journal articles
QUANTIFYING PERSONALIZED SHIFT-WORK MOLECULAR PORTRAITS UNDERLYING ALZHEIMER’S DISEASE THROUGH COMPUTATIONAL BIOLOGY
Y. Xu, G. Zhang, L. Yang, H. Qin, Z. Zhou, Q. Li, H. Liu, R. Wang, Z. Cai, L. Jing, Y. Li, Y. Yao, Z. Gong, P. Yuan, T. Fu, X. Zhao, T. Peng, Y. Jia
J Prev Alz Dis 2024;6(11):1721-1733
BACKGROUND: Shift work, the proven circadian rhythm-disrupting behavior, has been linked to the increased risk of Alzheimer’s disease (AD). However, the putative causal effect and potential mechanisms of shift work for AD were still unclear.
METHODS: Mendelian randomization (MR) analysis was performed to discover the putative causal effect of shift work for AD. Expression quantitative trait loci (eQTLs) and transcriptome data were integrated to identify genes causally associated with AD from circadian-related genes. An in vitro experiment was also conducted to validate the expression of target genes. Based on the identified genes, a novel integrative program and 4,077 samples from 16 microarray datasets were leveraged to assess the extent of circadian rhythm disruption (CRD), defined as the clock deviation level (CDL).
FINDINGS/RESULTS: Shift work causally increased the risk of AD [odds ratio (OR) = 2.49, 95% CI = 1.79 - 3.19, p = 0.01]. Seven circadian-related genes were causally associated with AD, including CCS, CDS2, MYRIP, NRP1, PLEKHA5, POLR1D, and PPP4C. These genes were significantly correlated with the circadian rhythm pathway. CDL was higher in CRD mice group, shift work group, sleep restriction group, and AD patients compared to control mice group (p = 0.043), non-shift group (p = 0.004), sleep extension group (p = 0.025), and health controls (multiple cohorts, p < 0.05). Additionally, CDL was also significantly correlated with AD’s clinical biomarkers.
INTERPRETATIONS/CONCLUSION: By combining GWAS and transcriptome data, this study demonstrated the causal role of CRD behavior in AD, identified the potential target genes in shift work-induced AD, and generated CDL to characterize CRD status, which provided evidence and prospects for disease prevention and future therapeutic interventions.
CITATION:
Y. Xu ; G. Zhang ; L. Yang ; H. Qin ; Z. Zhou ; Q. Li ; H. Liu ; R. Wang ; Z. Cai ; L. Jing ; Y. Li ; Y. Yao ; Z. Gong ; P. Yuan ; T. Fu ; X. Zhao ; T. Peng ; Y. Jia (2024): Quantifying Personalized Shift-Work Molecular Portraits Underlying Alzheimer’s Disease through Computational Biology. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2024.161