Viruses are well known as global threats to human and animal welfare. Viral diseases such as hepatitis caused by Hepatitis C virus (HCV) and cervical cancer caused by Human papillomavirus (HPV) each cause more than a quarter of a million deaths worldwide each year [1
]. Outbreaks also present an economic burden—the 2014 Ebola virus outbreak cost 2.2 billion USD to contain [2
], and the annual response to Influenza virus costs five times this amount in medical expenses in the US alone [3
]. Climate change and changing land use patterns are causing humans and livestock to be exposed to novel viruses for which there are currently no vaccines or antiviral drugs [4
]. This trend will continue as the habitats of vectors that carry arboviruses expand [5
], and as humans continue to come into contact with wildlife, creating opportunities for zoonosis [6
As obligate intracellular parasites, viruses act as metabolic engineers of the cells they infect as they commandeer the cell’s protein synthesis mechanisms to replicate [7
]. Thus, it is important to study their interactions with host cells in order to understand their biology, especially how their disruption of the host protein–protein interaction (PPI) network causes disease [8
]. Antiviral drugs have been highly effective at preventing the progression of HIV infection to AIDS [9
]; however, the effectiveness of antiviral drugs can decrease over time due to the development of drug resistant viral strains [10
]. A more complete understanding of the host–virus protein–protein interaction network provides more potential viral drug targets, and also enables alternative strategies such as targeting host proteins to attenuate viral infection [14
]. When available, vaccines are very effective at preventing diseases caused by viruses [15
]; however, vaccines are not available for all viruses, including HIV-1 and HCV, and a universal Influenza vaccine is still elusive [16
]. The development of modern vaccines such as subunit vaccines, which can be administered to immunocompromized patients, and which eliminate the chance that the vaccine could revert to an infectious virus [17
], also hinges on understanding the protein–protein interactions between viruses and their hosts.
Novel protein–protein interaction information is disseminated primarily in the scientific literature, but it is not always organized in ways that make it easy to find, access, or extract. Databases such as VirusMentha [18
] and HPIDB [19
] make strong efforts to organize virus–virus and virus–host PPIs into databases, where this information is available in an easily parsable format. However, with the volume of the biomedical literature growing exponentially at 4% per year [20
], it is not feasible for human curators to thoroughly review all new publications to add any new evidence to curated databases [21
]. Automated text-mining methods are thus required to get a comprehensive picture of what is already known about the viruses we study.
We have expanded the popular database STRING [22
] to include intra-virus and virus–host PPIs. The STRING database has been in constant development for 15 years, and the current version includes protein interaction data for over 2000 species; however, all of the interactions are exclusively intra-species. In this work, for the first time, we include cross species interactions into the STRING database. The PPIs reported by STRING represent functional associations between proteins. These interactions are not limited to physical interactions, and may also include interactions such as transcription factor binding, or the interaction may represent the fact that the associated proteins appear in the same biological pathway. In this paper, the terms “interaction” and “PPI” are used to refer to functional associations. STRING combines many different sources (channels) of information to give a confidence score that measures the probability that the interaction is true. In a similar fashion, we provide virus-related probabilistic interaction networks derived from text mining and experiment channels.
We were able to identify 177,425 protein–protein interactions for 239 viruses. Seventy-seven of these are human viruses, and the remainder infect a total of 318 other hosts. These interactions include more hosts than viruses since many viruses, such as arboviruses, infect multiple hosts. The median number of proteins coded for by these viruses is nine, with 24 viruses coding for more than 100 proteins, and 74 viruses coding for less than nine proteins. The majority of all types of interactions are between viruses and their hosts (as opposed to being intra-virus interactions), due to viral genomes encoding many fewer proteins than their host genomes and thus having fewer potential interactions. In this and the subsequent analysis, interactions are counted per channel, disregarding their scores. Excluding orthology transfer, 89% of the interactions are derived from text mining evidence, and the remaining untransferred evidence comes from curated experimental databases. For 154 viruses, representing 19.8% of all evidence in the database, only text mining evidence is present. This means that, although the interaction has not been curated into databases, we have found a protein from this virus mentioned together in the literature with either other proteins from this virus, or a protein from a host that this virus infects. For 77 viruses, representing 77.4% of all evidence, all of the experimental evidence is also supported by text mining evidence. The remaining eight viruses, representing 2.8% of evidence, have more experimental evidence than text mining, and likely represent opportunities to improve the text mining dictionaries. Despite the large efforts of database curators, the vast wealth of information on PPIs is accessible only in the literature. Furthermore, in addition to physical interactions, text mining will also uncover functional associations such as genetic interactions. As such, text mining provides a very important contribution to this database.
