Research Project 2 Alzheimer's disease (AD) is the most common cause of dementia, affecfing about 5 million individuals in the US. Small noncoding RNAs, 21 to 30 nucleotides in length, including microRNAs (miRNAs), small interfering RNAs (siRNAs), repeat-associated small interfering RNAs, and piwi-associated RNAs, shape diverse cellular pathways. MiRNAs are sequence specific regulators of posttranscriptlonal gene expression and are believed to regulate the expression of thousands of target mRNAs, with each mRNA targeted by mulfiple miRNAs. Although it has been esfimated that miRNAs could regulate as many as one-third of human genes, highthroughput sequencing data indicate that only a portion of small RNAs in the genome have been discovered so far. Researchers have shown that the expression of RNA is altered in AD brains;however, a role for miRNAs in the pathogenesis of AD has not been established. We have established high-throughput expression profiling of miRNAs and small RNAs in the lab. Using the brain fissues of AD pafients from Emory ADRC Neuropathology Core, we have analyzed the expression of all known miRNAs and identified selective miRNAs are altered specifically in AD. In the proposed study, we plan to test the hypothesis that selective miRNA(s) that are aberrantly expressed in the brain tissues of AD patients modulate the pathogenesis of AD by post-transcriptionally regulating the expression of speciflc mRNAs that are involved in AD. Specifically, we plan to: 1) Identify and validate the altered expression of selective small RNAs in AD brain tissues;2) Identify the mRNA targets of the miRNAs aberrantly expressed in AD brain tissues;and 3) Determine whether the miRNAs aberranfiy expressed in AD brain fissues modulate AD pathogenesis in AD mouse model.
Understanding of the role of small RNAs in AD will not only provide us insight into the molecular pathogenesis of AD, but also potentially provide new targets for further research and therapeutic development.
|Zhang, Qi; Ma, Cheng; Gearing, Marla et al. (2018) Integrated proteomics and network analysis identifies protein hubs and network alterations in Alzheimer's disease. Acta Neuropathol Commun 6:19|
|Gangishetti, Umesh; Christina Howell, J; Perrin, Richard J et al. (2018) Non-beta-amyloid/tau cerebrospinal fluid markers inform staging and progression in Alzheimer's disease. Alzheimers Res Ther 10:98|
|Wang, Qi; Guo, Lei; Thompson, Paul M et al. (2018) The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1. J Alzheimers Dis 64:149-169|
|Umoh, Mfon E; Dammer, Eric B; Dai, Jingting et al. (2018) A proteomic network approach across the ALS-FTD disease spectrum resolves clinical phenotypes and genetic vulnerability in human brain. EMBO Mol Med 10:48-62|
|Wang, Tingyan; Qiu, Robin G; Yu, Ming (2018) Predictive Modeling of the Progression of Alzheimer's Disease with Recurrent Neural Networks. Sci Rep 8:9161|
|Johnson, Erik C B; Dammer, Eric B; Duong, Duc M et al. (2018) Deep proteomic network analysis of Alzheimer's disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease. Mol Neurodegener 13:52|
|Agogo, George O; Ramsey, Christine M; Gnjidic, Danijela et al. (2018) Longitudinal associations between different dementia diagnoses and medication use jointly accounting for dropout. Int Psychogeriatr 30:1477-1487|
|Crum, Jana; Wilson, Jeffrey; Sabbagh, Marwan (2018) Does taking statins affect the pathological burden in autopsy-confirmed Alzheimer's dementia? Alzheimers Res Ther 10:104|
|An, Yang; Varma, Vijay R; Varma, Sudhir et al. (2018) Evidence for brain glucose dysregulation in Alzheimer's disease. Alzheimers Dement 14:318-329|
|Burke, Shanna L; Cadet, Tamara; Maddux, Marlaina (2018) Chronic Health Illnesses as Predictors of Mild Cognitive Impairment Among African American Older Adults. J Natl Med Assoc 110:314-325|
Showing the most recent 10 out of 444 publications