Representing virus-host interactions and other multi-organism processes in the Gene Ontology
© Foulger et al. 2015
Received: 17 December 2014
Accepted: 10 July 2015
Published: 28 July 2015
The Gene Ontology project is a collaborative effort to provide descriptions of gene products in a consistent and computable language, and in a species-independent manner. The Gene Ontology is designed to be applicable to all organisms but up to now has been largely under-utilized for prokaryotes and viruses, in part because of a lack of appropriate ontology terms.
To address this issue, we have developed a set of Gene Ontology classes that are applicable to microbes and their hosts, improving both coverage and quality in this area of the Gene Ontology. Describing microbial and viral gene products brings with it the additional challenge of capturing both the host and the microbe. Recognising this, we have worked closely with annotation groups to test and optimize the GO classes, and we describe here a set of annotation guidelines that allow the controlled description of two interacting organisms.
Building on the microbial resources already in existence such as ViralZone, UniProtKB keywords and MeGO, this project provides an integrated ontology to describe interactions between microbial species and their hosts, with mappings to the external resources above. Housing this information within the freely-accessible Gene Ontology project allows the classes and annotation structure to be utilized by a large community of biologists and users.
KeywordsAnnotation Gene Ontology Host Ontology Virus
The Gene Ontology (GO) is a bioinformatics resource to describe functional attributes of gene products across all kingdoms of life. The GO project is a collaborative effort developing three ontologies to describe the molecular actions of a gene product, the biological process those actions are part of, and the cellular locations in which they are active. First developed in 1998 , the Gene Ontologies arose from the need for standard descriptions to define a given object or process, and contained terms summarising the biology of three organisms (mouse, fly and yeast). GO has since grown to be the most popular bio-ontology used for describing gene product characteristics [2, 3]. It now contains more than 40,000 terms, expressed using the W3C standard ontology language OWL2 .
Subsequent to or in parallel with ontology construction, GO classes are associated with a gene product; this association is termed an annotation. Annotations are the result of manual analysis by trained curators and/or computational methods. Annotations are linked to an underlying source and include an evidence code  indicating the supporting data. Biological ontologies and related annotations have proven invaluable in interpreting accumulated biological data, where the volume of information and variations in terminology can pose problems. GO annotation in particular has been used in analyses ranging from small-scale queries about a protein or pathway of interest to large-scale high-throughput studies including, among others, gene enrichment analyses, microarray analyses, predicting gene functions and text-mining (e.g. [6–9] provide a representative overview). These analyses have allowed biologists to rapidly gain knowledge about their gene product or gene product set.
The need for a microbe-and virus project
History of multi-organism process terms in GO
Early iterations of the GO contained very few classes to describe processes involving hosts and their symbionts. This was largely because little annotation had been generated for the species typically participating in these processes. The exception was Plasmodium falciparum, which was fully annotated with GO classes in 2002 . The host/parasite classes that existed at the time had no common root class, and it was often ambiguous as to whether the process involved a second organism or not. For example, the process of cell lysis can either be induced in a host organism by its parasite, or can be an endogenous process whereby the immune system destroys its own infected cells. GO had only a single term for this process, which did not allow annotators to distinguish between endogenously and exogenously induced cytolysis.
In 2004 the PAMGO (Plant-Associated Microbe Gene Ontology) Consortium  worked with GO to develop a core set of terms for describing host-parasite interactions, specifically for the annotation of plant parasites [10, 27]. This branch, comprising 450 classes, subsumed the existing classes and formed a single branch in the biological process ontology with the root class ‘interaction between organisms’ (later renamed ‘multi-organism process’). It included host-parasite interactions involving macroparasites and microbial parasites as well as intra-species multi-organism processes such as biofilm formation.
Processes involving viruses were also sparse in early versions of GO. A set of classes was added in 2002 to accommodate a protein annotation set for Herpes simplex virus 1 (HSV-1), but not all of these classes were well suited for the annotation of other viral species, and the classes themselves were not well integrated with the rest of the biological process ontology. In 2009 we embarked on a project to improve the representation of viral processes in GO. This involved input from experts for a variety of viral species, including bacteriophages. We now report on the conclusion of these changes, including annotations for several viral species using the refactored terms.
With this project, we provide an integrated, comprehensive ontology to describe multi-species interactions and microbial biology, with mappings to relevant external resources, where possible. The advantage of housing these data in GO is that the Gene Ontology is a well-known and respected, sustainable, open-source resource, for which a battery of annotation and analysis tools have been built. Thus microbial annotations can reach a wide audience for analysis, and microbial researchers have a wide range of tools and services available for analysing and interpreting their data.
