithm, we have mapped and quantified proteins secreted by C. japonicus during growth on α‐ and β‐chitin. Hierarchical clustering of the detected protein quantities revealed groups of up‐regulated proteins that include all five putative C. japonicus chitinases as well as a chitin‐specific lytic polysaccharide monooxygenase (CjLPMO10A). A small set of secreted proteins were co‐regulated with known chitin‐specific enzymes, including several with unknown catalytic functions. These proteins provide interesting targets for further studies aimed at unraveling the enzymatic machineries used by C. japonicus for recalcitrant polysaccharide degradation. Studies of chitin degradation indicated that C. japonicus indeed produces an efficient chitinolytic enzyme cocktail. All MS data have been deposited in the ProteomeXchange with the dataset identifier PXD002843 ( http://proteomecentral.proteomexchange.org/dataset/PXD002843 ).     

Plant membrane proteomics

Plant membrane proteins are involved in many different functions according to their location in the cell. For instance, the chloroplast has two membrane systems, thylakoids and envelope, with specialized membrane proteins for photosynthesis and metabolite and ion transporters, respectively. Although recent advances in sample preparation and analytical techniques have been achieved for the study of membrane proteins, the characterization of these proteins, especially the hydrophobic ones, is still challenging. The present review highlights recent advances in methodologies for identification of plant membrane proteins from purified subcellular structures. The interest of combining several complementary extraction procedures to take into account specific features of membrane proteins is discussed in the light of recent proteomics data, notably for chloroplast envelope, mitochondrial membranes and plasma membrane from Arabidopsis. These examples also illustrate how, on one hand, proteomics can feed bioinformatics for a better definition of prediction tools and, on the other hand, although prediction tools are not 100% reliable, they can give valuable information for biological investigations. In particular, membrane proteomics brings new insights over plant membrane systems, on both the membrane compartment where proteins are working and their putative cellular function.     

Direct selection and phage display of a Gram-positive secretome

Surface, secreted and transmembrane protein-encoding open reading frames, collectively the secretome, can be identified in bacterial genome sequences using bioinformatics. However, functional analysis of translated secretomes is possible only if many secretome proteins are expressed and purified individually. We have now developed and applied a phage display system for direct selection, identification, expression and purification of bacterial secretome proteins.     

Identify submitochondria and subchloroplast locations with pseudo amino acid composition: Approach from the strategy of discrete wavelet transform feature extraction

It is very challenging and complicated to predict protein locations at the sub-subcellular level. The key to enhancing the prediction quality for protein sub-subcellular locations is to grasp the core features of a protein that can discriminate among proteins with different subcompartment locations. In this study, a different formulation of pseudoamino acid composition by the approach of discrete wavelet transform feature extraction was developed to predict submitochondria and subchloroplast locations. As a result of jackknife cross-validation, with our method, it can efficiently distinguish mitochondrial proteins from chloroplast proteins with total accuracy of 98.8% and obtained a promising total accuracy of 93.38% for predicting submitochondria locations. Especially the predictive accuracy for mitochondrial outer membrane and chloroplast thylakoid lumen were 82.93% and 82.22%, respectively, showing an improvement of 4.88% and 27.22% when other existing methods were compared. The results indicated that the proposed method might be employed as a useful assistant technique for identifying sub-subcellular locations. We have implemented our algorithm as an online service called SubIdent (http://bioinfo.ncu.edu.cn/services.aspx).     

Analysis of the Secretomes of Paracoccidioides Mycelia and Yeast Cells

Paracoccidioides, a complex of several phylogenetic species, is the causative agent of paracoccidioidomycosis. The ability of pathogenic fungi to develop a multifaceted response to the wide variety of stressors found in the host environment is important for virulence and pathogenesis. Extracellular proteins represent key mediators of the host-parasite interaction. To analyze the expression profile of the proteins secreted by Paracoccidioides, Pb01 mycelia and yeast cells, we used a proteomics approach combining two-dimensional electrophoresis with matrix-assisted laser desorption ionization quadrupole time-of-flight mass spectrometry (MALDI-Q-TOF MS/MS). From three biological replicates, 356 and 388 spots were detected, in mycelium and yeast cell secretomes, respectively. In this study, 160 non-redundant proteins/isoforms were indentified, including 30 and 24 proteins preferentially secreted in mycelia and yeast cells, respectively. In silico analyses revealed that 65% of the identified proteins/isoforms were secreted primarily via non-conventional pathways. We also investigated the influence of protein export inhibition in the phagocytosis of Paracoccidioides by macrophages. The addition of Brefeldin A to the culture medium significantly decreased the production of secreted proteins by both Paracoccidioides and internalized yeast cells by macrophages. In contrast, the addition of concentrated culture supernatant to the co-cultivation significantly increased the number of internalized yeast cells by macrophages. Importantly, the proteins detected in the fungal secretome were also identified within macrophages. These results indicate that Paracoccidioides extracellular proteins are important for the fungal interaction with the host.     

Mendel, a database of nomenclature for sequenced plant genes

The Mendel database contains names for plant-wide families of sequenced plant genes. The names have either been approved by the Commission on Plant Gene Nomenclature (CPGN), an organization of the International Society for Plant Molecular Biology (ISPMB), or are identified as provisional or temporary names. Mendel also identifies the corresponding genes in individual species of plants. Mendel can be searched through the mirror sites at Cornell (http://genome. cornell.edu/cgi-bin/WebAce/webace?db=mendel) and Stanford (http://genome-www.stanford.edu/Mendel/). In addition, parts of Mendel can be downloaded from the CPGN Web site (http://mbclserver. rutgers.edu/CPGN/).     

Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms

Information on subcellular localization of proteins is important to molecular cell biology, proteomics, system biology and drug discovery. To provide the vast majority of experimental scientists with a user-friendly tool in these areas, we present a package of Web servers developed recently by hybridizing the 'higher level' approach with the ab initio approach. The package is called Cell-PLoc and contains the following six predictors: Euk-mPLoc, Hum-mPLoc, Plant-PLoc, Gpos-PLoc, Gneg-PLoc and Virus-PLoc, specialized for eukaryotic, human, plant, Gram-positive bacterial, Gram-negative bacterial and viral proteins, respectively. Using these Web servers, one can easily get the desired prediction results with a high expected accuracy, as demonstrated by a series of cross-validation tests on the benchmark data sets that covered up to 22 subcellular location sites and in which none of the proteins included had > or =25% sequence identity to any other protein in the same subcellular-location subset. Some of these Web servers can be particularly used to deal with multiplex proteins as well, which may simultaneously exist at, or move between, two or more different subcellular locations. Proteins with multiple locations or dynamic features of this kind are particularly interesting, because they may have some special biological functions intriguing to investigators in both basic research and drug discovery. This protocol is a step-by-step guide on how to use the Web-server predictors in the Cell-PLoc package. The computational time for each prediction is less than 5 s in most cases. The Cell-PLoc package is freely accessible at http://chou.med.harvard.edu/bioinf/Cell-PLoc.