Categorization of metabolome in bacterial systems

Analyses of biological databases such as those of genome, proteome, metabolome etc., have given insights in organization of biological systems. However, current efforts do not utilize the complete potential of available metabolome data. In this study, metabolome of bacterial systems with reliable annotations are analyzed and a simple method is developed to categorize pathways hierarchically, using rational approach. Ninety-four bacterial systems having for each ≥ 250 annotated metabolic pathways were used to identify a set of common pathways. 42 pathways were present in all bacteria which are termed as Core/Stage I pathways. This set of pathways was used along with interacting compounds to categorize pathways in the metabolome hierarchically. In each metabolome non-interacting pathways were identified including at each stage. The case study of Escherichia coli O157, having 433 annotated pathways, shows that 378 pathways interact directly or indirectly with 41 core pathways while 14 pathways are noninteracting. These 378 pathways are distributed in Stage II (289), Stage III (75), Stage IV (13) and Stage V (1) category. The approach discussed here allows understanding of the complexity of metabolic networks. It has pointed out that core pathways could be most ancient pathways and compounds that interact with maximum pathways may be compounds with high biosynthetic potential, which can be easily identified. Further, it was shown that interactions of pathways at various stages could be one to one, one to many, many to one or many to many mappings through interacting compounds. The granularity of the method discussed being high; the impact of perturbation in a pathway on the metabolome and particularly sub networks can be studied precisely. The categorizations of metabolic pathways help in identifying choke point enzymes that are useful to identify probable drug targets. The Metabolic categorizations for 94 bacteria are available at http://115.111.37.202/mpe/

CEP: a conformational epitope prediction server

CEP server () provides a web interface to the conformational epitope prediction algorithm developed in-house. The algorithm, apart from predicting conformational epitopes, also predicts antigenic determinants and sequential epitopes. The epitopes are predicted using 3D structure data of protein antigens, which can be visualized graphically. The algorithm employs structure-based Bioinformatics approach and solvent accessibility of amino acids in an explicit manner. Accuracy of the algorithm was found to be 75% when evaluated using X-ray crystal structures of Ag–Ab complexes available in the PDB. This is the first and the only method available for the prediction of conformational epitopes, which is an attempt to map probable antibody-binding sites of protein antigens

Monoclonal antibody induced with inactived EV71-Hn2 virus protects mice against lethal EV71-Hn2 virus infection

<p>Abstract</p> <p>Background</p> <p>Enterovirus 71 (EV71) is a viral pathogen that belongs to the <it>Picornaviridae </it>family, EV71-infected children can develop severe neurological complications leading to rapid clinical deterioration and death.</p> <p>Results</p> <p>In this study, several monoclonal antibodies (MAbs) were produced by immunizing mice with the inactived EV71 Henan (Hn2) virus strain. The isolated MAbs were characterised by <it>in vitro </it>neutralizing analysis and peptide ELISA. ELISA assay showed that the neutralizing monoclonal antibody 4E8 specifically reacted with synthetic peptides which contain amino acid 240-250 and 250-260 of EV71 VP1. The <it>in vivo </it>protection assay showed that 4E8 can protect two-day-old BALB/c mice against the lethal challenge of EV71 virus.</p> <p>Conclusion</p> <p>The MAb 4E8 could be a promising candidate to be humanized and used for treatment of EV71 infection.</p

Impact of Immunization Technology and Assay Application on Antibody Performance – A Systematic Comparative Evaluation

Antibodies are quintessential affinity reagents for the investigation and determination of a protein's expression patterns, localization, quantitation, modifications, purification, and functional understanding. Antibodies are typically used in techniques such as Western blot, immunohistochemistry (IHC), and enzyme-linked immunosorbent assays (ELISA), among others. The methods employed to generate antibodies can have a profound impact on their success in any of these applications. We raised antibodies against 10 serum proteins using 3 immunization methods: peptide antigens (3 per protein), DNA prime/protein fragment-boost (“DNA immunization”; 3 per protein), and full length protein. Antibodies thus generated were systematically evaluated using several different assay technologies (ELISA, IHC, and Western blot). Antibodies raised against peptides worked predominantly in applications where the target protein was denatured (57% success in Western blot, 66% success in immunohistochemistry), although 37% of the antibodies thus generated did not work in any of these applications. In contrast, antibodies produced by DNA immunization performed well against both denatured and native targets with a high level of success: 93% success in Western blots, 100% success in immunohistochemistry, and 79% success in ELISA. Importantly, success in one assay method was not predictive of success in another. Immunization with full length protein consistently yielded the best results; however, this method is not typically available for new targets, due to the difficulty of generating full length protein. We conclude that DNA immunization strategies which are not encumbered by the limitations of efficacy (peptides) or requirements for full length proteins can be quite successful, particularly when multiple constructs for each protein are used

