Human coronaviruses pose a huge threat to global public health. The outbreak has led to significant morbidity, mortality and economic damage worldwide 1 These crucial differences between SARS-CoV-2 and more benign coronaviruses are a function of specific adaptations that SARS-CoV-2 has made to the human host. For example, the immune-evasive character of SARS-CoV-2 enables the COVID-19 pandemic to spread from asymptomatic individuals 13 , because symptoms are primarily produced by the host immune response. This spread from asymptomatic carriers is a key portion of COVID-19's rapid spread. Ongoing inquiry into these specific adaptations is hampered by a lack of structured integrative resources with standard data and metadata formats.
Deeper study of the interactions between hosts (human and others) and coronaviruses (SARS-CoV-2, SARS-CoV, MERS-CoV and others) would help our understanding of COVID-19 mechanisms and rational design of vaccines and drugs against COVID-19. However, to achieve this goal, two major bottlenecks exist. First, although intensive research has generated large amounts of data, the data are multidimensional, heterogeneous, disintegrated and non-interoperable. Such data cannot be efficiently interpreted by computers, preventing computer-assisted reasoning and analysis. The second bottleneck is the lack of efficient computational algorithms and bioinformatics tools to integrate and analyze the heterogeneous data and knowledge into deep scientific understanding. This bottleneck slows the integration of host-coronavirus interaction (HCI) data, discovery of HCI mechanisms, and new applications. Ontologies can solve these problems in analysis by integrating big data in a computer-interpretable way.
In the informatics field, an ontology is a human-and computer-interpretable set of terms and relations that represent entities in a specific domain and how they relate to each other. Ontology has played an important role in standard knowledge and data representation, integration, sharing, and analysis. A major field of artificial intelligence is knowledge representation and reasoning (KR², KR&R), and ontology is a foundation of the KR&R. The Coronavirus Infectious Disease Ontology (CIDO) is a new communitybased biomedical ontology in the domain of coronavirus diseases (https://github.com/cido-ontology/cido) 14 . To better study the HCI mechanisms to explain various COVID-19 phenomena, we propose to use CIDO as a platform for standard and computer-understandable representation of the host-coronavirus interactions and their influences on patient outcomes.
In this manuscript, we introduce a set of "HPI-Outcome postulates", laying out a set of basic rules for understanding host-pathogen interactions (HPIs) and their roles in the disease outcomes. Then, we propose a model to study the relations between hostcoronavirus interactions (HCIs) and coronavirus disease outcomes. We used our ontology-based strategy to annotate and classify human coronaviruses, hosts, and experimentally verified host-coronavirus interactions (HCIs) obtained from literature mining and bioinformatics studies. Using these annotations, we proposed an ontological model of the interactions between SARS-CoV-2 and human to explain how the human-SARS-CoV-2 interactions would induce different disease outcomes such as hypertension and cytokine storm. Furthermore, using known PPIs as baits, we developed a computational method to predict new PPIs and their associations with multi-organ disease phenotypes caused by SARS-CoV-2.
interacions. From the literature, we manually annotated and identified coronavirus virulence factors and how host genes/proteins interact with coronaviruses.
Experimentally confirmed coronavirus virulence factors were added to the Victors, a manually curated web-based database and analysis system 15 .
Ignet literature mining analysis. The Ignet interaction network tool (http://ignet.hegroup.org/), powered by our in-house SciMiner 16 literature mining tool, was used to mine gene-gene interactions in the specific domain of coronaviruses.
among the host genes were collected from the STRING database with a medium confidence score of 0.4.
host species were mapped to NCBITaxon taxonomy ontology 17 and Human Phenotype Ontology (HPO) 18 . The disease phenotype outcomes, including different symptoms and signs, were mapped to HPO. With these mapped terms, Ontofox 19 was used to generate subsets (or slimes) of the NCBITaxon or HPO ontologies.
Prediction of host-coronavirus PPIs. We used a "domain-inferred prediction" method to infer inter-species PPIs. Supported by the conservation and importance of domains in proteins 20 , such domain-domain interaction (DDI) infers the potential interaction between two proteins if one domain in a pathogen protein A interacted with another domain in a host protein B.
Visualization of host-coronavirus PPIs. Cytoscape 21 was utilized for visualization of the coronavirus-human PPIs.
