Dataset Information

Helminth secretome database (HSD): a collection of helminth excretory/secretory proteins predicted from expressed sequence tags (ESTs).

ABSTRACT: BACKGROUND: Helminths are important socio-economic organisms, responsible for causing major parasitic infections in humans, other animals and plants. These infections impose a significant public health and economic burden globally. Exceptionally, some helminth organisms like Caenorhabditis elegans are free-living in nature and serve as model organisms for studying parasitic infections. Excretory/secretory proteins play an important role in parasitic helminth infections which make these proteins attractive targets for therapeutic use. In the case of helminths, large volume of expressed sequence tags (ESTs) has been generated to understand parasitism at molecular level and for predicting excretory/secretory proteins for developing novel strategies to tackle parasitic infections. However, mostly predicted ES proteins are not available for further analysis and there is no repository available for such predicted ES proteins. Furthermore, predictions have, in the main, focussed on classical secretory pathways while it is well established that helminth parasites also utilise non-classical secretory pathways. RESULTS: We developed a free Helminth Secretome Database (HSD), which serves as a repository for ES proteins predicted using classical and non-classical secretory pathways, from EST data for 78 helminth species (64 nematodes, 7 trematodes and 7 cestodes) ranging from parasitic to free-living organisms. Approximately 0.9 million ESTs compiled from the largest EST database, dbEST were cleaned, assembled and analysed by different computational tools in our bioinformatics pipeline and predicted ES proteins were submitted to HSD. CONCLUSION: We report the large-scale prediction and analysis of classically and non-classically secreted ES proteins from diverse helminth organisms. All the Unigenes (contigs and singletons) and excretory/secretory protein datasets generated from this analysis are freely available. A BLAST server is available at http://estexplorer.biolinfo.org/hsd, for checking the sequence similarity of new protein sequences against predicted helminth ES proteins.

SUBMITTER: Garg G

PROVIDER: S-EPMC3546426 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Helminth secretome database (HSD): a collection of helminth excretory/secretory proteins predicted from expressed sequence tags (ESTs).

Garg Gagan G Ranganathan Shoba S

BMC genomics 20121213

<h4>Background</h4>Helminths are important socio-economic organisms, responsible for causing major parasitic infections in humans, other animals and plants. These infections impose a significant public health and economic burden globally. Exceptionally, some helminth organisms like Caenorhabditis elegans are free-living in nature and serve as model organisms for studying parasitic infections. Excretory/secretory proteins play an important role in parasitic helminth infections which make these pr ...[more]

PMID: 23281827

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Project description:Parasitic nematodes of humans, other animals and plants continue to impose a significant public health and economic burden worldwide, due to the diseases they cause. Promising antiparasitic drug and vaccine candidates have been discovered from excreted or secreted (ES) proteins released from the parasite and exposed to the immune system of the host. Mining the entire expressed sequence tag (EST) data available from parasitic nematodes represents an approach to discover such ES targets.In this study, we predicted, using EST2Secretome, a novel, high-throughput, computational workflow system, 4,710 ES proteins from 452,134 ESTs derived from 39 different species of nematodes, parasitic in animals (including humans) or plants. In total, 2,632, 786, and 1,292 ES proteins were predicted for animal-, human-, and plant-parasitic nematodes. Subsequently, we systematically analysed ES proteins using computational methods. Of these 4,710 proteins, 2,490 (52.8%) had orthologues in Caenorhabditis elegans, whereas 621 (13.8%) appeared to be novel, currently having no significant match to any molecule available in public databases. Of the C. elegans homologues, 267 had strong "loss-of-function" phenotypes by RNA interference (RNAi) in this nematode. We could functionally classify 1,948 (41.3%) sequences using the Gene Ontology (GO) terms, establish pathway associations for 573 (12.2%) sequences using Kyoto Encyclopaedia of Genes and Genomes (KEGG), and identify protein interaction partners for 1,774 (37.6%) molecules. We also mapped 758 (16.1%) proteins to protein domains including the nematode-specific protein family "transthyretin-like" and "chromadorea ALT," considered as vaccine candidates against filariasis in humans.We report the large-scale analysis of ES proteins inferred from EST data for a range of parasitic nematodes. This set of ES proteins provides an inventory of known and novel members of ES proteins as a foundation for studies focused on understanding the biology of parasitic nematodes and their interactions with their hosts, as well as for the development of novel drugs or vaccines for parasite intervention and control.

