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Virology Journal

Call for papers, viruses across borders, world aids day, food security and sustainability from a microbiology perspective, coronaviruses: emerging and re-emerging pathogens in humans and animals.

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Aims and scope

Virology Journal is an open access, peer reviewed journal that considers articles on all aspects of virology, including research on the viruses of animals, plants and microbes. The journal welcomes basic research as well as pre-clinical and clinical studies of novel diagnostic tools, vaccines and anti-viral therapies.

Journal Sections

Clinical Virology : Fred Kibenge, University of Prince Edward Island, Canada  Emerging viruses : Tom Geisbert,  University of Texas Medical Branch, USA Hepatitis viruses : Wan-Long Chuang,  Kaohsiung Medical University, Taiwan Herpes viruses : Tony Cunningham,  The Westmead Institute for Medical Research, Australia Influenza viruses : Hualan Chen,  Chinese Academy of Agricultural Sciences, China Negative-strand RNA viruses : John Barr,  University of Leeds, UK Other viruses : Erna Geessien Kroon,  Universidade Federal de Minas Gerais, Brazil Plant viruses : Supriya Chakraborty,  Jawaharlal Nehru University, India Positive-strand RNA viruses : Jaquelline Germano de Oliveira,  Fundação Oswaldo Cruz - Fiocruz, Brazil  Public health :  Kin On Kwok,  The Chinese University of Hong Kong Retroviruses : Aguinaldo Pinto, Universidade Federal de Santa Catarina, Brazil Veterinary DNA viruses : Walid Azab,  Freie Universität Berlin, Germany Veterinary RNA viruses : James Weger-Lucarelli,  Virginia Tech, USA Viruses of microbes : Joana Azeredo,  University of Minho, Portugal

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Alan McLachlan, Co-Editor-in-Chief

Alan McLachlan is a molecular geneticist and hepadnavirologist. He currently serves as a Professor in the Department of Microbiology and Immunology, University of Illinois Chicago, USA. His interests are focused on hepatitis viruses, primarily on hepatitis B virus (HBV) and its relationships to liver physiology.  His research is currently directed toward understanding the relationships between HBV transcription and viral biosynthesis using both cell culture and animal models.  His long-term goals include the identification of cellular gene products as targets for the development of small molecular weight antiviral compounds which, in combination with current nuceot(s)ide analog therapeutics, will resolve chronic HBV infections.

Leo Poon, Co-Editor-in-Chief

Leo Poon is a molecular virologist. He currently serves as a Professor in the School of Public Health, The University of Hong Kong and as a co-director of HKU-Pasteur Research Pole. He has strong interests in emerging viruses, including coronavirus and influenza virus. He researches on different aspects of these viruses, ranging from basic virology to clinical diagnosis. His ultimate goal is to use scientific findings to inform public health policy. Over the years, he has published about 290 peer-reviewed articles. Thus far, his work has been cited over 41,000 times and he has an H-index of 95 (Web of Science).

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Peer-reviewed

Research Article

Coordinating virus research: The Virus Infectious Disease Ontology

Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Philosophy, University at Buffalo, Buffalo, NY, United States of America, National Center for Ontological Research, Buffalo, NY, United States of America

Roles Conceptualization, Investigation, Validation, Writing – review & editing

Affiliations National Center for Ontological Research, Buffalo, NY, United States of America, Air Force Research Laboratory, Wright Patterson Air Force Base, Riverside, OH, United States of America

Roles Conceptualization, Investigation, Methodology, Project administration, Validation, Writing – review & editing

Affiliation Department of Cognitive Science, Northwestern University, Evanston, IL, United States of America

Roles Conceptualization, Formal analysis, Investigation, Writing – review & editing

Affiliation Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States of America

Roles Conceptualization, Formal analysis, Writing – review & editing

Affiliation Department of Philosophy, Loyola University, Chicago, IL, United States of America

Affiliation Computational Medicine and Bioinformatics, University of Michigan Medical School, He Group, Ann Arbor, MI, United States of America

Roles Conceptualization, Investigation, Methodology, Writing – review & editing

Affiliations National Center for Ontological Research, Buffalo, NY, United States of America, Department of Philosophy, Northwestern University, Evanston, IL, United States of America

Roles Conceptualization, Investigation, Methodology, Validation, Writing – review & editing

Roles Conceptualization, Methodology, Validation, Writing – review & editing

Affiliations Department of Informatics, J. Craig Venter Institute, La Jolla, CA, United States of America, Department of Pathology, University of California, San Diego, CA, United States of America, Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States of America

Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing – review & editing

• John Beverley, 
• Shane Babcock, 
• Gustavo Carvalho, 
• Lindsay G. Cowell, 
• Sebastian Duesing, 
• Yongqun He, 
• Regina Hurley, 
• Eric Merrell, 
• Richard H. Scheuermann, 
• Barry Smith

• Published: January 18, 2024
• https://doi.org/10.1371/journal.pone.0285093
• Peer Review
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The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies–structured, controlled, vocabularies–are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (IDO) Core, which provides terminological content common to investigations of all infectious diseases. We report here on the development of an IDO extension, the Virus Infectious Disease Ontology (VIDO), a reference ontology covering viral infectious diseases. We motivate term and definition choices, showcase reuse of terms from existing OBO ontologies, illustrate how ontological decisions were motivated by relevant life science research, and connect VIDO to the Coronavirus Infectious Disease Ontology (CIDO). We next use terms from these ontologies to annotate selections from life science research on SARS-CoV-2, highlighting how ontologies employing a common upper-level vocabulary may be seamlessly interwoven. Finally, we outline future work, including bacteria and fungus infectious disease reference ontologies currently under development, then cite uses of VIDO and CIDO in host-pathogen data analytics, electronic health record annotation, and ontology conflict-resolution projects.

Citation: Beverley J, Babcock S, Carvalho G, Cowell LG, Duesing S, He Y, et al. (2024) Coordinating virus research: The Virus Infectious Disease Ontology. PLoS ONE 19(1): e0285093. https://doi.org/10.1371/journal.pone.0285093

Editor: Barry L. Bentley, Cardiff Metropolitan University, UNITED KINGDOM

Received: November 28, 2022; Accepted: April 12, 2023; Published: January 18, 2024

Copyright: © 2024 Beverley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The Virus Infectious Disease Ontology artifact can be found in the following Github repository: https://github.com/infectious-disease-ontology-extensions/ido-virus . The Coronavirus Infectious Disease Ontology artifact can be found in the following Github repository: https://github.com/CIDO-ontology/cido .

Funding: Sources of funding for this article for John Beverley and Shane Babcock stem from the NIH / NLM T5 Biomedical Informatics and Data Science Research Training Programs. Barry Smith’s source of funding stemmed from the NIH under NCATS 1UL1TR001412 (Buffalo Clinical and Translational Research Center). No other co-authors were funded to pursue work on this project. Moreover, the funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The value of cross-discipline meta-data analysis has been evident in the COVID-19 pandemic. Early in the pandemic, for example, prostate oncologists [ 1 , 2 ] attempted to leverage existing research on enzymes crucial in host cell penetration by SARS-CoV-2 to explain differences in disease severity across sex [ 3 , 4 ]; immunologists combined insights from research on SARS-CoV-1 and MERS-CoV with chemical compound profiles to identify treatment options for SARS-CoV-2 [ 5 – 7 ]; pediatric researchers, observing that children have fewer nasal epithelia susceptible to SARS-CoV-2 infection than adults, suggested this difference may explain symptom disparities between the two groups [ 8 , 9 ]. The sheer volume of data collected by life-science researchers, the speed at which it is generated, range of its sources, quality, accuracy, and urgency of need for assessment of usefulness, has resulted in complex, multidimensional datasets, often annotated using discipline- or institution-specific terminologies and coding systems that lead to data silos [ 10 – 12 ].

Data silos emerge in life science research when data concerning an area of research is stored in a manner that makes it accessible to one group, but inaccessible to others. The use of proprietary information systems, differing storage methods, and distinct coding standards across life science that is characteristic of such silos undermines interoperability, meta-data analysis, pattern identification, and discovery across disciplines [ 13 , 14 ]. Ontologies–interoperable, logically well-defined, controlled vocabularies representing common entities and relations across disciplines using consensus terminologies–constitute a well-known solution to these problems through mitigation of the formation of data silos. The need for rapid analysis of evolving datasets representing coronavirus research motivated the development of the Virus Infectious Disease Ontology (VIDO; https://bioportal.bioontology.org/ontologies/VIDO ), comprised of textual definitions for terms and relations and logical axioms supporting automated consistency checking, querying over datasets, and interoperability with other ontologies. VIDO is an extension of the widely-used Infectious Disease Ontology Core (IDO Core; https://bioportal.bioontology.org/ontologies/IDO ) [ 15 , 16 ], which comprises terminological content common to all investigations of infectious disease. VIDO extends IDO with terms specific to the domain of infectious diseases caused by viruses and provides a foundation for ontologies representing specific viral infectious diseases, such as COVID-19.

VIDO is available under the Creative Commons Attribution 4.0 license ( https://creativecommons.org/licenses/by/4.0/ ) and its current and past versions can be found at the National Center for Biomedical Ontology (NCBO) Bioportal [ 17 ], the Ontobee repository ( http://www.ontobee.org/ ), and the Ontology Lookup Service ( https://www.ebi.ac.uk/ols/index ). VIDO was developed in collaboration with relevant domain experts, including immunologists and virologists, and by drawing on the expertise of the IDO developers to ensure alignment with principles outlined by the Open Biological and Biomedical Ontology (OBO) Foundry [ 18 ], thereby supporting interoperability with existing Foundry ontologies [ 19 ]. VIDO development is a transparent process, with all discussions available on GitHub ( https://github.com/infectious-disease-ontology-extensions ). All aspects of development, including addition of new terms, are driven by the needs of researchers investigating viruses and nearby domains. The ontology is thus not viewed as exhaustive of the domain of virus research but remains sensitive to evolving knowledge.

