A consideration of publication-derived immune-related associations in Coronavirus and related lung damaging diseases
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Abstract
Background
The severe acute respiratory syndrome virus SARS-CoV-2, a close relative of the SARS-CoV virus, is the cause of the recent COVID-19 pandemic affecting, to date, over 14 million individuals across the globe and demonstrating relatively high rates of infection and mortality. A third virus, the H5N1, responsible for avian influenza, has caused infection with some clinical similarities to those in COVID-19 infections. Cytokines, small proteins that modulate immune responses, have been directly implicated in some of the severe responses seen in COVID-19 patients, e.g. cytokine storms. Understanding the immune processes related to COVID-19, and other similar infections, could help identify diagnostic markers and therapeutic targets.
Methods
Here we examine data of cytokine, immune cell types, and disease associations captured from biomedical literature associated with COVID-19, Coronavirus in general, SARS, and H5N1 influenza, with the objective of identifying potentially useful relationships and areas for future research.
Results
Cytokine and cell-type associations captured from Medical Subject Heading (MeSH) terms linked to thousands of PubMed records, has identified differing patterns of associations between the four corpuses of publications (COVID-19, Coronavirus, SARS, or H5N1 influenza). Clustering of cytokine-disease co-occurrences in the context of Coronavirus has identified compelling clusters of co-morbidities and symptoms, some of which already known to be linked to COVID-19. Finally, network analysis identified sub-networks of cytokines and immune cell types associated with different manifestations, co-morbidities and symptoms of Coronavirus, SARS, and H5N1.
Conclusion
Systematic review of research in medicine is essential to facilitate evidence-based choices about health interventions. In a fast moving pandemic the approach taken here will identify trends and enable rapid comparison to the literature of related diseases.
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SciScore for 10.1101/2020.04.15.20065425: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Briefly, a list of cytokine MeSH descriptors (such as “interferon gamma,” “transforming growth factor beta,” and “chemokine CCL3”) was manually compiled by a domain expert, who browsed MeSH’s sub-trees and selected those descriptors that were deemed relevant to this work. MeSHsuggested: (MeSH, RRID:SCR_004750)MeSH’ssuggested: NonePubMed data: The PubMed database was searched on April 6th, 2020 for abstracts tagged with either the ‘Severe Acute Respiratory Syndrome’ MeSH term, the ‘influenza a virus, h5n1 subtype’ MeSH term, or with the ‘Coronavirus’ MeSH term (excluding those with the … SciScore for 10.1101/2020.04.15.20065425: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Briefly, a list of cytokine MeSH descriptors (such as “interferon gamma,” “transforming growth factor beta,” and “chemokine CCL3”) was manually compiled by a domain expert, who browsed MeSH’s sub-trees and selected those descriptors that were deemed relevant to this work. MeSHsuggested: (MeSH, RRID:SCR_004750)MeSH’ssuggested: NonePubMed data: The PubMed database was searched on April 6th, 2020 for abstracts tagged with either the ‘Severe Acute Respiratory Syndrome’ MeSH term, the ‘influenza a virus, h5n1 subtype’ MeSH term, or with the ‘Coronavirus’ MeSH term (excluding those with the SARS Mesh term). PubMedsuggested: (PubMed, RRID:SCR_004846)Network analysis: Network analysis was carried out with the Cytoscape software. Cytoscapesuggested: (Cytoscape, RRID:SCR_003032)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our approach to capturing immune-related associations from MeSH descriptors is not without limitations. We first assume that co-occurrences of MeSH descriptors within a PubMed record represent a true relationship or dependency; further, some of the associations and co-occurrences were low (i.e. present in only few abstracts). However, our previous investigation into the extent to which MeSH term co-occurrence captures real association has found that at least 70% of co-occurrences of different types of entities (disease, cell type, or cytokine) represent true direct or indirect dependencies, but it is likely to be higher than that [11]. Additionally, patterns of MeSH co-occurrence have shown to capture known medical associations, as well as identify potentially novel ones, thus providing further confidence in the approach. A second limitation is a lack of directionality and type for the associations captured by approach; nevertheless, we show that these mere co-occurrences may still hold valuable information. Finally, since at the time of writing, very few publications directly related to COVID-19 are available, our data mining has had to focus on Coronavirus- and other related lung-damaging diseases as a proxy for COVID-19 Nevertheless, we have created a paradigm for such research which is easy to use and apply, and demonstrated its utility.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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