Identification of Alzheimer associated differentially expressed gene through microarray data and transfer learning-based image analysis. (1st January 2022) - George, Benu
Graphical abstract: Highlights: Differentially expressed gene data is a resourceful repository identify genes for new and unique potential drug targets. Microarray data analysis of chronic unpredictable rat stress model aided in understanding up-/down regulated gene target. Among the ten identified unique genes in the microarray data, a Rho GTPase activating class ARHGAP32 gene was found to be a potential target that regulates two majorly investigated BCL2 and MMP9 in AD. Convolutional neural network model using VGG16 transfer learning technique enables in image classification of histopathological rat brain to its corresponding differential expressed gene. Abstract: Major factors contribute to mental stress and enhance the progression of late-onset Alzheimer's disease (AD). The factors that lead to neurodegeneration, such as tau protein hyperphosphorylation and increased amyloid-beta production, can be mimicked in animal stress models. The present study identifies differentially expressed genes (DEGs) data and its corresponding predictive image analysis in rat models. The gene expression profile of GSE72062, GSE85162, GSE143951 and GSE85238 was downloaded from NCBI, GEO archive to analyse DEGs. Functional enrichment and pathway relationship networks, gene signal, protein interaction and micro-RNA interaction DEGs networks were constructed and investigated. The image analysis of histopathological slides of rat brain images corresponding to AD microarray-based DEGs profile was undertaken using the convolution neural networks (ConvNets) model. Enrichment of network in terms of GO concluded with 10 DEGs, namely ARHGAP32, GNA11, NR5A1, GNAT3, FOSL1, HELZ2, NMUR2, BDKRB1, RPL3L and RPL39L as potential gene targets to control neurodegeneration and progression of sporadic AD. The image analysis of AD microarray-based DEGs profile builds a successful predictive model of 89% and 61% training and test accuracy with a minimum of 2.480% loss using transfer learning, VGG16 model. Interestingly, the ARHGAP32 gene, a Rho GTPase activating class, was identified to have a functional relationship with two significant genes BCL2 and MMP9, that are well explored in AD. The current investigation upgrades the traditional pre-clinical AD research using microarray data analysis and ConvNets. The model successfully predicts DEG from histopathology slides of rat brain samples, paving the way for image analysis to determine the underlying molecular makeup of the test samples.