Identification of Genes Involved in the Early Stages of Alzheimer Disease Using a Neural Network Algorithm

LncRNA Xist, X-chromosome Instability and Alzheimer's Disease

Neurodegenerative Diseases (NDD) are the major contributors to age-related causes of mental disability on a global scale. Most NDDs, like Alzheimer's Disease (AD), are complex in nature - implying that they are multi-parametric both in terms of heterogeneous clinical outcomes and underlying molecular paradigms. Emerging evidence from high throughput genomic, transcriptomic and small RNA sequencing experiments hint at the roles of long non-coding RNAs (lncRNAs) in AD. X-inactive Specific Transcript (XIST), a component of the Xic, the X-chromosome inactivation centre, is an RNA gene on the X chromosome of the placental mammals indispensable for the X inactivation process. An extensive literature survey shows that aberrations in Xist expression and in some cases, a disruption of the Xchromosome inactivation as a whole play a significant role in AD. Considering the enormous potential of Xist as an endogenous silencing molecule, the idea of using Xist as a non-conventional chromosome silencer to treat diseases harboring chromosomal alterations is also being implemented. Comprehensive knowledge about how Xist could play such a role in AD is still elusive. In this review, we have collated the available knowledge on the possible Xist involvement and deregulation from the perspective of molecular mechanisms governing NDDs with a primary focus on Alzheimer's disease. Possibilities of XIST mediated therapeutic intervention and linkages between XIC and preferential predisposition of females to AD have also been discussed.

Gene biomarker discovery at different stages of Alzheimer using gene co-expression network approach

Alzheimer's disease (AD) is a chronic neurodegenerative disorder. It is the most common type of dementia that has remained as an incurable disease in the world, which destroys the brain cells irreversibly. In this study, a systems biology approach was adopted to discover novel micro-RNA and gene-based biomarkers of the diagnosis of Alzheimer's disease. The gene expression data from three AD stages (Normal, Mild Cognitive Impairment, and Alzheimer) were used to reconstruct co-expression networks. After preprocessing and normalization, Weighted Gene Co-Expression Network Analysis (WGCNA) was used on a total of 329 samples, including 145 samples of Alzheimer stage, 80 samples of Mild Cognitive Impairment (MCI) stage, and 104 samples of the Normal stage. Next, three gene-miRNA bipartite networks were reconstructed by comparing the changes in module groups. Then, the functional enrichment analyses of extracted genes of three bipartite networks and miRNAs were done, respectively. Finally, a detailed analysis of the authentic studies was performed to discuss the obtained biomarkers. The outcomes addressed proposed novel genes, including MBOAT1, ARMC7, RABL2B, HNRNPUL1, LAMTOR1, PLAGL2, CREBRF, LCOR, and MRI1and novel miRNAs comprising miR-615-3p, miR-4722-5p, miR-4768-3p, miR-1827, miR-940 and miR-30b-3p which were related to AD. These biomarkers were proposed to be related to AD for the first time and should be examined in future clinical studies.