Construction and content
Ontology design challenges
Constructing and using GO terms for inter-species interactions presented a number of challenges and resulting ontology design decisions, which we describe below.
Diversity between organisms, and what constitutes an organism?
GO terms have to capture the diversity of biology seen across many species, including the great diversity seen within microbes themselves. Within the viruses alone, the genetic material can be DNA or RNA, double-stranded or single-stranded, and viruses come in all shapes and sizes. But it is not only the make-up of microbial organisms that exhibits diversity; they also vary in their biochemistry and replication mechanisms . The classes we created had to encompass this diversity whilst remaining general enough to be used to make inferences across species. For example, different microbes use a variety of mechanisms to adhere to their host organisms: via type IV pili in the case of bacteria, via specific infective structures such as the appressorium in parasitic fungi and via receptor-mediated binding in viruses. These disparate processes have a generic grouping under the GO multi-organism process sub-tree of ‘adhesion of symbiont to host’ (GO:0044406).
We also had to decide whether viral processes belonged in the ‘multi-organism process’ sub-tree, and this hinges on whether viruses are ‘living’ organisms or not. This is a question that has prompted much discussion e.g.  and the research community is split on this issue. Ultimately we decided that for the purposes of this project, viruses would be deemed to be organisms, and as such viral processes would be subtypes of multi-organism processes. We reached this decision to align GO with other bio-ontologies that have modelled viruses; for example, the Infectious Disease Ontology (IDO) , Experimental Factor Ontology (EFO)  and Ontology for Biomedical Investigation (OBI)  explicitly state that viruses  are a subclass of organism .
Symbiosis and mutualism
One of the key terms we created in this project was ‘symbiosis, encompassing mutualism through parasitism’ (GO:0044403). Here, the usage of symbiosis is not synonymous with mutualism (an interaction from which both organisms benefit). Instead it is used in its broad sense, to mean any intimate association between two organisms of different species, regardless of whether the outcome of the interaction is beneficial to both species (mutualism) or detrimental to one species (parasitism).
Upper-level multi-organism processes
Automating ontology development for multi-organism processes
The initial set of microbial terms required great manual input. In order for the multi-organism node of GO to be sustainable, new GO terms should be added, at least in-part, automatically. To this end we are developing a set of design patterns for multi-organism processes that include logical definitions . These will be used to construct templates  through which any registered user can request new terms which then need only minimal review by editors.
For example, we can make multi-organism specific subclasses of existing GO classes by specifying that the process must have multiple organisms as participants. For example: ‘multi-organism membrane fusion’ (GO:0044800) can be defined as any ‘membrane fusion’ (GO:0061025) in which there is more than one participating organism.1 Other logical definitions will take advantage of formally defined relations for recording relationships that hold between interacting organisms such as ‘host_of’ , ‘symbiont_of’ and ‘vector_for’ , developed in collaboration with the Population and Community Ontology (PCO) .
Summary of ontology changes
As a result of this project, the multi-organism process node of GO has been improved and extended to over 2000 classes under ‘multi-organism process’ (GO:0051704) (2241 as of October 8th 2014). The collaboration with ViralZone and the focus on creating terms suitable for annotation of viral gene products has generated 344 classes under ‘viral process’ (GO:0016032) and 65 classes of ‘virion part’ (GO:0044423) in the component ontology. The objective of this project was to produce a collaborative resource, and towards this goal we have linked multi-organism GO terms to a number of external resources with 126 multi-organism terms now mapped to UniProtKB keywords and 148 mapped to ViralZone pages, providing comprehensive information for users.
The new relations are partly defined with reference to GO process classes that indicate types of interaction. For example, we define ‘parasite_of’ as a relation between two organisms (X and Y) that participate in an instance of the GO process ‘parasitism’ where X (the parasite) gains some fitness advantage from participation in the process and the interaction is disadvantageous for the fitness of Y (the host). A more complete treatment of these relations will be the subject of a forthcoming paper on the Population and Community Ontology (PCO).
Multi-organism process GO annotations
Gaining annotations from mappings to external resources
Manual multi-organism annotations were provided by model organism databases and annotation groups. For viruses, UniProt focussed manual annotation efforts towards poliovirus and parvovirus B19, and EcoliWiki annotated gene products of Bacteriophage T4. To supplement the manual methods, annotations are also derived electronically from mappings of GO classes to relevant (sometimes equivalent) terms in external systems and databases. These mappings are useful both for checking consistency and for automatically deriving GO annotations. The mapping process is referenced for traceability, and most annotations assigned by this method have an ‘inferred by electronic annotation’ (IEA) evidence code. Below we describe the three key mapping methods employed for multi-organism process terms.
