Dataset Information

HPIDB--a unified resource for host-pathogen interactions.

ABSTRACT:

Background

Protein-protein interactions (PPIs) play a crucial role in initiating infection in a host-pathogen system. Identification of these PPIs is important for understanding the underlying biological mechanism of infection and identifying putative drug targets. Database resources for studying host-pathogen systems are scarce and are either host specific or dedicated to specific pathogens.

Results

Here we describe "HPIDB" a host-pathogen PPI database, which will serve as a unified resource for host-pathogen interactions. Specifically, HPIDB integrates experimental PPIs from several public databases into a single, non-redundant web accessible resource. The database can be searched with a variety of options such as sequence identifiers, symbol, taxonomy, publication, author, or interaction type. The output is provided in a tab delimited text file format that is compatible with Cytoscape, an open source resource for PPI visualization. HPIDB allows the user to search protein sequences using BLASTP to retrieve homologous host/pathogen sequences. For high-throughput analysis, the user can search multiple protein sequences at a time using BLASTP and obtain results in tabular and sequence alignment formats. The taxonomic categorization of proteins (bacterial, viral, fungi, etc.) involved in PPI enables the user to perform category specific BLASTP searches. In addition, a new tool is introduced, which allows searching for homologous host-pathogen interactions in the HPIDB database.

Conclusions

HPIDB is a unified, comprehensive resource for host-pathogen PPIs. The user interface provides new features and tools helpful for studying host-pathogen interactions. HPIDB can be accessed at http://agbase.msstate.edu/hpi/main.html.

SUBMITTER: Kumar R

PROVIDER: S-EPMC3026363 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

HPIDB--a unified resource for host-pathogen interactions.

Kumar Ranjit R Nanduri Bindu B

BMC bioinformatics 20101007

<h4>Background</h4>Protein-protein interactions (PPIs) play a crucial role in initiating infection in a host-pathogen system. Identification of these PPIs is important for understanding the underlying biological mechanism of infection and identifying putative drug targets. Database resources for studying host-pathogen systems are scarce and are either host specific or dedicated to specific pathogens.<h4>Results</h4>Here we describe "HPIDB" a host-pathogen PPI database, which will serve as a unif ...[more]

PMID: 20946599

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Project description:The goal of this project is to understand the etiology and pathology of host-pathogen interaction using high-throughput cellular phenotyping. By combining the expertise in pathogen biology, informatics and stem cell biology across the institute, this project will use targeted genetic modification in mouse embryonic stem cells to identify functions of host genes in innate immunity. We will also explore the biology of mouse embryonic stem cells and their differentiation into immune competent cells. To this end we will generate homozygous and inducible knockout lines in mouse embryonic stem cells, which will be differentiated into immune competent cells, such as macrophages or dendritic cells. These will be subjected to a panel of immune challenge and stimulation assays to characterise the functions of the disrupted genes. To identify new functions for mammalian genes in innate immunity we will:\Generate a panel of homozygous inducible gene knockout mouse ES cell lines working from a list of ca. 80 candidate genes (MGP phenotyping and GWAS hits). \Scale up differentiation protocols for immune challenge and stimulation assays.\Benchmark in vitro differentiated ES cell models against primary immune cells from selected MGP mouse lines and eventually against human iPS cells.\Screen a panel of 30 genetically modified cell lines in immunological challenge and stimulation assays, defining phenotypes at the cellular level by high content imaging, biochemically by assaying cytokine production, and at the molecular level by transcriptome analysis.\Define novel gene functions in innate immunity through network analysis of gene expression data from all challenged cell lines.\In the longer term scale up the production and phenotyping of genetically modified cell lines (mouse or human as appropriate).This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

Project description:MotivationAn important aspect of infectious disease research involves understanding the differences and commonalities in the infection mechanisms underlying various diseases. Systems biology-based approaches study infectious diseases by analyzing the interactions between the host species and the pathogen organisms. This work aims to combine the knowledge from experimental studies of host-pathogen interactions in several diseases to build stronger predictive models. Our approach is based on a formalism from machine learning called 'multitask learning', which considers the problem of building models across tasks that are related to each other. A 'task' in our scenario is the set of host-pathogen protein interactions involved in one disease. To integrate interactions from several tasks (i.e. diseases), our method exploits the similarity in the infection process across the diseases. In particular, we use the biological hypothesis that similar pathogens target the same critical biological processes in the host, in defining a common structure across the tasks.ResultsOur current work on host-pathogen protein interaction prediction focuses on human as the host, and four bacterial species as pathogens. The multitask learning technique we develop uses a task-based regularization approach. We find that the resulting optimization problem is a difference of convex (DC) functions. To optimize, we implement a Convex-Concave procedure-based algorithm. We compare our integrative approach to baseline methods that build models on a single host-pathogen protein interaction dataset. Our results show that our approach outperforms the baselines on the training data. We further analyze the protein interaction predictions generated by the models, and find some interesting insights.AvailabilityThe predictions and code are available at: http://www.cs.cmu.edu/?mkshirsa/ismb2013_paper320.html .Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

HPIDB--a unified resource for host-pathogen interactions.

Background

Results

Conclusions

Publications

HPIDB--a unified resource for host-pathogen interactions.

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