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Brain lesions in Alzheimer's disease (AD) include amyloid plaques made of Aβ peptides and neurofibrillary tangles composed of hyperphosphorylated tau protein with synaptic and neuronal loss and neuroinflammation. Aβ oligomers can trigger tau phosphorylation and neuronal alterations through activation of neuronal kinases leading to progressive cognitive decline. PKR is a ubiquitous pro-apoptotic serine/threonine kinase, and levels of activated PKR are increased in AD brains and AD CSF. In addition, PKR regulates negatively memory formation in mice. To assess the role of PKR in an AD in vivo model, we crossed 5xFAD transgenic mice with PKR knockout (PKRKO) mice and we explored the contribution of PKR on cognition and brain lesions in the 5xFAD mouse model of AD as well as in neuron-microglia co-cultures exposed to the innate immunity activator lipopolysaccharide (LPS). Nine-month-old double-mutant mice revealed significantly improved memory consolidation with the new object location test, starmaze test, and elevated plus maze test as compared to 5xFAD mice. Brain amyloid accumulation and BACE1 levels were statistically decreased in double-mutant mice. Apoptosis, neurodegeneration markers, and synaptic alterations were significantly reduced in double-mutant mice as well as neuroinflammation markers such as microglial load and brain cytokine levels. Using cocultures, we found that PKR in neurons was essential for LPS microglia-induced neuronal death. Our results demonstrate the clear involvement of PKR in abnormal spatial memory and brain lesions in the 5xFAD model and underline its interest as a target for neuroprotection in AD.
Pubmed ID: 30821420 RIS Download
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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