=Paper=
{{Paper
|id=Vol-2807/paperE
|storemode=property
|title=CIDO: The Community-Based Coronavirus Infectious Disease Ontology
|pdfUrl=https://ceur-ws.org/Vol-2807/paperE.pdf
|volume=Vol-2807
|authors=Yongqun He,Hong Yu,Edison Ong,Yang Wang,Yingtong Liu,Anthony Huffman,Hsin-hui Huang,John Beverley,Asiyah Yu Lin,William D. Duncan,Sivaram Arabandi,Jiangan Xie,Junguk Hur,Xiaolin Yang,Luonan Chen,Gilbert S. Omenn,Brian Athey,Barry Smith
|dblpUrl=https://dblp.org/rec/conf/icbo/He0OWLHHBLDAXHY20
}}
==CIDO: The Community-Based Coronavirus Infectious Disease Ontology==
https://ceur-ws.org/Vol-2807/paperE.pdf
CIDO: The Community-based Coronavirus
Infectious Disease Ontology
Yongqun Hea,1 Hong Yub2, Edison Onga, Yang Wanga,b, Yingtong Liua, Anthony
Huffmana, Hsin-hui Huanga,c, John Beverleyd, Asiyah Yu Line, William D. Duncanf,
Sivaram Arabandig, Jiangan Xieh, Junguk Huri, Xiaolin Yangj, Luonan Chenk, Gilbert
S. Omenna, Brian Atheya, Barry Smith l
a
University of Michigan Medical School, Ann Arbor, MI, USA. b People’s Hospital of
Guizhou University, Guiyang, Guizhou, China. c National Yang-Ming University,
Taipei, Taiwan. d Northwestern University, Evanston, IL, USA. e National Center for
Ontological Research, Buffalo, NY, USA. f Lawrence Berkeley National Laboratory,
Berkeley, CA, USA. g OntoPro LLC, Houston, TX, USA. hSchool of Bioinformatics,
Chongqing University of Posts and Telecommunications, Chongqing, China; i
University of North Dakota School of Medicine and Health Sciences, Grand Forks,
ND, USA. j Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences
& School of Basic Medicine, Peking Union Medical College, Beijing, China. k
Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences,
Shanghai, China. l University at Buffalo, Buffalo, NY 14260, USA.
Abstract. Current COVID-19 pandemic and previous SARS/MERS outbreaks have
caused a series of major crises to global public health. We must integrate the large
and exponentially growing amount of heterogeneous coronavirus data to better
understand coronaviruses and associated disease mechanisms, in the interest of
developing effective and safe vaccines and drugs. Ontologies have emerged to play
an important role in standard knowledge and data representation, integration,
sharing, and analysis. We have initiated the development of the community-based
Coronavirus Infectious Disease Ontology (CIDO). As an Open Biomedical
Ontology (OBO) library ontology, CIDO is an open source and interoperable with
other existing OBO ontologies. In this article, the general architecture and the design
patterns of the CIDO are introduced, CIDO representation of coronaviruses,
phenotypes, anti-coronavirus drugs and medical devices (e.g. ventilators) are
illustrated, and an application of CIDO implemented to identify repurposable drug
candidates for effective and safe COVID-19 treatment is presented.
Keywords. Coronavirus, COVID-19, ontology, drug repurposing, ventilator.
1. Introduction
Coronavirus diseases pose major crises to public health. In addition to the current
COVID-19 pandemic, Severe Acute Respiratory Syndrome (SARS) [1] and Middle East
respiratory syndrome (MERS) [2] are two other severe HCoV diseases that have
1
Yongqun Oliver He, Corresponding author. E-mail: yongqunh@med.umich.edu.
2
Hong Yu, Co-corresponding author. E-mail: yuhong20040416@sina.com.
1
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
occurred in the past two decades. More recently, the WHO declared the Coronavirus
Disease 2019 (COVID-19) outbreak as a pandemic on March 11, 2020, when there were
118,326 confirmed cases and 4,292 deaths. As of May 25, there have been over 5.5
million confirmed cases and approximately 350,000 deaths globally.
