scholarly journals The human brainome: network analysis identifies HSPA2 as a novel Alzheimer’s disease target

Brain ◽  
2018 ◽  
Author(s):  
Vladislav A Petyuk ◽  
Rui Chang ◽  
Manuel Ramirez-Restrepo ◽  
Noam D Beckmann ◽  
Marc Y R Henrion ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis

Co-expression Network Analysis Revealing the Potential Regulatory Roles of lncRNAs in Alzheimer’s Disease

2019 ◽  
Vol 11 (4) ◽  
pp. 645-654 ◽  
Author(s):  
Jiong Wu ◽  
Linhui Chen ◽  
Chaobo Zheng ◽  
Shanhu Xu ◽  
Yuhai Gao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis

Associations Between Obesity and Alzheimer’s Disease: Multiple Bioinformatic Analyses

2021 ◽  
pp. 1-11
Author(s):  
Qi-Shuai Zhuang ◽  
Lei Meng ◽  
Zhe Wang ◽  
Liang Shen ◽  
Hong-Fang Ji
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Drug Targets ◽  
Protective Factor ◽  
Genetic Locus ◽  
Meta Analysis ◽  
Current Evidence ◽  
Causal Association ◽  
Ppi Network

Background: Identifying modifiable risk factors, such as obesity, to lower the prevalence of Alzheimer’s disease (AD) has gained much interest. However, whether the association is causal remains to be evaluated. Objective: The present study was designed: 1) to make a quantitative assessment of the association between obesity and AD; 2) to validate whether there was a causal association between them; and 3) to provide genetic clues for the association through a network-based analysis. Methods: Two-sample Mendelian randomization (2SMR) analysis, meta-analysis, and protein-protein interaction (PPI) network analysis, were employed. Results: Firstly, the meta-analysis based on 9 studies comprising 6,986,436 subjects indicated that midlife obesity had 33%higher AD odds than controls (OR = 1.33, 95%CI = [1.03, 1.62]), while late-life obesity were inversely associated with AD risk (OR = 0.57, 95%CI = [0.47, 0.68]). Secondly, 2SMR analysis indicated that there was no causal association between them. Thirdly, CARTPT was identified to be shared by the anti-obesity drug targets and AD susceptibility genes. Further PPI network analysis found that CARTPT interacted with CD33, a strong genetic locus linked to AD. Finally, 2SMR analysis showed that CNR1 could be a protective factor for AD. Conclusion: Multiple bioinformatic analyses indicated that midlife obesity might increase the risk of AD, while current evidence indicated that there was no causal association between them. Further, CARTPT might be an important factor linking the two disease conditions. It could help to better understand the mechanisms underlying the associations between obesity and AD.

scholarly journals Integrated proteomics and network analysis identifies protein hubs and network alterations in Alzheimer’s disease

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Qi Zhang ◽  
Cheng Ma ◽  
Marla Gearing ◽  
Peng George Wang ◽  
Lih-Shen Chin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis

scholarly journals Metabolic Network Analysis Reveals Altered Bile Acid Synthesis and Metabolism in Alzheimer’s Disease

2020 ◽  
Vol 1 (8) ◽  
pp. 100138
Author(s):  
Priyanka Baloni ◽  
Cory C. Funk ◽  
Jingwen Yan ◽  
James T. Yurkovich ◽  
Alexandra Kueider-Paisley ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Bile Acid ◽  
Network Analysis ◽  
Metabolic Network ◽  
Acid Synthesis ◽  
Bile Acid Synthesis ◽  
Metabolic Network Analysis

scholarly journals Network Analysis of a Membrane-Enriched Brain Proteome across Stages of Alzheimer’s Disease

Proteomes ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 30 ◽  
Author(s):  
Lenora Higginbotham ◽  
Eric Dammer ◽  
Duc Duong ◽  
Erica Modeste ◽  
Thomas Montine ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Proteomic Analysis ◽  
Expression Patterns ◽  
Label Free ◽  
Post Mortem Brain ◽  
Brain Proteome ◽  
Associated Proteins ◽  
Membrane Bound