Koch's postulates [22] [23] [24] have long provided criteria designed to establish a causal relationship between exposure to a microbe and emergence of a disease. The four criteria of Koch's postulates are: (i) The microbe must be found in all organisms suffering from the disease, but should not be found in healthy organisms. Based on the HPI-Outcome postulates, we lay out the integrative host-coronavirus interaction framework as shown in Figure 1 . Basically, the framework includes three major processes from the viral side and three major processes from the host side. For the coronaviruses, crucial processes include: viral entry to host cell, viral replication, and viral release from the infected cell. For the host, three responses exist: the naïve response, the innate response, and the adaptive response. Initially, the host response to the coronavirus entry through a receptor-ligand interaction is naïve since the host does not realize the danger and react to the virus by a routine procedure. At this naïve host response stage, neither signalling molecules nor selected immune cell populations are yet present to coordinate an immune response. As signalling molecules are produced by infected cells (e.g. cytokines, interferons), the host will show a delayed innate responses to coronavirus infection. The innate immune response may or may not become effective and strong against the viral infection. Eventually, through a process of recombination followed by clonal selection in multiple immune cell subtypes, the host will mount an adaptive immune response more effective against the viral infection ( Figure 1 ). Each of the viral or host process includes many subprocesses and interactions. Dynamic hostvirus interactions can be used to explain disease outcomes according to their impact on these many subprocesses. If the viruses "wins", the result will be the viral survival and replication. If the host "wins", the host will eventually control viral replication and kill the viruses. To model and analyze the complex adversarial interactions between host and pathogen, we propose to apply ontology as an integrative and systematic platform due to the solid semantic logic behind ontology and its computer-interpretable nature.
We used ontology to represent coronavirus disease knowledge including the taxonomical structure of 9 coronaviruses in the NCBITaxon ontology, a taxonomy ontology based on the NCBI Taxonomy database 17 . As shown in Figure 2A , Human coronaviruses infect and transmit among mammal hosts. We identified over 10 organisms that can serve as hosts or reservoirs for human coronaviruses. Bats were found to be the reservoir of a wide variety of coronaviruses, including SARS-CoV and MERS-CoV viruses 27 . SARS-CoV was also found in masked palm civets and a raccoon dog, and antibodies against SARS-CoV were also found in Chinese ferret badgers in a live-animal market in Shenzhen, China 28 . Our analysis found many other animals that might have served as reservoir hosts of SARS-CoV ( Figure 2B ). The MERS-CoV virus likely transmitted from dromedary camels 29 .
These host organisms can further be classified using the NCBITaxon ontology ( Figure 2B ). All the host organisms belong to Boreoeutheria, a clade (magnorder) of placental mammals. Clades within Boreoeutheria include Euarchontoglires (Supraprimates, including humans and rodents) and Laurasiatheria. Laurasiatheria includes Artiodactyla (even toed ungulates, such as cattle and camels), Chiroptera (bats),
and Ferae (carnivores such as canines, felines, and civets, and relatives including pangolins) ( Figure 2B ). The taxonomy hierarchy was also extracted from the NCBITaxon ontology and become part of the CIDO.
Our NCBITaxon-based ontology analysis of all these coronavirus hosts further
shows that zoonotic human coronaviruses (including SARS-CoV, MERS-CoV, and SARS-CoV-2) are obligate mammal parasites ( Figure 2B ), suggesting that some molecular factor distinguishes mammals from other animals to host and transmit coronaviruses to humans. We hypothesize that zoonotic human coronaviruses (such as SARS-CoV-2) can only originate in mammals (in contrast to influenza, which can transmit to humans from avians). However, this hypothesis will need substantial additional study. A recent paper suggests snake as an intermediate host for human zoonotic coronavirus based on similarity in codon usage bias with 2019-nCoV (e.g., SARS-CoV-2) 30 . Since snakes are not mammals, this would disprove our hypothesis, but the overall pattern makes the proposed transmission via snakes an extreme claim, thus requiring extreme evidence.
According to US CDC, the common symptoms of COVID-19 include fever, cough, shortness of breath or difficulty breathing, chills, repeated shaking with chills, muscle pain, headache, sore throat, and new loss of taste or smell. Figure 3A is a hierarchical display of these 9 common symptoms and their associated hierarchical phenotype representation using the Human Phenotype Ontology (HPO) 31 .
Comorbidity is the presence of additional condition(s) such as a disease or phenotype that is concurrent with the coronavirus infection. Our further COVID-19
annotation and analysis show that the patients with many comorbid conditions (e.g., emphysema, chronic bronchitis, hypertension, coronary heart disease, and diabetes) are at heightened risk for severe symptoms and death. Figure 3B shows comorbidities and the COVID-19 outcome frequencies from case reports in peer-reviewed articles. To analyze the host interactions leading to these outcomes, it is very important to build data structures and data visualizations that can draw upon clinical electronic records.