OWL, Protégé, Mace4, and Prover9

VIDO is formally represented in the OWL2 Web Ontology Language ( https://www.w3.org/TR/owl2-overview/ ). OWL2 is an expansion of the Resource Description Framework (RDF; https://www.w3.org/TR/rdf-primer/ ) and of RDF Schema, which represent data as sets of subject- predicate-object directed graphs, and which can be queried using the SPARQL Protocol and RDF Query Language ( https://www.w3.org/TR/sparql11-query/ ). OWL2 supplements these languages by allowing for description of classes, members of classes, relations among individuals, and annotation properties. Formally, the OWL2 vocabulary can be mapped to a decidable fragment of first-order logic, meaning there is an algorithm which can determine the truth-value for any statement expressed in the language in a finite number of steps [ 20 ]. Restricting expressions to a decidable language allows automated consistency and satisfiability checking [ 21 ]. VIDO was developed using the Protégé-OWL editor ( https://protege.stanford.edu/ ) and tested against OWL reasoners such as HermiT [ 22 ] and Pellet [ 23 ]. Additionally, logical axioms underwriting these ontologies were translated into the Common Logic Interchange Format, and subsequently evaluated using the Mace4 model checker and Prover9 proof generator within the Macleod toolkit ( https://github.com/thahmann/macleod ).

Alignment with OBO Foundry ontologies

Ontologies are widely used in bioinformatics, supporting data standardization, integration, sharing, reproducibility, and automated reasoning. The Gene Ontology (GO; https://bioportal.bioontology.org/ontologies/GO ), for example, maintains species-neutral annotations of gene products and functions, and since its inception in 1998 it has inspired an explosion of biomedical ontologies covering all domains of the life sciences [ 19 , 24 , 25 ]. These early developments led to worries, however, that data silos–the very problem ontologies were designed to address–might reemerge [ 10 ] as researchers developed ontologies using concepts local to their discipline. By 2007, the Open Biomedical and Biological Ontologies (OBO) Foundry [ 18 ] was created to provide guidance for ontology developers and promote alignment and interoperability. OBO Foundry design principles require that ontologies: use a well-specified syntax that is unambiguous, with a common space of identifiers; that they be openly available in the public domain, have a specified scope, be developed in a modular fashion in a collaboration with ontologists covering nearby domains, and import a common set of relations from the Relations Ontology (RO; https://obofoundry.org/ontology/ro.html ). The OBO library ( http://obofoundry.org/ ) presently consists of over 250 ontologies, including some externally developed ontologies such as the NCI Thesaurus ( https://ncithesaurus.nci.nih.gov/ncitbrowser/ ) and the NCBI Taxonomy ( https://www.ncbi.nlm.nih.gov/taxonomy ). It also contains some constructed ab initio to satisfy OBO principles. At its core is Basic Formal Ontology (BFO; https://bioportal.bioontology.org/ontologies/BFO ), a top-level ontology covering general classes such as material entity , quality , process , function and role [ 10 , 26 – 29 ] which provides the architecture “on which OBO Foundry ontologies are built.” BFO is, moreover, an ISO/IEC approved standard 21838–2 ( https://www.iso.org/standard/74572.html ).

Where BFO is domain-neutral, other OBO Foundry ontologies represent types of entities in more specific domains, using terms such as disease , cell division , surgical procedure , and so forth. Ideally, domain ontologies are constructed using a methodology for formulating definitions through a process of downward population from BFO. The resulting alignment with BFO, and the conformance to OBO Foundry principles, foster integration across ontologies. VIDO was designed with alignment and conformance in mind. Development of each ontology follows metadata conventions adopted by many OBO Foundry ontologies [ 30 ]. These conventions require that every term introduced into the ontology has a unique IRI, textual definitions, definition source, designation of term editor(s), and preferred term label. In the interest of coordinating development with existing OBO ontologies, VIDO developers imported terms where possible from existing OBO library ontologies and constructed logical definitions using imported terms. Development was guided by best practices for definition construction [ 10 , 31 ]. New primitive terms were introduced when needed after consultation with domain experts, review of relevant literature, and careful examination of the OBO library to avoid redundancy.

Hub and spokes approach

VIDO follows the “hub and spokes” methodology [ 32 , 33 ] for ontology development. That is, VIDO is a spoke ontology, extending from the Infectious Disease Ontology Core (IDO Core; https://bioportal.bioontology.org/ontologies/IDO ) as its hub. IDO Core is an OBO ontology consisting of terms, relations, natural language definitions and associated logical axioms representing phenomena common across research in infectious diseases [ 15 ]. IDO Core has long provided a base from which more specific infectious disease ontologies extend, and it has been recently updated to keep pace with scientific and top-level architecture changes [ 16 ]. Extensions of IDO Core covering specific infectious diseases are created, first, by importing needed terms from IDO Core and other OBO Foundry ontologies, and second, by constructing the domain-specific terms where needed to adequately characterize entities in the relevant domain. Fig 1 illustrates example extensions, such as the Brucellosis Infectious Disease Ontology (IDOBRU; https://bioportal.bioontology.org/ontologies/IDOBRU ) the Influenza Infectious Disease Ontology (IDOFLU; https://bioportal.bioontology.org/ontologies/FLU ), and more recently the Coronavirus Infectious Disease Ontology (CIDO; https://bioportal.bioontology.org/ontologies/CIDO ). Each aims to be semantically interoperable with OBO library ontologies [ 11 , 34 – 36 ]. VIDO was designed to occupy the ontological space between such virus-specific ontologies and IDO Core. As a result, more specific virus-related ontologies such as CIDO [ 37 ] and IDOFLU are being curated to extend directly from VIDO, rather than directly from IDO Core.

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https://doi.org/10.1371/journal.pone.0285093.g001

The Virus Infectious Disease Ontology

VIDO takes IDO Core as its starting point, but also imports terms relevant to the domain of viruses from several other OBO Foundry ontologies, such as GO, the Ontology for General Medical Science (OGMS; https://bioportal.bioontology.org/ontologies/OGMS ) and the Ontology for Biomedical Investigation (OBI; https://bioportal.bioontology.org/ontologies/OBI ) [ 14 ]. The color- coded Fig 1 illustrates several importing relationships which provide the basis for VIDO definitions which we examine in what follows.

Acellular structure.

Like IDO Core, VIDO imports from OGMS. Examples of such imported terms are:

disorder = def Material entity that is a clinically abnormal part of an extended organism.

A part of a material entity is “clinically abnormal” if it is not expected in the life plan for entities of the relevant type and is causally linked to elevated risk–that is, risk exceeding some threshold–of illness, death, or disfunction [ 38 ]. Extended organism is imported from OGMS and organism from OBI, where they are defined as follows:

extended organism = def An object aggregate consisting of an organism and all material entities located within the organism, overlapping the organism, or occupying sites formed in part by the organism.

organism = def A material entity that is an individual living system, such as animal, plant, bacteria, or virus, that is capable of replicating or reproducing, growth and maintenance in the right environment. An organism may be unicellular or made up, like humans, of many billions of cells divided into specialized tissues and organs.

Here we run into the first of several ontological puzzles that emerged while developing VIDO. On the one hand, this definition aligns with common usage of the term “organism” among researchers for whom its instances are cellular entities [ 39 , 40 ]. On the other hand, the textual definition includes viruses among its instances, which are in every case acellular. Debates over organism ( https://github.com/OBOFoundry/COB/issues/6 ) among ontology developers have resulted in deprecation of the OBI term in favor of a nearby term from the Common Anatomy Reference Ontology ( https://bioportal.bioontology.org/ontologies/CARO ) with the label: organism or virus or viroid . At first glance, this appears to avoid the preceding worries, but further inspection reveals that this class is annotated as being an “exact synonym” of organism, and so suffers from the same issues raised above. Even if we put aside this latter issue, however, there are still two further concerns. First, introducing disjunctive classes is ad hoc [ 10 ]. Second, this disjunctive class leads naturally to debates over whether viruses are alive, since it classifies viruses alongside paradigmatic living entities. Decades of discussion have not resolved this question [ 41 – 46 ], and it is not obvious that we need an answer for the purposes of ontological modelling. It is unclear where consensus will land; in the interest of future-proofing our ontologies we should provide virus content in a way that neutral in regard to this issue to the maximal extent that this is possible. Rather than introduce an ad hoc disjunctive class, IDO Core, VIDO, and CIDO developers collaborated to add the following classes to IDO Core [ 47 ]:

self-replicating organic structure = def Object consisting of an organic structure that is able to initiate replication of its structure in a host.

acellular self-replicating organic structure = def Self-replicating organic structure comprised of acellular organic parts.

Which is imported to VIDO as the parent class of the term virus . The term virus is imported from the NCBITaxon [ 48 ] ( https://bioportal.bioontology.org/ontologies/NCBITAXON ), alongside terms relevant to virology, such as prion and satellite .

The NCBITaxon provides an extensive list of life science terms, but it has its limitations. As discussed in [ 16 ], NCBITaxon categorizes virus terms using the International Committee on Taxonomy of Viruses (ICTV). While an impressive taxonomy, the ICTV exhibits gaps in virus classification [ 49 , 50 ]. Additionally, NCBITaxon combined with standard ontology engineering tools such as Ontofox ( http://ontofox.hegroup.org/ ) [ 51 ] often leads to ontology developers importing superfluous portions of ICTV structured hierarchies, resulting in overwhelming taxonomies that are challenging for users to navigate. Fig 2 illustrates such a taxonomy found in IDOBRU, but importing an entire ICTV hierarchy is not uncommon (see for example the Schistosomiasis Ontology (IDOSCHISTO) [ 16 ]). Lastly, the NCBITaxon does not provide textual definitions for most terms within its scope. As stated, we seek to respect OBO Foundry metadata conventions [ 30 ] and ontology engineering best practices [ 10 , 31 ]; consequently, virus and other terminological content in VIDO must have textual definitions supplied.

https://doi.org/10.1371/journal.pone.0285093.g002

Standard definitions of “virus” provide a starting point for a textual definition, but caution is once again needed. Viruses are often described as obligate pathogens [ 52 , 53 ], since virus replication requires host machinery for production and assembly of viral components. However, defining a class virus solely in terms of what viruses typically do runs the risk of overlooking what viruses are, materially speaking. Compare: Homo sapiens are obligate aerobes, but this is no definition of the class. Insofar as we are defining the virus class, it is better to attend to genetic and structural components common to all viruses, and best to define the material entity in a way that captures obligate pathogenicity. VIDO accordingly defines:

virus = def Acellular self-replicating organic structure with RNA or DNA genetic material which uses host metabolic resources for RNA or DNA replication.

VIDO developers have contacted NCBITaxon developers proposing that the definitions we provide below be added to respective NCBITaxon terms. Other requests have been submitted, for example, to update the label of the NCBITaxon term from “Viruses” to “virus” in order to avoid ambiguous reference between classes and their instances [ 10 ].

