Dynamic Regulatory Network Reconstruction for Alzheimer’s Disease Based on Matrix Decomposition Techniques

Kong, Wei; Mou, Xiaoyang; Zhi, Xiefei; Zhang, Xing; Yang, Yang

doi:10.1155/2014/891761

Cited by 14 publications

(13 citation statements)

References 50 publications

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“…In our case, functional recovery would be obtained by expressing an E2F4 form unable to become Thr phosphorylated, thus counteracting the pathological environment that favors E2F4 phosphorylation. In this way, neuronal E2F4DN expression could efficiently target the complex etiology of AD (15)(16)(17)(18), thus becoming a promising molecule for a successful therapy against this devastating disease.…”

Section: Discussionmentioning

confidence: 99%

E2F4 as a single multifactorial target against Alzheimer’s disease

López-Sánchez

Ramón-Landreau

Trujillo

et al. 2020

Preprint

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Alzheimer's disease (AD) has a multifactorial etiology, which requires a single multi-target approach for an efficient treatment. We have focused on E2F4, a transcription factor that regulates cell quiescence and tissue homeostasis, controls gene networks affected in AD, and is upregulated in the brain of Alzheimer's patients and of APP/PS1 and 5xFAD transgenic mice. E2F4 contains an evolutionarily-conserved Thr-motif that, when phosphorylated, modulates its activity, thus constituting a potential target for intervention. Here we show that neuronal expression in 5xFAD mice of a dominant negative form of E2F4 lacking this Thrmotif (E2F4DN) potentiates a transcriptional program consistent with global brain homeostasis. The latter correlates with attenuation of both microglial immune response and astrogliosis, modulation of A proteostasis, and blockade of neuronal tetraploidization. Moreover, E2F4DN prevents cognitive impairment and body weight loss, a known somatic alteration associated with AD. We propose E2F4DN-based gene therapy as a promising multifactorial approach against AD.

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Section: Discussionmentioning

confidence: 99%

E2F4 as a single multifactorial target against Alzheimer’s disease

López-Sánchez

Ramón-Landreau

Trujillo

et al. 2020

Preprint

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“…In order to compare the rules found by GeRNeT with the ones reported in Kong et al (Kong et al, 2014), the first ones were reduced using the same Gene Regulators and Gene Targets as those reported by Kong Figures 13 to 16 contrast the association rules obtained by GeRNeT and Kong et al for each AD dataset. The interactions highlighted in blue corresponds to the rules found by Kong et al that are also inferred by GRNCOP2, without using BiHEA tools.…”

Section: Resultsmentioning

confidence: 99%

“…As stated before, the rules found by GeRNeT were highlighted in different colors according to the source of its finding (see figures 13 to 16). In blue are the rules reported by Kong (Kong et al, 2014) and found by the GRNCOP2 without the integration of the biclustering technique. In orange are the rules also found by Kong and GRNCOP2 hybridized with BiHEA.…”

Section: Biological Relevance Of the Resultsmentioning

confidence: 99%

“…The analysis involved a contrast between the results obtained with the algorithm GRNCOP2 and the results added by the integration of BiHEA using the GeRNeT tool. These results were compared with the ones reported by Kong et al (Kong et al, 2014) using the same datasets. From these studies, it was possible to conclude that the knowledge extracted by BiHEA provides relevant associations that cannot be detected by GRNCOP2 nor by Kong et al In this way, it is evident that the collaboration between two model-free strategies of different characteristics allows to reach GRNs with more information.…”

Section: Discussionmentioning

confidence: 96%

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GeRNet: a gene regulatory network tool

et al. 2017

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Gene regulatory networks (GRNs) are crucial in every process of life since they govern the majority of the molecular processes. Therefore, the task of assembling these networks is highly important. In particular, the so called model-free approaches have an advantage modeling the complexities of dynamic molecular networks, since most of the gene networks are hard to be mapped with accuracy by any other mathematical model. A highly abstract model-free approach, called rule-based approach, offers several advantages performing data-driven analysis; such as the requirement of the least amount of data. They also have an important ability to perform inferences: its simplicity allows the inference of large size models with a higher speed of analysis. However, regarding these techniques, the reconstruction of the relational structure of the network is partial, hence incomplete, for an effective biological analysis. This situation motivated us to explore the possibility of hybridizing with other approaches, such as biclustering techniques. This led to incorporate a biclustering tool that finds new relations between the nodes of the GRN. In this work we present a new software, called GeRNeT that integrates the algorithms of GRNCOP2 and BiHEA along a set of tools for interactive visualization, statistical analysis and ontological enrichment of the resulting GRNs. In this regard, results associated with Alzheimer disease datasets are presented that show the usefulness of integrating both bioinformatics tools.

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“…For example, a cluster analysis of microarray data indicated the association between COPA and AD (Guttula et al, 2012). Dynamic regulatory network reconstruction analysis showed gradually depressed activity of COPA (Kong et al, 2014). Bettayeb et al (2016) highlighted 12 SNPs including rs7531886, rs12033011, rs72868007, rs73022058, rs3132828, rs498872, rs34280607, rs61614746, rs757352, rs9898218, rs7216504, and rs11650615 in COPI genes COPA , COPB1 , COPD/IFT46 , COPD/PHLDB1 , COPZ1 , COPZ2 , and COPZ2/NFE2L1 to be significantly associated with increased AD risk.…”

Section: Discussionmentioning

confidence: 99%

Genetic and Expression Analysis of COPI Genes and Alzheimer’s Disease Susceptibility

Wang

et al. 2019

Front. Genet.

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Alzheimer’s disease (AD) is the most common neurodegenerative disease in the elderly and the leading cause of dementia in humans. Evidence shows that cellular trafficking and recycling machineries are associated with AD risk. A recent study found that the coat protein complex I (COPI)–dependent trafficking in vivo could significantly reduce amyloid plaques in the cortex and hippocampus of neurological in the AD mouse models and identified 12 single-nucleotide polymorphisms in COPI genes to be significantly associated with increased AD risk using 6,795 samples. Here, we used a large-scale GWAS dataset to investigate the potential association between the COPI genes and AD susceptibility by both SNP and gene-based tests. The results showed that only rs9898218 was associated with AD risk with P = 0.017. We further conducted an expression quantitative trait loci (eQTLs) analysis and found that rs9898218 G allele was associated with increased COPZ2 expression in cerebellar cortex with P = 0.0184. Importantly, the eQTLs analysis in whole blood further indicated that 11 of these 12 genetic variants could significantly regulate the expression of COPI genes. Hence, these findings may contribute to understand the association between COPI genes and AD susceptibility.

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Dynamic Regulatory Network Reconstruction for Alzheimer’s Disease Based on Matrix Decomposition Techniques

Cited by 14 publications

References 50 publications

E2F4 as a single multifactorial target against Alzheimer’s disease

E2F4 as a single multifactorial target against Alzheimer’s disease

GeRNet: a gene regulatory network tool

Genetic and Expression Analysis of COPI Genes and Alzheimer’s Disease Susceptibility

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