A major bottleneck for coronavirus disease research, however, is that valuable
research data and knowledge are siloed in non-integratable and non-interoperable data
repositories. Big data has the characteristics of 5 V’s: volume, veracity (i.e., quality of
data), velocity, variety, and value [3]. In the era of Information Technology and big data,
biomedical research has become data-intensive with increasingly large, complex,
multidimensional, and diverse datasets generated. Knowledge is a special type of data
that embodies awareness and understanding. Non-integrated and non-interoperable data
and knowledge inhibit computer-assisted reasoning, which is the essence of Artificial
Intelligence. To address this bottleneck, we can rely on computer-interpretable
integrative and interoperable ontology.
With that in mind, to study coronaviruses efficiently we have initiated the
development of a community-based interoperable Coronavirus Infectious Disease
Ontology (CIDO) for standardized and integrative coronavirus disease representation,
integration, and analysis. This manuscript provides details on current status of CIDO
development and applications.
2. Methods
2.1. Coronavirus disease-related data collection
The coronavirus disease related data were collected from the literature and openly
available databases. We have focused on coronavirus and host taxonomy data, phenotype,
drug, and vaccine data.
2.2. Ontology development
CIDO development followed the OBO Foundry ontology development principles [4] and
the eXtensible Ontology Development (XOD) strategy [5]. The CIDO development
started with the reuse and alignment of terms and relations from existing ontologies using
the Ontofox tool [6]. We used the Basic Formal Ontology (BFO) [7], Chemical Entities
of Biological Interest (ChEBI) [8], Human Disease Ontology (DOID) [9], Human
Phenotype Ontology (HPO) [10], Infectious Disease Ontology (IDO) [11], etc. The
Protégé-OWL editor was used for ontology editing.
2.3. CIDO status, source code, deposition, and license
CIDO has been accepted as an OBO library ontology
(http://www.obofoundry.org/ontology/cido.html). CIDO source code is freely available
on the GitHub website https://github.com/CIDO-ontology/cido. The source code uses
the license CC-BY. CIDO has been deposited to the Ontobee ontology repository
(http://www.ontobee.org/ontology/CIDO) the BioPortal repository
(https://bioportal.bioontology.org/ontologies/CIDO), and the OLS repository
(https://www.ebi.ac.uk/ols/ontologies/cido).
2
2.4. CIDO representation demonstrations and drug repurposing use case
In this manuscript, we provide demonstrations of different CIDO ontological
representations of coronaviruses, drugs, vaccines, and medical devices. We also show
how CIDO can be used to support rational design for drug repurposing. Description
Logic (DL) and SPARQL queries are used in Protégé–OWL editor for ontology content
query.
3. Results
3.1. High level structure
Figure 1 lays out the high-level hierarchical structure of CIDO, as well as the various
ontologies which it imports. Abbreviations in parentheses indicate an entity source
ontology, and red text indicates terms introduced by CIDO:
Figure 1. Top level hierarchical structure of CIDO. Abbreviations not defined earlier: GO – Gene
Ontology, NCBITaxon – NCBI Taxonomy Ontology, OBI – Ontology for Biomedical Investigations, OGMS
– Ontology for General Medical Science, OHPI – Ontology of Host-Pathogen Interaction, OPMI – Ontology
of Precision Medicine and Investigation.
As illustrated above, CIDO uses the Basic Formal Ontology (BFO) as the top-level
ontology. BFO has been approved to become ISO/IEC standard 21838. BFO contains
two branches, ‘continuant’ and ‘occurrent’. The ‘continuant’ represents time-
independent entities such as material entity, and the ‘occurrent’ represents time-related
entities such as process. BFO has been adopted by >300 ontologies. BFO provides us a
mechanism to reuse and integrate with these many ontologies without interoperability
issues, and will ultimately facilitate CIDO becoming a standard widely-used ontology
for coordinating and integrating coronavirus research.
Figure 2 describes the general CIDO design pattern that lays out the relations among
selected major entities modeled in the ontology.
3
Figure 2. Design pattern of CIDO for logically representing and linking different components related to
coronavirus disease.
One central term in CIDO is ‘COVID-19 disease process’ (Figure 2), which is
defined as a subclass of ‘disease process’, which is defined as a subclass of ‘pathological
bodily process’ (OGMS_0000061). The ‘COVID-19 disease process’ has many features
including those defined using the following axioms:
- ‘occurs in’ some animal
- ‘occurs in’ some lung, where lung is part of animal.
- ‘caused by infection with’ some SARS-CoV-2
- realizes some ‘COVID-19 disease’, which is a disposition defined in Human
Disease Ontology (DOID).
