E-GEOD-46131 - A Study of Small RNAs from Cerebral Neocortex of Pathology-Verified Alzheimer’s Disease, Dementia with Lewy Bodies, Hippocampal Sclerosis, Frontotemporal Lobar Dementia, and Non-Demented Human Controls
Released on 18 April 2013, last updated on 12 June 2013
MicroRNAs (miRNAs) are small (20-22 nucleotides) regulatory non-coding RNAs that strongly influence gene expression. Most prior studies addressing the role of miRNAs in neurodegenerative diseases (NDs) have focused on individual controls (n = 2), AD (n = 5), dementia with Lewy bodies (n = 4), hippocampal sclerosis of aging (n = 4), and frontotemporal lobar dementia (FTLD) (n = 5) cases, together accounting for the most prevalent ND subtypes. All cases had short postmortem intervals, relatively high-quality RNA, and state-of-the-art neuropathological diagnoses. The resulting data (over 113 million reads in total, averaging 5.6 million reads per sample) and secondary expression analyses constitute an unprecedented look into the human cerebral cortical miRNome at single nucleotide resolution. While we find no apparent changes in isomiR or miRNA editing patterns in correlation with ND pathology, our results validate and extend previous miRNA profiling studies with regard to quantitative changes in NDs. In agreement with this idea, we provide independent cohort validation for changes in miR-132 expression levels in AD (n = 8) and FTLD (n = 14) cases when compared to controls (n = 8). The identification of common and ND-specific putative novel brain miRNAs and/or short-hairpin molecules is also presented. The challenge now is to better understand the impact of these and other alterations on neuronal gene expression networks and neuropathologies. Using RNA deep sequencing, we sought to analyze in detail the small RNAs (including miRNAs) in the temporal neocortex gray matter from non-demented controls (n = 2), AD (n = 5), dementia with Lewy bodies (n = 4), hippocampal sclerosis of aging (n = 4), and frontotemporal lobar dementia (FTLD) (n = 5) cases, together accounting for the most prevalent ND subtypes.
RNA-seq of non coding RNA
Tsaiwei Shen <firstname.lastname@example.org>, Peter T Nelson, Qi Zhu, Sebastien S Hebert, Wang-Xia Wang