Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specific ICAM1-associated pathways
Introduction: Intercellular adhesion molecule 1 (ICAM-1) is a critical molecule responsible for interactions between cells. Previous studies have suggested that ICAM-1 triggers cell-to-cell transmission of HIV-1 or HTLV-1, that SARS-CoV-2 shares several features with these viruses via interactions b...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-08-01
|
| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1250545/full |
| _version_ | 1797730163538001920 |
|---|---|
| author | Mitsuhiro Odaka Mitsuhiro Odaka Mitsuhiro Odaka Mitsuhiro Odaka Morgan Magnin Morgan Magnin Katsumi Inoue Katsumi Inoue Katsumi Inoue |
| author_facet | Mitsuhiro Odaka Mitsuhiro Odaka Mitsuhiro Odaka Mitsuhiro Odaka Morgan Magnin Morgan Magnin Katsumi Inoue Katsumi Inoue Katsumi Inoue |
| author_sort | Mitsuhiro Odaka |
| collection | DOAJ |
| description | Introduction: Intercellular adhesion molecule 1 (ICAM-1) is a critical molecule responsible for interactions between cells. Previous studies have suggested that ICAM-1 triggers cell-to-cell transmission of HIV-1 or HTLV-1, that SARS-CoV-2 shares several features with these viruses via interactions between cells, and that SARS-CoV-2 cell-to-cell transmission is associated with COVID-19 severity. From these previous arguments, it is assumed that ICAM-1 can be related to SARS-CoV-2 cell-to-cell transmission in COVID-19 patients. Indeed, the time-dependent change of the ICAM-1 expression level has been detected in COVID-19 patients. However, signaling pathways that consist of ICAM-1 and other molecules interacting with ICAM-1 are not identified in COVID-19. For example, the current COVID-19 Disease Map has no entry for those pathways. Therefore, discovering unknown ICAM1-associated pathways will be indispensable for clarifying the mechanism of COVID-19.Materials and methods: This study builds ICAM1-associated pathways by gene network inference from single-cell omics data and multiple knowledge bases. First, single-cell omics data analysis extracts coexpressed genes with significant differences in expression levels with spurious correlations removed. Second, knowledge bases validate the models. Finally, mapping the models onto existing pathways identifies new ICAM1-associated pathways.Results: Comparison of the obtained pathways between different cell types and time points reproduces the known pathways and indicates the following two unknown pathways: (1) upstream pathway that includes proteins in the non-canonical NF-κB pathway and (2) downstream pathway that contains integrins and cytoskeleton or motor proteins for cell transformation.Discussion: In this way, data-driven and knowledge-based approaches are integrated into gene network inference for ICAM1-associated pathway construction. The results can contribute to repairing and completing the COVID-19 Disease Map, thereby improving our understanding of the mechanism of COVID-19. |
| first_indexed | 2024-03-12T11:41:18Z |
| format | Article |
| id | doaj.art-1db6826c08e64267908081d09751a2b7 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-12T11:41:18Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-1db6826c08e64267908081d09751a2b72023-08-31T15:41:49ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-08-011410.3389/fgene.2023.12505451250545Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specific ICAM1-associated pathwaysMitsuhiro Odaka0Mitsuhiro Odaka1Mitsuhiro Odaka2Mitsuhiro Odaka3Morgan Magnin4Morgan Magnin5Katsumi Inoue6Katsumi Inoue7Katsumi Inoue8The Graduate University for Advanced Studies, SOKENDAI, Tokyo, JapanPrinciples of Informatics Research Division, National Institute of Informatics, Tokyo, JapanLaboratoire des Sciences du Numérique de Nantes, École Centrale de Nantes, Nantes Université, UMR 6004, Nantes, FranceJapan Society for the Promotion of Science, Tokyo, JapanPrinciples of Informatics Research Division, National Institute of Informatics, Tokyo, JapanLaboratoire des Sciences du Numérique de Nantes, École Centrale de Nantes, Nantes Université, UMR 6004, Nantes, FranceThe Graduate University for Advanced Studies, SOKENDAI, Tokyo, JapanPrinciples of Informatics Research Division, National Institute of Informatics, Tokyo, JapanLaboratoire des Sciences du Numérique de Nantes, École Centrale de Nantes, Nantes Université, UMR 6004, Nantes, FranceIntroduction: Intercellular adhesion molecule 1 (ICAM-1) is a critical molecule responsible for interactions between cells. Previous studies have