Mappings between InterPro and GO
InterPro is an integrated resource of protein families , and InterPro entries are mapped to GO terms such that the GO function, process or component is applicable for all members of that family. This allows any new members to automatically be assigned the relevant GO term. For example, InterPro entry IPR008768 (Bacteriophage T7 capsid assembly) has a mapping to GO term ‘viral capsid assembly’ (GO:0019069). Any viral protein that contains IPR008768 will inherit an annotation to GO:0019069. As GO classes are created and modified, we work with the InterPro group to keep mappings current. Mappings are available from the GO website .
Mappings between ViralZone, UniProtKB keywords and GO
Integration with the viral resource ViralZone  and UniProtKB keywords  describing host-viral processes has been crucial to the success of the revised viral GO node, and we have generated reciprocal mappings to provide interplay between all three resources;  describes this collaborative effort in more detail. UniProt have applied virus-host keywords to thousands of viral proteins, and GO term mappings from UniProtKB keywords are the largest source of electronic GO annotations for viral gene products (Fig. 7).
Mappings between Reactome and GO
Reactome pathways  are mapped to GO terms, where possible, by Reactome curators. For example, ‘Entry of Influenza Virion into Host Cell via Endocytosis’ (REACT_6147.2) is mapped to GO term ‘receptor-mediated endocytosis of virus by host cell’ (GO:0019065) such that any Reactome protein that takes part in REACT_6147.2 receives an annotation to GO:0019065. There are two principal viral infection pathways currently annotated in Reactome; 'HIV infection' (REACT_6185.3) and 'Influenza infection' (REACT_6167.2), and one bacterial multi-organism process ‘Latent infection of Homo sapiens with Mycobacterium tuberculosis ’(REACT_121237.1). Annotations derived from these mappings are, at present, restricted to these HIV and Influenza viruses, Mycobacterium tuberculosis, and the human host.
Taxon distribution amongst annotations
The most annotated taxons in UniProtKB/Swiss-Prot for ‘viral process’ (GO:0016032) subclasses
UniProtKB/Swiss-Prot annotations per taxon
Influenza A virus (strain A/Puerto Rico/8/1934 H1N1)
Vaccinia virus (strain Western Reserve)
Human cytomegalovirus (strain Merlin)
Vaccinia virus (strain Copenhagen)
Human immunodeficiency virus type 1 group M subtype B (isolate HXB2)
Human cytomegalovirus (strain AD169)
Variola virus (isolate Human/India/Ind3/1967)
Human herpesvirus 1 (strain 17)
Human herpesvirus 8 type P (isolate GK18)
Epstein-Barr virus (strain B95-8)
Human herpesvirus 2 (strain HG52)
Human adenovirus C serotype 2
Epstein-Barr virus (strain AG876)
Epstein-Barr virus (strain GD1)
Human adenovirus C serotype 5
Varicella-zoster virus (strain Dumas)
Equine herpesvirus 1 (strain Ab4p)
Human SARS coronavirus
Tools and software
The GO ontology structure was developed in both Web Ontology Language (OWL) and Open Biomedical Ontologies (OBO) languages using the ontology editing software OBO-Edit-2.3  and Protégé 4.3 . The viral GO slim was developed in OBO-Edit-2.3. We also used TermGenie [35, 44], a web-based system for template-based ontology term addition for adding new classes to GO. Gene products were manually annotated using the UniProt protein annotation tool, Protein2GO .
Utility & discussion
The ontology and annotation model presented here will also allow, in the future, for the construction of complex logical queries across GO annotation data, such as “return all the host proteins involved in viral budding for Retroviridae” or “return all the symbiont proteins involved in the formation of a root nodule in soybean (Glycine max)”. This powerful search capability has potential uses in many areas of biological research, for example in drug discovery - “return all viral receptors for cell type X” or “return all host proteins that interact with viruses of the taxon Paramyxoviridae”.
The capability to make complex queries of this sort will in the future be built in to the GO tools AmiGO [45, 46] and QuickGO [12, 47], and we hope that other tool developers will leverage the information encoded into this annotation model to develop new tools and services.
GO ‘slims’ - subsets of the GO tailored for a particular application - have been employed widely in the analysis of large datasets e.g. [48–50]. With the volume of data arising from metagenomics and metatranscriptomics studies increasing apace, and the interest in the study of the microbiome and viriome of different environments, it is ever more important that bioinformatics tools be available to analyse these data. The GO provides both a general metagenomics slim  and a newly developed viral GO slim . These slims are already deployed in many tools that utilize the GO, for example the EBI Metagenomics Portal  uses the metagenomics slim to summarize the functional profile of submitted environmental samples.
