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

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

Identification of Continuous Human B-Cell Epitopes in the Envelope Glycoprotein of Dengue Virus Type 3 (DENV-3)

BACKGROUND:Dengue virus infection is a growing global public health concern in tropical and subtropical regions of the world. Dengue vaccine development has been hampered by concerns that cross-reactive immunological memory elicited by a candidate vaccine could increase the risk of development of more severe clinical forms. One possible strategy to reduce risks associated with a dengue vaccine is the development of a vaccine composed of selected critical epitopes of each of the serotypes. METHODOLOGY/PRINCIPAL FINDINGS:Synthetic peptides were used to identify B-cell epitopes in the envelope (E) glycoprotein of dengue virus type 3 (DENV-3). Eleven linear, immunodominant epitopes distributed in five regions at amino acid (aa) positions: 51-65, 71-90, 131-170, 196-210 and 246-260 were identified by employing an enzyme- linked immunosorbent assay (ELISA), using a pool of human sera from dengue type 3 infected individuals. Peptides 11 (aa51-65), 27 and 28 (aa131-150) also reacted with dengue 1 (DENV-1) and dengue 2 (DENV-2) patient sera as analyzed through the ROC curves generated for each peptide by ELISA and might have serotype specific diagnostic potential. Mice immunized against each one of the five immunogenic regions showed epitopes 51-65, 131-170, 196-210 and 246-260 elicited the highest antibody response and epitopes131-170, 196-210 and 246-260, elicited IFN-gamma production and T CD4+ cell response, as evaluated by ELISA and ELISPOT assays respectively. CONCLUSIONS/SIGNIFICANCE:Our study identified several useful immunodominant IgG-specific epitopes on the envelope of DENV-3. They are important tools for understanding the mechanisms involved in antibody dependent enhancement and immunity. If proven protective and safe, in conjunction with others well-documented epitopes, they might be included into a candidate epitope-based vaccine

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

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

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

Histone H2A (H2A.X and H2A.Z) Variants in Molluscs: Molecular Characterization and Potential Implications For Chromatin Dynamics

Histone variants are used by the cell to build specialized nucleosomes, replacing canonical histones and generating functionally specialized chromatin domains. Among many other processes, the specialization imparted by histone H2A (H2A.X and H2A.Z) variants to the nucleosome core particle constitutes the earliest response to DNA damage in the cell. Consequently, chromatin-based genotoxicity tests have been developed in those cases where enough information pertaining chromatin structure and dynamics is available (i.e., human and mouse). However, detailed chromatin knowledge is almost absent in most organisms, specially protostome animals. Molluscs (which represent sentinel organisms for the study of pollution) are not an exception to this lack of knowledge. In the present work we first identified the existence of functionally differentiated histone H2A.X and H2A.Z variants in the mussel Mytilus galloprovincialis (MgH2A.X and MgH2A.Z), a marine organism widely used in biomonitoring programs. Our results support the functional specialization of these variants based on: a) their active expression in different tissues, as revealed by the isolation of native MgH2A.X and MgH2A.Z proteins in gonad and hepatopancreas; b) the evolutionary conservation of different residues encompassing functional relevance; and c) their ability to confer specialization to nucleosomes, as revealed by nucleosome reconstitution experiments using recombinant MgH2A.X and MgH2A.Z histones. Given the seminal role of these variants in maintaining genomic integrity and regulating gene expression, their preliminary characterization opens up new potential applications for the future development of chromatin-based genotoxicity tests in pollution biomonitoring programs

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