Fatemeh Kaviani,1,*Samaneh Dalali,2Maryam Peymani,3
1. Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran 2. Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran 3. Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
Introduction: Alzheimer’s disease (AD) dementia refers to a particular onset and course of cognitive and functional decline associated with age together with a particular neuropathology. Deficits in the ability to encode and store new memories characterizes the initial stages of the disease. The risk of Alzheimer’s disease is 60–80% dependent on heritable factors. This disease is the most common cause of dementia in elderly population characterized by the presence of neurotoxic senile amyloid plaques, hyper-phosphorylated tau tangles, massive neuron death, and neuro-inflammation.
Circular RNAs (circRNAs) comprise a large class of non- coding RNAs that are produced by a non- canonical splicing event called backsplicing. CircRNAs are single-stranded, covalently closed RNA molecules that are ubiquitous across species ranging from viruses to mammals. These structures have the 3′ and 5′ ends joined together by covalent bonds giving a circular appearance. prominence of stable circRNAs in the synapse provides both the stability and flexibility of neuronal networks which are vital to all behavior, including learning and memory.
In this descriptive-analytical study, we investigate circRNA to be an important player in the development of neurodegenerative diseases such as Alzheimer's disease.
Methods: Parietal cortex RNA-sequencing (RNA-seq) data were generated from individuals with and without Alzheimer disease (AD; ncontrol= 13; nAD= 83) from the Knight Alzheimer Disease Research Center (Knight ADRC). Using this and an independent (Mount Sinai Brain Bank (MSBB)) AD RNA-seq dataset, cortical circular RNA (circRNA) expression was quantified in the context of AD.
STAR software was used in chimeric read-detection mode to align the reads from both RNA-seq datasets to the GENCODE- annotated human reference genome .
Chimeric reads were further processed and filtered using DCC software to identify backsplice junctions. Finally, the backsplice junction counts were collapsed on to their linear gene of origin to generate a set of highconfidence circRNA counts for downstream analyses.
DESeq2 software was used to perform circRNA differential expression analyses for neuropathological AD case–control status as well as for correlation with AD quantitative traits
Results: Significant associations were identified between circRNA expression and AD diagnosis, clinical dementia severity and neuropathological severity. It was demonstrated that most circRNA–AD associations are independent of changes in cognate linear messenger RNA expression or estimated brain cell-type proportions. Evidence was provided for circRNA expression changes occurring early in presymptomatic AD and in autosomal dominant AD. It was also observed that AD associated circRNAs coexpressed with known AD genes. Finally, potential microRNA-binding sites were identified in AD-associated circRNAs for miRNAs predicted to target AD genes.
Finally, the AD relevance and potential disease-influencing mechanisms of AD-associated circRNAs were investigated through relative importance, network co-expression and miRNA-binding site-prediction analyses.
Conclusion: circRNA function and their relationships with Alzheimer’s disease and other neuropathies remain to be fully elucidated. circRNAs are usually abundant and found to be stable in vivo, which might attribute to their importance in molecular diagnostics. Importantly, the potential role of circRNAs as miRNA sponges can be utilized as an innovative approach to regulate gene expression. Further research on circRNA will enhance our understanding in relation to neuropathies like AD and lead to new diagnostic biomarkers and promising therapeutic.