Based on the “all-some rule” in ontology [12], the axiom (‘occurs in’ some lung)
means that every COVID-19 disease process occurs in some lung. We know that the
disease can also occur in other organs such as kidney and brain; however, the occurrence
in kidney or brain does not always happen. In this case, we can use the relation
‘susceptibly occurs in’, which represents a susceptibility of the occurrence rather than an
assurance.
In addition, ‘COVID-19 vaccine’ ‘immunizes against disease process’ the ‘COVID-
19 disease process’, where the relation is a Vaccine Ontology (VO) relation (Figure 2).
Furthermore, ‘COVID-19 drug’ ‘is substance that treats’ the ‘COVID-19 disease
process’, and here the relation is a Relation Ontology (RO) term.
Instead of directly using the disposition term ‘COVID-19 disease’, we chose to use
‘COVID-19 disease process’ due to multiple reasons. First, the relation ‘occurs in’ only
links a process to an anatomic entity (e.g., lung), and it cannot link a disposition to an
anatomic entity. The relation ‘caused by infection with’ is also defined to link a
pathological process (not a disposition) with a pathogen. Furthermore, the vaccines and
drugs are used for an existing disease process instead of a disease disposition. Meanwhile,
the ‘COVID-19 disease’ disposition is realized in the ‘COVID-19 disease process’,
which closely links the two terms together.
Figure 2 also lays out the genes, cells, quality types (e.g., phenotypes, age) and their
relations with each other and other entities in CIDO. For example, human ACE2 gene,
which encodes for the Angiotensin I Converting Enzyme 2 (ACE2), is a receptor of the
SARS-CoV-2 S protein. The ACE2-S binding in the epithelial cells of the lung would
4
stimulate a list of host genes up- or down-regulated in the cells, which may further
contribute to the disease development.
3.2. Classification of coronaviruses, hosts, and host phenotypes
CIDO includes taxonomic representations of coronaviruses and hosts, and the
phenotypes shown in coronavirus patients. Figure 2A represents the taxonomic hierarchy
of representative coronaviruses. SARS-CoV and SAR-CoV-2 belong to the Sarbecovirus,
a subclass of Betacoronavirus genus. MERS-CoV belongs to Merbecovirus subgenus
under the same Betacoronavirus genus. Four human coronavirus strains (229E, NL63,
HKU1, and OC43) cause mild common colds in humans, and they belong to different
taxonomic locations compared to SARS-CoV, SARS-CoV-2, and MERS-CoV. Figure
2B shows the common phenotypes shown in COVID-19 patients, including fever, chills,
cough, shortness of breath (dyspnea), loss of smells and taste, etc. These terms have all
been extracted from NCBITaxon and HPO and imported to CIDO.
Figure 3. Ontological representation of representative coronaviruses and COVID-19 phenotypes.
(A) NCBITaxon representation of the hierarchy of representative coronaviruses. (B) HP representation of
common phenotypes shown in COVID-19 patients. These hierarchical terms are imported to CIDO.
3.3. Anti-coronavirus drug classification
Extending our previous preprint work [13], we collected 136 anti-coronavirus drugs,
including 26 coronavirus protein-specific monoclonal or polyclonal antibody drugs and
110 general purpose drugs. These drugs have been experimentally verified in vivo or in
vitro to be effective against the infections of various human coronaviruses including
SARS-CoV, MERS-CoV, and SARS-CoV-2.
We have mapped all these drugs to three ontologies: ChEBI [8], National Drug File
– Reference Terminology (NDF-RT) [14], and the Drug Ontology (DrON) [15]. The
subsets of these ontologies that contain the mapped drugs and their related characteristics
were then extracted and imported to CIDO using the Ontofox tool [6].
5
Through the mechanism of Emergency Use Authorization (EUA), FDA has
authorized two COVID-19 drugs for for the emergency treatment of COVID-19 patients:
Remdesivir drug (https://www.fda.gov/media/141477/download) and COVID-19
Convalescent Plasma (CCP) (https://www.fda.gov/media/137566/download). The
following axiom defines the usage of Remdesivir and CCP for treating COVID-19:
‘is substance that treatments’ some ‘COVID-19 disease process’
The following axiom represents the relation between the drug ‘Remdesivir drug’
and its active ingredient:
‘has active ingredient’ some remdesivir
As defined in ChEBI, remdesivir (http://purl.obolibrary.org/obo/CHEBI_145994)
has many properties such as the antiviral role and anticoronaviral agent role.
