Using literature-based discovery to identify candidate genes for the interaction between myocardial infarction and depression
Abstract Background A multidirectional relationship has been demonstrated between myocardial infarction (MI) and depression. However, the causal genetic factors and molecular mechanisms underlying this interaction remain unclear. The main purpose of this study was to identify potential candidate gen...
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| Format: | Article |
| Language: | English |
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BMC
2019-06-01
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| Series: | BMC Medical Genetics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12881-019-0841-8 |
| _version_ | 1818430227425001472 |
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| author | Zhenguo Dai Qian Li Guang Yang Yini Wang Yang Liu Zhilei Zheng Yingfeng Tu Shuang Yang Bo Yu |
| author_facet | Zhenguo Dai Qian Li Guang Yang Yini Wang Yang Liu Zhilei Zheng Yingfeng Tu Shuang Yang Bo Yu |
| author_sort | Zhenguo Dai |
| collection | DOAJ |
| description | Abstract Background A multidirectional relationship has been demonstrated between myocardial infarction (MI) and depression. However, the causal genetic factors and molecular mechanisms underlying this interaction remain unclear. The main purpose of this study was to identify potential candidate genes for the interaction between the two diseases. Methods Using a bioinformatics approach and existing gene expression data in the biomedical discovery support system (BITOLA), we defined the starting concept X as “Myocardial Infarction” and end concept Z as “Major Depressive Disorder” or “Depressive disorder”. All intermediate concepts relevant to the “Gene or Gene Product” for MI and depression were searched. Gene expression data and tissue-specific expression of potential candidate genes were evaluated using the Human eFP (electronic Fluorescent Pictograph) Browser, and intermediate concepts were filtered by manual inspection. Results Our analysis identified 128 genes common to both the “MI” and “depression” text mining concepts. Twenty-three of the 128 genes were selected as intermediates for this study, 9 of which passed the manual filtering step. Among the 9 genes, LCAT, CD4, SERPINA1, IL6, and PPBP failed to pass the follow-up filter in the Human eFP Browser, due to their low levels in the heart tissue. Finally, four genes (GNB3, CNR1, MTHFR, and NCAM1) remained. Conclusions GNB3, CNR1, MTHFR, and NCAM1 are putative new candidate genes that may influence the interactions between MI and depression, and may represent potential targets for therapeutic intervention. |
| first_indexed | 2024-12-14T15:30:04Z |
| format | Article |
| id | doaj.art-be26d200d74b423c82c1ea215595c0f8 |
| institution | Directory Open Access Journal |
| issn | 1471-2350 |
| language | English |
| last_indexed | 2024-12-14T15:30:04Z |
| publishDate | 2019-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genetics |
| spelling | doaj.art-be26d200d74b423c82c1ea215595c0f82022-12-21T22:55:55ZengBMCBMC Medical Genetics1471-23502019-06-0120111010.1186/s12881-019-0841-8Using literature-based discovery to identify candidate genes for the interaction between myocardial infarction and depressionZhenguo Dai0Qian Li1Guang Yang2Yini Wang3Yang Liu4Zhilei Zheng5Yingfeng Tu6Shuang Yang7Bo Yu8Department of Cardiology, The Second Affiliated Hospital of Harbin Medical UniversityDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical UniversityDepartment of Cardiology, The Second Affiliated Hospital of Harbin Medical UniversityDepartment of Cardiology, The Second Affiliated Hospital of Harbin Medical UniversityDepartment of Cardiology, The Second Affiliated Hospital of Harbin Medical UniversityDepartment of Cardiology, The Second Affiliated Hospital of Harbin Medical UniversityDepartment of Cardiology, The Second Affiliated Hospital of Harbin Medical UniversityDepartment of Cardiology, The Second Affiliated Hospital of Harbin Medical UniversityDepartment of Cardiology, The Second Affiliated Hospital of Harbin Medical UniversityAbstract Background A multidirectional relationship has been demonstrated between myocardial infarction (MI) and depression. However, the causal genetic factors and molecular mechanisms underlying this interaction remain unclear. The main purpose of this study was to identify potential candidate genes for the interaction between the two diseases. Methods Using a bioinformatics approach and existing gene expression data in the biomedical discovery support system (BITOLA), we defined the starting concept X as “Myocardial Infarction” and end concept Z as “Major Depressive Disorder” or “Depressive disorder”. All intermediate concepts relevant to the “Gene or Gene Product” for MI and depression were searched. Gene expression data and tissue-specific expression of potential candidate genes were evaluated using the Human eFP (electronic Fluorescent Pictograph) Browser, and intermediate concepts were filtered by manual inspection. Results Our analysis identified 128 genes common to both the “MI” and “depression” text mining concepts. Twenty-three of the 128 genes were selected as intermediates for this study, 9 of which passed the manual filtering step. Among the 9 genes, LCAT, CD4, SERPINA1, IL6, and PPBP failed to pass the follow-up filter in the Human eFP Browser, due to their low levels in the heart tissue. Finally, four genes (GNB3, CNR1, MTHFR, and NCAM1) remained. Conclusions GNB3, CNR1, MTHFR, and NCAM1 are putative new candidate genes that may influence the interactions between MI and depression, and may represent potential targets for therapeutic intervention.http://link.springer.com/article/10.1186/s12881-019-0841-8Myocardial infarctionDepressionBITOLACandidate genesText miningGene expression profiling |
| spellingShingle | Zhenguo Dai Qian Li Guang Yang Yini Wang Yang Liu Zhilei Zheng Yingfeng Tu Shuang Yang Bo Yu Using literature-based discovery to identify candidate genes for the interaction between myocardial infarction and depression BMC Medical Genetics Myocardial infarction Depression BITOLA Candidate genes Text mining Gene expression profiling |
| title | Using literature-based discovery to identify candidate genes for the interaction between myocardial infarction and depression |
| title_full | Using literature-based discovery to identify candidate genes for the interaction between myocardial infarction and depression |
| title_fullStr | Using literature-based discovery to identify candidate genes for the interaction between myocardial infarction and depression |
| title_full_unstemmed | Using literature-based discovery to identify candidate genes for the interaction between myocardial infarction and depression |
| title_short | Using literature-based discovery to identify candidate genes for the interaction between myocardial infarction and depression |
| title_sort | using literature based discovery to identify candidate genes for the interaction between myocardial infarction and depression |
| topic | Myocardial infarction Depression BITOLA Candidate genes Text mining Gene expression profiling |
| url | http://link.springer.com/article/10.1186/s12881-019-0841-8 |
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