Identification of Blood Biomarkers for Alzheimer's Disease Through Computational Prediction and Experimental Validation

Fang Yao; Kaoyuan Zhang; Yan Zhang; Yi Guo; Aidong Li; Shifeng Xiao; Qiong Liu; Liming Shen; Jiazuan Ni

doi:10.3389/fneur.2018.01158

Identification of Blood Biomarkers for Alzheimer's Disease Through Computational Prediction and Experimental Validation

Front Neurol. 2019 Jan 8:9:1158. doi: 10.3389/fneur.2018.01158. eCollection 2018.

Authors

Fang Yao^{1

2}, Kaoyuan Zhang¹, Yan Zhang¹, Yi Guo³, Aidong Li⁴, Shifeng Xiao¹, Qiong Liu¹, Liming Shen¹, Jiazuan Ni¹

Affiliations

¹ Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Science and Oceanography, Shenzhen University, Shenzhen, China.
² Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen, China.
³ Department of Neurology, Shenzhen People's Hospital, Shenzhen, China.
⁴ Department of Rehabilitation, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.

Abstract

Background: Alzheimer's disease (AD) is the major cause of dementia in population aged over 65 years, accounting up to 70% dementia cases. However, validated peripheral biomarkers for AD diagnosis are not available up to present. In this study, we adopted a new strategy of combination of computational prediction and experimental validation to identify blood protein biomarkers for AD. Methods: First, we collected tissue-based gene expression data of AD patients and healthy controls from GEO database. Second, we analyzed these data and identified differentially expressed genes for AD. Third, we applied a blood-secretory protein prediction program on these genes and predicted AD-related proteins in blood. Finally, we collected blood samples of AD patients and healthy controls to validate the potential AD biomarkers by using ELISA experiments and Western blot analyses. Results: A total of 2754 genes were identified to express differentially in brain tissues of AD, among which 296 genes were predicted to encode AD-related blood-secretory proteins. After careful analysis and literature survey on these predicted blood-secretory proteins, ten proteins were considered as potential AD biomarkers, five of which were experimentally verified with significant change in blood samples of AD vs. controls by ELISA, including GSN, BDNF, TIMP1, VLDLR, and APLP2. ROC analyses showed that VLDLR and TIMP1 had excellent performance in distinguishing AD patients from controls (area under the curve, AUC = 0.932 and 0.903, respectively). Further validation of VLDLR and TIMP1 by Western blot analyses has confirmed the results obtained in ELISA experiments. Conclusion: VLDLR and TIMP1 had better discriminative abilities between ADs and controls, and might serve as potential blood biomarkers for AD. To our knowledge, this is the first time to identify blood protein biomarkers for AD through combination of computational prediction and experimental validation. In addition, VLDLR was first reported here as potential blood protein biomarker for AD. Thus, our findings might provide important information for AD diagnosis and therapies.

Keywords: Alzheimer's disease; biomarker; blood; computation; protein.