Identification of molecular signatures associated with sleep disorder and Alzheimer's disease

Lucong Liang; Jing Yan; Xiaohua Huang; Chun Zou; Liechun Chen; Rongjie Li; Jieqiong Xie; Mika Pan; Donghua Zou; Ying Liu

doi:10.3389/fpsyt.2022.925012

Identification of molecular signatures associated with sleep disorder and Alzheimer's disease

Front Psychiatry. 2022 Aug 4:13:925012. doi: 10.3389/fpsyt.2022.925012. eCollection 2022.

Authors

Lucong Liang¹, Jing Yan², Xiaohua Huang³, Chun Zou¹, Liechun Chen¹, Rongjie Li^{2

4}, Jieqiong Xie¹, Mika Pan¹, Donghua Zou¹, Ying Liu^{2

4}

Affiliations

¹ Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China.
² Department of Geriatrics, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China.
³ Department of Neurology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.
⁴ Department of Geriatrics, The First People's Hospital of Nanning, Nanning, China.

Abstract

Background: Alzheimer's disease (AD) and sleep disorders are both neurodegenerative conditions characterized by impaired or absent sleep. However, potential common pathogenetic mechanisms of these diseases are not well characterized.

Methods: Differentially expressed genes (DEGs) were identified using publicly available human gene expression profiles GSE5281 for AD and GSE40562 for sleep disorder. DEGs common to the two datasets were used for enrichment analysis, and we performed multi-scale embedded gene co-expression network analysis (MEGENA) for common DEGs. Fast gene set enrichment analysis (fGSEA) was used to obtain common pathways, while gene set variation analysis (GSVA) was applied to quantify those pathways. Subsequently, we extracted the common genes between module genes identified by MEGENA and genes of the common pathways, and we constructed protein-protein interaction (PPI) networks. The top 10 genes with the highest degree of connectivity were classified as hub genes. Common genes were used to perform Metascape enrichment analysis for functional enrichment. Furthermore, we quantified infiltrating immune cells in patients with AD or sleep disorder and in controls.

Results: DEGs common to the two disorders were involved in the citrate cycle and the HIF-1 signaling pathway, and several common DEGs were related to signaling pathways regulating the pluripotency of stem cells, as well as 10 other pathways. Using MEGENA, we identified 29 modules and 1,498 module genes in GSE5281, and 55 modules and 1,791 module genes in GSE40562. Hub genes involved in AD and sleep disorder were ATP5A1, ATP5B, COX5A, GAPDH, NDUFA9, NDUFS3, NDUFV2, SOD1, UQCRC1, and UQCRC2. Plasmacytoid dendritic cells and T helper 17 cells had the most extensive infiltration in both AD and sleep disorder.

Conclusion: AD pathology and pathways of neurodegeneration participate in processes contributing in AD and sleep disorder. Hub genes may be worth exploring as potential candidates for targeted therapy of AD and sleep disorder.

Keywords: Alzheimer’s disease; bioinformatics analysis; differentially expressed gene (DEG); gene expression; sleep disorder.