suggested that ICAM-1 triggers cell-to-cell transmission of HIV-1 or HTLV-1, that SARS-CoV-2 shares several features with these viruses via interactions between cells, and that SARS-CoV-2 cell-to-cell transmission is associated with COVID-19 severity. From these previous arguments, it is assumed that ICAM-1 can be related to SARS-CoV-2 cell-to-cell transmission in COVID-19 patients. Indeed, the time-dependent change of the ICAM-1 expression level has been detected in COVID-19 patients. However, signaling pathways that consist of ICAM-1 and other molecules interacting with ICAM-1 are not identified in COVID-19. For example, the current COVID-19 Disease Map has no entry for those pathways. Therefore, discovering unknown ICAM1-associated pathways will be indispensable for clarifying the mechanism of COVID-19.Materials and methods: This study builds ICAM1-associated pathways by gene network inference from single-cell omics data and multiple knowledge bases. First, single-cell omics data analysis extracts coexpressed genes with significant differences in expression levels with spurious correlations removed. Second, knowledge bases validate the models. Finally, mapping the models onto existing pathways identifies new ICAM1-associated pathways.Results: Comparison of the obtained pathways between different cell types and time points reproduces the known pathways and indicates the following two unknown pathways: (1) upstream pathway that includes proteins in the non-canonical NF-κB pathway and (2) downstream pathway that contains integrins and cytoskeleton or motor proteins for cell transformation.Discussion: In this way, data-driven and knowledge-based approaches are integrated into gene network inference for ICAM1-associated pathway construction. The results can contribute to repairing and completing the COVID-19 Disease Map, thereby improving our understanding of the mechanism of COVID-19.https://www.frontiersin.org/articles/10.3389/fgene.2023.1250545/fullICAM-1cell-to-cell transmissionCOVID-19single-cell omics data analysismodel validationpathway enrichment analysis |
| spellingShingle | Mitsuhiro Odaka Mitsuhiro Odaka Mitsuhiro Odaka Mitsuhiro Odaka Morgan Magnin Morgan Magnin Katsumi Inoue Katsumi Inoue Katsumi Inoue Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specific ICAM1-associated pathways Frontiers in Genetics ICAM-1 cell-to-cell transmission COVID-19 single-cell omics data analysis model validation pathway enrichment analysis |
| title | Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specific ICAM1-associated pathways |
| title_full | Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specific ICAM1-associated pathways |
| title_fullStr | Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specific ICAM1-associated pathways |
| title_full_unstemmed | Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specific ICAM1-associated pathways |
| title_short | Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specific ICAM1-associated pathways |
| title_sort | gene network inference from single cell omics data and domain knowledge for constructing covid 19 specific icam1 associated pathways |
| topic | ICAM-1 cell-to-cell transmission COVID-19 single-cell omics data analysis model validation pathway enrichment analysis |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1250545/full |
| work_keys_str_mv | AT mitsuhiroodaka genenetworkinferencefromsinglecellomicsdataanddomainknowledgeforconstructingcovid19specificicam1associatedpathways AT mitsuhiroodaka genenetworkinferencefromsinglecellomicsdataanddomainknowledgeforconstructingcovid19specificicam1associatedpathways AT mitsuhiroodaka genenetworkinferencefromsinglecellomicsdataanddomainknowledgeforconstructingcovid19specificicam1associatedpathways AT mitsuhiroodaka genenetworkinferencefromsinglecellomicsdataanddomainknowledgeforconstructingcovid19specificicam1associatedpathways AT morganmagnin genenetworkinferencefromsinglecellomicsdataanddomainknowledgeforconstructingcovid19specificicam1associatedpathways AT morganmagnin genenetworkinferencefromsinglecellomicsdataanddomainknowledgeforconstructingcovid19specificicam1associatedpathways AT katsumiinoue genenetworkinferencefromsinglecellomicsdataanddomainknowledgeforconstructingcovid19specificicam1associatedpathways AT katsumiinoue genenetworkinferencefromsinglecellomicsdataanddomainknowledgeforconstructingcovid19specificicam1associatedpathways AT katsumiinoue genenetworkinferencefromsinglecellomicsdataanddomainknowledgeforconstructingcovid19specificicam1associatedpathways |