Cross study transcriptomic investigation of Alzheimer’s brain tissue discoveries and limitations
Abstract Developing effective treatments for Alzheimer’s disease (AD) likely requires a deep understanding of molecular mechanisms. Integration of transcriptomic datasets and developing innovative computational analyses may yield novel molecular targets with broad applicability. The motivation for t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01017-y |
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| author | Fernando Koiti Tsurukawa Yixiang Mao Cesar Sanchez-Villalobos Nishtha Khanna Chiquito J. Crasto J. Josh Lawrence Ranadip Pal |
| author_facet | Fernando Koiti Tsurukawa Yixiang Mao Cesar Sanchez-Villalobos Nishtha Khanna Chiquito J. Crasto J. Josh Lawrence Ranadip Pal |
| author_sort | Fernando Koiti Tsurukawa |
| collection | DOAJ |
| description | Abstract Developing effective treatments for Alzheimer’s disease (AD) likely requires a deep understanding of molecular mechanisms. Integration of transcriptomic datasets and developing innovative computational analyses may yield novel molecular targets with broad applicability. The motivation for this study was conceived from two main observations: (a) most transcriptomic analyses of AD data consider univariate differential expression analysis, and (b) insights are often not transferable across studies. We designed a machine learning-based framework that can elucidate interpretable multivariate relationships from multiple human AD studies to discover robust transcriptomic AD biomarkers transferable across multiple studies. Our analysis of three human hippocampus datasets revealed multiple robust synergistic associations from unrelated pathways along with inconsistencies of gene associations across different studies. Our study underscores the utility of developing AI-assisted next-gen metrics for integration, robustness, and generalization and also highlights the potential benefit of elucidating molecular mechanisms and pathways that are important in targeting a single population. |
| format | Article |
| id | doaj-art-9a3699e840c74200a0a355d4a86b354a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9a3699e840c74200a0a355d4a86b354a2025-08-20T03:53:12ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-01017-yCross study transcriptomic investigation of Alzheimer’s brain tissue discoveries and limitationsFernando Koiti Tsurukawa0Yixiang Mao1Cesar Sanchez-Villalobos2Nishtha Khanna3Chiquito J. Crasto4J. Josh Lawrence5Ranadip Pal6Department of Electrical and Computer Engineering, Texas Tech UniversityDepartment of Electrical and Computer Engineering, Texas Tech UniversityDepartment of Electrical and Computer Engineering, Texas Tech UniversityCenter for Biotechnology and Genomics, Texas Tech UniversityCenter for Biotechnology and Genomics, Texas Tech UniversityDepartment of Pharmacology and Neuroscience, Garrison Institute on Aging, Center of Excellence for Translational Neuroscience and Therapeutics, and Center of Excellence in Integrative Health, Texas Tech University Health Sciences CenterDepartment of Electrical and Computer Engineering, Texas Tech UniversityAbstract Developing effective treatments for Alzheimer’s disease (AD) likely requires a deep understanding of molecular mechanisms. Integration of transcriptomic datasets and developing innovative computational analyses may yield novel molecular targets with broad applicability. The motivation for this study was conceived from two main observations: (a) most transcriptomic analyses of AD data consider univariate differential expression analysis, and (b) insights are often not transferable across studies. We designed a machine learning-based framework that can elucidate interpretable multivariate relationships from multiple human AD studies to discover robust transcriptomic AD biomarkers transferable across multiple studies. Our analysis of three human hippocampus datasets revealed multiple robust synergistic associations from unrelated pathways along with inconsistencies of gene associations across different studies. Our study underscores the utility of developing AI-assisted next-gen metrics for integration, robustness, and generalization and also highlights the potential benefit of elucidating molecular mechanisms and pathways that are important in targeting a single population.https://doi.org/10.1038/s41598-025-01017-yAlzheimer’s diseaseMultivariate analysisMachine learningRNA sequencingTranscriptomicsKCNIP1 |
| spellingShingle | Fernando Koiti Tsurukawa Yixiang Mao Cesar Sanchez-Villalobos Nishtha Khanna Chiquito J. Crasto J. Josh Lawrence Ranadip Pal Cross study transcriptomic investigation of Alzheimer’s brain tissue discoveries and limitations Scientific Reports Alzheimer’s disease Multivariate analysis Machine learning RNA sequencing Transcriptomics KCNIP1 |
| title | Cross study transcriptomic investigation of Alzheimer’s brain tissue discoveries and limitations |
| title_full | Cross study transcriptomic investigation of Alzheimer’s brain tissue discoveries and limitations |
| title_fullStr | Cross study transcriptomic investigation of Alzheimer’s brain tissue discoveries and limitations |
| title_full_unstemmed | Cross study transcriptomic investigation of Alzheimer’s brain tissue discoveries and limitations |
| title_short | Cross study transcriptomic investigation of Alzheimer’s brain tissue discoveries and limitations |
| title_sort | cross study transcriptomic investigation of alzheimer s brain tissue discoveries and limitations |
| topic | Alzheimer’s disease Multivariate analysis Machine learning RNA sequencing Transcriptomics KCNIP1 |
| url | https://doi.org/10.1038/s41598-025-01017-y |
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