Rather than import in accordance with the ICTV taxonomy, subclasses of virus are imported from NCBITaxon in alignment with the Baltimore Classification [ 54 ], which groups viruses into seven exhaustive classes based on genetic structure. For example, one subclass of virus is:

positive-sense single-stranded RNA virus = def Virus with genetic material encoded in single-stranded RNA that can be translated directly into proteins.

Which is a class representing one of the seven Baltimore Classification categories. These Baltimore Classification classes provide parent classes from which terms for more specific viruses can extend. Fig 3 illustrates how the Baltimore Classification appears in the Protégé visualization of the class positive-sense single-stranded RNA virus alongside viral replication pathways underwritten by genetic differences in viruses.

https://doi.org/10.1371/journal.pone.0285093.g003

By incorporating the Baltimore Classification, we provide developers of virus-specific domain ontologies an ontological representation of viruses that is simpler and easier to navigate than the ICTV, which currently underwrites the NCBITaxon.

VIDO subclasses of virus include those common in virology research, such as bacteriophage– viruses that infect bacteria– virophage– viruses that infect viruses– oncovirus– viruses that cause cancer–and mycovirus– viruses that infect fungi. As well as:

virion = def Virus that is in its assembled state consisting of genomic material (DNA or RNA) surrounded by coating molecules.

Some researchers use “virion” and “virus” synonymously [ 55 ]. Some define “virion” so that instances only exist outside host cells [ 56 ], or they distinguish virions outside host cells from those inside host cells, calling the former “mature virions.” Some claim “virion” is best understood as analogous to a sperm cell [ 57 , 58 ]. Ontologically speaking, one might model the relationship between a virus and its virion in a variety of ways: virion is to a virus as human infant is to human, or as human student is to human, or as human gamete is to human. Treating virions as akin to gametes is uncommon among researchers. Between the remaining options, we adopt the first, treating virion as a type of virus , since adopting the alternative would suggest a virion is simply a virus that is in a specific context, a result that overlooks the importance of genomic assembly to identifying virions.

Incidentally, some viruses do not replicate faithfully, perhaps resulting in genetically distinct mutants or–in extreme cases–an inactive aggregate of virion components. Virus mutations may undermine host immune system recognition of viral threats, as evidenced by the difficulty in developing vaccines for certain influenza strains. If there are too many mutations, however, then a virus may lose its ability to replicate, an observation used in development of treatments for polio and hepatitis C which exacerbate respective virus mutations [ 59 , 60 ]. VIDO thus provides the term:

disordered virus = def Acellular self-replicating organic structure having some clinically abnormal arrangement of viral components (e.g. viral capsid, viral DNA/RNA).

Viruses falling in this class may be associated with diseases much different from those associated with viruses that do not exhibit disorder. Terms for viral components are imported to VIDO: from GO, viral nucleocapsid , viral capsid , capsomere , viral envelope ; from the Chemical Entities of Biological Interest (ChEBI) ontology ( https://bioportal.bioontology.org/ontologies/CHEBI ) [ 61 , 62 ], nucleic acid and ribonucleic acid ; from the Protein Ontology ( https://bioportal.bioontology.org/ontologies/PR ), protein and viral protein .

Infectious structure.

The term “pathogen” is indexed to species or to stages in the developmental cycle of a species. A given virus may engage in mutual symbiosis with one species, while exhibiting pathogenic behavior towards others [ 63 , 64 ]. Mature plants are often susceptible to different pathogens than developing plants [ 65 – 68 ]. We capture virus pathogenicity in VIDO in steps. From IDO Core [ 16 ], we import dispositions borne by pathogens and infectious agents, as follows:

pathogenic disposition = def Disposition borne by a material entity to establish localization in, or produce toxins that can be transmitted to, an organism or acellular structure, either of which may form disorder in the entity or immunocompetent members of the entity’s species.

infectious disposition = def Pathogenic disposition borne by a pathogen to be transmitted to a host and then become part of an infection in that host or in immunocompetent members of the same species as the host.

The class infectious agent in IDO Core is a subclass of organism , and so cannot include instances of virus . To address this issue, the term infectious structure was developed to parallel the IDO Core term infectious agent and to provide a logically defined subclass of acellular self-replicating organic structure . The term infectious disposition bridges infectious acellular structures and infectious organisms since instances of each bear an infectious disposition. Moreover, the logical definitions of infectious structure and infectious agent are such that, though the former is a defined subclass of acellular self-replicating organic structure and the latter a subclass of organism , they are both inferred subclasses of pathogen .

As discussed in [ 16 ], establishment of localization used in pathogenic disposition is characterized using the IDO Core term establishment of localization in host representing tethering or adhesion to a host, while “formation of disorder” abbreviates appearance of disorder , which is a process that results in formation of a disorder . The definition of pathogenic disposition is meant to reflect a temporal ordering between establishment of localization and appearance of disorder. This is reflected explicitly in the logical axioms associated with the class. Similarly, in the definition of infectious disposition there is an intended temporal ordering between transmission to a host–represented by pathogen transmission process imported from the Pathogen Transmission Ontology ( https://bioportal.bioontology.org/ontologies/PTRANS )–and becoming part of an infection–represented by the IDO Core process of establishing an infection . A pathogen bearing an infectious disposition that generates disorder in a host will have been transmitted to the host prior to establishing localization in the host and will have established an infection prior to the appearance of disorder.

The complexity of the definitions of pathogenic disposition and infectious disposition reflect the variety of pathogen examples documented in contemporary literature. Consider S . aureus , an opportunistic pathogen [ 56 ] in humans. We count S . aureus as a pathogen, even when it does not realize disorder in a host, since it is nevertheless disposed to localize in a human host and generate disorder if given the opportunity. This is a BFO disposition of S . aureus as it is an “internally-grounded” property of the entity [ 32 ]. That is, it is part of the material basis of S . aureus to generate disorder in human hosts if given the chance. This is analogous to the way salt has a disposition to dissolve, based on its lattice structure, independently of whether it ever realizes this disposition. Opportunistic pathogens are not pathogens because of an opportunity; they are pathogens because they are disposed to localize and cause disorder in a host.

Consider now, C . botulinum , a pathogen which produces a toxin and which may produce a spore ingested by humans. This bacterium is a pathogen for adult humans since the toxins often result in disorder when ingested. Furthermore, C . botulinum may cause infection in human infants if, say honey colonized by C . botulinum is ingested. The sugar content of honey inhibits C . botulinum growth, but in the low-oxygen, low-acid intestines of human infants, spores can localize, grow, and produce toxins resulting in disorder. Thus, C . botulinum counts as a human infant pathogen. Nevertheless, because C . botulinum is not itself disposed to invade or be transmitted to human infants, we do not say the bacterium is infectious [ 69 ]. Being part of an infection is not itself sufficient for something to be counted as infectious. Pathogens bearing an infectious disposition must be disposed to both transmit and become part of an infection. Many opportunistic pathogens, for example, are not infectious.

Consider lastly, the respective definitions of infectious disposition and pathogenic disposition address instances where mutations in hosts may block realization of disorder or infection. In such cases, an infectious pathogen may nevertheless be transmissible and cause disorder or infection in others. For example, HIV-1 is a pathogen that may localize in a host with CCR-5 mutations [ 70 ] that block the virus from attaching to host cells, and so block pathogenesis to AIDS. Similarly, P . falciparum may be transmitted to a host with a sickle-cell trait that blocks manifestation of the disease malaria [ 71 , 72 ]. However, P . falciparum and HIV-1 count as pathogens even if they do not result in the formation of disorders for hosts with a sickle-cell trait or CCR-5 mutation, respectively. IDO Core reflects this characterization. Each pathogen may be transmitted to immunocompetent members of the same species as the host, and so count as bearing instances of infectious disposition and pathogenic disposition . Note, the fact that P . falciparum and HIV-1 do not result in the formation of disorders in hosts with sickle-cell traits or CCR-5 mutations should not suggest that there are no clinical abnormalities associated with these traits or mutations. Individuals with, say, CCR-5 mutations do exhibit clinical abnormalities, and so do exhibit disorders. But these disorders are due to the CCR-5 mutation rather than the HIV-1 infection.

Whenever an infectious disposition is realized, this is always in some site in a host, via some transmission to that host, and some generation of infection and disorder in that host. Infectious structures– such as viruses–bear this disposition. For example, each SARS-CoV-2 virus is disposed to be transmitted to hosts, localize, cause infection, and result in disorder.

Pathogen host.

Until recently, microbiologists, immunologists, virologists, and others studying pathogenesis have engaged in either host-centered or pathogen-centered pathogenesis research [ 73 – 77 ]. Each approach has led to impressive research results. But emphasizing one aspect of host-pathogen interactions at the expense of the other may leave valuable questions unanswered [ 78 ]. Emphasis, for example, solely on pathogenic factors of SARS-CoV-2 will provide only a partial explanation of various pathogenesis pathways observed in clinical settings. IDO Core and VIDO prioritize neither host nor pathogen in representation of pathogens and associated diseases, adopting the Damage Response Framework (DRF) for guidance in development of relevant terms [ 79 – 82 ]. According to the DRF, pathogenesis results from interactions between both host and pathogen interacting primarily through host damage, which is a function of the intensity and degree of host response and pathogen factors. Host and pathogen interactions thus influence manifestations of signs, symptoms, and disease. IDO Core now defines hosts and pathogens in terms of roles and allows that acellular structures may also be pathogen hosts, such as when a virus hosts a virophage:

host role = def Role borne by either an organism whose extended organism contains a distinct material entity, or an acellular structure containing a distinct material entity, realized in use of that structure or organism as a site of reproduction or replication.

pathogen host role = def Host role borne by an organism or acellular structure having a pathogen as part.

Following BFO, roles are “externally grounded” realizable entities that may be gained or lost based on circumstance without necessarily involving material change to their bearer, such as the role a student acquired once enrolled in a university.

The Symptom Ontology ( https://bioportal.bioontology.org/ontologies/SYMP ) provides a extensive terminological content for representing symptoms owing to viral infection, such as fever , taste alteration , and so on [ 83 ]. Given the importance of asymptomatic carriers in viral infection spread, moreover, attention is also given in IDO Core to:

symptomatic carrier role = def Pathogen host role borne by an organism whose extended organism contains a pathogen bearing an infectious disposition towards the host, and the host has manifested symptoms of the infectious disease caused by the pathogen.

asymptomatic carrier role = def Pathogen host role borne by an organism whose extended organism contains a pathogen bearing an infectious disposition towards the host, and the host has no symptoms of the infectious disease caused by the pathogen.

subclinical infection = def Infection that is part of an asymptomatic carrier.