The top GO terms that are enriched in the set of 1835 human proteins that interact with any virus protein with a confidence of 0.5 or greater are shown in Table 1
. The fact that this list includes terms such as viral process, protein binding and cell surface receptor signaling pathways provides a sanity check that the human protein partners in the found interactions are valid.
Orthology transfer gives a 2.7 times increase in the number of interactions with text mining results being more readily transferred than experimental results. A handful of well studied viruses (EBV, HIV-1, Influenza A) are the subjects of high-throughput studies that make up the bulk of the interactions in curated experimental databases. These viruses happen to have few close relatives (e.g., HIV, Influenza A), and infect a limited number of hosts (EBV, HIV), which is why their PPIs are not as readily transferred via orthology as interactions found by text mining for other virus proteins. Viruses with many close relatives will have more populated orthology groups, and thus more candidates to transfer evidence from and to. At the level of ssRNA viruses, the maximum size of any orthology group containing an Influenza A protein is 5, and at the level of retrotranscribing viruses the same for any HIV protein is 9. This is in contrast to the viruses that receive the most experimental transfer data, which have 193 (Swinepox virus), 16 (Canine oral papillomavirus) and 30 (Murine cytomegalovirus) proteins in their most populated orthology group. The viruses that receive the most text mining transfer data are Gallid herpesvirus, Murine cytomegalovirus and Equine herpesvirus 2, which at the level of dsDNA have maximum orthology group sizes of 62, 30 and 62 proteins, respectively.
More than half (55%) of pre-transfer evidence relates to human viruses. However, evidence transferred to humans comprises only 26% of all transferred experimental evidence and 18% of all transferred text mining evidence, which implies that the majority of transferred evidence is to a new host (panel c or d in Figure 1
). This is due to the fact that gene duplication events occur less frequently in viruses compared to their host organisms [34
], and additionally also because the viral taxonomic tree has overall been sequenced much less densely than the tree for cellular organisms, even though the viral tree has been extensively sequenced around specific human viruses [35
]. In all, this makes potential transfer partners rarer for transfer between viruses than between hosts.
The distribution of interactions for the 20 viruses with the most interactions is shown in Figure 2
. The viruses with the largest number of intra-virus interactions include the relatively large double-stranded DNA Herpesviruses and well studied RNA viruses including Influenza and HIV. The same viruses also show the highest proportion of interactions from the experimental channel. An example of two viruses that share interactions based on orthology transfer are human and murine cytomegalovirus (HCMV and MCMV, respectively). The majority of the evidence for HCMV is direct evidence, and conversely, the majority of evidence for MCMV is evidence from transfer, which has come from interactions with HCMV.
The virus–virus and virus–host PPI networks are made publicly accessible as a resource which is available at viruses.string-db.org. The data can be browsed online, downloaded from the website, or accessed through the REST API. Furthermore, the data can also be imported into Cytoscape directly [36
] using the STRING Cytoscape app [22
3.1. Utility and Examples
3.1.1. Web Interface
The Viruses.STRING website enables three variants of protein search: for the complete set of proteins in a virus, for a single protein in a virus, or for multiple proteins in a virus. Since most viral genomes encode only a small number of proteins (the viruses included in the database have a median of nine proteins), they can easily be displayed in a network together with the most strongly interacting host proteins.
The network interface has a similar appearance to STRING, but the visual styling has been modified to be more flat. The nodes in the network are coloured only based on their origin, either as viral proteins (brick red) or as host proteins (blue-green slate).
