A database of human genes and a gene network involved in response to tick-borne encephalitis virus infection

Abstract

BackgroundTick-borne encephalitis is caused by the neurotropic, positive-sense RNA virus, tick-borne encephalitis virus (TBEV). TBEV infection can lead to a variety of clinical manifestations ranging from slight fever to severe neurological illness. Very little is known about genetic factors predisposing to severe forms of disease caused by TBEV. The aims of the study were to compile a catalog of human genes involved in response to TBEV infection and to rank genes from the catalog based on the number of neighbors in the network of pairwise interactions involving these genes and TBEV RNA or proteins.ResultsBased on manual review and curation of scientific publications a catalog comprising 140 human genes involved in response to TBEV infection was developed. To provide access to data on all genes, the TBEVhostDB web resource (http://icg.nsc.ru/TBEVHostDB/) was created. We reconstructed a network formed by pairwise interactions between TBEV virion itself, viral RNA and viral proteins and 140 genes/proteins from TBEVHostDB. Genes were ranked according to the number of interactions in the network. Two genes/proteins (CCR5 and IFNAR1) that had maximal number of interactions were revealed. It was found that the subnetworks formed by CCR5 and IFNAR1 and their neighbors were a fragments of two key pathways functioning during the course of tick-borne encephalitis: (1) the attenuation of interferon-I signaling pathway by the TBEV NS5 protein that targeted peptidase D; (2) proinflammation and tissue damage pathway triggered by chemokine receptor CCR5 interacting with CD4, CCL3, CCL4, CCL2. Among nine genes associated with severe forms of TBEV infection, three genes/proteins (CCR5, IL10, ARID1B) were found to have protein-protein interactions within the network, and two genes/proteins (IFNL3 and the IL10, that was just mentioned) were up- or down-regulated in response to TBEV infection. Based on this finding, potential mechanisms for participation of CCR5, IL10, ARID1B, and IFNL3 in the host response to TBEV infection were suggested.ConclusionsA database comprising 140 human genes involved in response to TBEV infection was compiled and the TBEVHostDB web resource, providing access to all genes was created. This is the first effort of integrating and unifying data on genetic factors that may predispose to severe forms of diseases caused by TBEV. The TBEVHostDB could potentially be used for assessment of risk factors for severe forms of tick-borne encephalitis and for the design of personalized pharmacological strategies for the treatment of TBEV infection.Electronic supplementary materialThe online version of this article (10.1186/s12862-017-1107-8) contains supplementary material, which is available to authorized users.

Exome-wide search and functional annotation of genes associated in patients with severe tick-borne encephalitis in a Russian population

“…In our recent report [46], we have presented a catalog of human genes that can be involved in response to TBEV infection (TBEVHostDB, http://icg.nsc.ru/TBEVHostDB/). TBEVHostDB includes data on 140 candidate genes identified based on manually collected information from a number of research papers.…”

“…TBEVHostDB includes data on 140 candidate genes identified based on manually collected information from a number of research papers. Genes were classified into five functional categories according to the type of evidence that confirms their involvement in the host response: (1) genes encoding proteins involved in direct physical interactions with TBE viral particle, TBEV proteins or RNA; (2) genes encoding mRNAs (or proteins) that were up- or down-regulated in response to TBEV infection; (3) genes that had allelic variant associated with susceptibility or resistance to TBEV infection; (4) genes encoding proteins required for the inhibitory effect of other proteins against TBEV or proteins that attenuated antiviral activity of other proteins; (5) genes that have been knocked out in mice and, as a consequence, mortality rates or other clinical manifestations of the disease were increased [46].…”

To the question of the digital platform “bioinformatics” creating and its system-forming solutions

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Prioritization of genes associated with the pathogenesis of leukosis in cattle

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