Abstract
BackgroundWith 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.MethodsThe 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.ResultsThe 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.ConclusionUsing 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.
Highlights
With COVID-19 still in its pandemic stage, extensive research has generated increasing amounts of data and knowledge
SARS-CoV-2 viral S protein related host-coronavirus interaction (HCI) knowledge was emphasized for Coronavirus Infectious Disease Ontology (CIDO) modeling, including its binding with angiotensinconverting-enzyme 2 (ACE2), mutations causing different variants, and epitope homology by comparison with other coronavirus S proteins
ImmPort data exploration on the basis of existing CIDO development The method implemented in our study is the recursive XOD strategy (Fig. 1)
Summary
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. It is critical to systematically study the molecular mechanisms of COVID-19 disease formation and host responses in order to fully understand, prevent, and treat COVID-19. To better study and understand the disease mechanism, extensive research has been conducted in a relatively short period of time. With tens of thousands of papers published on host-coronavirus interactions (HCIs), a major bottleneck is how to incorporate all the studies into a more comprehensive understanding of the HCI mechanisms. As of April 22, 2021, ImmPort has included 7 studies on COVID-19, and 6 of these studies have included unique and large data sets
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