As is possible on the main STRING site, the viruses.STRING web interface provides more information about each protein, which is accessed by clicking on the node. Similarly, clicking on any edge displays a summary of the information that contributes to that interaction, and provides links to further inspect the evidence from each channel. Text mining evidence shows highlighted phrases from relevant publications, whereas experiments’ evidence shows the specific database and publication from which it was obtained.
3.1.2. Example: HIV-1
In this example, we will query for all proteins present in Human Immunodeficiency virus type 1. If the host field on the search page is left empty, the server will auto detect the host species with the most interactions with the specified virus, in this case, humans. An interaction network will then be shown for the virus proteins and for the 10 human proteins that have the highest interaction scores with these virus proteins, as shown in Figure 3
. By default, only interaction scores with a confidence score greater than 0.4 will be shown, the same as the main STRING site. This confidence can be changed using the interface.
HIV-1 consists of 19 proteins, 10 of which are cleaved from three polyproteins. The polyproteins are translated as a single long protein, and then the long polyprotein is cleaved by the viral protease into functional protein units. The database includes 24 proteins as it includes some partial cleavage products, such as both gp160 and gp120, which is cleaved from gp160.
3.1.3. Cytoscape STRING App
The Viruses.STRING interaction data can also be queried from the Cytoscape STRING app. This requires version 3.6 of Cytoscape or greater and version 1.4 of the STRING app or greater, which is available for free in the Cytoscape app store (http://apps.cytoscape.org/apps/stringapp
The STRING app allows for more flexible queries than the Viruses.STRING website, such as choosing specific additional host proteins to be included in the network, and displaying multiple hosts and multiple viruses in the same network. In addition to the Viruses.STRING interaction data, the app automatically fetches node and edge information, which can be used for further analysis. The former includes the protein sequence for host and virus nodes, subcellular localization data from the COMPARTMENTS database for human proteins, and tissue expression data taken from the TISSUES database for human, mouse, rat and pig proteins [37
]. Edge information includes the combined confidence score from all channels as a probability that the interaction is true.
illustrates the combined interaction network for HPV 16 and HPV 1a proteins with the top 50 human proteins they interact with ranked by combined interaction score. Since HPV is known to disrupt the cell cycle [38
], many of the proteins that interact with E6 and E7 are associated with the nucleus and the GO term for cell cycle. A tutorial to reproduce this network in Cytoscape is available at http://jensenlab.org/training/stringapp/
The Viruses.STRING database provides a single unified interface to virus–virus and host–virus PPIs from text mining and many experimental sources. With a simple web interface, the database can easily be queried to immediately retrieve the interaction partners for a protein of interest, and the corresponding evidence can be inspected. The Cytoscape STRINGapp, although it requires software to be installed, provides more versatility than the website, and can handle much larger networks—up to at least as large as the human interaction network. This provides the researcher with more opportunities to answer interesting biological questions about viruses and their hosts. For example, the virus–host network could potentially be used to select candidate host proteins as drug targets to inhibit viral infection, possibly by repurposing existing drugs. This approach would likely generate less viral resistance to the drug since the host protein is being targeted, instead of a viral protein that can mutate easily [14
As this is the first iteration of Viruses.STRING, there are currently some limitations to the data. The virus data is provided only at the species level, with the exception of Dengue types 1–4, even though there is some evidence that different influenza strains show differential protein interactions [40
]. This fine grained resolution will be added in a future version for those viruses where sufficient data is available, such as Influenza A.
Text mining reveals many more virus–host PPIs in the literature than have been collected into databases. The text mining gives good precision and recall for virus species, and good precision for virus proteins [28
]. However, the method performs less well for virus proteins in terms of recall, meaning that many interactions may still be missed by this approach [28
Both text mining and experimental evidence is transferred via orthology using the system designed for STRING for cellular organisms. Since viruses have a much higher mutation rate than cellular organisms, this transfer method may over-discount contributions from viruses that appear to be distant relatives but that have diverged relatively recently. This will result in lower transfer scores for viruses than we would see between cellular organisms that have diverged for the same amount of time.
Just as a having a broader view of intra-species PPIs has provided a deeper understanding of cellular function [41
], having a similar understanding between pathogens and their hosts will provide new information to combat clinically and economically relevant viral infections and diseases.