A new framework for host-pathogen interaction research.

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.

Ontology Modeling and Analysis of COVID-19 associated Acute Kidney Injury and Its Underlying Molecular Mechanisms

Acute kidney injury (AKI) is found to be common among COVID-19 patients. In this study, we performed extensive literature mining and used the BioGRID COVID-19 interaction data to bridge the mechanistic and molecular link between COVID-19 and AKI. DAVID GO enrichment analysis of the BioGRID data allowed for further filtration of COVID-19 related interactors by their relevance to untoward kidney manifestations. Key physiological processes involved in this pathway include Renin-Angiotensin system (RAS) activation, complement activation, and most importantly, systemic inflammation. Discovered interactors like CD147, CD209, CypA, and MASP2 were found to be heavily implicated in the mentioned processes. The Coronavirus Infectious Disease Ontology (CIDO) was used to represent our analyzed results, leading to further understanding of the COVID-19 associated AKI mechanisms. © 2021 Copyright for this paper by its authors.

Differential COVID-19 Symptoms Given Pandemic Locations, Time, and Comorbidities During the Early Pandemic.

BACKGROUND: COVID-19 pandemic is disaster to public health worldwide. Better perspective on COVID's features early in its course-prior to the development of vaccines and widespread variants-may prove useful in the understanding of future pandemics. Ontology provides a standardized integrative method for knowledge modeling and computer-assisted reasoning. In this study, we systematically extracted and analyzed clinical phenotypes and comorbidities in COVID-19 patients found at different countries and regions during the early pandemic using an ontology-based bioinformatics approach, with the aim to identify new insights and hidden patterns of the COVID-19 symptoms. RESULTS: A total of 48 research articles reporting analysis of first-hand clinical data from over 40,000 COVID-19 patients were surveyed. The patients studied therein were diagnosed with COVID-19 before May 2020. A total of 18 commonly-occurring phenotypes in these COVID-19 patients were first identified and then classified into different hierarchical groups based on the Human Phenotype Ontology (HPO). This meta-analytic approach revealed that fever, cough, and the loss of smell and taste were ranked as the most commonly-occurring phenotype in China, the US, and Italy, respectively. We also found that the patients from Europe and the US appeared to have more frequent occurrence of many nervous and abdominal symptom phenotypes (e.g., loss of smell, loss of taste, and diarrhea) than patients from China during the early pandemic. A total of 22 comorbidities, such as diabetes and kidney failure, were found to commonly exist in COVID-19 patients and positively correlated with the severity of the disease. The knowledge learned from the study was further modeled and represented in the Coronavirus Infectious Disease Ontology (CIDO), supporting semantic queries and analysis. Furthermore, also considering the symptoms caused by new viral variants at the later stages, a spiral model hypothesis was proposed to address the changes of specific symptoms during different stages of the pandemic. CONCLUSIONS: Differential patterns of symptoms in COVID-19 patients were found given different locations, time, and comorbidity types during the early pandemic. The ontology-based informatics provides a unique approach to systematically model, represent, and analyze COVID-19 symptoms, comorbidities, and the factors that influence the disease outcomes.

The Infectious Disease Ontology in the age of COVID-19.

BACKGROUND: Effective response to public health emergencies, such as we are now experiencing with COVID-19, requires data sharing across multiple disciplines and data systems. Ontologies offer a powerful data sharing tool, and this holds especially for those ontologies built on the design principles of the Open Biomedical Ontologies Foundry. These principles are exemplified by the Infectious Disease Ontology (IDO), a suite of interoperable ontology modules aiming to provide coverage of all aspects of the infectious disease domain. At its center is IDO Core, a disease- and pathogen-neutral ontology covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is extended by disease and pathogen-specific ontology modules. RESULTS: To assist the integration and analysis of COVID-19 data, and viral infectious disease data more generally, we have recently developed three new IDO extensions: IDO Virus (VIDO); the Coronavirus Infectious Disease Ontology (CIDO); and an extension of CIDO focusing on COVID-19 (IDO-COVID-19). Reflecting the fact that viruses lack cellular parts, we have introduced into IDO Core the term acellular structure to cover viruses and other acellular entities studied by virologists. We now distinguish between infectious agents - organisms with an infectious disposition - and infectious structures - acellular structures with an infectious disposition. This in turn has led to various updates and refinements of IDO Core's content. We believe that our work on VIDO, CIDO, and IDO-COVID-19 can serve as a model for yielding greater conformance with ontology building best practices. CONCLUSIONS: IDO provides a simple recipe for building new pathogen-specific ontologies in a way that allows data about novel diseases to be easily compared, along multiple dimensions, with data represented by existing disease ontologies. The IDO strategy, moreover, supports ontology coordination, providing a powerful method of data integration and sharing that allows physicians, researchers, and public health organizations to respond rapidly and efficiently to current and future public health crises.