The definition of the term subclinical infection reflects standard use of the terms “subclinical” and “asymptomatic” while allowing for asymptomatic clinically abnormal infections. VIDO extends subclinical infection to subclinical virus infection , namely, those subclinical infections caused by a virus. These remarks bring us full circle to the term disorder introduced above, since clinical abnormality is associated with disorder. When that disorder stems from infection it counts as an:

infectious disorder = def Disorder that is part of an extended organism which has an infectious pathogen part, that exists as a result of a process of formation of disorder initiated by the infectious pathogen.

And when the adverted pathogen is a virus, it falls in the VIDO class:

virus disorder = def Infectious disorder that exists as a result of a process of formation of disorder initiated by a virus.

Viral disease.

Medical researchers draw a distinction between symptoms and signs, a distinction which OBO Foundry ontologies respect (from OGMS [ 38 ]):

symptom = def Process experienced by the patient which can only be experienced by the patient, that is hypothesized to be clinically relevant.

qualitative sign = def Abnormal observable quality of a part of a patient that is hypothesized to be clinically relevant.

processual sign = def Abnormal processual entity occurring in a patient that is hypothesized to be clinically relevant.

An asymptomatic carrier infected with SARS-CoV-2 likely exhibits signs indicating that the infection is clinically abnormal, such as ground-glass opacities. Such asymptomatic carriers exhibit an instance of the VIDO class virus disorder which is the material basis of a viral disease :

infectious disease = def Disease whose physical basis is an infectious disorder.

viral disease = def Infectious disease inhering in a virus disorder that is a disorder due to the presence of the virus.

Worth noting is that these definitions are consistent with the CDC’s case criteria definitions adopted between April 5, 2020 and February 28, 2023, which indicate that the presence of the SARS-CoV-2 genome or relevant antigens in an individual is sufficient to count as a case of COVID-19, asymptomatic or not [ 84 – 86 ]. A viral disease may be realized in a disease course:

infectious disease course = def Disease course that is the realization of an infectious disease.

viral disease course = def Infectious disease course whose physical basis is a virus disorder that is clinically abnormal in virtue of the presence of the relevant virus.

Here infectious disease and infectious disease course are imported from IDO Core, and are subclasses, respectively, of disease and disease course , which are imported from OGMS.

Viral epidemiology.

Changes in viral disease and infection incidence are among the targets of epidemiological investigation. VIDO imports from IDO Core:

infectious disease incidence = def Quality that inheres in an organism population and is the number of realizations of an infectious disease for which the infectious disease course begins during a specified period.

infectious disease incidence rate = def Quality that inheres in an organism population and is the infectious disease incidence proportion per unit time.

infectious disease incidence proportion = def Quality that inheres in an organism population and is the proportion of members of the population not experiencing an infectious disease course at the beginning of a specified period and in whom the infectious disease begins during the specified period.

organism population = def Aggregate of organisms of the same species.

Additionally, VIDO imports from IDO Core other important epidemiological terms, such as infection prevalence , infectivity , and infectious disease mortality rate . Each are specifically dependent entities inhering in some material entity, though not always in some organism population. For example, infectivity is a quality inhering in instances of pathogen . Additionally, VIDO imports from IDO Core:

infection incidence = def Quality that inheres in an organism population and is the number of organisms in the population that become infected with a pathogen during a specified period.

on which infectious disease incidences depend, as infectious disease realizations require infection.

A quality process profile is a type of process which tracks changes of specific qualities in material entities over time [ 26 ]. For example, a patient’s temperature will likely fluctuate over time, as will many other qualities of the patient. The specific fluctuations of temperature in the patient over time is a process profile , which reflects common abstractions used in clinical diagnosis and testing and manifesting in charts prepared from time-series data. Changes in qualities of clinical interest may follow several patterns, each of which can be defined as a subclass of process profile . A patient’s temperature may exhibit a linear increase followed by a linear decrease. Similarly, there are process profile instances of cyclical patterns, for instance the seasonal patterns of influenza [ 87 ]. Such patterns can be tracked in VIDO by classes such as:

viral disease incidence profile = def Infectious disease incidence profile comprised of a series of determinate viral disease incidence qualities caused by a specific virus in a population over time.

viral disease incidence proportion profile = def Infectious disease incidence proportion profile comprised of a series of viral disease incidence proportion qualities caused by a specific virus per unit time.

viral disease incidence rate profile = def Infectious disease incidence rate profile comprised of a series of viral disease rate qualities caused by a specific virus per unit time.

Extending VIDO to CIDO

VIDO serves as a bridge between IDO Core and the IDO extension ontologies representing specific viral diseases. Of particular importance during the pandemic has been the Coronavirus Infectious Disease Ontology (CIDO; https://bioportal.bioontology.org/ontologies/CIDO ), developed by the He Group at the University of Michigan. CIDO provides terminological content that facilitates representations of coronavirus genome, protein structures, epidemiological surveillance, vaccine development, and treatment options. The ontology has been used to annotate data pertaining to 136 known anti-coronavirus drugs [ 6 ], as well as in the identification of approximately 110 candidate drugs [ 7 ] for potential drug repurposing projects with respect to COVID-19 [ 88 ]. More recently, CIDO has been employed as a general framework for understanding host-pathogen interactions [ 78 ]. IDO Core, VIDO, and CIDO development teams work together closely in the interest of ensuring ontology alignment.

CIDO will extend from VIDO by adopting, among other terms:

coronavirus disease = def Viral disease inhering in a coronavirus disorder.

coronavirus disease course = def Viral disease course that is the realization of some coronavirus disease and has as a participant a coronavirus.

This extension example illustrates a downward population recipe useful for aligning CIDO terms to VIDO, by starting with a given virus term from the latter, and restricting subclasses based on features of coronaviruses and associated diseases. Moreover, common coronavirus features can be reused from OBO ontologies to complement the CIDO characterization of the virus, such as the viral envelop glycoprotein spikes [ 89 , 90 ]. Some such terms are specializations of terms from the Protein Ontology ( https://bioportal.bioontology.org/ontologies/PRO ), e.g. spike glycoprotein (SARS- CoV-2) . CIDO covers existing and novel coronaviruses in general, and so provides resources for detailed comparison of coronavirus biological profiles. Fig 4 illustrates various links between VIDO and CIDO, and the IDO Core and GO ontologies.

https://doi.org/10.1371/journal.pone.0285093.g004

SARS-CoV-2 pathogenesis.

Characterizing pathogenesis to COVID-19 is aided by terms such as:

COVID-19 = def Coronavirus disease inhering in a SARS-CoV-2 disorder.

COVID-19 disease course = def Coronavirus disease course that is the realization of some COVID-19 disease and has participant SARS-CoV-2.

Ontologically precise representation of COVID-19 pathogenesis is crucial for understanding the range of symptoms and signs which appear across demographics [ 91 – 94 ]. Ontological representation of COVID-19 pathogenesis is aided by reusing OBO Foundry ontology terms, resulting in the following definition:

SARS-CoV-2 pathogenesis = def Coronavirus pathogenesis process realization of an infectious

disposition inhering in SARS-CoV-2 or a SARS-CoV-2 population, having at least the proper process parts:

• pathogen transmission,
• establishment of localization in host,
• process of establishing an infection, and
• appearance of a virus disorder.

Instances of SARS-CoV-2 pathogenesis are asserted as part of some COVID-19 disease course . The term coronavirus pathogenesis will ultimately be imported to CIDO, as a subclass of the VIDO term viral pathogenesis , itself a subclass of:

pathogenesis = def Process that generates the ability of a pathogen to induce disorder in an organism.

which is imported from GO. As defined, pathogenesis is a success term [ 25 ], in that it encompasses formation of disorder in an entity. Of course, this is not meant to imply that SARS-CoV-2 infections necessarily lead to successful pathogenesis. SARS-CoV-2 infections, for example, may not lead to host disorder, in which case there would be no pathogenesis. Just as important as it is to represent SARS-CoV-2 pathogenesis to COVID-19, adequate representation of the target domain requires representation of pathogenesis to acute respiratory distress syndrome (ARDS), one of the leading causes of death in those infected by SARS-CoV-2 [ 95 , 96 ]:

acute respiratory distress syndrome = def Progressive and life-threatening pulmonary distress in the absence of an underlying pulmonary condition, usually following major trauma or surgery.

which may be imported from the Experimental Factor Ontology ( https://bioportal.bioontology.org/ontologies/EFO ). Similar remarks apply to other diseases associated with SARS-CoV-2 pathogenesis.

SARS-CoV-2 pathogenesis involves transmission of SARS-CoV-2 virions. From PTRANS ( https://bioportal.bioontology.org/ontologies/PTRANS ) is imported:

pathogen transmission process = def Process during which a pathogen is transmitted directly or indirectly to a new host.

From which SARS-CoV-2 specific terms can be constructed. Additionally, IDO Core provides important role terms relevant to pathogen transmission, such as:

pathogen transporter role = def Role borne by a material entity in or on which a pathogen is located, from which the pathogen may be transmitted to a new host.

An important subclass fomite role– roughly, a pathogen transporter role borne by a non-living entity–may feature in SARS-CoV-2 transmission via instances of fomite role bearing:

respiratory droplet = def Respiratory secretion composed of a bounded portion of liquid which maintains its shape due to surface tension.

respiratory droplet SARS-CoV-2 fomite = def Respiratory droplet fomite with SARS-CoV-2 part.

Knowledge of transmission steps supports strategies designed to break the transmission chain.

Worth noting is that the OBO library ontology APOLLO-SV ( https://bioportal.bioontology.org/ontologies/APOLLO-SV ) also contains terms, such as contact tracing and quarantine control strategy , which may be leveraged to represent virus-specific transmission control strategies.

SARS-CoV-2 replication.

SARS-CoV-2 pathogenesis involves replication in a host. The term virus replication is defined in VIDO as a subclass of the IDO Core term replication , specifically:

virus replication = def Replication process in which a virus containing some portion of genetic material inherited from a parent virus is replicated.

And instances of viral disease course and virus pathogenesis have virus replication as parts. SARS-CoV-2 replication occurs within an:

incubation process = def Process beginning with the establishment of an infection in a host and ending with the onset of symptoms by the host, during which pathogens are multiplying in the host.

