Evidence map›Paper›PMID 34454610›Full record

ArticleJournal of biomedical semantics2021

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

Abstract read
In one paragraph

Article in Journal of biomedical semantics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
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  5. Big knowledge visualization of the COVID-19 CIDO ontology evolution.BMC medical informatics and decision making · 2023
    Article
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  7. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Anthony HuffmanDepartment of Computational Medicine and Biology, University of Michigan, Ann Arbor, MI, 48109, USA.
Anna Maria MasciDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27710, USA.
Jie ZhengDepartment of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
Nasim SanatiDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, 97239, USA.
Timothy BrunsonDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, 97239, USA.
Guanming WuDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, 97239, USA.
Yongqun HeDepartment of Computational Medicine and Biology, University of Michigan, Ann Arbor, MI, 48109, USA. yongqunh@med.umich.edu.ORCID 0000-0001-9189-9661

Funding

Reactome - CoreU41HG003751 · NHGRI · ONTARIO INSTITUTE FOR CANCER RESEARCH · PI DEUSTACHIO, PETER G, HERMJAKOB, HENNING · 2012 to 2021
$14.5M
Ontology-supported Integrative Analysis and Visualization of Vaccine-induced Pathways and NetworksUH2AI132931 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HE, YONGQUN, WU, GUANMING · 2019 to 2020
$437k
NHGRI NIH HHS U41 HG003751NIAID NIH HHS UH2 AI132931
6 · The paper itself

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.

Indexed as

Biological OntologiesCOVID-19SARS-CoV-2Databases, FactualHumansCIDOCoronavirus infectious disease ontologyCOVID-19Host-coronavirus interactionImmPortMetadataOBO foundryOntologySARS-CoV-2Sex difference

Identifiers

PMID34454610
PMCPMC8400831

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.