Previous systems-based proteomic approaches have characterized alterations in protein co-expression networks of unfractionated asymptomatic (AsymAD) and symptomatic Alzheimer’s disease (AD) brains. However, it remains unclear how sample fractionation and sub-proteomic analysis influences the organization of these protein networks and their relationship to clinicopathological traits of disease. In this proof-of-concept study, we performed a systems-based sub-proteomic analysis of membrane-enriched post-mortem brain samples from pathology-free control, AsymAD, and AD brains (n = 6 per group). Label-free mass spectrometry based on peptide ion intensity was used to quantify the 18 membrane-enriched fractions. Differential expression and weighted protein co-expression network analysis (WPCNA) were then used to identify and characterize modules of co-expressed proteins most significantly altered between the groups. We identified a total of 27 modules of co-expressed membrane-associated proteins. In contrast to the unfractionated proteome, these networks did not map strongly to cell-type specific markers. Instead, these modules were principally organized by their associations with a wide variety of membrane-bound compartments and organelles. Of these, the mitochondrion was associated with the greatest number of modules, followed by modules linked to the cell surface compartment. In addition, we resolved networks with strong associations to the endoplasmic reticulum, Golgi apparatus, and other membrane-bound organelles. A total of 14 of the 27 modules demonstrated significant correlations with clinical and pathological AD phenotypes. These results revealed that the proteins within individual compartments feature a heterogeneous array of AD-associated expression patterns, particularly during the preclinical stages of disease. In conclusion, this systems-based analysis of the membrane-associated AsymAD brain proteome yielded a unique network organization highly linked to cellular compartmentalization. Further study of this membrane-associated proteome may reveal novel insight into the complex pathways governing the earliest stages of disease.

scholarly journals Expression Profiles of Long Noncoding RNAs in Intranasal LPS-Mediated Alzheimer’s Disease Model in Mice

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Liang Tang ◽  
Lan Liu ◽  
Guangyi Li ◽  
Pengcheng Jiang ◽  
Yan Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Expression Profiles ◽  
Disease Model ◽  
Noncoding Rnas ◽  
Memory Loss ◽  
Long Noncoding Rnas ◽  
Specific Effects ◽  
Microarray Expression

Alzheimer’s disease (AD), characterized by memory loss, cognitive decline, and dementia, is a progressive neurodegenerative disease. Although the long noncoding RNAs (lncRNAs) have recently been identified to play a role in the pathogenesis of AD, the specific effects of lncRNAs in AD remain unclear. In present study, we have investigated the expression profiles of lncRNAs in hippocampal of intranasal LPS-mediated Alzheimer’s disease models in mice by microarray method. A total of 395 lncRNAs and 123 mRNAs was detected to express differently in AD models and controls (>2.0 folds,p<0.05). The microarray expression was validated by Quantitative Real-Time-PCR (qRT-PCR). The pathway analysis showed the mRNAs that correlated with lncRNAs were involved in inflammation, apoptosis, and nervous system related pathways. The lncRNA-TFs network analysis suggested the lncRNAs were mostly regulated by HMGA2, ONECUT2, FOXO1, and CDC5L. Additionally, lncRNA-target-TFs network analysis indicated the FOXL1, CDC5L, ONECUT2, and CDX1 to be the TFs most likely to regulate the production of these lncRNAs. This is the first study to investigate lncRNAs expression pattern in intranasal LPS-mediated Alzheimer’s disease model in mice. And these results may facilitate the understanding of the pathogenesis of AD targeting lncRNAs.

P4-031: Integrative Network Analysis of Multiple Alzheimer's Disease Rnaseq Studies From the Accelerating Medicine Partnership-Alzheimer's Disease Consortium

2016 ◽  
Vol 12 ◽  
pp. P1026-P1027 ◽  
Author(s):  
Benjamin A. Logsdon ◽  
Bin Zhang ◽  
Vitalina Komashko ◽  
Sara Mostafavi ◽  
Mingming Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Integrative Network
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