Integrated network-based multiple computational analyses for identification of co-expressed candidate genes associated with neurological manifestations of COVID-19
Abstract ‘Tripartite network’ (TN) and ‘combined gene network’ (CGN) were constructed and their hub-bottleneck and driver nodes (44 genes) were evaluated as ‘target genes’ (TG) to identify 21 ‘candidate genes’ (CG) and their relationship with neurological manifestations of COVID-19. TN was developed...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-10-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-21109-3 |
| _version_ | 1828343219068338176 |
|---|---|
| author | Suvojit Hazra Alok Ghosh Chaudhuri Basant K. Tiwary Nilkanta Chakrabarti |
| author_facet | Suvojit Hazra Alok Ghosh Chaudhuri Basant K. Tiwary Nilkanta Chakrabarti |
| author_sort | Suvojit Hazra |
| collection | DOAJ |
| description | Abstract ‘Tripartite network’ (TN) and ‘combined gene network’ (CGN) were constructed and their hub-bottleneck and driver nodes (44 genes) were evaluated as ‘target genes’ (TG) to identify 21 ‘candidate genes’ (CG) and their relationship with neurological manifestations of COVID-19. TN was developed using neurological symptoms of COVID-19 found in literature. Under query genes (TG of TN), co-expressed genes were identified using pair-wise mutual information to genes available in RNA-Seq autopsy data of frontal cortex of COVID-19 victims. CGN was constructed with genes selected from TN and co-expressed in COVID-19. TG and their connecting genes of respective networks underwent functional analyses through findings of their enrichment terms and pair-wise ‘semantic similarity scores’ (SSS). A new integrated ‘weighted harmonic mean score’ was formulated assimilating values of SSS and STRING-based ‘combined score’ of the selected TG-pairs, which provided CG-pairs with properties of CGs as co-expressed and ‘indispensable nodes’ in CGN. Finally, six pairs sharing seven ‘prevalent CGs’ (ADAM10, ADAM17, AKT1, CTNNB1, ESR1, PIK3CA, FGFR1) showed linkages with the phenotypes (a) directly under neurodegeneration, neurodevelopmental diseases, tumour/cancer and cellular signalling, and (b) indirectly through other CGs under behavioural/cognitive and motor dysfunctions. The pathophysiology of ‘prevalent CGs’ has been discussed to interpret neurological phenotypes of COVID-19. |
| first_indexed | 2024-04-13T23:39:29Z |
| format | Article |
| id | doaj.art-060678378c59415aa329b329692bfc35 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T23:39:29Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-060678378c59415aa329b329692bfc352022-12-22T02:24:33ZengNature PortfolioScientific Reports2045-23222022-10-0112111710.1038/s41598-022-21109-3Integrated network-based multiple computational analyses for identification of co-expressed candidate genes associated with neurological manifestations of COVID-19Suvojit Hazra0Alok Ghosh Chaudhuri1Basant K. Tiwary2Nilkanta Chakrabarti3CPEPA-UGC Centre for “Electro-Physiological and Neuro-Imaging Studies Including Mathematical Modelling”, University of CalcuttaDepartment of Physiology, Vidyasagar CollegeDepartment of Bioinformatics, School of Life Sciences, Pondicherry UniversityCPEPA-UGC Centre for “Electro-Physiological and Neuro-Imaging Studies Including Mathematical Modelling”, University of CalcuttaAbstract ‘Tripartite network’ (TN) and ‘combined gene network’ (CGN) were constructed and their hub-bottleneck and driver nodes (44 genes) were evaluated as ‘target genes’ (TG) to identify 21 ‘candidate genes’ (CG) and their relationship with neurological manifestations of COVID-19. TN was developed using neurological symptoms of COVID-19 found in literature. Under query genes (TG of TN), co-expressed genes were identified using pair-wise mutual information to genes available in RNA-Seq autopsy data of frontal cortex of COVID-19 victims. CGN was constructed with genes selected from TN and co-expressed in COVID-19. TG and their connecting genes of respective networks underwent functional analyses through findings of their enrichment terms and pair-wise ‘semantic similarity scores’ (SSS). A new integrated ‘weighted harmonic mean score’ was formulated assimilating values of SSS and STRING-based ‘combined score’ of the selected TG-pairs, which provided CG-pairs with properties of CGs as co-expressed and ‘indispensable nodes’ in CGN. Finally, six pairs sharing seven ‘prevalent CGs’ (ADAM10, ADAM17, AKT1, CTNNB1, ESR1, PIK3CA, FGFR1) showed linkages with the phenotypes (a) directly under neurodegeneration, neurodevelopmental diseases, tumour/cancer and cellular signalling, and (b) indirectly through other CGs under behavioural/cognitive and motor dysfunctions. The pathophysiology of ‘prevalent CGs’ has been discussed to interpret neurological phenotypes of COVID-19.https://doi.org/10.1038/s41598-022-21109-3 |
| spellingShingle | Suvojit Hazra Alok Ghosh Chaudhuri Basant K. Tiwary Nilkanta Chakrabarti Integrated network-based multiple computational analyses for identification of co-expressed candidate genes associated with neurological manifestations of COVID-19 Scientific Reports |
| title | Integrated network-based multiple computational analyses for identification of co-expressed candidate genes associated with neurological manifestations of COVID-19 |
| title_full | Integrated network-based multiple computational analyses for identification of co-expressed candidate genes associated with neurological manifestations of COVID-19 |
| title_fullStr | Integrated network-based multiple computational analyses for identification of co-expressed candidate genes associated with neurological manifestations of COVID-19 |
| title_full_unstemmed | Integrated network-based multiple computational analyses for identification of co-expressed candidate genes associated with neurological manifestations of COVID-19 |
| title_short | Integrated network-based multiple computational analyses for identification of co-expressed candidate genes associated with neurological manifestations of COVID-19 |
| title_sort | integrated network based multiple computational analyses for identification of co expressed candidate genes associated with neurological manifestations of covid 19 |
| url | https://doi.org/10.1038/s41598-022-21109-3 |
| work_keys_str_mv | AT suvojithazra integratednetworkbasedmultiplecomputationalanalysesforidentificationofcoexpressedcandidategenesassociatedwithneurologicalmanifestationsofcovid19 AT alokghoshchaudhuri integratednetworkbasedmultiplecomputationalanalysesforidentificationofcoexpressedcandidategenesassociatedwithneurologicalmanifestationsofcovid19 AT basantktiwary integratednetworkbasedmultiplecomputationalanalysesforidentificationofcoexpressedcandidategenesassociatedwithneurologicalmanifestationsofcovid19 AT nilkantachakrabarti integratednetworkbasedmultiplecomputationalanalysesforidentificationofcoexpressedcandidategenesassociatedwithneurologicalmanifestationsofcovid19 |