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A set of common-acting iron-responsive 5'untranslated region (5'UTR) motifs can fold into RNA stem loops that appear significant to the biology of cognitive declines of Parkinson's disease dementia (PDD), Lewy body dementia (LDD), and Alzheimer's disease (AD). Neurodegenerative diseases exhibit perturbations of iron homeostasis in defined brain subregions over characteristic time intervals of progression. While misfolding of Aß from the amyloid-precursor-protein (APP), alpha-synuclein, prion protein (PrP) each cause neuropathic protein inclusions in the brain subregions, iron-responsive-like element (IRE-like) RNA stem-loops reside in their transcripts. APP and asyn have a role in iron transport while gene duplications elevate the expression of their products to cause rare familial cases of AD and PDD. Of note, IRE-like sequences are responsive to excesses of brain iron in a potential feedback loop to accelerate neuronal ferroptosis and cognitive declines as well as amyloidosis. This pathogenic feedback is consistent with the translational control of the iron storage protein ferritin. We discuss how the IRE-like RNA motifs in the 5'UTRs of APP, alpha-synuclein and PrP mRNAs represent uniquely folded drug targets for therapies to prevent perturbed iron homeostasis that accelerates AD, PD, PD dementia (PDD) and Lewy body dementia, thus preventing cognitive deficits. Inhibition of alpha-synuclein translation is an option to block manganese toxicity associated with early childhood cognitive problems and manganism while Pb toxicity is epigenetically associated with attention deficit and later-stage AD. Pathologies of heavy metal toxicity centered on an embargo of iron export may be treated with activators of APP and ferritin and inhibitors of alpha-synuclein translation.

Manganese (Mn) is an essential heavy metal in the human body, while excess Mn leads to neurotoxicity, as observed in this study, where 100 µM of Mn was administered to the human neuroblastoma (SH-SY5Y) cell model of dopaminergic neurons in neurodegenerative diseases. We quantitated pathway and gene changes in homeostatic cell-based adaptations to Mn exposure. Utilizing the Gene Expression Omnibus, we accessed the GSE70845 dataset as a microarray of SH-SY5Y cells published by Gandhi et al. (2018) and applied statistical significance cutoffs at p < 0.05. We report 74 pathway and 10 gene changes with statistical significance. ReactomeGSA analyses demonstrated upregulation of histones (5 out of 10 induced genes) and histone deacetylases as a neuroprotective response to remodel/mitigate Mn-induced DNA/chromatin damage. Neurodegenerative-associated pathway changes occurred. NF-κB signaled protective responses via Sirtuin-1 to reduce neuroinflammation. Critically, Mn activated three pathways implicating deficits in purine metabolism. Therefore, we validated that urate, a purine and antioxidant, mitigated Mn-losses of viability in SH-SY5Y cells. We discuss Mn as a hypoxia mimetic and trans-activator of HIF-1α, the central trans-activator of vascular hypoxic mitochondrial dysfunction. Mn induced a 3-fold increase in mRNA levels for antioxidant metallothionein-III, which was induced 100-fold by hypoxia mimetics deferoxamine and zinc. [ABSTRACT FROM AUTHOR]

Abstract Background Computational genomics of Alzheimer disease (AD), the most common form of senile dementia, is a nascent field in AD research. The field includes AD gene clustering by computing gene order which generates higher quality gene clustering patterns than most other clustering methods. However, there are few available gene order computing methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Further, their performance in gene order computation using AD microarray data is not known. We thus set forth to evaluate the performances of current gene order computing methods with different distance formulas, and to identify additional features associated with gene order computation. Methods Using different distance formulas- Pearson distance and Euclidean distance, the squared Euclidean distance, and other conditions, gene orders were calculated by ACO and GA (including standard GA and improved GA) methods, respectively. The qualities of the gene orders were compared, and new features from the calculated gene orders were identified. Results Compared to the GA methods tested in this study, ACO fits the AD microarray data the best when calculating gene order. In addition, the following features were revealed: different distance formulas generated a different quality of gene order, and the commonly used Pearson distance was not the best distance formula when used with both GA and ACO methods for AD microarray data. Conclusion Compared with Pearson distance and Euclidean distance, the squared Euclidean distance generated the best quality gene order computed by GA and ACO methods.

The authors employed a novel approach to identify therapeutics effective in Alzheimer disease (AD). The 5' untranslated region (5'UTR) of the mRNA of AD amyloid precursor protein (APP) is a [...]