Clinical coronavirus disease outcomes result from host-coronavirus interactions.
We have collected 10, 4, and 1 virulence factors from SARS-CoV, MERS-CoV, and SARS-CoV-2, respectively (Table 1, Figure 4 ). Table 1 
Based on our HPI-Outcome postulates, the model given in Figure 1 , and our literature mining results, we generated an integrative framework to formally specify the interactions between host and SARS-CoV-2 factors with associated disease outcomes. which can be attenuated by blocking the renin-angiotensin system (RAS) pathway 32 . As we know (Figure 3) , patients with hypertension are more susceptible to death and severe outcomes. This phenomenon can be explained if the down-regulation of the ACE2 due to the infection is associated with hypertension ( Figure 5) . Specifically, the downregulation of ACE2 would lead to excessive production of angiotensin II by ACE, which would then bind to its receptor and induce vasocontraction and hypertension 42 .
Although ACE and ACE2 both act as key players in the RAS, they serve oppositional roles to angiotensin II. An angiotensin II receptor blocker drug (e.g., Azilsartan) would block the angiotensin II binding to its receptor, and thus help treat hypertension. In this way, the ontological model links host-pathogen interactions to the hypertension outcome. If a patient already has hypertension, the further increased blood pressure due to the interactions would exacerbate the underlying comorbidity, which may explain the increased death rate of patients with superimposed Covid and hypertension.
However, other pathogen factors likely play a role in the extreme pathogenicity of SARS-CoV-2. This is evidenced by the fact that despite markedly weak pathogenicity, HCoV-NL63 (a prevalent human coronavirus) also uses an S-ACE2 interaction for cellular entry 43 . However, HCoV-NL63 is considerably more benign than SARS-CoV-2.
This may be a consequence of molecular differences in the S protein-ACE2 interaction between the two strains. The ontology-driven comparative analysis of CoVs provides a formal representation for both similarities and differences among the S proteins from different viruses. If the S proteins do not differ significantly, we hypothesize that some other factor (e.g., a co-receptor) of SARS-CoV-2 contributes to viral binding and entry.
Our recent vaccine design study 44 predicts many SARS-CoV-2 nonstructural proteins as possible adhesins, some of which may serve as a possible co-receptor, which are testable predictions.
A cytokine storm is an excessive immune response to external stimuli associated with high levels of cytokine production 45 . Cytokine storm is a major cause of acute respiratory distress syndrome (ARDS) and multiple-organ failure in severe COVID-19
patients, resulting in death within a short time 46 . IL-6 is a major cytokine involved in the cytokine storm 45 . IL-6 can be induced by the angiotensin II in human cells 47 , which links the cytokine storm to the S-ACE2 binding-associated interaction network ( Figure 5 ).
Tocilizumab, a recombinant humanized anti-human IL-6 receptor monoclonal antibody, can bind to the IL-6 receptor with high affinity, and thus prevent IL-6 from binding to its native receptor and inducing downstream immune damage. It was recently reported the Tocilizumab improved the clinical outcome in severe and critical COVID-19 patients and effectively reduced mortality 48 . Figure 5 provides a logical and structured graphical summary of the knowledge introduced above. This representation covers knowledge learned from many studies.
Without such an integrative representation, it is difficult to put all the pieces together.
Another unique feature of the Figure 5 representation is that it is ontology-oriented, so that all the terms and relations are represented using a computer-interpretable ontological format. Figure 5 is a subset of the comprehensive host-coronavirus interaction network, which will be expanded as our understanding of host-interactions by the coronavirus grows.
To demonstrate the ontological representation, the process 'SARS-CoV-2 S-ACE2
binding' can be defined by two logical axioms ( Figure 5 ):
The above ontological logical axioms define the relations where a SARS-CoV-2 S-ACE2 binding process has two participants: a SARS-CoV-2 S protein that serves as a ligand role, and an ACE2 protein that serves as a receptor role.