3.4. Anti-coronavirus vaccine representation in CIDO
In another recent study published in a journal article [17], we reported our COVID-19
vaccine design using reverse vaccinology and machine learning. This study collected 44
vaccine candidates experimentally verified to be effective against coronavirus challenges
in laboratory animal models and/or used in clinical trials. The Vaccine Ontology (VO)
[18] has been used to represent these vaccines. The VO-represented terms and relations
were then imported to CIDO (data not shown). Furthermore, we have used CIDO and
other ontologies including the Ontology of Adverse Events (OAE) to systematically
examine the adverse events associated with SARS/MERS/COVID-19 vaccine
candidates, and found differential profiles in different types of vaccines (data not shown).
3.5. Medical device for COVID-19 treatment
During this unprecedented time of COVID-19 pandemic, due to the rapid spread of
SARS-CoV-2, the control of the pandemic requires large amounts of medical devices in
all healthcare settings, especially the nursing homes and or long-term care facilities
where the most vulnerable population lives in high density. A medical device is any
device intended to be used for medical purposes. The medical devices used for the
pandemic include diagnosis testing kits, ventilators, and personal protective equipment
(PPE) for medical use such as surgical masks, face shields, respirators, gowns, gloves
etc. In the USA, the FDA is responsible for regulating medical devices used to diagnose,
prevent and treat COVID-19. The NIH has also developed COVID-19 treatment
guidelines to inform clinicians how to care for patients with COVID-19
(https://www.covid19treatmentguidelines.nih.gov).
Some COVID-19 patients may develop acute respiratory failure and require
ventilatory support. As a specific use case, we focus the ontological representation on
ventilators. A ventilator is defined as a machine that supports breathing when a person is
unable to breathe enough on their own. The modern ventilator technology has evolved
to enable a single device to operate in numerous modes, from basic continuous positive
pressure (CPAP and bi-level PAP) to traditional pressure and volume ventilator modes.
As a complicated machine, the modern ventilator system includes an operator interface
where parameters will be set for proper management of the automatic ventilation
supported by a mechanical ventilator. The central part of the ventilator is like a “black
box”. The output of the ventilator can be measured and monitored to fit the patient’s
condition. We developed a framework to demonstrate how CIDO models the entities
involved in a mechanical ventilation assistance and management process (Figure 5).
6
Figure 4. Medical device design pattern for COVID-19 treatment. Mechanical ventilator is used as an
example for the ontology modeling here.
A ‘mechanical ventilator’ is a subclass of ‘ventilator’, which is a subclass of
‘medical device’. The function of the ‘ventilator’ is to ‘deliver mixed gas into lung’.
Multiple entities involved in a ‘mechanical ventilator operation’ process: a ‘mechanical
ventilator’, a clinical ‘ventilator operator’, a ‘patient’ and the preset parameter of ‘tidal
volume’. As a planned process, the operation of the mechanical ventilator aims to achieve
the objective of assisting patient’s breathing that ‘is about treating’ the ‘acute respiratory
failure disease process’, a pathological process that a ‘COVID-19 patient’ may be
susceptible to. Note that the low tidal volume (4–8 mL/kg of predicted body weight) is
an important parameter for the mechanical ventilation operation in the case of caring
acute respiratory failure without damaging the lung
(https://www.covid19treatmentguidelines.nih.gov/critical-care/).
3.6. Statistics of CIDO
As of August 30, 2020, CIDO has 6,758 terms, including imported terms from over 40
existing ontologies. CIDO includes 190 CIDO-specific terms and hundreds of newly
generated axioms that link different entities. The detailed statistics of CIDO is available
at the Ontobee website: http://www.ontobee.org/ontostat/CIDO.
3.7. Use cases of CIDO
CIDO can be applied for many use cases. Below we will focus on drug repurposing
demonstration and then introduce a few other use cases.
Drug repurposing is a strategy to identify new uses for approved drugs that are
outside the scope of the original medical indication. We previously identified 110 anti-
coronavirus drugs [13], most of which were not originally targeted for COVID-19
treatment. We hypothesize that some of these drugs could be repurposed for treating
COVID-19 patients. To address this hypothesis, we performed ontology-based
bioinformatics analysis with the assumption that ontology-formatted computer-
interpretable results provided us a new way to identify more scientific insights. As shown
in Figure 5, a DL query of CIDO identified 6 chemical compounds that have the antiviral
7
agent role and antimalarial role and has been found effective against the infection of
SARS-CoV, SARS-CoV-2, or MERS-CoV. Our further analysis found that 3 out of these
6 chemicals (amodiaquine, chloroquine, hydroxychloroquine) all have the role of
inhibiting viral entry to host cells, and all these three chemicals are all under the class of
quinolines. Furthermore, among these anticoronavirus chemicals, a query to find an
entity having all of antiviral, antimalarial, and anti-inflammatory agent roles retrieved
only one chemical compound - amodiaquine (Figure 6).
