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Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer’s disease
BACKGROUND: The human brain is complex and interconnected structurally. Brain connectome change is associated with Alzheimer’s disease (AD) and other neurodegenerative diseases. Genetics and genomics studies have identified molecular changes in AD; however, the results are often limited to isolated...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7003435/ https://www.ncbi.nlm.nih.gov/pubmed/32024511 http://dx.doi.org/10.1186/s12916-019-1488-1 |
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author | Woo, Young Jae Roussos, Panos Haroutunian, Vahram Katsel, Pavel Gandy, Samuel Schadt, Eric E. Zhu, Jun |
author_facet | Woo, Young Jae Roussos, Panos Haroutunian, Vahram Katsel, Pavel Gandy, Samuel Schadt, Eric E. Zhu, Jun |
author_sort | Woo, Young Jae |
collection | PubMed |
description | BACKGROUND: The human brain is complex and interconnected structurally. Brain connectome change is associated with Alzheimer’s disease (AD) and other neurodegenerative diseases. Genetics and genomics studies have identified molecular changes in AD; however, the results are often limited to isolated brain regions and are difficult to interpret its findings in respect to brain connectome. The mechanisms of how one brain region impacts the molecular pathways in other regions have not been systematically studied. And how the brain regions susceptible to AD pathology interact with each other at the transcriptome level and how these interactions relate to brain connectome change are unclear. METHODS: Here, we compared structural brain connectomes defined by probabilistic tracts using diffusion magnetic resonance imaging data in Alzheimer’s Disease Neuroimaging Initiative database and a brain transcriptome dataset covering 17 brain regions. RESULTS: We observed that the changes in diffusion measures associated with AD diagnosis status and the associations were replicated in an independent cohort. The result suggests that disease associated white matter changes are focal. Analysis of the brain connectome by genomic data, tissue-tissue transcriptional synchronization between 17 brain regions, indicates that the regions connected by AD-associated tracts were likely connected at the transcriptome level with high number of tissue-to-tissue correlated (TTC) gene pairs (P = 0.03). And genes involved in TTC gene pairs between white matter tract connected brain regions were enriched in signaling pathways (P = 6.08 × 10(−9)). Further pathway interaction analysis identified ionotropic glutamate receptor pathway and Toll receptor signaling pathways to be important for tissue-tissue synchronization at the transcriptome level. Transcript profile entailing Toll receptor signaling in the blood was significantly associated with diffusion properties of white matter tracts, notable association between fractional anisotropy and bilateral cingulum angular bundles (P(permutation) = 1.0 × 10(−2) and 4.9 × 10(−4) for left and right respectively). CONCLUSIONS: In summary, our study suggests that brain connectomes defined by MRI and transcriptome data overlap with each other. |
format | Online Article Text |
id | pubmed-7003435 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-70034352020-02-10 Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer’s disease Woo, Young Jae Roussos, Panos Haroutunian, Vahram Katsel, Pavel Gandy, Samuel Schadt, Eric E. Zhu, Jun BMC Med Research Article BACKGROUND: The human brain is complex and interconnected structurally. Brain connectome change is associated with Alzheimer’s disease (AD) and other neurodegenerative diseases. Genetics and genomics studies have identified molecular changes in AD; however, the results are often limited to isolated brain regions and are difficult to interpret its findings in respect to brain connectome. The mechanisms of how one brain region impacts the molecular pathways in other regions have not been systematically studied. And how the brain regions susceptible to AD pathology interact with each other at the transcriptome level and how these interactions relate to brain connectome change are unclear. METHODS: Here, we compared structural brain connectomes defined by probabilistic tracts using diffusion magnetic resonance imaging data in Alzheimer’s Disease Neuroimaging Initiative database and a brain transcriptome dataset covering 17 brain regions. RESULTS: We observed that the changes in diffusion measures associated with AD diagnosis status and the associations were replicated in an independent cohort. The result suggests that disease associated white matter changes are focal. Analysis of the brain connectome by genomic data, tissue-tissue transcriptional synchronization between 17 brain regions, indicates that the regions connected by AD-associated tracts were likely connected at the transcriptome level with high number of tissue-to-tissue correlated (TTC) gene pairs (P = 0.03). And genes involved in TTC gene pairs between white matter tract connected brain regions were enriched in signaling pathways (P = 6.08 × 10(−9)). Further pathway interaction analysis identified ionotropic glutamate receptor pathway and Toll receptor signaling pathways to be important for tissue-tissue synchronization at the transcriptome level. Transcript profile entailing Toll receptor signaling in the blood was significantly associated with diffusion properties of white matter tracts, notable association between fractional anisotropy and bilateral cingulum angular bundles (P(permutation) = 1.0 × 10(−2) and 4.9 × 10(−4) for left and right respectively). CONCLUSIONS: In summary, our study suggests that brain connectomes defined by MRI and transcriptome data overlap with each other. BioMed Central 2020-02-06 /pmc/articles/PMC7003435/ /pubmed/32024511 http://dx.doi.org/10.1186/s12916-019-1488-1 Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Woo, Young Jae Roussos, Panos Haroutunian, Vahram Katsel, Pavel Gandy, Samuel Schadt, Eric E. Zhu, Jun Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer’s disease |
title | Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer’s disease |
title_full | Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer’s disease |
title_fullStr | Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer’s disease |
title_full_unstemmed | Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer’s disease |
title_short | Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer’s disease |
title_sort | comparison of brain connectomes by mri and genomics and its implication in alzheimer’s disease |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7003435/ https://www.ncbi.nlm.nih.gov/pubmed/32024511 http://dx.doi.org/10.1186/s12916-019-1488-1 |
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