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. 2020 Apr:88:24-32.
doi: 10.1016/j.neurobiolaging.2019.12.001. Epub 2019 Dec 10.

Dysregulated Fc gamma receptor-mediated phagocytosis pathway in Alzheimer's disease: network-based gene expression analysis

Young Ho Park  1 , Angela Hodges  2 , Shannon L Risacher  3 , Kuang Lin  2 , Jae-Won Jang  4 , Soyeon Ahn  5 , SangYun Kim  6 , Simon Lovestone  7 , Andrew Simmons  2 , Michael W Weiner  8 , Andrew J Saykin  9 , Kwangsik Nho  10 ; AddNeuroMed consortium and the Alzheimer's Disease Neuroimaging Initiative
Affiliations

Dysregulated Fc gamma receptor-mediated phagocytosis pathway in Alzheimer's disease: network-based gene expression analysis

Young Ho Park et al. Neurobiol Aging. 2020 Apr.

Abstract

Transcriptomics has become an important tool for identification of biological pathways dysregulated in Alzheimer's disease (AD). We performed a network-based gene expression analysis of blood-based microarray gene expression profiles using 2 independent cohorts, Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 661) and AddNeuroMed (N = 674). Weighted gene coexpression network analysis identified 17 modules from ADNI and 13 from AddNeuroMed. Four of the modules derived in ADNI were significantly related to AD; 5 modules in AddNeuroMed were significant. Gene-set enrichment analysis of the AD-related modules identified and replicated 3 biological pathways including the Fc gamma receptor-mediated phagocytosis pathway. Module-based association analysis showed the AD-related module, which has the 3 pathways, to be associated with cognitive function and neuroimaging biomarkers. Gene-based association analysis identified PRKCD in the Fc gamma receptor-mediated phagocytosis pathway as being significantly associated with cognitive function and cerebrospinal fluid biomarkers. The identification of the Fc gamma receptor-mediated phagocytosis pathway implicates the peripheral innate immune system in the pathophysiology of AD. PRKCD is known to be related to neurodegeneration induced by amyloid-β.

Keywords: Alzheimer's disease; Coexpression; Network; PRKCD; Phagocytosis; Transcriptome.

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Conflict of interest statement

Declarations of interest: none

Figures

Figure 1.
Figure 1.. Clustering dendrogram with correlation between modules and AD diagnosis (CN vs. AD)
Weighted correlation network analysis constructed 17 and 13 modules in ADNI (A; discovery data) and AddNeuroMed (B; replication data) cohorts, respectively. The modules are represented in the rows with the clustering dendrogram. Colors were assigned to the modules arbitrarily according to their size by the WGCNA software. Each cell contains the correlation between the corresponding module and AD diagnosis (CN vs. AD) with its FDR-corrected p-value. It is color-coded by the correlation according to the color legend. The p-value was derived from a linear regression analysis with AD diagnosis, age and sex as independent variables and module eigengene as an outcome. Abbreviation: AD: Alzheimer’s disease; CN: cognitively normal older adults; FDR: false discovery rate; WGCNA: weighted correlation network analysis
Figure 2.
Figure 2.. Relationship between hippocampal volume and the brown module in ADNI
The relationship between hippocampal volume and module eigengene (ME) of the brown module in ADNI was represented in a scatter plot. The blue line was obtained from a linear regression analysis (FDR-corrected p-value = 1.55×10−2), and the gray zone around the blue line indicates 95% confidence interval. Abbreviation: FDR: false discovery rate
Figure 3.
Figure 3.. LocusZoom plot for eQTL analysis of ASAP1 and LILRA2
SNPs’ positions on chromosome and their color-coded association with expression levels of ASAP1 are plotted in ADNI (A) and AddNeuroMed (B). The SNP with the lowest p-value in a meta-analysis (rs11774659) is indicated. The eQTL results of LILRA2 are plotted for ADNI (C) and AddNeuroMed (D) with an indication of the SNP with the lowest p-value (rs28516458) in a meta-analysis. Abbreviation: eQTL: expression quantitative trait locus
See this image and copyright information in PMC

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