Which occupies an incubation interval and may precede a communicability interval . The corresponding process during which SARS-CoV-2 hosts bear a contagiousness disposition has proper part some latency process which itself has an eclipse process as part::

communicability interval = def One-dimensional temporal region during which a pathogen host bears a contagiousness disposition.

latency process = def Process beginning with the establishing of an infection in a host and ending when the host becomes contagious, during which pathogens are multiplying in the host.

eclipse process = def Process beginning with the establishment of a virus in a host and ending with the first appearance of a virion following viral release, during which an infecting virus is uncoating to begin genome replication.

The last are specific to viruses, and so specific to VIDO. Viral dormancy is a virus-specific term from VIDO occurring over a:

viral dormancy interval = def One-dimensional temporal region on which a virus is no longer replicating but remains within a host cell and which may be reactivated to begin replication again.

Viral dormancy is characteristic of familiar viruses such as varicella zoster and herpes simplex .

VIDO includes as a temporal subdivision of a virus developmental process:

virus generative stage = def Infectious structure generative stage that is a temporal subdivision of a virus developmental process.

Subclasses of which include the stages through which viruses may proceed during replication:

virus attachment stage = def Virus generative stage during which a virion protein binds to molecules on the host surface or host cell surface projection.

virus penetration stage = def Virus generative stage during which a virion or viral nucleic acid breaches the barriers of a host.

SARS-CoV-2 attachment stage = def Virus attachment stage during which SARS-CoV-2 bonds with a host cell.

SARS-CoV-2 penetration stage = def Virus penetration stage during which SARS-CoV-2 penetrates a host cell.

SARS-CoV-2 susceptibility.

Only cells with certain features are susceptible to SARS-CoV-2 infection [ 16 ]. For example, successful infection in humans typically involves SARS-CoV-2 attachment to alveolar epithelial cells through angiotensin-converting enzyme 2 (ACE2) receptors [ 97 – 99 ]. Cells lacking ACE2 receptors seem protected from attachment by SARS-CoV-2. Those with receptors can be represented in CIDO using:

SARS-CoV-2 adhesion susceptible cell = def Virus adhesion susceptible cell with a functional receptor part bearing an adhesion disposition realized in a SARS-CoV-2 attachment stage.

adhesion disposition = def Disposition borne by a macromolecule that is the disposition to participate in an adhesion process.

Where adhesion disposition is imported from IDO Core and virus adhesion susceptible cell defined in VIDO. The ACE2 functional receptor is defined in the Protein Ontology ( https://bioportal.bioontology.org/ontologies/PRO ):

angiotensin-converting enzyme 2 = def A protein that is a translation product of the human ACE2 gene or a 1:1 ortholog thereof.

Attachment is frequently followed by cell penetration, where cell cleavage is aided by transmembrane protease serine 2 (TMPRSS2) prior to SARS-CoV-2 cell membrane fusion [ 100 , 101 ]. These observations motivate introducing terminological content for defining SARS-CoV-2 penetration susceptible cell such as:

SARS-CoV-2 penetration disposition = def Virus penetration disposition borne by a functional receptor complex that is the disposition to participate in a SARS-CoV-2 penetration process.

Ontological representation of the SARS-CoV-2 replication cycle provides targets for disruption or regulation of that cycle, which is important to rational drug design [ 102 – 106 ]:

negative regulation of SARS-CoV-2 attachment = def Negative regulation of coronavirus replication process that stops, prevents, or reduces the frequency of some SARS-CoV- 2 attachment stage.

negative regulation of SARS-CoV-2 penetration = def Negative regulation of coronavirus replication that stops, prevents, or reduces the frequency of some SARS-CoV-2 penetration stage.

Following our strategy of linking VIDO and CIDO, parent classes of negative regulation of coronavirus classes have a proper home in CIDO, while their parent classes–negative regulation of viruses more generally–have a proper home in VIDO.

Annotations.

Coverage in VIDO and CIDO can be illustrated by annotation of coronavirus research articles.

Consider the following overview of SARS-CoV-2 pathogenesis (compare bold with Fig 5 ): Following replication , cell lysis of SARS-CoV-2 coronavirus virions causes host cells to release molecules which function to warn nearby cells . When recognized by epithelial cells , endothelial , and alveolar macrophages , proteins such as IL-6 , IP-10 , and MCPI , are released which attract T cells , macrophages , and monocytes to the site of infection , promoting inflammation . In disordered immune systems , immune cells accumulate in the lungs , then propagate to and damage other organs . In normal immune systems , inflammation attracts T cells which neutralize the virus at the site of infection . Antibodies circulate , preventing SARS-CoV-2 infection , and alveolar macrophages recognize SARS-CoV-2 and eliminate virions via phagocytosis [ 92 , 107 , 108 ].

https://doi.org/10.1371/journal.pone.0285093.g005

In a more ontologically oriented language, we speak of the relevant part of a host’s immune response as being disposed to manifest a response that eliminates SARS-CoV-2 infection, while SARS-CoV-2 has a disposition to block manifestation of this immune system response. Consider next a color-coded selection from the Lancet [ 109 ] concerning SARS-CoV-2:

“The viral load s in throat swab s and sputum sample s peaked at around 5–6 days after symptom onset , ranging from around 10^4 to 10^7 copies per mL during this time .”

SARS-CoV-2 infected hosts contain the highest concentration of SARS-CoV-2 virions–the viral load– during the incubation interval [ 110 ]. Viral load is a common measurement of the proportion of virions to fluid, and for SARS-CoV-2 is frequently measured from host sputum. VIDO provides the resources for annotating virus quantification:

viral load = def Quality inhering in a portion of fluid that is the proportion of virions to volume of that portion of fluid

Our color-coding of the above passage from the Lancet models term reuse across existing ontologies. For example, developers can use VIDO and CIDO terms alongside terms from the Common Core Ontology ( https://github/com/CommonCoreOntology/CommonCoreOntologies ) such as is measured by , measurement information content entity , has integer value , uses measurement unit , and milliliter measurement unit . Other virus quantification metric terms, such as multiplicity of viral infection ‐ the ratio of virions to susceptible cells in a target area–can be found in VIDO as well.

Motivated to standardize virus ontology extensions of IDO Core, we have developed VIDO and provided a recipe for connecting VIDO to more virus-specific extensions of IDO Core, illustrated by connecting VIDO to CIDO. Summarizing our results, we have introduced acellular structure as a parent class to virus , motivated using the Baltimore classification to model viruses rather than the International Committee on Taxonomy of Viruses classification, revised IDO Core’s pathogen and host classes to accommodate acellular structures, and extended IDO Core’s infectious disease , infectious disease course , and infectious disease epidemiology classes to cover viruses. We then introduced bridge classes in VIDO to better align IDO Core and CIDO, illustrating throughout how CIDO terminological content can be extended and enriched to represent SARS-CoV-2 pathogenesis, associated transmission processes, virus transporters, replication stages and associated temporal extents, as well as pathogenesis regulation. Our attention was then turned to annotations of texts concerning SARS-CoV-2 and COVID-19, by which we highlighted how ontologies using common vocabularies may be seamlessly interwoven to provide broad annotation coverage for the domain.

VIDO and CIDO are not the only ontologies developed to support curation of COVID-19 data [ 111 – 114 ]. However, most alternatives are stand-alone initiatives, and so subject to the silo problems typically found in ontologies developed outside the scope of the OBO Foundry and with no attention to its principles. That said, VIDO and CIDO developers have participated in harmonization efforts aimed at semantic integration across COVID-19 ontologies [ 115 ]. Notably, harmonization efforts have resulted in the deprecation of the COVID-19 Infectious Disease Ontology (IDO-COVID-19)–introduced in a preprint version of the current paper [ 116 ]–with parties agreeing its scope was subsumed by CIDO. Additionally, VIDO and CIDO have been used to highlight ontology conflict resolution strategies [ 117 ]. It is not uncommon for ontology researchers working independently in nearby domains to construct overlapping ontology content.

The harmonization efforts of the VIDO and CIDO development teams signal to the wider ontology community our willingness to reuse terms where possible and obsolete terms or cede terms to other ontologies when needed.

VIDO and CIDO enable extensive representation of virus-related research. The very scope of VIDO provides challenges, however, as does the specificity of CIDO. For these reasons, attempts have been made to foster community-driven development of both. The development team for each ontology spanned disciplines in the life sciences, and to ensure the computational viability of the formal representation of each ontology, included specialists in logic. Often, terms were developed then presented to domain specialists for vetting, after which they were refined through discussion.

As in the case of all scientific ontologies, refinement will continue as research advances, and further collaborators are welcome. Interested parties may contact the corresponding author to be invited to on-going VIDO development meetings and may contact co-author He for invitation to development meetings concerning CIDO. Additionally, collaborators are encouraged to raise issues on respective GitHub issue trackers for VIDO ( https://github.com/infectious-disease-ontology-extensions/VIDO ) and CIDO ( https://github.com/CIDO-ontology/cido ).

The existence of IDO Core extensions covering infectious disease-causing entities other than viruses suggests a need for the creation of reference ontology extensions of IDO covering bacteria, fungi, and parasites. To that end, development of the Bacteria Infectious Disease Ontology is underway ( https://github.com/infectious-disease-ontology-extensions/Bacteria-Infectious-Disease- O ntology) as is the development of the Fungal Infectious Disease Ontology ( https://github.com/SydCo99/MIDO ). The methodology illustrated in the development of VIDO provides a recipe for such reference ontology creation. Additionally, the methodology illustrated in the development of CIDO provides a recipe for the creation of novel virus-specific ontologies, namely, by extending them from existing virus ontologies. Adoption of these methodologies by developers during ontology construction will significantly reduce the labor involved in ontology creation. Related, linking research on infectious diseases to developments on non-infectious diseases is no less important than our focus here. In this respect, VIDO and CIDO benefit from alignment with IDO Core, which itself aligns with the Ontology of General Medical Science (OGMS), whose scope extends beyond infectious disease. What this means in practice is that, for example, kidney disease [ 118 ] and cancer [ 119 ] researchers accurately using the OGMS vocabulary to represent data, invariably use ontology terms and methodologies common to IDO Core, VIDO, and CIDO, thereby lowering barriers to data integration and interoperability.

