Loss of parkin function is responsible for the majority of autosomal recessive parkinsonism. Here, we show that parkin is not only a stress-protective, but also a stress-inducible protein. Both mitochondrial and endoplasmic reticulum (ER) stress induce an increase in parkin-specific mRNA and protein levels. The stress-induced upregulation of parkin is mediated by ATF4, a transcription factor of the unfolded protein response (UPR) that binds to a specific CREB/ATF site within the parkin promoter. Interestingly, c-Jun can bind to the same site, but acts as a transcriptional repressor of parkin gene expression. We also present evidence that mitochondrial damage can induce ER stress, leading to the activation of the UPR, and thereby to an upregulation of parkin expression. Vice versa, ER stress results in mitochondrial damage, which can be prevented by parkin. Notably, the activity of parkin to protect cells from stress-induced cell death is independent of the proteasome, indicating that proteasomal degradation of parkin substrates cannot explain the cytoprotective activity of parkin. Our study supports the notion that parkin has a role in the interorganellar crosstalk between the ER and mitochondria to promote cell survival under stress, suggesting that both ER and mitochondrial stress can contribute to the pathogenesis of Parkinson's disease.

BackgroundMicroRNAs (miRNAs) are post-transcriptional regulators involved in numerous biological processes including the pathogenesis of Alzheimer’s disease (AD). A key gene of AD, ADAM10, controls the proteolytic processing of APP and the formation of the amyloid plaques and is known to be regulated by miRNA in hepatic cancer cell lines. To predict miRNAs regulating ADAM10 expression concerning AD, we developed a computational approach.MethodsMiRNA binding sites in the human ADAM10 3' untranslated region were predicted using the RNA22, RNAhybrid and miRanda programs and ranked by specific selection criteria with respect to AD such as differential regulation in AD patients and tissue-specific expression. Furthermore, target genes of miR-103, miR-107 and miR-1306 were derived from six publicly available miRNA target site prediction databases. Only target genes predicted in at least four out of six databases in the case of miR-103 and miR-107 were compared to genes listed in the AlzGene database including genes possibly involved in AD. In addition, the target genes were used for Gene Ontology analysis and literature mining. Finally, we used a luciferase assay to verify the potential effect of these three miRNAs on ADAM10 3'UTR in SH-SY5Y cells.ResultsEleven miRNAs were selected, which have evolutionary conserved binding sites. Three of them (miR-103, miR-107, miR-1306) were further analysed as they are linked to AD and most strictly conserved between different species. Predicted target genes of miR-103 (p-value = 0.0065) and miR-107 (p-value = 0.0009) showed significant overlap with the AlzGene database except for miR-1306. Interactions between miR-103 and miR-107 to genes were revealed playing a role in processes leading to AD. ADAM10 expression in the reporter assay was reduced by miR-1306 (28%), miR-103 (45%) and miR-107 (52%).ConclusionsOur approach shows the requirement of incorporating specific, disease-associated selection criteria into the prediction process to reduce the amount of false positive predictions. In summary, our method identified three miRNAs strongly suggested to be involved in AD, which possibly regulate ADAM10 expression and hence offer possibilities for the development of therapeutic treatments of AD.

The molecular mechanisms and genetic risk factors underlying Alzheimer's disease (AD) pathogenesis are only partly understood. To identify new factors, which may contribute to AD, different approaches are taken including proteomics, genetics, and functional genomics. Here, we used a bioinformatics approach and found that distinct AD-related genes share modules of transcription factor binding sites, suggesting a transcriptional coregulation. To detect additional coregulated genes, which may potentially contribute to AD, we established a new bioinformatics workflow with known multivariate methods like support vector machines, biclustering, and predicted transcription factor binding site modules by using in silico analysis and over 400 expression arrays from human and mouse. Two significant modules are composed of three transcription factor families: CTCF, SP1F, and EGRF/ZBPF, which are conserved between human and mouse APP promoter sequences. The specific combination of in silico promoter and multivariate analysis can identify regulation mechanisms of genes involved in multifactorial diseases.

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