PP2A subunit PPP2R2C is downregulated in the brains of Alzheimer’s transgenic mice

Leong, Waiian; Xu, Wei; Wang, Bo; Gao, Shuaiyun; Zhai, Xiao; Wang, Cuicui; Gilson, Éric; Ye, Jing; Lu, Yiming

doi:10.18632/aging.103048

Cited by 12 publications

(4 citation statements)

References 20 publications

(24 reference statements)

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“…Interestingly, our data showed a marked correlation between increased phosphorylation of AMPK and decreased protein expression of PP2A, a phosphatase with a broad spectrum of substrates including AMPK and tau (Martin et al, 2013;Salminen et al, 2016). While much efforts have been dedicated to therapeutic strategies targeting kinases, roles of phosphatases (particularly PP2A) in AD etiology have drawn a lot of attention recently (Leong et al, 2020;Nicholls et al, 2016;Torrent and Ferrer, 2012;Wei et al, 2020).…”

Section: Discussionmentioning

confidence: 70%

Characterization of early Alzheimer’s-like pathological alterations in non-human primates with aging: a pilot study

Jester

Gosrani

Ding

et al. 2021

Preprint

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Sporadic or late onset Alzheimer's disease (LOAD) is a multifactorial neurodegenerative disease with aging the most known risk factor. Non-human primates (NHPs) may serve as an excellent model to study LOAD because of their close similarity to humans in many aspects including neuroanatomy and neurodevelopment. Recent studies reveal AD-like pathology in old NHPs. In this pilot study, we took advantage of brain samples from 6 Cynomolgus macaques that were divided into two groups: middle aged (average age 14.81 years) and older (average age 19.33 years). We found multiple AD-like pathological alteration in the prefrontal cortex (but not in the hippocampus) of the older NHPs including tau hyperphosphorylation, increased activity of AMP-activated protein kinase (AMPK), decreased expression of protein phosphatase 2A (PP2A), impairments in mitochondrial morphology and postsynaptic densities (PSDs) formation. These findings may provide insights into the factors contributing to the development of LOAD, particularly during the early stage transitioning from middle to old age. Future endeavors are warranted to elucidate mechanisms underlying the regional (and perhaps cellular) vulnerability with aging and the functional correlation of such pathological changes in NHPs.

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Section: Discussionmentioning

confidence: 70%

Characterization of early Alzheimer’s-like pathological alterations in non-human primates with aging: a pilot study

Jester

Gosrani

Ding

et al. 2021

Preprint

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“…In another two imaging genetics studies, EOMES and RGS6 were also identified as risk genes [40][41]. The unregulated expression of PPP2R2C may be related to the onset of AD [42]. The amyloid precursor protein (APP) plays a central role in AD, and CASK is an interactor of the APP intracellular domain (AICD) [43].…”

Section: Results On Adni Databasementioning

confidence: 99%

Integration of Imaging Genomics Data for the Study of Alzheimer's Disease Using Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization

2021

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Image genetics reveal the connection between microscopic genetics and macroscopic imaging and then detect diseases' biomarkers. In this research, we make full use of the prior knowledge that has significant reference value for exploring the correlation of brain and genetics to explore more biologically substantial biomarkers. In this paper, we propose Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization (JCB-SNMF). The algorithm simultaneously projects structural Magnetic Resonance Imaging (sMRI), Single Nucleotide Polymorphism sites (SNPs), and gene expression data onto a common feature space, where heterogeneous variables with large coefficients in the same projection direction form a common module. In addition, the connectivity information of each region of the brain and genetic data are added as prior knowledge to identify regions of interest (ROI), SNP, and gene-related risks related to Alzheimer's disease (AD) patients. GraphNet regularization increases the anti-noise performance of the algorithm and the biological interpretability of the results. The simulation results show that compared with other NMF-based algorithms (JNMF, JSNMNMF), JCB-SNMF has better anti-noise performance and can identify and predict biomarkers closely related to AD from significant modules. By constructing a proteinprotein interaction network (PPI), we identified SF3B1, RPS20, and RBM14 as potential biomarkers of AD. We also find some significant SNP-ROI and Gene-ROI pairs. Among them, two SNPs of rs4472239 and rs11918049 and three genes of KLHL8, ZC3H11A, and OSGEPL1 may have effects on the gray matter volume of multiple brain regions. This model provides a new way to further integrate multimodal impact genetic data to identify complex disease association patterns.

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“…In another two imaging genetics studies, EOMES and RGS6 were also identified as risk genes [40] [41]. The unregulated expression of PPP2R2C may be related to the onset of AD [42]. The amyloid precursor protein (APP) plays a central role in AD, and CASK is an interactor of the APP intracellular domain (AICD) [43].…”

Section: Biological Significancementioning

confidence: 99%

Integration of Imaging Genomics Data for the Study of Alzheimer's Disease Using Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization

Wei

Kong

Wang

2021

Preprint

View full text Add to dashboard Cite

Image genetics reveal the connection between microscopic genetics and macroscopic imaging and then detect diseases’ biomarkers. In this research, we make full use of the prior knowledge that has significant reference value for exploring the correlation of brain and genetics to explore more biologically substantial biomarkers. In this paper, we propose Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization (JCB-SNMF). The algorithm simultaneously projects structural Magnetic Resonance Imaging (sMRI), Single Nucleotide Polymorphism sites (SNPs), and gene expression data onto a common feature space, where heterogeneous variables with large coefficients in the same projection direction form a common module. In addition, the connectivity information of each region of the brain and genetic data are added as prior knowledge to identify regions of interest (ROI), SNP, and gene-related risks related to Alzheimer's disease (AD) patients. GraphNet regularization increases the anti-noise performance of the algorithm and the biological interpretability of the results. The simulation results show that compared with other NMF-based algorithms (JNMF, JSNMNMF), JCB-SNMF has better anti-noise performance and can identify and predict biomarkers closely related to AD from significant modules. By constructing a protein-protein interaction network (PPI), we identified SF3B1, RPS20, and RBM14 as potential biomarkers of AD. We also find some significant SNP-ROI and Gene-ROI pairs. Among them, two SNPs of rs4472239 and rs11918049 and three genes of KLHL8, ZC3H11A, and OSGEPL1 may have effects on the gray matter volume of multiple brain regions. This model provides a new way to further integrate multimodal impact genetic data to identify complex disease association patterns.

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PP2A subunit PPP2R2C is downregulated in the brains of Alzheimer’s transgenic mice

Cited by 12 publications

References 20 publications

Characterization of early Alzheimer’s-like pathological alterations in non-human primates with aging: a pilot study

Characterization of early Alzheimer’s-like pathological alterations in non-human primates with aging: a pilot study

Integration of Imaging Genomics Data for the Study of Alzheimer's Disease Using Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization

Integration of Imaging Genomics Data for the Study of Alzheimer's Disease Using Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization

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