Global Gene Expression Profiling and Transcription Factor Network Analysis of Cognitive Aging in Monozygotic Twins
Cognitive aging is one of the major problems worldwide, especially as people get older. This study aimed to perform global gene expression profiling of cognitive function to identify associated genes and pathways and a novel transcriptional regulatory network analysis to identify important regulons....
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-06-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.675587/full |
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| author | Afsaneh Mohammadnejad Weilong Li Weilong Li Jesper Beltoft Lund Jesper Beltoft Lund Shuxia Li Martin J. Larsen Martin J. Larsen Jonas Mengel-From Jonas Mengel-From Tanja Maria Michel Tanja Maria Michel Tanja Maria Michel Lene Christiansen Lene Christiansen Kaare Christensen Kaare Christensen Jacob Hjelmborg Jan Baumbach Jan Baumbach Qihua Tan Qihua Tan |
| author_facet | Afsaneh Mohammadnejad Weilong Li Weilong Li Jesper Beltoft Lund Jesper Beltoft Lund Shuxia Li Martin J. Larsen Martin J. Larsen Jonas Mengel-From Jonas Mengel-From Tanja Maria Michel Tanja Maria Michel Tanja Maria Michel Lene Christiansen Lene Christiansen Kaare Christensen Kaare Christensen Jacob Hjelmborg Jan Baumbach Jan Baumbach Qihua Tan Qihua Tan |
| author_sort | Afsaneh Mohammadnejad |
| collection | DOAJ |
| description | Cognitive aging is one of the major problems worldwide, especially as people get older. This study aimed to perform global gene expression profiling of cognitive function to identify associated genes and pathways and a novel transcriptional regulatory network analysis to identify important regulons. We performed single transcript analysis on 400 monozygotic twins using an assumption-free generalized correlation coefficient (GCC), linear mixed-effect model (LME) and kinship model and identified six probes (one significant at the standard FDR < 0.05 while the other results were suggestive with 0.18 ≤ FDR ≤ 0.28). We combined the GCC and linear model results to cover diverse patterns of relationships, and meaningful and novel genes like APOBEC3G, H6PD, SLC45A1, GRIN3B, and PDE4D were detected. Our exploratory study showed the downregulation of all these genes with increasing cognitive function or vice versa except the SLC45A1 gene, which was upregulated with increasing cognitive function. Linear models found only H6PD and SLC45A1, the other genes were captured by GCC. Significant functional pathways (FDR < 3.95e-10) such as focal adhesion, ribosome, cysteine and methionine metabolism, Huntington's disease, eukaryotic translation elongation, nervous system development, influenza infection, metabolism of RNA, and cell cycle were identified. A total of five regulons (FDR< 1.3e-4) were enriched in a transcriptional regulatory analysis in which CTCF and REST were activated and SP3, SRF, and XBP1 were repressed regulons. The genome-wide transcription analysis using both assumption-free GCC and linear models identified important genes and biological pathways implicated in cognitive performance, cognitive aging, and neurological diseases. Also, the regulatory network analysis revealed significant activated and repressed regulons on cognitive function. |
| first_indexed | 2024-12-21T22:00:51Z |
| format | Article |
| id | doaj.art-cf895cc48d0f4db89e1efc6523e8f03d |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-21T22:00:51Z |
| publishDate | 2021-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-cf895cc48d0f4db89e1efc6523e8f03d2022-12-21T18:48:50ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-06-011210.3389/fgene.2021.675587675587Global Gene Expression Profiling and Transcription Factor Network Analysis of Cognitive Aging in Monozygotic TwinsAfsaneh Mohammadnejad0Weilong Li1Weilong Li2Jesper Beltoft Lund3Jesper Beltoft Lund4Shuxia Li5Martin J. Larsen6Martin J. Larsen7Jonas Mengel-From8Jonas Mengel-From9Tanja Maria Michel10Tanja Maria Michel11Tanja Maria Michel12Lene Christiansen13Lene Christiansen14Kaare Christensen15Kaare Christensen16Jacob Hjelmborg17Jan Baumbach18Jan Baumbach19Qihua Tan20Qihua Tan21Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, DenmarkEpidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, DenmarkPopulation Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, FinlandEpidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, DenmarkDigital Health & Machine Learning Research Group, Hasso Plattner Institute for Digital Engineering, Potsdam, GermanyEpidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, DenmarkUnit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, DenmarkDepartment of Clinical Genetics, Odense University