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Association between gene expression and functional‐metabolic architecture in Parkinson's disease

Gene expression plays a critical role in the pathogenesis of Parkinson's disease (PD). How gene expression profiles are correlated with functional‐metabolic architecture remains obscure. We enrolled 34 PD patients and 25 age‐and‐sex‐matched healthy controls for simultaneous (18)F‐FDG‐PET/functi...

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Autores principales: Zang, Zhenxiang, Zhang, Xiaolong, Song, Tianbin, Li, Jiping, Nie, Binbin, Mei, Shanshan, Hu, Zhi'an, Zhang, Yuqing, Lu, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543112/
https://www.ncbi.nlm.nih.gov/pubmed/37605831
http://dx.doi.org/10.1002/hbm.26443
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author Zang, Zhenxiang
Zhang, Xiaolong
Song, Tianbin
Li, Jiping
Nie, Binbin
Mei, Shanshan
Hu, Zhi'an
Zhang, Yuqing
Lu, Jie
author_facet Zang, Zhenxiang
Zhang, Xiaolong
Song, Tianbin
Li, Jiping
Nie, Binbin
Mei, Shanshan
Hu, Zhi'an
Zhang, Yuqing
Lu, Jie
author_sort Zang, Zhenxiang
collection PubMed
description Gene expression plays a critical role in the pathogenesis of Parkinson's disease (PD). How gene expression profiles are correlated with functional‐metabolic architecture remains obscure. We enrolled 34 PD patients and 25 age‐and‐sex‐matched healthy controls for simultaneous (18)F‐FDG‐PET/functional MRI scanning during resting state. We investigated the functional gradients and the ratio of standard uptake value. Principal component analysis was used to further combine the functional gradients and glucose metabolism into functional‐metabolic architecture. Using partial least squares (PLS) regression, we introduced the transcriptomic data from the Allen Institute of Brain Sciences to identify gene expression patterns underlying the affected functional‐metabolic architecture in PD. Between‐group comparisons revealed significantly higher gradient variation in the visual, somatomotor, dorsal attention, frontoparietal, default mode, and subcortical network (p (FDR) < .048) in PD. Increased FDG‐uptake was found in the somatomotor and ventral attention network while decreased FDG‐uptake was found in the visual network (p (FDR) < .008). Spatial correlation analysis showed consistently affected patterns of functional gradients and metabolism (p = 2.47 × 10(−8)). PLS analysis and gene ontological analyses further revealed that genes were mainly enriched for metabolic, catabolic, cellular response to ions, and regulation of DNA transcription and RNA biosynthesis. In conclusion, our study provided genetic pathological mechanism to explain imaging‐defined brain functional‐metabolic architecture of PD.
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spelling pubmed-105431122023-10-03 Association between gene expression and functional‐metabolic architecture in Parkinson's disease Zang, Zhenxiang Zhang, Xiaolong Song, Tianbin Li, Jiping Nie, Binbin Mei, Shanshan Hu, Zhi'an Zhang, Yuqing Lu, Jie Hum Brain Mapp Research Articles Gene expression plays a critical role in the pathogenesis of Parkinson's disease (PD). How gene expression profiles are correlated with functional‐metabolic architecture remains obscure. We enrolled 34 PD patients and 25 age‐and‐sex‐matched healthy controls for simultaneous (18)F‐FDG‐PET/functional MRI scanning during resting state. We investigated the functional gradients and the ratio of standard uptake value. Principal component analysis was used to further combine the functional gradients and glucose metabolism into functional‐metabolic architecture. Using partial least squares (PLS) regression, we introduced the transcriptomic data from the Allen Institute of Brain Sciences to identify gene expression patterns underlying the affected functional‐metabolic architecture in PD. Between‐group comparisons revealed significantly higher gradient variation in the visual, somatomotor, dorsal attention, frontoparietal, default mode, and subcortical network (p (FDR) < .048) in PD. Increased FDG‐uptake was found in the somatomotor and ventral attention network while decreased FDG‐uptake was found in the visual network (p (FDR) < .008). Spatial correlation analysis showed consistently affected patterns of functional gradients and metabolism (p = 2.47 × 10(−8)). PLS analysis and gene ontological analyses further revealed that genes were mainly enriched for metabolic, catabolic, cellular response to ions, and regulation of DNA transcription and RNA biosynthesis. In conclusion, our study provided genetic pathological mechanism to explain imaging‐defined brain functional‐metabolic architecture of PD. John Wiley & Sons, Inc. 2023-08-22 /pmc/articles/PMC10543112/ /pubmed/37605831 http://dx.doi.org/10.1002/hbm.26443 Text en © 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Zang, Zhenxiang
Zhang, Xiaolong
Song, Tianbin
Li, Jiping
Nie, Binbin
Mei, Shanshan
Hu, Zhi'an
Zhang, Yuqing
Lu, Jie
Association between gene expression and functional‐metabolic architecture in Parkinson's disease
title Association between gene expression and functional‐metabolic architecture in Parkinson's disease
title_full Association between gene expression and functional‐metabolic architecture in Parkinson's disease
title_fullStr Association between gene expression and functional‐metabolic architecture in Parkinson's disease
title_full_unstemmed Association between gene expression and functional‐metabolic architecture in Parkinson's disease
title_short Association between gene expression and functional‐metabolic architecture in Parkinson's disease
title_sort association between gene expression and functional‐metabolic architecture in parkinson's disease
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543112/
https://www.ncbi.nlm.nih.gov/pubmed/37605831
http://dx.doi.org/10.1002/hbm.26443
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