RESUMO

Several cellular responses depend on translational regulation and in most cases, this involves the formation of cytoplasmic granules that contain repressed mRNAs. In neurons, numerous mRNAs travel along dendrites to be locally regulated upon synapse activity and we have recently shown that the exoribonuclease XRN1 forms dynamic aggregates at the post synapse that respond to specific stimuli.(1) These foci were termed SX-bodies and are distinct from stress granules (SGs), processing bodies (PBs) and other RNA granules previously described. Together with Smaug1-foci and FMRP-granules, the SX-bodies contribute to dynamically shape the transcriptome available for translation at the post-synapse.

RESUMO

Repression of mRNA translation is linked to the formation of specific cytosolic foci such as stress granules and processing bodies, which store or degrade mRNAs. In neurons, synaptic activity regulates translation at the post-synapse and this is important for plasticity. N-methyl-D-aspartate (NMDA) receptor stimulation downregulates translation, and we speculate that this is linked to the formation of unknown mRNA-silencing foci. Here, we show that the 5'-3' exoribonuclease XRN1 forms discrete clusters associated with the post-synapse that are different from processing bodies or stress granules, and we named them synaptic XRN1 bodies (SX-bodies). Using primary neurons, we found that the SX-bodies respond to synapse stimulation and that their formation correlates inversely with the local translation rate. SX-bodies increase in size and number upon NMDA stimulation, and metabotropic glutamate receptor activation provokes SX-body dissolution, along with increased translation. The response is specific and the previously described Smaug1 foci and FMRP granules show a different response. Finally, XRN1 knockdown impairs the translational repression triggered by NMDA. Collectively, these observations support a role for the SX-bodies in the reversible masking and silencing of mRNAs at the synapse.

Assuntos

Proteínas de Ligação a DNA/metabolismo , Exorribonucleases/metabolismo , RNA Mensageiro/genética , Receptores de Glutamato Metabotrópico/metabolismo , Receptores de N-Metil-D-Aspartato/metabolismo , Sinapses/metabolismo , Sequência de Aminoácidos , Animais , Células Cultivadas , Regulação da Expressão Gênica , Camundongos , Dados de Sequência Molecular , Neurônios/metabolismo , Organelas , Biossíntese de Proteínas , Ratos Sprague-Dawley

RESUMO

Late-onset Alzheimer's disease (LOAD) is a complex and multifactorial disease. So far ten loci have been identified for LOAD, including APOE, PICALM, CLU, BIN1, CD2AP, CR1, CD33, EPHA1, ABCA7, and MS4A4A/MS4A6E, but they explain about 50% of the genetic risk and thus additional risk genes need to be identified. Amyloid beta (Aß) plaques develop in the brains of LOAD patients and are considered to be a pathological hallmark of this disease. Recently 12 new Aß toxicity modifier genes (ADSSL1, PICALM, SH3KBP1, XRN1, SNX8, PPP2R5C, FBXL2, MAP2K4, SYNJ1, RABGEF1, POMT2, and XPO1) have been identified that potentially play a role in LOAD risk. In this study, we have examined the association of 222 SNPs in these 12 candidate genes with LOAD risk in 1291 LOAD cases and 958 cognitively normal controls. Single site and haplotype analyses were performed using PLINK. Following adjustment for APOE genotype, age, sex, and principal components, we found single nucleotide polymorphisms (SNPs) in PPP2R5C, PICALM, SH3KBP1, XRN1, and SNX8 that showed significant association with risk of LOAD. The top SNP was located in intron 3 of PPP2R5C (P=0.009017), followed by an intron 19 SNP in PICALM (P=0.0102). Haplotype analysis revealed significant associations in ADSSL1, PICALM, PPP2R5C, SNX8, and SH3KBP1 genes. Our data indicate that genetic variation in these new candidate genes affects the risk of LOAD. Further investigation of these genes, including additional replication in other case-control samples and functional studies to elucidate the pathways by which they affect Aß, are necessary to determine the degree of involvement these genes have for LOAD risk.