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: 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.

Alzheimer’s disease (AD) is a dementing neurological disorder that results in progressive memory loss and cognitive decline thought to be associated with buildup of amyloid plaques and neurofibrillary tangles in the brain. Vascular Dementia (VaD) is another common dementing disorder characterized by decreased brain perfusion. Together, AD and VaD constitute mixed dementia, an extremely common type of dementia associated with aging. Neuroimaging research suggests that brain vascular atrophy results in mild cognitive impairment (MCI), a possible precursor for AD. Additionally, literature suggests that attention to cardiovascular risk factors such as hypertension could reduce or delay the incidence of mixed dementia. Furthermore, foods and beverages rich in natural antioxidant flavanoids (i.e. epicatechin and catechin) are currently being advocated as possible preventative agents for a number of pathological conditions ranging from coronary heart disease to dementia. Experimental evidence is mounting that oxidative stress is involved in the pathophysiology of AD, and numerous studies are indicating that polyphenolic antioxidants found in fruits and vegetables can be useful in countering this and blocking neuronal death. More specifically, several cocoa studies suggest that daily intake of cocoa flavanols leads to cardiovascular benefits including vasodilatation via a nitric oxide mechanism and increased brain perfusion. The following text will consider an important question that thus arises regarding the potential of flavanols as effective agents for the prevention and delay of the onset of brain vascular atrophy and subsequently MCI and AD. It will also review the molecular mechanisms through which flavanols operate to accomplish their protective effects.

Emerging evidence highlights the importance of the interplay between the bone and immune systems. That evidence bolsters a longstanding recognition that estrogen deficiency, infection, inflammation, and autoimmune disorders are associated with systemic and local bone loss. Yet, only recently has an understanding emerged that T lymphocytes and their products act as key regulators of osteoclast formation, life span, and activity. This review presents this understanding of the process of T lymphocytes and their products mediating osteoporosis and explores some of the most recent findings and hypotheses to explain their action in bone. A more complete appreciation of the interactions between immune and bone cells should lead to targeted therapeutic strategies for diseases that affect either or both systems.