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

Anthony Huffman, Anna Maria Masci, Jie Zheng, Nasim Sanati, Timothy Brunson, Guanming Wu, Yongqun He
Author Information
  1. Anthony Huffman: Department of Computational Medicine and Biology, University of Michigan, Ann Arbor, MI, 48109, USA.
  2. Anna Maria Masci: Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27710, USA.
  3. Jie Zheng: Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
  4. Nasim Sanati: Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, 97239, USA.
  5. Timothy Brunson: Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, 97239, USA.
  6. Guanming Wu: Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, 97239, USA.
  7. Yongqun He: Department of Computational Medicine and Biology, University of Michigan, Ann Arbor, MI, 48109, USA. yongqunh@med.umich.edu. ORCID

Abstract

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.

Keywords

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Grants

  1. U41 HG003751/NHGRI NIH HHS
  2. UH2 AI132931/NIAID NIH HHS

MeSH Term

COVID-19
Humans
Biological Ontologies
SARS-CoV-2
Databases, Factual

Word Cloud

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