Application of GO microbial annotation sets
Microbial GO datasets have been used to answer key questions in a wide range of biological areas including those of environmental, medical and agricultural significance. For example, in a study to examine the impact of the microbial community on the distribution of arsenic pollution in Mediterranean, Plewniak et al.  used GO to compare the functional composition of metagenomes from different geographical areas. The data have also been used to address medical questions such as the origin of outer membrane vesicles (OMVs) in the food-borne pathogen Listeria monocytogenes , as well as being able to examine viral protein dynamics in influenza virus H1N1 infection . And in agrigenomics, GO multi-organism annotation has been used to predict drug targets for Pseudomonas syringae , an important plant pathogen that causes halo blight disease, while Peng et al.  used GO to help examine effectors in the potato rot nematode, Ditylenchus destuctor.
Intended use of microbial multi-organism process GO terms
GO multi-species data can be used in various ways. It can be used by research scientists to interrogate large datasets, giving a functional perspective on, for example, RNASeq data. It can also be used on a gene-centric basis to provide detailed information on a particular microbe or host protein or RNA. In addition, these annotations have the potential to provide unique cross-species comparisons to allow users to identify proteins and RNA with common functions in diverse species.
By importing functional data on hosts and parasites from other resources, this project also offers the possibility of being able to consolidate data from multiple species into a single resource, thus allowing users to ask questions of a unified data set. Data from ViralZone and Reactome is already incorporated, and in the future we hope to work with the ACLAME database to align our ontologies and import their annotation data, and with the IMG resource to find ways to import their data. We will also encourage other external groups to create microbial GO annotations by providing training and advice to these groups.
As the ontology terms described in this paper are used to annotate microbial protein and RNAs, data annotators will be requesting modifications and additions to the terms, gradually improving the ontology and highlighting areas where further development is needed. We also welcome ideas for new terms, and improvements and refinements for existing terms, from members of the microbial research community. Combined with an expanding annotation set, this resource will continue to grow and improve over time.
We describe here an integrated resource for providing functional data for multiple microbial species and their hosts that has applications in human and animal health, infectious disease, drug discovery, agriculture and environmental studies. In the future we will provide templates in the TermGenie tool for automated creation of these multi-organism terms, allowing users to add new terms without requiring an in-depth understanding of the ontology structure. This is a resource we expect to continue to grow and improve over time, as more groups begin to use the datasets and contribute annotations and ontology terms.
Availability and requirements
The GO ontology classes under ‘multi-organism process’ (GO:0051704) can be viewed from any GO tool that allows visualisation of the GO, many of which require only a web-browser. To view the branch in the GO Consortium browser AmiGO 2 , search for ‘multi-organism process’ and then explore down through the tree via the ‘inferred tree view’ or ‘ancestors and children’ tabs. Annotated genes and proteins can be viewed at any level in the tree by selecting the ‘Associations’ tab. The multi-organism process ontology and annotated proteins can also be viewed and downloaded from the UniProt GO browser QuickGO . This is done by searching for ‘multi-organism process’ and navigating down the tree using the ‘Child terms’ tab. Annotated proteins for the selected term can be viewed in the ‘Protein Annotation’ tab. The annotated proteins can be further filtered e.g. by taxon using the ‘Filter’ option and downloaded with the ‘Download’ button.
The multi-organism process ontology can be downloaded as a part of the GO ontology resources in OWL or OBO format .
Common Anatomy Reference Ontology
Experimental Factor Ontology
High-quality Automated and Manual Annotation of microbial Proteins
Infectious Disease Ontology
Integrated Microbial Genome Network
Ontology for Biomedical Investigations
Plant-Associated Microbe Gene Ontology consortium
Population and Community Ontology
The authors would like to thank Amelia Ireland and the PAMGO Consortium, especially Candace Colmer, Michelle Gwinn-Giglio and Trudy Torto-Alalibo for work developing the original set of multi-organism GO terms. We would also like to acknowledge Ariane Toussaint for work on phage terms, and Fiona McCarthy for work on animal virus terms. Paola Roncaglia provided helpful comments on the manuscript, for which we are very grateful. The Gene Ontology Consortium is supported by National Human Genome Research Institute (NHGRI) U41 grant HG22073 to PIs JA Blake, JM Cherry, S Lewis, PW Sternberg and P Thomas; this grant supported RF and DOS. JL was funded by the European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute Outstation (EMBL-EBI) core funds. BM at EcoliWiki was funded as a component of PortEco from subcontracts from grant U24 GM077905-01 (2006–2009) and 1U24GM088849-01 (2009-present) from the NIH/NIGMS. PLM, PM, CH and SP are funded by the Swiss Federal Government through the state Secretariat for Education, Research and Innovation (SERI), and by the National Institute of Health (NIH) grant 1 U41 HG006104.
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