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pmid:36225326

Differentially Expressed Genes Study Shown Potential for BCG Stimulation in Reducing the Severity of COVID-19.

Irandi Putra Pratomo · Aryo Tedjo · Dimas R Noor · Wisnu Ananta Kusuma

International journal of inflammation · 2022 doi:10.1155/2022/1490408

Paper

complete

Experiment

complete

Data

partial

Analysis

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Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:36225326
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science-record
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2026-11-01T18:15:35Z
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2026-11-01T22:07:48Z
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paper object · 10 fields
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title
Differentially Expressed Genes Study Shown Potential for BCG Stimulation in Reducing the Severity of COVID-19.
doi
10.1155/2022/1490408
pmid
36225326
authors array · 4 items
[0]
Irandi Putra Pratomo
[1]
Aryo Tedjo
[2]
Dimas R Noor
[3]
Wisnu Ananta Kusuma
journal
International journal of inflammation
year
2022
abstract
The incidence of COVID-19 infection and death is known to be lower in tuberculosis (TB) endemic countries than in nonendemic countries. The Bacillus Calmette-Guerin (BCG) vaccination, which is commonly administered in TB endemic countries, was previously reported to have a nonspecific protective effect against several infections, including COVID-19. In this study, we used a differentially expressed genes (DEG) approach to analyze the genes modulated by BCG vaccination and COVID-19 infection. The Gene Expression Omnibus (GEO) database was used to select a COVID-19 gene expression data set with GSE164805, GSE14408, and GSE58636, and DEG in each data set were identified using the GEO2R online tools and selected using the adjusted p value (padj) 0.05 criteria. The protein-protein interaction (PPI) network was constructed from DEGs with the same trend of expression (upregulation or downregulation) using STRING version 11. The PPI network was performed by using the highest confidence number (0.9). DEGs that have a high-trust network were collected and functional cluster analysis of biological processes from Gene Ontology (GO), pathway analysis from the Human KEGG pathway, and COVID-19-related gene analysis was carried out using the Enrichr database. We found that either BCG or tuberculin increased the expression of several genes related to hyperinflammation, such as CCL3, CCL4, CSF2, IL1B, and LTA. In severe COVID-19, these genes were downregulated. This leads to the hypothesis that revaccination may have a protective effect against the severity of COVID-19 by reducing the hyperinflammatory status.
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Differently expressed genes in each data set were identified using the online tools of GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) provided by the NCBI. The DEG in each data set was selected using the adjusted p value (padj) 0.05 criteria.
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sections[5] "2.3. Data Processing and Identification of Differentially Expressed Genes" paragraph 2
quote
Differently expressed genes in each data set were identified using the online tools of GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) provided by the NCBI. The DEG in each data set was selected using the adjusted p value (padj) 0.05 criteria.
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sections[12] "Data Availability" paragraph 2
quote
Data are available on the database mentioned in this article, as it used secondary data from the Gene Omnibus Ontology (GEO) database.
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Transcriptional profiling of mycobacterial antigen-induced responses in infants vaccinated with BCG at birth
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The transcriptional profiles of severe COVID-19
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Gene expression and cytokine profile correlate with mycobacterial growth in a human BCG challenge model
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Differently expressed genes in each data set were identified using the online tools of GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) provided by the NCBI. The DEG in each data set was selected using the adjusted p value (padj) 0.05 criteria.
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review.machine_critique
assays array · 1 item
[0] object · 11 fields
role
analysis
definition_id
network_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 3 items
[0]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
protein-protein interaction
order_basis
analysis_only
id
assay:1
Complete file
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The Bacillus Calmette-Guerin (BCG) vaccination, which is commonly administered in TB endemic countries, was previously reported to have a nonspecific protective effect against several infections, including COVID-19. In this study, we used a differentially expressed genes (DEG) approach to analyze the genes modulated by BCG vaccination and COVID-19 infection. The Gene Expression Omnibus (GEO) database was used to select a COVID-19 gene expression data set with GSE164805, GSE14408, and GSE58636, and DEG in each data set were identified using the GEO2R online tools and selected using the adjusted p value (padj) 0.05 criteria. The protein-protein interaction (PPI) network was constructed from DEGs with the same trend of expression (upregulation or downregulation) using STRING version 11. The PPI network was performed by using the highest confidence number (0.9). DEGs that have a high-trust network were collected and functional cluster analysis of biological processes from Gene Ontology (GO), pathway analysis from the Human KEGG pathway, and COVID-19-related gene analysis was carried out using the Enrichr database. We found that either BCG or tuberculin increased the expression of several genes related to hyperinflammation, such as CCL3, CCL4, CSF2, IL1B, and LTA. In severe COVID-19, these genes were downregulated. This leads to the hypothesis that revaccination may have a protective effect against the severity of COVID-19 by reducing the hyperinflammatory status.", "claims": [{"id": "claim-method", "statement": "Differently expressed genes in each data set were identified using the online tools of GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) provided by the NCBI. 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The DEG in each data set was selected using the adjusted p value (padj) 0.05 criteria."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data are available on the database mentioned in this article, as it used secondary data from the Gene Omnibus Ontology (GEO) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36225326", "locator": "sections[12] \"Data Availability\" paragraph 2", "quote": "Data are available on the database mentioned in this article, as it used secondary data from the Gene Omnibus Ontology (GEO) database."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36225326"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE14408"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE14408"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36225326", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/1490408", "sha256": 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