Tsetse GmmSRPN10 has anti-complement activity and is important for successful establishment of trypanosome infections in the fly midgut

The complement cascade in mammalian blood can damage the alimentary tract of haematophagous arthropods. As such, these animals have evolved their own repertoire of complement-inactivating factors, which are inadvertently exploited by blood-borne pathogens to escape complement lysis. Unlike the bloodstream stages, the procyclic (insect) stage of Trypanosoma brucei is highly susceptible to complement killing, which is puzzling considering that a tsetse takes a bloodmeal every 2–4 days. In this study, we identified four tsetse (Glossina morsitans morsitans) serine protease inhibitors (serpins) from a midgut expressed sequence tag (EST) library (GmmSRPN3, GmmSRPN5, GmmSRPN9 and GmmSRPN10) and investigated their role in modulating the establishment of a T. brucei infection in the midgut. Although not having evolved in a common blood-feeding ancestor, all four serpins have an active site sharing remarkable homology with the human complement C1-inhibitor serpin, SerpinG1. RNAi knockdown of individual GmmSRPN9 and GmmSRPN10 genes resulted in a significant decreased rate of infection by procyclic form T. brucei. Furthermore, recombinant GmmSRPN10 was both able to inhibit the activity of human complement-cascade serine proteases, C1s and Factor D, and to protect the in vitro killing of procyclic trypanosomes when incubated with complement-activated human serum. Thus, the secretion of serpins, which may be part of a bloodmeal complement inactivation system in tsetse, is used by procyclic trypanosomes to evade an influx of fresh trypanolytic complement with each bloodmeal. This highlights another facet of the complicated relationship between T. brucei and its tsetse vector, where the parasite takes advantage of tsetse physiology to further its chances of propagation and transmission

FungalRV: adhesin prediction and immunoinformatics portal for human fungal pathogens

<p>Abstract</p> <p>Background</p> <p>The availability of sequence data of human pathogenic fungi generates opportunities to develop Bioinformatics tools and resources for vaccine development towards benefitting at-risk patients.</p> <p>Description</p> <p>We have developed a fungal adhesin predictor and an immunoinformatics database with predicted adhesins. Based on literature search and domain analysis, we prepared a positive dataset comprising adhesin protein sequences from human fungal pathogens <it>Candida albicans, Candida glabrata, Aspergillus fumigatus, Coccidioides immitis, Coccidioides posadasii, Histoplasma capsulatum, Blastomyces dermatitidis, Pneumocystis carinii, Pneumocystis jirovecii and Paracoccidioides brasiliensis</it>. The negative dataset consisted of proteins with high probability to function intracellularly. We have used 3945 compositional properties including frequencies of mono, doublet, triplet, and multiplets of amino acids and hydrophobic properties as input features of protein sequences to Support Vector Machine. Best classifiers were identified through an exhaustive search of 588 parameters and meeting the criteria of best Mathews Correlation Coefficient and lowest coefficient of variation among the 3 fold cross validation datasets. The "FungalRV adhesin predictor" was built on three models whose average Mathews Correlation Coefficient was in the range 0.89-0.90 and its coefficient of variation across three fold cross validation datasets in the range 1.2% - 2.74% at threshold score of 0. We obtained an overall MCC value of 0.8702 considering all 8 pathogens, namely, <it>C. albicans, C. glabrata, A. fumigatus, B. dermatitidis, C. immitis, C. posadasii, H. capsulatum </it>and <it>P. brasiliensis </it>thus showing high sensitivity and specificity at a threshold of 0.511. In case of <it>P. brasiliensis </it>the algorithm achieved a sensitivity of 66.67%. A total of 307 fungal adhesins and adhesin like proteins were predicted from the entire proteomes of eight human pathogenic fungal species. The immunoinformatics analysis data on these proteins were organized for easy user interface analysis. A Web interface was developed for analysis by users. The predicted adhesin sequences were processed through 18 immunoinformatics algorithms and these data have been organized into MySQL backend. A user friendly interface has been developed for experimental researchers for retrieving information from the database.</p> <p>Conclusion</p> <p>FungalRV webserver facilitating the discovery process for novel human pathogenic fungal adhesin vaccine has been developed.</p

Analysis of the Effects of Polymorphism on Pollen Profilin Structural Functionality and the Generation of Conformational, T- and B-Cell Epitopes

