Systematic Characterization of Heterogeneity caused by Neuroinflammation in Alzheimer’s Disease based on Integrative Network Analysis

2021 ◽  
Vol 19 ◽  
Author(s):  
Yingying Wang ◽  
Jianfeng Liu ◽  
Yufeng Li ◽  
Yu Yang ◽  
Keshen Li

Background: Alzheimer's disease (AD) is the most common cause of dementia. As a heterogenous disease, there are several clinically and pathobiological defined subtypes with different molecular signatures. Neuroinflammation contributed to AD pathogenesis, however, the roles it played in the heterogeneity of AD was unclear. Objective: We aimed to illustrate the roles neuroinflammation played in the heterogeneity of AD. Method: An integrative network analysis based on transcriptomics, miRNOmics, and proteomics was performed to illustrate the heterogeneous characters of AD. Combined-functional-networks and hypothesis-network were constructed and analyzed to explore the roles neuroinflammation played in AD heterogeneity. Results: Astrocytes, microglia, ‘M2 macrophage-Neuron’, and ‘Microglia- Neuron’ were shown to be enriched in neuroinflammation related functional terms in a cell- and spatial-specific way. The microglia and neurons could interact with each other in three different ways including indirect interactions via intermediate cells, indirect interactions via soluble factors, and direct interactions established localized and functionally distinct signaling, all of which were used to control different biological processes. The combined network analyses exhibited the key roles neuroinflammation plays in the 'AD hypothesis network’. Conclusion : The AD heterogeneity may be caused by the heterogeneous cells involved in neuroinflammation and the crosstalks between spatial-specific molecular signatures.

2016 ◽  
Vol 12 ◽  
pp. P1026-P1027 ◽  
Author(s):  
Benjamin A. Logsdon ◽  
Bin Zhang ◽  
Vitalina Komashko ◽  
Sara Mostafavi ◽  
Mingming Chen ◽  
...  

BMC Genomics ◽  
2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Xianglian Meng ◽  
◽  
Jin Li ◽  
Qiushi Zhang ◽  
Feng Chen ◽  
...  

Abstract Background Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer’s disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism. Results In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer’s disease, Legionellosis, Pertussis, and Serotonergic synapse. Conclusions The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer’s Disease and will be of value to novel gene discovery and functional genomic studies.


2019 ◽  
Vol 11 (4) ◽  
pp. 645-654 ◽  
Author(s):  
Jiong Wu ◽  
Linhui Chen ◽  
Chaobo Zheng ◽  
Shanhu Xu ◽  
Yuhai Gao ◽  
...  

2021 ◽  
pp. 1-11
Author(s):  
Qi-Shuai Zhuang ◽  
Lei Meng ◽  
Zhe Wang ◽  
Liang Shen ◽  
Hong-Fang Ji

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.


Author(s):  
Qi Zhang ◽  
Cheng Ma ◽  
Marla Gearing ◽  
Peng George Wang ◽  
Lih-Shen Chin ◽  
...  

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