CIDO ontology updates and secondary analysis of host responses to COVID-19 infection based on ImmPort reports and literature.

BACKGROUND: With COVID-19 still in its pandemic stage, extensive research has generated increasing amounts of data and knowledge. As many studies are published within a short span of time, we often lose an integrative and comprehensive picture of host-coronavirus interaction (HCI) mechanisms. As of early April 2021, the ImmPort database has stored 7 studies (with 6 having details) that cover topics including molecular immune signatures, epitopes, and sex differences in terms of mortality in COVID-19 patients. The Coronavirus Infectious Disease Ontology (CIDO) represents basic HCI information. We hypothesize that the CIDO can be used as the platform to represent newly recorded information from ImmPort leading the reinforcement of CIDO. METHODS: The CIDO was used as the semantic platform for logically modeling and representing newly identified knowledge reported in the 6 ImmPort studies. A recursive eXtensible Ontology Development (XOD) strategy was established to support the CIDO representation and enhancement. Secondary data analysis was also performed to analyze different aspects of the HCI from these ImmPort studies and other related literature reports. RESULTS: The topics covered by the 6 ImmPort papers were identified to overlap with existing CIDO representation. SARS-CoV-2 viral S protein related HCI knowledge was emphasized for CIDO modeling, including its binding with ACE2, mutations causing different variants, and epitope homology by comparison with other coronavirus S proteins. Different types of cytokine signatures were also identified and added to CIDO. Our secondary analysis of two cohort COVID-19 studies with cytokine panel detection found that a total of 11 cytokines were up-regulated in female patients after infection and 8 cytokines in male patients. These sex-specific gene responses were newly modeled and represented in CIDO. A new DL query was generated to demonstrate the benefits of such integrative ontology representation. Furthermore, IL-10 signaling pathway was found to be statistically significant for both male patients and female patients. CONCLUSION: Using the recursive XOD strategy, six new ImmPort COVID-19 studies were systematically reviewed, the results were modeled and represented in CIDO, leading to the enhancement of CIDO. The enhanced ontology and further seconary analysis supported more comprehensive understanding of the molecular mechanism of host responses to COVID-19 infection.

Visual comprehension and orientation into the COVID-19 CIDO ontology.

The current intensive research on potential remedies and vaccinations for COVID-19 would greatly benefit from an ontology of standardized COVID terms. The Coronavirus Infectious Disease Ontology (CIDO) is the largest among several COVID ontologies, and it keeps growing, but it is still a medium sized ontology. Sophisticated CIDO users, who need more than searching for a specific concept, require orientation and comprehension of CIDO. In previous research, we designed a summarization network called "partial-area taxonomy" to support comprehension of ontologies. The partial-area taxonomy for CIDO is of smaller magnitude than CIDO, but is still too large for comprehension. We present here the "weighted aggregate taxonomy" of CIDO, designed to provide compact views at various granularities of our partial-area taxonomy (and the CIDO ontology). Such a compact view provides a "big picture" of the content of an ontology. In previous work, in the visualization patterns used for partial-area taxonomies, the nodes were arranged in levels according to the numbers of relationships of their concepts. Applying this visualization pattern to CIDO's weighted aggregate taxonomy resulted in an overly long and narrow layout that does not support orientation and comprehension since the names of nodes are barely readable. Thus, we introduce in this paper an innovative visualization of the weighted aggregate taxonomy for better orientation and comprehension of CIDO (and other ontologies). A measure for the efficiency of a layout is introduced and is used to demonstrate the advantage of the new layout over the previous one. With this new visualization, the user can "see the forest for the trees" of the ontology. Benefits of this visualization in highlighting insights into CIDO's content are provided. Generality of the new layout is demonstrated.