Abstract Background Alzheimer’s disease (AD) is intimately tied to amyloid-β (Aβ) peptide. Extraneuronal brain plaques consisting primarily of Aβ aggregates are a hallmark of AD. Intraneuronal Aβ subunits are strongly implicated in disease progression. Protein sequence mutations of the Aβ precursor protein (APP) account for a small proportion of AD cases, suggesting that regulation of the associated gene (APP) may play a more important role in AD etiology. The APP promoter possesses a novel 30 nucleotide sequence, or “proximal regulatory element” (PRE), at −76/−47, from the +1 transcription start site that confers cell type specificity. This PRE contains sequences that make it vulnerable to epigenetic modification and may present a viable target for drug studies. We examined PRE-nuclear protein interaction by gel electrophoretic mobility shift assay (EMSA) and PRE mutant EMSA. This was followed by functional studies of PRE mutant/reporter gene fusion clones. Results EMSA probed with the PRE showed DNA-protein interaction in multiple nuclear extracts and in human brain tissue nuclear extract in a tissue-type specific manner. We identified transcription factors that are likely to bind the PRE, using competition gel shift and gel supershift: Activator protein 2 (AP2), nm23 nucleoside diphosphate kinase/metastatic inhibitory protein (PuF), and specificity protein 1 (SP1). These sites crossed a known single nucleotide polymorphism (SNP). EMSA with PRE mutants and promoter/reporter clone transfection analysis further implicated PuF in cells and extracts. Functional assays of mutant/reporter clone transfections were evaluated by ELISA of reporter protein levels. EMSA and ELISA results correlated by meta-analysis. Conclusions We propose that PuF may regulate the APP gene promoter and that AD risk may be increased by interference with PuF regulation at the PRE. PuF is targeted by calcium/calmodulin-dependent protein kinase II inhibitor 1, which also interacts with the integrins. These proteins are connected to vital cellular and neurological functions. In addition, the transcription factor PuF is a known inhibitor of metastasis and regulates cell growth during development. Given that APP is a known cell adhesion protein and ferroxidase, this suggests biochemical links among cell signaling, the cell cycle, iron metabolism in cancer, and AD in the context of overall aging.

Abstract Background Gene microarray technology is an effective tool to investigate the simultaneous activity of multiple cellular pathways from hundreds to thousands of genes. However, because data in the colossal amounts generated by DNA microarray technology are usually complex, noisy, high-dimensional, and often hindered by low statistical power, their exploitation is difficult. To overcome these problems, two kinds of unsupervised analysis methods for microarray data: principal component analysis (PCA) and independent component analysis (ICA) have been developed to accomplish the task. PCA projects the data into a new space spanned by the principal components that are mutually orthonormal to each other. The constraint of mutual orthogonality and second-order statistics technique within PCA algorithms, however, may not be applied to the biological systems studied. Extracting and characterizing the most informative features of the biological signals, however, require higher-order statistics. Results ICA is one of the unsupervised algorithms that can extract higher-order statistical structures from data and has been applied to DNA microarray gene expression data analysis. We performed FastICA method on DNA microarray gene expression data from Alzheimer's disease (AD) hippocampal tissue samples and consequential gene clustering. Experimental results showed that the ICA method can improve the clustering results of AD samples and identify significant genes. More than 50 significant genes with high expression levels in severe AD were extracted, representing immunity-related protein, metal-related protein, membrane protein, lipoprotein, neuropeptide, cytoskeleton protein, cellular binding protein, and ribosomal protein. Within the aforementioned categories, our method also found 37 significant genes with low expression levels. Moreover, it is worth noting that some oncogenes and phosphorylation-related proteins are expressed in low levels. In comparison to the PCA and support vector machine recursive feature elimination (SVM-RFE) methods, which are widely used in microarray data analysis, ICA can identify more AD-related genes. Furthermore, we have validated and identified many genes that are associated with AD pathogenesis. Conclusion We demonstrated that ICA exploits higher-order statistics to identify gene expression profiles as linear combinations of elementary expression patterns that lead to the construction of potential AD-related pathogenic pathways. Our computing results also validated that the ICA model outperformed PCA and the SVM-RFE method. This report shows that ICA as a microarray data analysis tool can help us to elucidate the molecular taxonomy of AD and other multifactorial and polygenic complex diseases.

Abstract A number of studies have shown that increased APP levels, resulting from either a genomic locus duplication or alteration in APP regulatory sequences, can lead to development of early-onset dementias, including Alzheimer's disease (AD). Therefore, understanding how APP levels are regulated could provide valuable insight into the genetic basis of AD and illuminate novel therapeutic avenues for AD. Here we test the hypothesis that APP protein levels can be regulated by miRNAs, evolutionarily conserved small noncoding RNA molecules that play an important role in regulating gene expression. Utilizing human cell lines, we demonstrate that miRNAs hsa-mir-106a and hsa-mir-520c bind to their predicted target sequences in the APP 3'UTR and negatively regulate reporter gene expression. Over-expression of these miRNAs, but not control miRNAs, results in translational repression of APP mRNA and significantly reduces APP protein levels. These results are the first to demonstrate that levels of human APP can be regulated by miRNAs.