Figure 5. DL query of potential drugs for potential COVID-19 treatment. (A) DL query of chemicals
that have two roles (antiviral agent role and antimalarial role). (B) DL query of chemicals that have three roles
including the two roles above and the anti-inflammatory agent role.
CIDO is also being used in many other applications. For example, CIDO is being
used by OHNLP (http://www.ohnlp.org/), the Open Health Natural Language Processing
(NLP) Consortium aimed to promote clinical COVID-19 data NLP. The OHNLP
COVID-19 research is also an effort of the National COVID Cohort Collaborative (N3C)
program (https://ncats.nih.gov/n3c). CIDO has also been applied to support COVID-19
vaccine study and semantic calculation and prediction.
4. Discussion
This manuscript introduces the initial development and applications of the community-
based Coronavirus Infectious Disease Ontology (CIDO). Our study demonstrates that
CIDO provides an ideal platform to integrate important data needed to research different
coronavirus disease-related entities such as viruses, phenotypes, drugs, vaccines, medical
devices (e.g. ventilators),), supporting integrative analysis of COVID-19 and other
human coronavirus diseases.
We provide one CIDO use case concerning drug repurposing for COVID-19
treatment. Despite extensive research and clinical trials, we as of now have found only
one potentially effective therapy for COVID-19, Remdesivir [19]. Our ontology-based
analysis provides a new strategy for rational drug repurposing design (Figure 4),
complementing others [20, 21]. Eight active drug chemicals were found to have antiviral
and antimalarial roles, and amodiaquine was found to not only have these two roles but
also an additional anti-inflammatory role. Similar to chloroquine, Amodiaquine is a 4‐
aminoquinoline that has been used widely to treat and prevent uncomplicated malaria
[22]. A comparative study showed that amodiaquine is more effective than chloroquine
in the treatment therapy of falciparum malaria in Kenya [22]. It has also been
recommended that combining amodiaquine with artesunate to reduce the risk of drug
8
resistance [23]. The performance of amodiaquine for possible COVID-19 treatment
deserve further evaluation [24].
An ongoing CIDO development is to model and represent the mechanisms of the
molecular and cellular interaction between the hosts and coronaviruses. Such modeling
will provide the foundation for our rational drug repurposing and vaccine development.
In our drug preprint study [13], we extracted and analyzed the interactions between anti-
coronavirus drugs and their target proteins. These anti-coronavirus drugs were identified
to be effective against coronavirus infections in vitro or in vivo. It is likely that some of
the drug targets participate in active host-SARS-CoV-2 interactions leading to severe
COVID-19 disease outcomes. Deeper modeling and representation of the intricate host-
virus-drug interactions would help us in better drug repurposing analysis.
Being a “community-based” ontology, CIDO is committed to serve the community.
CIDO is created to be open and freely available for the community to use. It is an
interoperable ontology that reuses and interlinks to existing ontologies and resources.
We also expect that the community gets involved in its development, and we are always
ready to accept new ideas and critiques.
In future work we will use CIDO as a platform to standardize different coronavirus
research-related metadata types and apply them for the standardization and enhanced
analysis of specific conditions defined in different experimental and clinical studies, and
how these conditions would affect the disease outcomes. We will also identify and
develop more applications tools that implementing CIDO for different purposes. More
researchers and developers are welcome to join our community-based effort to advance
the CIDO ontology and its applications.
Acknowledgements
This project is supported by NIH grants U24CA210967 and P30ES017885 (to GSO);
R01GM080646, 1UL1TR001412, 1U24CA199374, and 1T15LM012495 (to BS); the
National Natural Science Foundation of China 61801067 (to JX); the Natural Science
Foundation of Chongqing CSTC2018JCYJAX0243 (to JX); the non-profit Central
Research Institute Fund of Chinese Academy of Medical Sciences 2019PT320003 (to
HY); and University of Michigan Medical School Global Reach award (to YH).
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