In silico modeling of pathogenic point mutations in PSEN1 as studied in South-east Asia

Abstract

In Asia, particularly in Korea and Japan, there is an increase in the aging population, and a subsequent increase in the number of Alzheimer’s disease (AD) patients. Early onset AD (EOAD) represents a minority of all cases. Three genes are associated with EOAD: APP, PSEN1, and PSEN2. PSEN1 is the most common causative gene of EOAD. Recently, more than 200 pathogenic mutations associated with EOAD were discovered in the coding region of PSEN1. To verify the damaging properties of mutations in pre-senilins, cell studies such as COS-1, HEK-293 were performed. The disadvantages of transfection analyses are that they are expensive and time-consuming. In addition, these studies cannot be performed for all mutations. In silico analyses (e.g., Sorting Intolerant from Tolerant [SIFT], PolyPhen2) can be useful in predicting which mutations are involved in disease progression. In addition, 3-D modeling can be also help to estimate the role of mutations in AD onset. This work focuses on AD-causative point mutations in PSEN1 as studied in South-East Asian countries. Predictions were performed for PSEN1 with point mutations to estimate their pathogenic nature and potential role in disease progression. In this study, PolyPhen 2 and SIFT software predictions, as well as 3-D modeling, were performed for all mutations. Most of the mutations were confirmed as pathogenic mutations by these algorithms; however, in silico modeling cannot replace clinical, functional, segregation, and association studies. © 2016, Korean Society of Environmental Risk Assessment and Health Science and Springer Science+Business Media Dordrecht.