Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s disease
Abstract Despite decades of genetic studies on late-onset Alzheimer’s disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets....
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-05-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-023-04791-5 |
| _version_ | 1827943244755894272 |
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| author | Julie P. Merchant Kuixi Zhu Marc Y. R. Henrion Syed S. A. Zaidi Branden Lau Sara Moein Melissa L. Alamprese Richard V. Pearse David A. Bennett Nilüfer Ertekin-Taner Tracy L. Young-Pearse Rui Chang |
| author_facet | Julie P. Merchant Kuixi Zhu Marc Y. R. Henrion Syed S. A. Zaidi Branden Lau Sara Moein Melissa L. Alamprese Richard V. Pearse David A. Bennett Nilüfer Ertekin-Taner Tracy L. Young-Pearse Rui Chang |
| author_sort | Julie P. Merchant |
| collection | DOAJ |
| description | Abstract Despite decades of genetic studies on late-onset Alzheimer’s disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer’s pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer’s-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer’s disease. |
| first_indexed | 2024-03-13T10:12:32Z |
| format | Article |
| id | doaj.art-59af61a1276a4d158efbd9462249793a |
| institution | Directory Open Access Journal |
| issn | 2399-3642 |
| language | English |
| last_indexed | 2024-03-13T10:12:32Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj.art-59af61a1276a4d158efbd9462249793a2023-05-21T11:23:49ZengNature PortfolioCommunications Biology2399-36422023-05-016111910.1038/s42003-023-04791-5Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s diseaseJulie P. Merchant0Kuixi Zhu1Marc Y. R. Henrion2Syed S. A. Zaidi3Branden Lau4Sara Moein5Melissa L. Alamprese6Richard V. Pearse7David A. Bennett8Nilüfer Ertekin-Taner9Tracy L. Young-Pearse10Rui Chang11Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital and Harvard Medical SchoolThe Center for Innovation in Brain Sciences, University of ArizonaLiverpool School of Tropical Medicine, Pembroke PlaceThe Center for Innovation in Brain Sciences, University of ArizonaThe Center for Innovation in Brain Sciences, University of ArizonaThe Center for Innovation in Brain Sciences, University of ArizonaThe Center for Innovation in Brain Sciences, University of ArizonaAnn Romney Center for Neurologic Diseases, Brigham and Women’s Hospital and Harvard Medical SchoolRush Alzheimer’s Disease Center, Rush University Medical CenterDepartment of Neuroscience, Mayo Clinic FloridaAnn Romney Center for Neurologic Diseases, Brigham and Women’s Hospital and Harvard Medical SchoolThe Center for Innovation in Brain Sciences, University of ArizonaAbstract Despite decades of genetic studies on late-onset Alzheimer’s disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer’s pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer’s-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer’s disease.https://doi.org/10.1038/s42003-023-04791-5 |
| spellingShingle | Julie P. Merchant Kuixi Zhu Marc Y. R. Henrion Syed S. A. Zaidi Branden Lau Sara Moein Melissa L. Alamprese Richard V. Pearse David A. Bennett Nilüfer Ertekin-Taner Tracy L. Young-Pearse Rui Chang Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s disease Communications Biology |
| title | Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s disease |
| title_full | Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s disease |
| title_fullStr | Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s disease |
| title_full_unstemmed | Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s disease |
| title_short | Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s disease |
| title_sort | predictive network analysis identifies jmjd6 and other potential key drivers in alzheimer s disease |
| url | https://doi.org/10.1038/s42003-023-04791-5 |
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