HPIDB 2.0: a curated database for host–pathogen interactions

Ammari, Mais; Gresham, Cathy; McCarthy, Fiona M.; Nanduri, Bindu

doi:10.1093/database/baw103

Cited by 215 publications

(161 citation statements)

References 38 publications

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“…Co-opting these pathways by CP leads to negative regulation of cellular machineries and ultimately apoptosis, which could be the case in the 42 hours samples. In our recent study we showed an increase in reactive oxygen species (ROS) and the breakdown of mitochondrial membrane potential (∆ᴪm) in a timedependent manner that accompanied an increase in apoptotic BT cells infected with CP but not with NCP BVDV [28], supporting our current proteomics findings. A rapidly growing literature suggests that a transient increase in ROS levels results in the activation of various signalling molecules and pathways to regulate responses to cellular oxidative stresses [18].…”

Section: Functional Analysis Of Cellular Proteins Associated With Difsupporting

confidence: 86%

See 1 more Smart Citation

Evaluation of bovine viral diarrhea virus interactions with its host proteome during the course of infection

Ammari¹,

Pharr²,

Pendarvis³

et al. 2017

Biomed Res Clin Prac

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Identifying viral-host interactions is central in understanding the pathogenesis of intracellular agents, such as bovine viral diarrhea viruses (BVDVs). A high expression level of BVDV NS3 protein is associated with its cytopathic (CP) biotype and is essential in viral replication and viral-induced apoptosis. Although the differences between CP and non-cytopathic BVDV biotypes are well established, especially the importance of the BVDV NS3 viral protein in giving rise to different biotypes, only a few studies identified BVDV-cellular partners. In this study, using a proteomics approach, we identified 71 bovine proteins that interact with the CP NS3 protein at three different time-points post-infection. Our results show that BVDV-host interaction dynamic network involves simultaneously and sequentially interacting proteins in different compartments of the host cells. System analysis of targeted proteins shows that CP BVDVs manipulate multiple and different pathways, primarily the ribosome/translation pathway, specifically at early stages of infection. At later stages of infection, when high levels of NS3 are present, apoptosis can be detected in infected cells. In addition, combining results from this study with previously identified protein interactions adds an extra dimension and higher connectivity to the BVDV strain NADL-host interaction network. Overall, our results indicate correlations among an increase in viral RNA translation, free NS3 level, and cytopathic effect associated with CP biotype infection. Finally, our approach highlights the dynamic interplay between BVDVs and host cells and identifies specific CP BVDV-host interactions that can be used as specific targets for further investigation as BVDV antiviral therapies.

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Section: Functional Analysis Of Cellular Proteins Associated With Difsupporting

confidence: 86%

“…The Host-Pathogen Interaction Database (HPIDB) [28], a resource that integrates dataset of pathogen-host interactions, was used to determine host proteins that are previously known to be targeted by BVDV NADL proteins.…”

Section: Network Modellingmentioning

confidence: 99%

Evaluation of bovine viral diarrhea virus interactions with its host proteome during the course of infection

Ammari¹,

Pharr²,

Pendarvis³

et al. 2017

Biomed Res Clin Prac

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“…The demand for such experimental data can be seen by the increase in databases (e.g. IntAct (Orchard et al, 2014) and HPIDB (Ammari, Gresham, McCarthy, & Nanduri, 2016)) that collect both experimentally verified molecular interactions and the conditions under which they occur. HPI data that can be utilized to study infections can be as simple as host protein A interacts with pathogen protein B.…”

Section: Translating An Infectious Disease Question Into Knowledgementioning

confidence: 99%

“…The systematic annotation of molecular interaction data to IMEx standards can be done using the European Bioinformatics Institute (EBI) IntAct interface (Orchard et al, 2014). IMEx databases are part of PSICQUIC and include 11 databases, of which IntAct, The Host-Pathogen Interaction DataBase (HPIDB) (Ammari et al, 2016), MINT (Orchard et al, 2014) and UniProtKB (The UniProt, 2017) are examples of IMEx databases with HPIs. For more information regarding standard data formats and databases for molecular interactions see review (Orchard et al, 2014).…”

Section: Translating An Infectious Disease Question Into Knowledgementioning

confidence: 99%

Leveraging Experimental Details for an Improved Understanding of Host‐Pathogen Interactome

Ammari

McCarthy

Nanduri

2018

CP in Bioinformatics

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An increasing proportion of curated host-pathogen interaction (HPI) information is becoming available in interaction databases. These data represent detailed, experimentally-verified, molecular interaction data, which may be used to better understand infectious diseases. By their very nature, HPIs are context dependent, where the outcome of two proteins as interacting or not depends on the precise biological conditions studied and approaches used for identifying these interactions. The associated biology and the technical details of the experiments identifying interacting protein molecules are increasing being curated using defined curation standards but are overlooked in current HPI network modeling. Given the increase in data size and complexity, awareness of the process and variables included in HPI identification and curation, and their effect on data analysis and interpretation is crucial in understanding pathogenesis. We describe the use of HPI data for network modeling, aspects of curation that can help researchers to more accurately model specific infection conditions, and provide examples to illustrate these principles. © 2018 by John Wiley & Sons, Inc.

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“…We obtained high-confidence interaction pairs of pathogens and human proteins from the Host-Pathogen Interaction Database (HPIDB) (Ammari et al, 2016). We then trained an ANN to predict host-pathogen interactions using the feature vectors of pathogens and human proteins as input.…”

Section: Phenotypic and Functional Prediction Of Interaction Partnersmentioning

confidence: 99%

Phenotypic, functional and taxonomic features predict host-pathogen interactions

Liu-Wei

Kafkas

Hoehndorf

2018

Preprint

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Motivation: Identification of host-pathogen interactions (HPIs) can reveal mechanistic insights of infectious diseases for potential treatments and drug discoveries. Current computational methods for the prediction of HPIs often rely on our knowledge on the sequences and functions of pathogen proteins, which is limited for many species, especially for emerging pathogens. Matching the phenotypes elicited by pathogens with phenotypes associated with host proteins might improve the prediction of HPIs. Results: We developed an ontology-based machine learning method that predicts potential interaction protein partners for pathogens. Our method exploits information about disease mechanisms through features learned from phenotypic, functional and taxonomic knowledge about pathogens and human proteins. Additionally, by embedding the phenotypic information of the pathogens within a formal representation of pathogen taxonomy, we demonstrate that our model can accurately predict interaction partners for pathogens without known phenotypes, using a combination of their taxonomic relationships with other pathogens and information from ontologies as background knowledge. Our results show that the integration of phenotypic, functional and taxonomic knowledge not only improves the prediction of HPIs, but also enables us to investigate novel pathogens in emerging infectious diseases. Availability:https://github.com/bio-ontology-research-group/hpi-predict Contact:

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HPIDB 2.0: a curated database for host–pathogen interactions

Cited by 215 publications

References 38 publications

Evaluation of bovine viral diarrhea virus interactions with its host proteome during the course of infection

Evaluation of bovine viral diarrhea virus interactions with its host proteome during the course of infection

Leveraging Experimental Details for an Improved Understanding of Host‐Pathogen Interactome

Phenotypic, functional and taxonomic features predict host-pathogen interactions

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