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Whole transcriptome profiling of Late-Onset Alzheimer’s Disease patients provides insights into the molecular changes involved in the disease

Alzheimer’s Disease (AD) is the most common cause of dementia affecting the elderly population worldwide. We have performed a comprehensive transcriptome profiling of Late-Onset AD (LOAD) patients using second generation sequencing technologies, identifying 2,064 genes, 47 lncRNAs and 4 miRNAs whose...

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Detalles Bibliográficos
Autores principales: Annese, Anita, Manzari, Caterina, Lionetti, Claudia, Picardi, Ernesto, Horner, David S., Chiara, Matteo, Caratozzolo, Mariano Francesco, Tullo, Apollonia, Fosso, Bruno, Pesole, Graziano, D’Erchia, Anna Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5844946/
https://www.ncbi.nlm.nih.gov/pubmed/29523845
http://dx.doi.org/10.1038/s41598-018-22701-2
Descripción
Sumario:Alzheimer’s Disease (AD) is the most common cause of dementia affecting the elderly population worldwide. We have performed a comprehensive transcriptome profiling of Late-Onset AD (LOAD) patients using second generation sequencing technologies, identifying 2,064 genes, 47 lncRNAs and 4 miRNAs whose expression is specifically deregulated in the hippocampal region of LOAD patients. Moreover, analyzing the hippocampal, temporal and frontal regions from the same LOAD patients, we identify specific sets of deregulated miRNAs for each region, and we confirm that the miR-132/212 cluster is deregulated in each of these regions in LOAD patients, consistent with these miRNAs playing a role in AD pathogenesis. Notably, a luciferase assay indicates that miR-184 is able to target the 3’UTR NR4A2 - which is known to be involved in cognitive functions and long-term memory and whose expression levels are inversely correlated with those of miR-184 in the hippocampus. Finally, RNA editing analysis  reveals a general RNA editing decrease in LOAD hippocampus, with 14 recoding sites significantly and differentially edited in 11 genes. Our data underline specific transcriptional changes in LOAD brain and provide an important source of information for understanding the molecular changes characterizing LOAD progression.