VIDO and CIDO are being used to annotate host-coronavirus protein-protein interactions, in the interest of developing more effective treatment strategies for those infected by SARS-CoV-2 or variants [ 37 ]. While various treatments have been authorized for emergency use [ 120 ], there is significant room for improvement. Rather than focus on a single drug to treat infected patients, VIDO and CIDO developers have pursued investigating drug cocktail strategies to improve treatment outcomes. Foundational to these investigations has been proper characterization of viral proteins playing different roles in host-coronavirus interactions which impact pathogenesis [ 78 ].

From another direction, VIDO and CIDO have been used in automated electronic health-record annotation [ 121 ], in particular those involving COVID-19 data, which highlights the importance of providing researchers with terminological content relevant to nearby domains. Recent developments at the intersection of ontology engineering and machine learning research have, moreover, motivated the need for formally well-defined ontologies in machine learning pipelines using minimal data [ 122 ]. By exploiting formal axioms in, for example, the Gene Ontology, impressive zero-shot predictions for protein functions can be generated [ 123 ]. We believe the formal axiomatization of VIDO makes it a particularly promising ontology for inclusion in zero-shot research and intend to explore how VIDO may supplement machine learning efforts in future work.

Ontologies provide important tools for overcoming contemporary big data challenges. It is incumbent on working ontologists representing life science research to seek harmonization with nearby ontologies, else we run the risk of reinstating the same big data challenges ontologies have previously been so successful at addressing. VIDO represents a substantial effort to characterize viruses in general, in a collaborative, computationally tractable manner. CIDO too represents a significant effort to characterize coronaviruses in a specific, no less collaborative, no less computationally tractable manner. Connecting VIDO to CIDO improves semantic interoperability among IDO Core-conformant infectious disease ontologies and, moreover, improves interoperability with other BFO-conformant ontologies, ranging from the OBO Foundry to numerous other ontology projects employing BFO as a top-level architecture. Consequently, our work provides researchers resources for gathering and coordinating life science data while avoiding issues that so frequently undermine automating integration and analyses of the data flood in which we so often find ourselves [ 124 – 126 ].

Acknowledgments

Many thanks to Asiyah Yu Lin for assistance in VIDO development and harmonization; to Darren Natale and Sydney Cohen for helpful critical feedback on earlier drafts; to Amanda Hicks and Neil Otte for comments on the VIDO rdf files; to participants at the 2022 International Conference on Biomedical Ontologies, with particular thanks to Alexander Diehl and Chris Stoeckert for their helpful feedback before, during, and after the conference. Figures were designed by or in consultation with Rain Yuan.

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• Published: 08 May 2024

A meta-analysis on global change drivers and the risk of infectious disease

• Michael B. Mahon   ORCID: orcid.org/0000-0002-9436-2998 1 , 2   na1 ,
• Alexandra Sack 1 , 3   na1 ,
• O. Alejandro Aleuy 1 ,
• Carly Barbera 1 ,
• Ethan Brown   ORCID: orcid.org/0000-0003-0827-4906 1 ,
• Heather Buelow   ORCID: orcid.org/0000-0003-3535-4151 1 ,
• David J. Civitello 4 ,
• Jeremy M. Cohen   ORCID: orcid.org/0000-0001-9611-9150 5 ,
• Luz A. de Wit   ORCID: orcid.org/0000-0002-3045-4017 1 ,
• Meghan Forstchen 1 , 3 ,
• Fletcher W. Halliday 6 ,
• Patrick Heffernan 1 ,
• Sarah A. Knutie 7 ,
• Alexis Korotasz 1 ,
• Joanna G. Larson   ORCID: orcid.org/0000-0002-1401-7837 1 ,
• Samantha L. Rumschlag   ORCID: orcid.org/0000-0003-3125-8402 1 , 2 ,
• Emily Selland   ORCID: orcid.org/0000-0002-4527-297X 1 , 3 ,
• Alexander Shepack 1 ,
• Nitin Vincent   ORCID: orcid.org/0000-0002-8593-1116 1 &
• Jason R. Rohr   ORCID: orcid.org/0000-0001-8285-4912 1 , 2 , 3   na1  

Nature volume  629 ,  pages 830–836 ( 2024 ) Cite this article

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• Infectious diseases

Anthropogenic change is contributing to the rise in emerging infectious diseases, which are significantly correlated with socioeconomic, environmental and ecological factors 1 . Studies have shown that infectious disease risk is modified by changes to biodiversity 2 , 3 , 4 , 5 , 6 , climate change 7 , 8 , 9 , 10 , 11 , chemical pollution 12 , 13 , 14 , landscape transformations 15 , 16 , 17 , 18 , 19 , 20 and species introductions 21 . However, it remains unclear which global change drivers most increase disease and under what contexts. Here we amassed a dataset from the literature that contains 2,938 observations of infectious disease responses to global change drivers across 1,497 host–parasite combinations, including plant, animal and human hosts. We found that biodiversity loss, chemical pollution, climate change and introduced species are associated with increases in disease-related end points or harm, whereas urbanization is associated with decreases in disease end points. Natural biodiversity gradients, deforestation and forest fragmentation are comparatively unimportant or idiosyncratic as drivers of disease. Overall, these results are consistent across human and non-human diseases. Nevertheless, context-dependent effects of the global change drivers on disease were found to be common. The findings uncovered by this meta-analysis should help target disease management and surveillance efforts towards global change drivers that increase disease. Specifically, reducing greenhouse gas emissions, managing ecosystem health, and preventing biological invasions and biodiversity loss could help to reduce the burden of plant, animal and human diseases, especially when coupled with improvements to social and economic determinants of health.

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Towards common ground in the biodiversity–disease debate

Biological invasions facilitate zoonotic disease emergences

Measuring the shape of the biodiversity-disease relationship across systems reveals new findings and key gaps

Data availability.

All the data for this Article have been deposited at Zenodo ( https://doi.org/10.5281/zenodo.8169979 ) 52 and GitHub ( https://github.com/mahonmb/GCDofDisease ) 53 .

Code availability

All the code for this Article has been deposited at Zenodo ( https://doi.org/10.5281/zenodo.8169979 ) 52 and GitHub ( https://github.com/mahonmb/GCDofDisease ) 53 . R markdown is provided in Supplementary Data 1 .

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Acknowledgements

We thank C. Mitchell for contributing data on enemy release; L. Albert and B. Shayhorn for assisting with data collection; J. Gurevitch, M. Lajeunesse and G. Stewart for providing comments on an earlier version of this manuscript; and C. Carlson and two anonymous reviewers for improving this paper. This research was supported by grants from the National Science Foundation (DEB-2109293, DEB-2017785, DEB-1518681, IOS-1754868), National Institutes of Health (R01TW010286) and US Department of Agriculture (2021-38420-34065) to J.R.R.; a US Geological Survey Powell grant to J.R.R. and S.L.R.; University of Connecticut Start-up funds to S.A.K.; grants from the National Science Foundation (IOS-1755002) and National Institutes of Health (R01 AI150774) to D.J.C.; and an Ambizione grant (PZ00P3_202027) from the Swiss National Science Foundation to F.W.H. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

These authors contributed equally: Michael B. Mahon, Alexandra Sack, Jason R. Rohr

Authors and Affiliations

Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA

Michael B. Mahon, Alexandra Sack, O. Alejandro Aleuy, Carly Barbera, Ethan Brown, Heather Buelow, Luz A. de Wit, Meghan Forstchen, Patrick Heffernan, Alexis Korotasz, Joanna G. Larson, Samantha L. Rumschlag, Emily Selland, Alexander Shepack, Nitin Vincent & Jason R. Rohr

Environmental Change Initiative, University of Notre Dame, Notre Dame, IN, USA

Michael B. Mahon, Samantha L. Rumschlag & Jason R. Rohr

Eck Institute of Global Health, University of Notre Dame, Notre Dame, IN, USA

Alexandra Sack, Meghan Forstchen, Emily Selland & Jason R. Rohr

Department of Biology, Emory University, Atlanta, GA, USA

David J. Civitello

Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

Jeremy M. Cohen

Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA

Fletcher W. Halliday

Department of Ecology and Evolutionary Biology, Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA

Sarah A. Knutie

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Contributions

J.R.R. conceptualized the study. All of the authors contributed to the methodology. All of the authors contributed to investigation. Visualization was performed by M.B.M. The initial study list and related information were compiled by D.J.C., J.M.C., F.W.H., S.A.K., S.L.R. and J.R.R. Data extraction was performed by M.B.M., A.S., O.A.A., C.B., E.B., H.B., L.A.d.W., M.F., P.H., A.K., J.G.L., E.S., A.S. and N.V. Data were checked for accuracy by M.B.M. and A.S. Analyses were performed by M.B.M. and J.R.R. Funding was acquired by D.J.C., J.R.R., S.A.K. and S.L.R. Project administration was done by J.R.R. J.R.R. supervised the study. J.R.R. and M.B.M. wrote the original draft. All of the authors reviewed and edited the manuscript. J.R.R. and M.B.M. responded to reviewers.

Corresponding author

Correspondence to Jason R. Rohr .

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The authors declare no competing interests.

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Nature thanks Colin Carlson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended data fig. 1 prisma flowchart..

The PRISMA flow diagram of the search and selection of studies included in this meta-analysis. Note that 77 studies came from the Halliday et al. 3 database on biodiversity change.

Extended Data Fig. 2 Summary of the number of studies (A-F) and parasite taxa (G-L) in the infectious disease database across ecological contexts.

The contexts are global change driver ( A , G ), parasite taxa ( B , H ), host taxa ( C , I ), experimental venue ( D , J ), study habitat ( E , K ), and human parasite status ( F , L ).

Extended Data Fig. 3 Summary of the number of effect sizes (A-I), studies (J-R), and parasite taxa (S-a) in the infectious disease database for various parasite and host contexts.

Shown are parasite type ( A , J , S ), host thermy ( B , K , T ), vector status ( C , L , U ), vector-borne status ( D , M , V ), parasite transmission ( E , N , W ), free living stages ( F , O , X ), host (e.g. disease, host growth, host survival) or parasite (e.g. parasite abundance, prevalence, fecundity) endpoint ( G , P , Y ), micro- vs macroparasite ( H , Q , Z ), and zoonotic status ( I , R , a ).

Extended Data Fig. 4 The effects of global change drivers and subsequent subcategories on disease responses with Log Response Ratio instead of Hedge’s g.

