PHISTO: pathogen–host interaction search tool

Tekir, Saliha Durmuş; Çakır, Tunahan; Ardıç, Emre; Sayılırbaş, Ali Semih; Konuk, Gökhan; Konuk, Mithat; Sarıyer, Hasret; Uğurlu, Azat; Karadeniz, İlknur; Özgür, Arzucan; Sevilgen, Fatih Erdogan; Ülgen, Kutlu Ö.

doi:10.1093/bioinformatics/btt137

Cited by 144 publications

(89 citation statements)

References 4 publications

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“…Here, we comparatively analyzed the current experimental PHI data belonging to DNA and RNA viruses, covering 19 033 PHIs between 1061 viral proteins and 4943 human proteins. The PHI data were obtained from phisto which is a comprehensive database of pathogen–human PPIs . This study presents the first comprehensive comparison between DNA and RNA viruses in terms of their infection strategies, providing an initial systems‐level understanding of viral pathogenesis through PHIs.…”

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confidence: 99%

Comparative interactomics for virus–human protein–protein interactions: DNA viruses versus RNA viruses

Durmuş

Ülgen

2017

FEBS Open Bio

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Viruses are obligatory intracellular pathogens and completely depend on their hosts for survival and reproduction. The strategies adopted by viruses to exploit host cell processes and to evade host immune systems during infections may differ largely with the type of the viral genetic material. An improved understanding of these viral infection mechanisms is only possible through a better understanding of the pathogen–host interactions (PHIs) that enable viruses to enter into the host cells and manipulate the cellular mechanisms to their own advantage. Experimentally‐verified protein–protein interaction (PPI) data of pathogen–host systems only became available at large scale within the last decade. In this study, we comparatively analyzed the current PHI networks belonging to DNA and RNA viruses and their human host, to get insights into the infection strategies used by these viral groups. We investigated the functional properties of human proteins in the PHI networks, to observe and compare the attack strategies of DNA and RNA viruses. We observed that DNA viruses are able to attack both human cellular and metabolic processes simultaneously during infections. On the other hand, RNA viruses preferentially interact with human proteins functioning in specific cellular processes as well as in intracellular transport and localization within the cell. Observing virus‐targeted human proteins, we propose heterogeneous nuclear ribonucleoproteins and transporter proteins as potential antiviral therapeutic targets. The observed common and specific infection mechanisms in terms of viral strategies to attack human proteins may provide crucial information for further design of broad and specific next‐generation antiviral therapeutics.

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confidence: 99%

Comparative interactomics for virus–human protein–protein interactions: DNA viruses versus RNA viruses

Durmuş

Ülgen

2017

FEBS Open Bio

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“…Prior knowledge about interactions in GRNs originates from published results that were transferred to databases. PHI databases like PHISTO (Tekir et al, 2013 ), PHI-base (Winnenburg et al, 2006 ), and HPIDB (Kumar and Nanduri, 2010 ) have been established. Mukherjee et al ( 2013 ) listed various web sources of interaction data.…”

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confidence: 99%

Computational prediction of molecular pathogen-host interactions based on dual transcriptome data

Schulze

Henkel²,

Driesch³

et al. 2015

Front. Microbiol.

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Inference of inter-species gene regulatory networks based on gene expression data is an important computational method to predict pathogen-host interactions (PHIs). Both the experimental setup and the nature of PHIs exhibit certain characteristics. First, besides an environmental change, the battle between pathogen and host leads to a constantly changing environment and thus complex gene expression patterns. Second, there might be a delay until one of the organisms reacts. Third, toward later time points only one organism may survive leading to missing gene expression data of the other organism. Here, we account for PHI characteristics by extending NetGenerator, a network inference tool that predicts gene regulatory networks from gene expression time series data. We tested multiple modeling scenarios regarding the stimuli functions of the interaction network based on a benchmark example. We show that modeling perturbation of a PHI network by multiple stimuli better represents the underlying biological phenomena. Furthermore, we utilized the benchmark example to test the influence of missing data points on the inference performance. Our results suggest that PHI network inference with missing data is possible, but we recommend to provide complete time series data. Finally, we extended the NetGenerator tool to incorporate gene- and time point specific variances, because complex PHIs may lead to high variance in expression data. Sample variances are directly considered in the objective function of NetGenerator and indirectly by testing the robustness of interactions based on variance dependent disturbance of gene expression values. We evaluated the method of variance incorporation on dual RNA sequencing (RNA-Seq) data of Mus musculus dendritic cells incubated with Candida albicans and proofed our method by predicting previously verified PHIs as robust interactions.

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“…The experimentally determined host-pathogen PPIs from PHISTO database (Durmus Tekir, et al 2013) and PPIs present in the DIP (Database of Interacting Proteins) database (Salwinski, et al 2004) were used as a template to reconstruct interologs (Yu, et al 2004) based PPIs networks of human-ZE, human-Tb and human-NM. The stand-alone InParanoid program version 4.1 was used to assign proteins from the three pathogens to orthology groups.…”

Section: Inferring Protein-protein Interactions Based On Interologsmentioning

confidence: 99%

Protein network analysis indicates that Ebola virus, Neisseria meningitidis and Trypanosoma brucei trigger common host defense response pathways

Gupta

Ponte-Sucre

Bencúrová

et al. 2019

Preprint

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Background: Sepsis is the most severe final state of infection. Might pathogens belonging to different kingdoms of life and pathogen classes trigger analogous responses pathways in the host? Is there a common denominator or strategy to prevent it? Here we use interactomics and comparative evolutionary analysis of three serious infectious pathogens, spanning viruses, bacteria and parasites: Filovirus ebolavirus (ZE; Zaire ebolavirus , Bundibugyo ebolavirus), Neisseria meningitides (NM), and Trypanosoma brucei (Tb) that target the blood during their infectious life cycle. Results: We analyze 2797 unique human proteins targeted by the pathogens. The comparison resulted in specific and shared protein-protein interactions (PPIs) with each pathogen, derived from orthology searches of experimentally validated PPIs. Furthermore, we narrow down the data set of effected genes of the human (defense) immune response and describe human proteins predicted to participate in at least two of the compared host-pathogen networks. Only four proteins were common to all three host-pathogen interactomes. This interaction clade of four proteins, common to all three host-pathogen networks, suggests a central cluster of host response formed by elongation factor 1-alpha 1 (EF-1-alpha-1), the SWI/SNF complex subunit SMARCC2 (matrix-associated actin-dependent regulator of chromatin subfamily C), the dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 1, and the tubulin beta-5 chain. These hub proteins are functionally connected, suggesting according to the collected data including experimental information on VP24 from Ebola virus that these, as well as some joined interaction partners, may orchestrate a common host response to infection.

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PHISTO: pathogen–host interaction search tool

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 144 publications

References 4 publications

Comparative interactomics for virus–human protein–protein interactions: DNA viruses versus RNA viruses

Comparative interactomics for virus–human protein–protein interactions: DNA viruses versus RNA viruses

Computational prediction of molecular pathogen-host interactions based on dual transcriptome data

Protein network analysis indicates that Ebola virus, Neisseria meningitidis and Trypanosoma brucei trigger common host defense response pathways

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