Apolipoprotein E4 domain interaction: synaptic and cognitive deficits in mice

Sterols in the central nervous system

PURPOSE OF REVIEW

Aberrations in cerebral cholesterol homeostasis can lead to severe neurological diseases and have been linked to Alzheimer's disease. Many proteins involved in peripheral cholesterol metabolism are also present in the brain. Yet, brain cholesterol metabolism is very different from that in the remainder of the body. This review reports on present insights into the regulation of cerebral cholesterol homeostasis, focusing on cholesterol trafficking between astrocytes and neurons.

RECENT FINDINGS

Astrocytes are a major site of cholesterol synthesis. They secrete cholesterol in the form of apolipoprotein E-containing HDL-like particles. After birth, neurons are thought to reduce their cholesterol synthesis and rely predominantly on astrocytes for their cholesterol supply. How exactly neurons regulate their cholesterol supply is largely unknown. A role for the brain-specific cholesterol metabolite, 24(S)-hydroxycholesterol, in this process was recently proposed. Recent findings strengthen the link between brain cholesterol metabolism and factors involved in synaptic plasticity, a process essential for learning and memory functions, as well as regeneration, which are affected in Alzheimer's disease.

SUMMARY

Insight into the regulation of cerebral cholesterol homeostasis will provide possibilities to modulate the key steps involved and may lead to the development of therapies for the prevention as well as treatment of neurodegenerative diseases such as Alzheimer's disease.

Independent component analysis of Alzheimer's DNA microarray gene expression data

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.

Insight on the molecular envelope of lipid-bound apolipoprotein E from electron paramagnetic resonance spectroscopy

Although a high-resolution X-ray structure for the N-terminal domain of apolipoprotein E (apoE) in the lipid-free state has been solved, our knowledge of the structure of full-length apoE in a lipid-bound state is limited to an X-ray model fitting a molecular envelope at 10-A resolution. To add molecular detail to the molecular envelope, we used cysteine mutagenesis to incorporate spin labels for analysis with electron paramagnetic resonance (EPR) spectroscopy. Twelve cysteine residues were introduced singly and in pairs at unique locations throughout apoE4 and labeled with an EPR spin probe. The labeled apoE4 was combined with dipalmitoylphosphatidylcholine, the particles were purified, and spectra were determined for 24 combinations (single and double) of the cysteine mutants. Data on the conformation, mobility, distance, and surface exposure of regions revealed by the cysteine probes were modeled into the molecular envelope of apoE bound to dipalmitoylphosphatidylcholine that had been determined by X-ray analysis. This EPR model of apoE in a native lipid-bound state validates the structural model derived from X-ray analysis and provides additional insight into apoE structure-function relationships.

Understanding the association of apolipoprotein E4 with Alzheimer disease: clues from its structure

Despite intense interest, the molecular mechanisms underlying the association of apoE4 with Alzheimer disease are not clear. Because the function (or dysfunction) of a protein is based on its structure, this review focuses on the effects of the structural differences among the isoforms on neurodegeneration. Understanding how apoE4 structure impacts neurodegeneration is likely to provide mechanistic insight as well as potential therapeutic approaches to blunt or reduce its effects.