Here, Log Response Ratio shows similar trends to that of Hedge’s g presented in the main text. The displayed points represent the mean predicted values (with 95% confidence intervals) from a meta-analytical model with separate random intercepts for study. Points that do not share letters are significantly different from one another (p < 0.05) based on a two-sided Tukey’s posthoc multiple comparison test with adjustment for multiple comparisons. See Table S 3 for pairwise comparison results. Effects of the five common global change drivers ( A ) have the same directionality, similar magnitude, and significance as those presented in Fig. 2 . Global change driver effects are significant when confidence intervals do not overlap with zero and explicitly tested with two-tailed t-test (indicated by asterisks; t 80.62  = 2.16, p = 0.034 for CP; t 71.42  = 2.10, p = 0.039 for CC; t 131.79  = −3.52, p < 0.001 for HLC; t 61.9  = 2.10, p = 0.040 for IS). The subcategories ( B ) also show similar patterns as those presented in Fig. 3 . Subcategories are significant when confidence intervals do not overlap with zero and were explicitly tested with two-tailed one sample t-test (t 30.52  = 2.17, p = 0.038 for CO 2 ; t 40.03  = 4.64, p < 0.001 for Enemy Release; t 47.45  = 2.18, p = 0.034 for Mean Temperature; t 110.81  = −4.05, p < 0.001 for Urbanization); all other subcategories have p > 0.20. Note that effect size and study numbers are lower here than in Figs. 3 and 4 , because log response ratios cannot be calculated for studies that provide coefficients (e.g., odds ratio) rather than raw data; as such, all observations within BC did not have associated RR values. Despite strong differences in sample size, patterns are consistent across effect sizes, and therefore, we can be confident that the results presented in the main text are not biased because of effect size selection.

Extended Data Fig. 5 Average standard errors of the effect sizes (A) and sample sizes per effect size (B) for each of the five global change drivers.

The displayed points represent the mean predicted values (with 95% confidence intervals) from the generalized linear mixed effects models with separate random intercepts for study (Gaussian distribution for standard error model, A ; Poisson distribution for sample size model, B ). Points that do not share letters are significantly different from one another (p < 0.05) based on a two-sided Tukey’s posthoc multiple comparison test with adjustment for multiple comparisons. Sample sizes (number of studies, n, and effect sizes, k) for each driver are as follows: n = 77, k = 392 for BC; n = 124, k = 364 for CP; n = 202, k = 380 for CC; n = 517, k = 1449 for HLC; n = 96, k = 355 for IS.

Extended Data Fig. 6 Forest plots of effect sizes, associated variances, and relative weights (A), Funnel plots (B), and Egger’s Test plots (C) for each of the five global change drivers and leave-one-out publication bias analyses (D).

In panel A , points are the individual effect sizes (Hedge’s G), error bars are standard errors of the effect size, and size of the points is the relative weight of the observation in the model, with larger points representing observations with higher weight in the model. Sample sizes are provided for each effect size in the meta-analytic database. Effect sizes were plotted in a random order. Egger’s tests indicated significant asymmetries (p < 0.05) in Biodiversity Change (worst asymmetry – likely not bias, just real effect of positive relationship between diversity and disease), Climate Change – (weak asymmetry, again likely not bias, climate change generally increases disease), and Introduced Species (relatively weak asymmetry – unclear whether this is a bias, may be driven by some outliers). No significant asymmetries (p > 0.05) were found in Chemical Pollution and Habitat Loss/Change, suggesting negligible publication bias in reported disease responses across these global change drivers ( B , C ). Egger’s test included publication year as moderator but found no significant relationship between Hedge’s g and publication year (p > 0.05) implying no temporal bias in effect size magnitude or direction. In panel D , the horizontal red lines denote the grand mean and SE of Hedge’s g and (g = 0.1009, SE = 0.0338). Grey points and error bars indicate the Hedge’s g and SEs, respectively, using the leave-one-out method (grand mean is recalculated after a given study is removed from dataset). While the removal of certain studies resulted in values that differed from the grand mean, all estimated Hedge’s g values fell well within the standard error of the grand mean. This sensitivity analysis indicates that our results were robust to the iterative exclusion of individual studies.

Extended Data Fig. 7 The effects of habitat loss/change on disease depend on parasite taxa and land use conversion contexts.

A) Enemy type influences the magnitude of the effect of urbanization on disease: helminths, protists, and arthropods were all negatively associated with urbanization, whereas viruses were non-significantly positively associated with urbanization. B) Reference (control) land use type influences the magnitude of the effect of urbanization on disease: disease was reduced in urban settings compared to rural and peri-urban settings, whereas there were no differences in disease along urbanization gradients or between urban and natural settings. C) The effect of forest fragmentation depends on whether a large/continuous habitat patch is compared to a small patch or whether disease it is measured along an increasing fragmentation gradient (Z = −2.828, p = 0.005). Conversely, the effect of deforestation on disease does not depend on whether the habitat has been destroyed and allowed to regrow (e.g., clearcutting, second growth forests, etc.) or whether it has been replaced with agriculture (e.g., row crop, agroforestry, livestock grazing; Z = 1.809, p = 0.0705). The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variables included enemy type (A), reference land use type (B), or land use conversion type (C). Data for (A) and (B) were only those studies that were within the “urbanization” subcategory; data for (C) were only those studies that were within the “deforestation” and “forest fragmentation” subcategories. Sample sizes (number of studies, n, and effect sizes, k) in (A) for each enemy are n = 48, k = 98 for Virus; n = 193, k = 343 for Protist; n = 159, k = 490 for Helminth; n = 10, k = 24 for Fungi; n = 103, k = 223 for Bacteria; and n = 30, k = 73 for Arthropod. Sample sizes in (B) for each reference land use type are n = 391, k = 1073 for Rural; n = 29, k = 74 for Peri-urban; n = 33, k = 83 for Natural; and n = 24, k = 58 for Urban Gradient. Sample sizes in (C) for each land use conversion type are n = 7, k = 47 for Continuous Gradient; n = 16, k = 44 for High/Low Fragmentation; n = 11, k = 27 for Clearcut/Regrowth; and n = 21, k = 43 for Agriculture.

Extended Data Fig. 8 The effects of common global change drivers on mean infectious disease responses in the literature depends on whether the endpoint is the host or parasite; whether the parasite is a vector, is vector-borne, has a complex or direct life cycle, or is a macroparasite; whether the host is an ectotherm or endotherm; or the venue and habitat in which the study was conducted.

A ) Parasite endpoints. B ) Vector-borne status. C ) Parasite transmission route. D ) Parasite size. E ) Venue. F ) Habitat. G ) Host thermy. H ) Parasite type (ecto- or endoparasite). See Table S 2 for number of studies and effect sizes across ecological contexts and global change drivers. See Table S 3 for pairwise comparison results. The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variables included the main effects and an interaction between global change driver and the focal independent variable (whether the endpoint measured was a host or parasite, whether the parasite is vector-borne, has a complex or direct life cycle, is a macroparasite, whether the study was conducted in the field or lab, habitat, the host is ectothermic, or the parasite is an ectoparasite).

Extended Data Fig. 9 The effects of five common global change drivers on mean infectious disease responses in the literature only occasionally depend on location, host taxon, and parasite taxon.

A ) Continent in which the field study occurred. Lack of replication in chemical pollution precluded us from including South America, Australia, and Africa in this analysis. B ) Host taxa. C ) Enemy taxa. See Table S 2 for number of studies and effect sizes across ecological contexts and global change drivers. See Table S 3 for pairwise comparison results. The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variables included the main effects and an interaction between global change driver and continent, host taxon, and enemy taxon.

Extended Data Fig. 10 The effects of human vs. non-human endpoints for the zoonotic disease subset of database and wild vs. domesticated animal endpoints for the non-human animal subset of database are consistent across global change drivers.

(A) Zoonotic disease responses measured on human hosts responded less positively (closer to zero when positive, further from zero when negative) than those measured on non-human (animal) hosts (Z = 2.306, p = 0.021). Note, IS studies were removed because of missing cells. (B) Disease responses measured on domestic animal hosts responded less positively (closer to zero when positive, further from zero when negative) than those measured on wild animal hosts (Z = 2.636, p = 0.008). These results were consistent across global change drivers (i.e., no significant interaction between endpoint and global change driver). As many of the global change drivers increase zoonotic parasites in non-human animals and all parasites in wild animals, this may suggest that anthropogenic change might increase the occurrence of parasite spillover from animals to humans and thus also pandemic risk. The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variable of global change driver and human/non-human hosts. Data for (A) were only those diseases that are considered “zoonotic”; data for (B) were only those endpoints that were measured on non-human animals. Sample sizes in (A) for zoonotic disease measured on human endpoints across global change drivers are n = 3, k = 17 for BC; n = 2, k = 6 for CP; n = 25, k = 39 for CC; and n = 175, k = 331 for HLC. Sample sizes in (A) for zoonotic disease measured on non-human endpoints across global change drivers are n = 25, k = 52 for BC; n = 2, k = 3 for CP; n = 18, k = 29 for CC; n = 126, k = 289 for HLC. Sample sizes in (B) for wild animal endpoints across global change drivers are n = 28, k = 69 for BC; n = 21, k = 44 for CP; n = 50, k = 89 for CC; n = 121, k = 360 for HLC; and n = 29, k = 45 for IS. Sample sizes in (B) for domesticated animal endpoints across global change drivers are n = 2, k = 4 for BC; n = 4, k = 11 for CP; n = 7, k = 20 for CC; n = 78, k = 197 for HLC; and n = 1, k = 2 for IS.

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Mahon, M.B., Sack, A., Aleuy, O.A. et al. A meta-analysis on global change drivers and the risk of infectious disease. Nature 629 , 830–836 (2024). https://doi.org/10.1038/s41586-024-07380-6

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Genetic analyses reveal new viruses on the horizon

by German Cancer Research Center

Suddenly they appear, and like the SARS-CoV-2 coronavirus, can trigger major epidemics: Viruses that nobody had on their radar. They are not really new, but they have changed genetically. In particular, the exchange of genetic material between different virus species can lead to the sudden emergence of threatening pathogens with significantly altered characteristics.

This is suggested by current genetic analyses carried out by an international team of researchers. Virologists from the German Cancer Research Center (DKFZ) were in charge of the large-scale study, published in the journal PLOS Pathogens .

"Using a new computer-assisted analysis method, we discovered 40 previously unknown nidoviruses in various vertebrates from fish to rodents, including 13 coronaviruses," reports DKFZ group leader Stefan Seitz. With the help of high-performance computers, the research team, which also includes Chris Lauber's working group from the Helmholtz Center for Infection Research in Hanover, has sifted through almost 300,000 data sets . According to virologist Seitz, the fact that we can now analyze such huge amounts of data at once opens up completely new perspectives.

