Biomarkers

Abstract

Background: Tau protein abnormalities have been attracting more and more atten-tion in Alzheimer’s disease (AD) pathophysiology. Tau spreads in a hierarchical pattern in the AD brain, likely by trans-synaptic propagation between neurons. Discovering geneticriskfactorsassociatedwithtaupathologycouldbekeyforimprovingourability to predict and treat AD. In this study, we leveraged the non-linear analytic approach of deep learning to identify genes that are associated with cerebrospinal fluid (CSF) total tau levels. Method: We selected 384 subjects [118 normal control and 266 mild cognitive impair-ment (MCI)] from ADNI cohort. We designed a deep neuron network model that predicted CSF total tau levels using peripheral blood transcriptomic data. More specif-ically, we developed an occlusion map approach to rank 2,364 transcripts, which were initially selected from 49,386 transcripts by dimension reduction. The occlusion map aimed to rank the transcripts and identified the critical ones. Result: Our deep neural network successfully predicted CSF total tau levels with an accuracy of 67% and the occlusion map identified the critical genes for the prediction. The list included genes like TTL5, AKIRIN2, CPEB1, PTPN7, CIRBP and RPS23, which have been previously reported to be related to AD. For example, TTL5 relates to mis-sorting of tau protein. AKIRIN2 is essential for the formation of the cerebral cortex. CPEB is found to be an interacting factor of amyloid-precursor protein (APP). PTPN7 belongs to protein tyrosine phosphatases family, among which