Hospital, Odense, DenmarkEpidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, DenmarkUnit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, DenmarkDepartment of Psychiatry, Department of Clinical Research, University of Southern Denmark, Odense, DenmarkPsychiatry in the Region of Southern Denmark, Odense University Hospital, Odense, DenmarkBrain Research—Inter-Disciplinary Guided Excellence, Department of Clinical Research, University of Southern Denmark, Odense, DenmarkEpidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, DenmarkDepartment of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, DenmarkEpidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, DenmarkUnit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, DenmarkEpidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark0Computational Biomedicine, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark1Chair of Computational Systems Biology, University of Hamburg, Hamburg, GermanyEpidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, DenmarkUnit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, DenmarkCognitive aging is one of the major problems worldwide, especially as people get older. This study aimed to perform global gene expression profiling of cognitive function to identify associated genes and pathways and a novel transcriptional regulatory network analysis to identify important regulons. We performed single transcript analysis on 400 monozygotic twins using an assumption-free generalized correlation coefficient (GCC), linear mixed-effect model (LME) and kinship model and identified six probes (one significant at the standard FDR < 0.05 while the other results were suggestive with 0.18 ≤ FDR ≤ 0.28). We combined the GCC and linear model results to cover diverse patterns of relationships, and meaningful and novel genes like APOBEC3G, H6PD, SLC45A1, GRIN3B, and PDE4D were detected. Our exploratory study showed the downregulation of all these genes with increasing cognitive function or vice versa except the SLC45A1 gene, which was upregulated with increasing cognitive function. Linear models found only H6PD and SLC45A1, the other genes were captured by GCC. Significant functional pathways (FDR < 3.95e-10) such as focal adhesion, ribosome, cysteine and methionine metabolism, Huntington's disease, eukaryotic translation elongation, nervous system development, influenza infection, metabolism of RNA, and cell cycle were identified. A total of five regulons (FDR< 1.3e-4) were enriched in a transcriptional regulatory analysis in which CTCF and REST were activated and SP3, SRF, and XBP1 were repressed regulons. The genome-wide transcription analysis using both assumption-free GCC and linear models identified important genes and biological pathways implicated in cognitive performance, cognitive aging, and neurological diseases. Also, the regulatory network analysis revealed significant activated and repressed regulons on cognitive function.https://www.frontiersin.org/articles/10.3389/fgene.2021.675587/fullcognitive agingtranscriptgeneralized correlation coefficientlinear regressiontwinsregulons |
| spellingShingle | Afsaneh Mohammadnejad Weilong Li Weilong Li Jesper Beltoft Lund Jesper Beltoft Lund Shuxia Li Martin J. Larsen Martin J. Larsen Jonas Mengel-From Jonas Mengel-From Tanja Maria Michel Tanja Maria Michel Tanja Maria Michel Lene Christiansen Lene Christiansen Kaare Christensen Kaare Christensen Jacob Hjelmborg Jan Baumbach Jan Baumbach Qihua Tan Qihua Tan Global Gene Expression Profiling and Transcription Factor Network Analysis of Cognitive Aging in Monozygotic Twins Frontiers in Genetics cognitive aging transcript generalized correlation coefficient linear regression twins regulons |
| title | Global Gene Expression Profiling and Transcription Factor Network Analysis of Cognitive Aging in Monozygotic Twins |
| title_full | Global Gene Expression Profiling and Transcription Factor Network Analysis of Cognitive Aging in Monozygotic Twins |
| title_fullStr | Global Gene Expression Profiling and Transcription Factor Network Analysis of Cognitive Aging in Monozygotic Twins |
| title_full_unstemmed | Global Gene Expression Profiling and Transcription Factor Network Analysis of Cognitive Aging in Monozygotic Twins |
| title_short | Global Gene Expression Profiling and Transcription Factor Network Analysis of Cognitive Aging in Monozygotic Twins |
| title_sort | global gene expression profiling and transcription factor network analysis of cognitive aging in monozygotic twins |
| topic | cognitive aging transcript generalized correlation coefficient linear regression twins regulons |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.675587/full |
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