Project description:BackgroundThe cestode Echinococcus multilocularis is the causative agent of human alveolar echinococcosis (AE). However, this life-threatening disease is still difficult to treat and control, due to the lack of efficient drugs and vaccines. Excretory/secretory (ES) proteins are crucial for parasite survival and represent potential preferred targets for novel intervention strategies. However, the ES protein features in this parasite have been poorly investigated until now. The current study was carried out to identify and characterise a repertoire of ES proteins in E. multilocularis at the genome-wide level.MethodsHere we predicted and functionally annotated the classical and non-classical ES proteins, and comprehensively compared the features and evolution of ES and non-ES proteins in E. multilocularis genome using an integration of bioinformatics tools. The intervention target and antigen potentials as well as the transcription information were also investigated.ResultsComputational analysis of the E. multilocularis proteins identified 673 putative ES proteins (6.4%), of which 617 (91.68%) could be supported by transcription analyses. The predicted ES proteins in E. multilocularis were mostly represented by molecular functions of protease inhibitors, proteases, glycoside hydrolases, immunoglobulin-like folds and growth factors. Analysis of the ratio between synonymous and non-synonymous substitution rates (dN/dS) revealed no significant difference of the evolution selection pressure on ES and non-ES protein coding genes. ES proteins showed higher antigenic density measured by AAR values as compared with the transmembrane proteins but had no significant difference of that feature with intracellular proteins. Additionally, 383 possible ideal drug targets were identified in ES proteins, of which four proteins have corresponding known drugs.ConclusionsThe present study is the first to identify a repertoire of predicted ES proteins at the genome-wide level in E. multilocularis. The comprehensive analysis provides some novel understanding of the parasite ES protein features and a valuable resource of potential targets for future experimental studies to develop new intervention tools against this parasite.

Project description:BACKGROUND: The turbot (Scophthalmus maximus; Scophthalmidae; Pleuronectiformes) is a flatfish species of great relevance for marine aquaculture in Europe. In contrast to other cultured flatfish, very few genomic resources are available in this species. Aeromonas salmonicida and Philasterides dicentrarchi are two pathogens that affect turbot culture causing serious economic losses to the turbot industry. Little is known about the molecular mechanisms for disease resistance and host-pathogen interactions in this species. In this work, thousands of ESTs for functional genomic studies and potential markers linked to ESTs for mapping (microsatellites and single nucleotide polymorphisms (SNPs)) are provided. This information enabled us to obtain a preliminary view of regulated genes in response to these pathogens and it constitutes the basis for subsequent and more accurate microarray analysis. RESULTS: A total of 12584 cDNAs partially sequenced from three different cDNA libraries of turbot (Scophthalmus maximus) infected with Aeromonas salmonicida, Philasterides dicentrarchi and from healthy fish were analyzed. Three immune-relevant tissues (liver, spleen and head kidney) were sampled at several time points in the infection process for library construction. The sequences were processed into 9256 high-quality sequences, which constituted the source for the turbot EST database. Clustering and assembly of these sequences, revealed 3482 different putative transcripts, 1073 contigs and 2409 singletons. BLAST searches with public databases detected significant similarity (e-value < or = 1e-5) in 1766 (50.7%) sequences and 816 of them (23.4%) could be functionally annotated. Two hundred three of these genes (24.9%), encoding for defence/immune-related proteins, were mostly identified for the first time in turbot. Some ESTs showed significant differences in the number of transcripts when comparing the three libraries, suggesting regulation in response to these pathogens. A total of 191 microsatellites, with 104 having sufficient flanking sequences for primer design, and 1158 putative SNPs were identified from these EST resources in turbot. CONCLUSION: A collection of 9256 high-quality ESTs was generated representing 3482 unique turbot sequences. A large proportion of defence/immune-related genes were identified, many of them regulated in response to specific pathogens. Putative microsatellites and SNPs were identified. These genome resources constitute the basis to develop a microarray for functional genomics studies and marker validation for genetic linkage and QTL analysis in turbot.

Dataset Information

Helminth secretome database (HSD): a collection of helminth excretory/secretory proteins predicted from expressed sequence tags (ESTs).

Publications

Helminth secretome database (HSD): a collection of helminth excretory/secretory proteins predicted from expressed sequence tags (ESTs).

Similar Datasets

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