Virus research is still in its relative infancy. Only a fraction of all viruses occurring in nature are known, especially those that cause diseases in humans, domestic animals and crops. The new method therefore promises a quantum leap in knowledge with regard to the natural virus reservoir. Stefan Seitz and his colleagues sent genetic data from vertebrates stored in scientific databases through their high-performance computers with new questions. They searched for virus-infected animals in order to obtain and study viral genetic material on a large scale. The main focus was on so-called nidoviruses, which include the coronavirus family.

Nidoviruses, whose genetic material consists of RNA (ribonucleic acid), are widespread in vertebrates. This species-rich group of viruses has some common characteristics that distinguish them from all other RNA viruses and document their relationship. Otherwise, however, nidoviruses are very different from each other, i.e., in terms of the size of their genome.

One discovery is particularly interesting with regard to the emergence of new viruses: In host animals that are simultaneously infected with different viruses, a recombination of viral genes can occur during virus replication.

"Apparently, the nidoviruses we discovered in fish frequently exchange genetic material between different virus species, even across family boundaries," says Seitz. And when distant relatives "crossbreed," this can lead to the emergence of viruses with completely new properties. According to Seitz, such evolutionary leaps can affect the aggressiveness and dangerousness of the viruses, but also their attachment to certain host animals.

"A genetic exchange, as we have found in fish viruses, will probably also occur in mammalian viruses," explains Seitz. Bats, which—like shrews—are often infected with a large number of different viruses, are considered a true melting pot. The SARS-CoV-2 coronavirus probably also developed in bats and jumped from there to humans.

After gene exchange between nidoviruses, the spike protein with which the viruses dock onto their host cells often changes. Chris Lauber, first author of the study, was able to show this by means of family tree analyses. Modifying this anchor molecule can significantly change the properties of the viruses to their advantage—by increasing their infectiousness or enabling them to switch hosts.

A change of host, especially from animals to humans, can greatly facilitate the spread of the virus, as the corona pandemic has emphatically demonstrated. Viral "game-changers" can suddenly appear at any time, becoming a massive threat, and—if push comes to shove—triggering a pandemic. The starting point can be a single double-infected host animal.

The new high-performance computer process could help to prevent the spread of new viruses. It enables a systematic search for virus variants that are potentially dangerous for humans, explains Seitz.

The DKFZ researcher sees another important possible application with regard to his special field of research, virus-associated carcinogenesis: "I could imagine that we could use the new High Performance Computing (HPC) to systematically examine cancer patients or immunocompromised people for viruses. We know that cancer can be triggered by viruses, the best-known example being human papillomaviruses. But we are probably only seeing the tip of the iceberg so far. The HPC method offers the opportunity to track down viruses that, previously undetected, nestle in the human organism and increase the risk of malignant tumors."

Journal information: PLoS Pathogens

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Preparedness for HPAI A(H5N1) virus varies across jurisdictions

by Elana Gotkine

Variation is seen in preparedness and response to highly pathogenic avian influenza (HPAI) A(H5N1) viruses, according to a research letter published online May 21 in the Journal of the American Medical Association .

Noting that HPAI A(H5N1) clade 2.3.4.4b viruses pose pandemic potential, Noah Kojima, M.D., from the U.S. Centers for Disease Control and Prevention in Atlanta, and colleagues examined components of public health preparedness and response to HPAI A(H5N1) viruses. State and territorial epidemiologists in 55 jurisdictions were surveyed.

The researchers found that 91 percent of jurisdictions (50 jurisdictions) reported persons exposed to A(H5N1) virus-infected animals and monitored for symptoms. Of these, human exposures were reported in backyard flocks, commercial poultry, wild birds , and sick or dead mammals in 88, 82, 54, and 18 percent of jurisdictions, respectively.

Overall, 59 percent of 49 jurisdictions with A(H5) virus testing capacity reported testing respiratory specimens from symptomatic persons since January 2022.

Public health authorities reported difficulties in monitoring A(H5N1) virus-exposed persons due to personnel shortages or lack of funding in 66 percent of 50 jurisdictions.

Overall, 19 of 50 respondents (38 percent) reported recommending empirical antiviral treatment before performing influenza testing for persons monitored after exposure to A(H5N1) virus who developed symptoms. One-third of the jurisdictions would recommend postexposure prophylaxis for close contacts of those with laboratory-confirmed A(H5N1).

"Challenges reported in monitoring exposed persons and differences in antiviral recommendations highlight the need to strengthen and standardize public health preparedness and response to HPAI A(H5N1) viruses in the U.S., particularly if additional animal-to-human A(H5N1) virus transmission events are reported," the authors write.

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U.S. Tightens Rules on Risky Virus Research

A long-awaited new policy broadens the type of regulated viruses, bacteria, fungi and toxins, including those that could threaten crops and livestock.

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By Carl Zimmer and Benjamin Mueller

The White House has unveiled tighter rules for research on potentially dangerous microbes and toxins, in an effort to stave off laboratory accidents that could unleash a pandemic.

The new policy, published Monday evening, arrives after years of deliberations by an expert panel and a charged public debate over whether Covid arose from an animal market or a laboratory in China.

A number of researchers worried that the government had been too lax about lab safety in the past, with some even calling for the creation of an independent agency to make decisions about risky experiments that could allow viruses, bacteria or fungi to spread quickly between people or become more deadly. But others warned against creating restrictive rules that would stifle valuable research without making people safer.

The debate grew sharper during the pandemic, as politicians raised questions about the origin of Covid. Those who suggested it came from a lab raised concerns about studies that tweaked pathogens to make them more dangerous — sometimes known as “gain of function” research.

The new policy, which applies to research funded by the federal government, strengthens the government’s oversight by replacing a short list of dangerous pathogens with broad categories into which more pathogens might fall. The policy pays attention not only to human pathogens, but also those that could threaten crops and livestock. And it provides more details about the kinds of experiments that would draw the attention of government regulators.

The rules will take effect in a year, giving government agencies and departments time to update their guidance to meet the new requirements.

“It’s a big and important step forward,” said Dr. Tom Inglesby, the director of the Johns Hopkins Center for Health Security and a longtime proponent of stricter safety regulations. “I think this policy is what any reasonable member of the public would expect is in place in terms of oversight of the world’s most transmissible and lethal organisms.”

Still, the policy does not embrace the most aggressive proposals made by lab safety proponents, such as creating an independent regulatory agency. It also makes exemptions for certain types of research, including disease surveillance and vaccine development. And some parts of the policy are recommendations rather than government-enforced requirements.

“It’s a moderate shift in policy, with a number of more significant signals about how the White House expects the issue to be treated moving forward,” said Nicholas Evans, an ethicist at University of Massachusetts Lowell.

Experts have been waiting for the policy for more than a year. Still, some said they were surprised that it came out at such a politically fraught moment . “I wasn’t expecting anything, especially in an election year,” Dr. Evans said. “I’m pleasantly surprised.”

Under the new policy, scientists who want to carry out experiments will need to run their proposals past their universities or research institutions, which will to determine if the work poses a risk. Potentially dangerous proposals will then be reviewed by government agencies. The most scrutiny will go to experiments that could result in the most dangerous outcomes, such as those tweaking pathogens that could start a pandemic.

In a guidance document , the White House provided examples of research that would be expected to come under such scrutiny. In one case, they envisioned scientists trying to understand the evolutionary steps a pathogen needed to transmit more easily between humans. The researchers might try to produce a transmissible strain to study, for example, by repeatedly infecting human cells in petri dishes, allowing the pathogens to evolve more efficient ways to enter the cells.

Scientists who do not follow the new policy could become ineligible for federal funding for their work. Their entire institution may have its support for life science research cut off as well.

One of the weaknesses of existing policies is that they only apply to funding given out by the federal government. But for years , the National Institutes of Health and other government agencies have struggled with stagnant funding, leading some researchers to turn instead to private sources. In recent years, for example, crypto titans have poured money into pandemic prevention research.

The new policy does not give the government direct regulation of privately funded research. But it does say that research institutions that receive any federal money for life-science research should apply a similar oversight to scientists doing research with support from outside the government.

“This effectively limits them, as the N.I.H. does a lot of work everywhere in the world,” Dr. Evans said.

The new policy takes into account the advances in biotechnology that could lead to new risks. When pathogens become extinct, for example, they can be resurrected by recreating their genomes. Research on extinct pathogens will draw the highest levels of scrutiny.

Dr. Evans also noted that the new rules emphasize the risk that lab research can have on plants and animals. In the 20th century, the United States and Russia both carried out extensive research on crop-destroying pathogens such as wheat-killing fungi as part of their biological weapons programs. “It’s significant as a signal the White House is sending,” Dr. Evans said.

Marc Lipsitch, an epidemiologist at Harvard and a longtime critic of the government’s policy, gave the new one a grade of A minus. “I think it’s a lot clearer and more specific in many ways than the old guidance,” he said. But he was disappointed that the government will not provide detailed information to the public about the risky research it evaluates. “The transparency is far from transparent,” he said.

Scientists who have warned of the dangers of impeding useful virus research were also largely optimistic about the new rules.

Gigi Gronvall, a biosafety specialist at the Johns Hopkins Bloomberg School of Public Health, said the policy’s success would depend on how federal health officials interpreted it, but applauded the way it recognized the value of research needed during a crisis, such as the current bird flu outbreak .

“I was cautiously optimistic in reading through it,” she said of the policy. “It seems like the orientation is for it to be thoughtfully implemented so it doesn’t have a chilling effect on needed research.”

Anice Lowen, an influenza virologist at Emory University, said the expanded scope of the new policy was “reasonable.” She said, for instance, that the decision not to create an entirely new review body helped to alleviate concerns about how unwieldy the process might become.

Still, she said, ambiguities in the instructions for assessing risks in certain experiments made it difficult to know how different university and health officials would police them.

“I think there will be more reviews carried out, and more research will be slowed down because of it,” she said.

Carl Zimmer covers news about science for The Times and writes the Origins column . More about Carl Zimmer

Benjamin Mueller reports on health and medicine. He was previously a U.K. correspondent in London and a police reporter in New York. More about Benjamin Mueller

U.S. Government Accountability Office

Virus Field Research: Reduce Risks and Enhance Benefits

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