An extensive polymorphism analysis of pollen profilin, a fundamental regulator of the actin cytoskeleton dynamics, has been performed with a major focus in 3D-folding maintenance, changes in the 2-D structural elements, surface residues involved in ligands-profilin interactions and functionality, and the generation of conformational and lineal B- and T-cell epitopes variability. Our results revealed that while the general fold is conserved among profilins, substantial structural differences were found, particularly affecting the special distribution and length of different 2-D structural elements (i.e. cysteine residues), characteristic loops and coils, and numerous micro-heterogeneities present in fundamental residues directly involved in the interacting motifs, and to some extension these residues nearby to the ligand-interacting areas. Differential changes as result of polymorphism might contribute to generate functional variability among the plethora of profilin isoforms present in the olive pollen from different genetic background (olive cultivars), and between plant species, since biochemical interacting properties and binding affinities to natural ligands may be affected, particularly the interactions with different actin isoforms and phosphoinositides lipids species. Furthermore, conspicuous variability in lineal and conformational epitopes was found between profilins belonging to the same olive cultivar, and among different cultivars as direct implication of sequences polymorphism. The variability of the residues taking part of IgE-binding epitopes might be the final responsible of the differences in cross-reactivity among olive pollen cultivars, among pollen and plant-derived food allergens, as well as between distantly related pollen species, leading to a variable range of allergy reactions among atopic patients. Identification and analysis of commonly shared and specific epitopes in profilin isoforms is essential to gain knowledge about the interacting surface of these epitopes, and for a better understanding of immune responses, helping design and development of rational and effective immunotherapy strategies for the treatment of allergy diseases. [EN]This study was supported by the following European Regional Development Fund co-financed grants: MCINN BFU 2004-00601/BFI, BFU 2008-00629, BFU2011-22779, CICE (Junta de Andalucía) P2010-CVI15767, P2010-AGR6274 and P2011-CVI-7487, and by the coordinated project Spain/Germany MEC HA2004-0094. JCJ-L thanks Spanish CSIC and the European Marie Curie research program for his I3P-BPD-CSIC, and PIOF-GA-2011-301550 grants, respectively.Peer reviewe

Proteomics and in silico approaches to extend understanding of the glutathione transferase superfamily of the tropical liver fluke Fasciola gigantica

Fasciolosis is an important foodborne, zoonotic disease of livestock and humans, with global annual health and economic losses estimated at several billion US$. Fasciola hepatica is the major species in temperate regions, while F. gigantica dominates in the tropics. In the absence of commercially available vaccines to control fasciolosis, increasing reports of resistance to current chemotherapeutic strategies and the spread of fasciolosis into new areas, new functional genomics approaches are being used to identify potential new drug targets and vaccine candidates. The glutathione transferase (GST) superfamily is both a candidate drug and vaccine target. This study reports the identification of a putatively novel Sigma class GST, present in a water-soluble cytosol extract from the tropical liver fluke F. gigantica. The GST was cloned and expressed as an enzymically active recombinant protein. This GST shares a greater identity with the human schistosomiasis GST vaccine currently at Phase II clinical trials than previously discovered F. gigantica GSTs, stimulating interest in its immuno-protective properties. In addition, in silico analysis of the GST superfamily of both F. gigantica and F. hepatica has revealed an additional Mu class GST, Omega class GSTs, and for the first time, a Zeta class member

Pathogen Proteins Eliciting Antibodies Do Not Share Epitopes with Host Proteins: A Bioinformatics Approach

The best way to prevent diseases caused by pathogens is by the use of vaccines. The advent of genomics enables genome-wide searches of new vaccine candidates, called reverse vaccinology. The most common strategy to apply reverse vaccinology is by designing subunit recombinant vaccines, which usually generate an humoral immune response due to B-cell epitopes in proteins. A major problem for this strategy is the identification of protective immunogenic proteins from the surfome of the pathogen. Epitope mimicry may lead to auto-immune phenomena related to several human diseases. A sequence-based computational analysis has been carried out applying the BLASTP algorithm. Therefore, two huge databases have been created, one with the most complete and current linear B-cell epitopes, and the other one with the surface-protein sequences of the main human respiratory bacterial pathogens. We found that none of the 7353 linear B-cell epitopes analysed shares any sequence identity region with human proteins capable of generating antibodies, and that only 1% of the 2175 exposed proteins analysed contain a stretch of shared sequence with the human proteome. These findings suggest the existence of a mechanism to avoid autoimmunity. We also propose a strategy for corroborating or warning about the viability of a protein linear B-cell epitope as a putative vaccine candidate in a reverse vaccinology study; so, epitopes without any sequence identity with human proteins should be very good vaccine candidates, and the other way around

Prediction of Peptide Reactivity with Human IVIg through a Knowledge-Based Approach

The prediction of antibody-protein (antigen) interactions is very difficult due to the huge variability that characterizes the structure of the antibodies. The region of the antigen bound to the antibodies is called epitope. Experimental data indicate that many antibodies react with a panel of distinct epitopes (positive reaction). The Challenge 1 of DREAM5 aims at understanding whether there exists rules for predicting the reactivity of a peptide/epitope, i.e., its capability to bind to human antibodies. DREAM 5 provided a training set of peptides with experimentally identified high and low reactivities to human antibodies. On the basis of this training set, the participants to the challenge were asked to develop a predictive model of reactivity. A test set was then provided to evaluate the performance of the model implemented so far