In addition, we can define a logical axiom between 'SARS-CoV-2 S-ACE2
binding' and TMPRSS2 that defines the role of TMPRSS2 to activate the SARS-CoV-2 S-ACE2 binding process:
A major difference between ontology and taxonomy is that a taxonomy is built only based on the is_a relation. Beside the representation of such is_a relation, ontology can also provide additional relations between entities under different hierarchies, such as the above 'activates' relation that links the TMPRSS2 protein in the protein hierarchy and the SARS-CoV-2 S-ACE2 binding under the interaction process hierarchy. The excessive production of pro-inflammatory cytokines and chemokines, esp. IL-1b, IL-8, IL-6, CXCL10, and CCL2, were detected in SARS patients [52] [53] [54] , indicating the initiation of a cytokine storm, which is eventually associated with the progression to ARDS and death. It is obvious that the timing here is critical. The early IFN response would be beneficial. However, coronaviruses, especially SARS-CoV-2, manipulate the immune system to delay such a response allowing widespread infection prior to an onset of symptoms. The understanding of such dynamics helps our rational design of proper treatments and the timing of the treatments.
It appears that similar to SARS-CoV and MERS-CoV, SARS-CoV-2 also affects the IFN response 51, 55 . SARS-CoV-2 even displayed a higher sensitivity to IFN-I in vitro compared with SARS-CoV-1 in infected Vero cells 56 . The difference in terms of IFN-I activation between SARS-CoV and SAR-CoV-2 is likely due to the difference in the sequences for two proteins (Orf 3b and Orf 6) of these two human coronaviruses 56 .
Considering that different human coronaviruses induce varied phenotype outcomes, it is important to include more coronaviruses in the systematic comparison and identify their differential action on IFN production and on ISGs, which likely plays a critical role in the delayed host recognition and responses to the infection, supporting the survival and replication of the viruses inside the host. Our proposed HPI postulates combined with our structured ontology support an systems-level investigation into these mechanisms and into potential avenues of prophylaxis or treatment.
The second key component of the host response to coronavirus interaction is the activation of an adaptive host response, which is also key to long term immunity. The severe SARS disease is also related to the lack of or delayed activation of adaptive immunity 42 . The SARS patients with severe outcomes maintained high levels of many innate immune system-linked cytokines (e.g., CXCL10, CCL2) and ISG-encoded proteins with low levels of spike-specific antibodies 57 . Conversely, surviving patients had higher levels of spike-specific antibodies. These findings suggest that the prompt formation of adaptive immunity is important to reduce disease severity and improve outcomes. The adaptive immune response can be induced by effective vaccines against COVID-19. Both SARS-CoV and MERS-CoV S and S1 have been used as vaccine antigens/immunogens due to their ability to induce neutralizing antibodies that prevent host cell attachment and infection 44, 58 . Numerous SARS-CoV-2 vaccine trials are underway 44 , with the hope that a safe and effective vaccine(s) will be ready in the near future to control the COVID-19 pandemic.
Our previous Victors PPI predictions are based on the analyses of protein domains and phylogeny 15 . By nature, each PPI is a domain-domain interaction. Therefore, we can identify the domains of virus proteins, and based on known interactions with domains of host proteins, predict how the virus proteins interact with host proteins. Using the known PPIs as baits, we developed a domain-inferred PPI prediction strategy to predict more PPIs, and how these PPIs could possibly affect organs and tissues. Our prediction found that SARS-CoV-2 could induce 3,111 human-virus interactions with 133 human proteins.
Many PPIs are tissue-specific, and the tissue-specific information can be identified from the Human Protein Atlas 59 . Using our predicted PPIs, SARS-CoV-2 was predicted to affect many tissues such as skin, liver, intestine, blood, glands and reproductive tissues ( Figure 6 ).
In addition to computational methods, the prediction described above also depends on prior knowledge of the pre-identified PPIs and tissue-specific protein The intergrative ontological modeling can also support different applications such as rational drug repurposing design and vaccine development. As shown in Figure 5 , our ontological representation provides several paths for rational drug repurposing design for COVID-19 treatment. Camostat mesylate (a serine protease inhibitor) is capable of inhibiting the function of TMPRSS2 41 , which is crucial for the effective S-ACE2 binding TMPRSS2 41 . Angiotensin II receptor blockers can be used to block the activation of the angiotensin II 60 , therefore blocking the vacoconstriction and following hypertension.
Tocilizumab is able to inhibit IL-6 activity 48 so block the formation of cytokine storm.
SARS-CoV-2 appears to use many pathways to cause different phenotypes. It is reasonable to hypothesize that the drugs blocking early stages of the disease pathways would be more effective than the later starges of the pathways. It is also important to consider drugs that block the paths leading to severe disease outcomes such as hypertension and cytokine storm. In addition, we also need to consider the side effects of the drugs being used for the treatment. The integrative ontology framework provides a foundation for these rational design. Ontology-based software tools and algorithms can also be developed to support intelligence queries 61 