Person:

Rogers, Jack

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.

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.

We reported that iron influx drives the translational expression of the neuronal amyloid precursor protein (APP), which has a role in iron efflux. This is via a classic release of repressor interaction of APP mRNA with iron-regulatory protein-1 (IRP1) whereas IRP2 controls the mRNAs encoding the L- and H-subunits of the iron storage protein, ferritin. Here, we identified thirteen potent APP translation blockers that acted selectively towards the uniquely configured iron-responsive element (IRE) RNA stem loop in the 5′ untranslated region (UTR) of APP mRNA. These agents were 10-fold less inhibitory of 5′UTR sequences of the related prion protein (PrP) mRNA. Western blotting confirmed that the ‘ninth’ small molecule in the series selectively reduced neural APP production in SH-SY5Y cells at picomolar concentrations without affecting viability or the expression of α-synuclein and ferritin. APP blocker-9 (JTR-009), a benzimidazole, reduced the production of toxic Aβ in SH-SY5Y neuronal cells to a greater extent than other well tolerated APP 5′UTR-directed translation blockers, including posiphen, that were shown to limit amyloid burden in mouse models of Alzheimer's disease (AD). RNA binding assays demonstrated that JTR-009 operated by preventing IRP1 from binding to the IRE in APP mRNA, while maintaining IRP1 interaction with the H-ferritin IRE RNA stem loop. Thus, JTR-009 constitutively repressed translation driven by APP 5′UTR sequences. Calcein staining showed that JTR-009 did not indirectly change iron uptake in neuronal cells suggesting a direct interaction with the APP 5′UTR. These studies provide key data to develop small molecules that selectively reduce neural APP and Aβ production at 10-fold lower concentrations than related previously characterized translation blockers. Our data evidenced a novel therapeutic strategy of potential impact for people with trisomy of the APP gene on chromosome 21, which is a phenotype long associated with Down syndrome (DS) that can also cause familial Alzheimer's disease.

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.

Parkinson’s disease (PD) is the second most common progressive neurodegenerative disorder after Alzheimer’s disease (AD) and represents a large health burden to society. Genetic and oxidative risk factors have been proposed as possible causes, but their relative contribution remains unclear. Dysfunction of alpha-synuclein ((\alpha)-syn) has been associated with PD due to its increased presence, together with iron, in Lewy bodies. Brain oxidative damage caused by iron may be partly mediated by (\alpha)-syn oligomerization during PD pathology. Also, (\alpha)-syn gene dosage can cause familial PD and inhibition of its gene expression by blocking translation via a newly identified Iron Responsive Element-like RNA sequence in its 5’-untranslated region may provide a new PD drug target.

Metal toxicity has been identified as a possible risk factor for amyotrophic lateral sclerosis (ALS) and other neurodegenerative disorders. We conducted a retrospective chart review of urinary, hair and blood metal levels and serum ferritin in 321 people with ALS seen over a ten-year period at the Massachusetts General Hospital (MGH). We found that hair lead levels and serum ferritin levels were elevated in ALS patients compared to published normal values. Metal levels of arsenic, lead, mercury, cadmium, thallium, cobalt and aluminum in 24-hour urine specimens and lead, mercury and arsenic in serum were within the normal range. We conclude that twenty-four hour urine or blood testing for metals is not warranted as part of the evaluation of ALS. Elevated levels of serum ferritin in ALS population could reflect an underlying perturbation in iron metabolism.

Abnormal accumulation of brain metals is a key feature of Alzheimer’s disease (AD). Formation of amyloid-β plaque cores (APC) is related to interactions with biometals, especially Fe, Cu and Zn, but their particular structural associations and roles remain unclear. Using an integrative set of advanced transmission electron microscopy (TEM) techniques, including spherical aberration-corrected scanning transmission electron microscopy (Cs-STEM), nano-beam electron diffraction, electron holography and analytical spectroscopy techniques (EDX and EELS), we demonstrate that Fe in APC is present as iron oxide (Fe3O4) magnetite nanoparticles. Here we show that Fe was accumulated primarily as nanostructured particles within APC, whereas Cu and Zn were distributed through the amyloid fibers. Remarkably, these highly organized crystalline magnetite nanostructures directly bound into fibrillar Aβ showed characteristic superparamagnetic responses with saturated magnetization with circular contours, as observed for the first time by off-axis electron holography of nanometer scale particles.

Acid fibroblast growth factor (aFGF) has shown neuroprotection in Alzheimer’s disease (AD) models in previous studies, yet its mechanism is still uncertain. Here we report that the efficacy of Tat-haFGF14–154 is markedly increased when loaded cationic liposomes for intranasal delivery are intranasally administered to APP/PS1 mice. Our results demonstrated that liposomal Tat-haFGF14–154 treatment significantly ameliorated behavioral deficits, relieved brain Aβ burden, and increased the expression and activity of disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) in the brain. Tat-haFGF14–154 antagonized Aβ1–42-induced cell death and structural damage in rat primary neurons in an ADAM10-dependent manner, which, in turn, was promoted by the activation of XBP1 splicing and modulated by the PI3K-CREB pathway. Both knockdown of ADAM10 and inhibition of PI3K (LY294002) negated Tat-haFGF14–154 rescue. Thus, Tat-haFGF14–154 activates the IRE1α/XBP1 pathway of the unfolded protein response (UPR) against the endoplasmic reticulum (ER) stress induced by Aβ, and, subsequently, the nuclear translocation of spliced XBP1 (XBP1s) promotes transcription of ADAM10. These results highlight the important role of ADAM10 and its activation through the PI3K-CREB-IRE1α/XBP1 pathway as a key factor in the mechanism of neuroprotection for Tat-haFGF14–154.

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.

Memantine, a partial antagonist of N-methyl-D-aspartate receptor (NMDAR), approved for moderate to severe Alzheimer’s disease (AD) treatment within the US and Europe under brand name Namenda (Forest), Axura and Akatinol (Merz), and Ebixa and Abixa (Lundbeck), may have potential in alleviating additional neurological conditions, such as vascular dementia (VD) and Parkinson’s disease (PD). In various animal models, memantine has been reported to be a neuroprotective agent that positively impacts both neurodegenerative and vascular processes. While excessive levels of glutamate result in neurotoxicity, in part through the over-activation of NMDARs, memantine—as a partial NMDAR antagonist, blocks the NMDA glutamate receptors to normalize the glutamatergic system and ameliorate cognitive and memory deficits. The key to memantine’s therapeutic action lies in its uncompetitive binding to the NMDAR through which low affinity and rapid off-rate kinetics of memantine at the level of the NMDAR-channel preserves the physiological function of the receptor, underpinning memantine’s tolerability and low adverse event profile. As the biochemical pathways evoked by NMDAR antagonism also play a role in PD and since no other drug is sufficiently effective to substitute for the first-line treatment of L-dopa despite its side effects, memantine may be useful in PD treatment with possibly fewer side effects. In spite of the relative modest nature of its adverse effects, memantine has been shown to provide only a moderate decrease in clinical deterioration in AD and VD, and hence efforts are being undertaken in the design of new and more potent memantine-based drugs to hopefully provide greater efficacy.