This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. The amygdala, hippocampus, and cingular cortex of Alzheimer disease (AD) patients show signs of significant changes in physiological function that accompany the amyloid plaques and neurofibrillary tangles that are hallmarks of the disease. A substantial portion of the post-transcriptional gene regulation is controlled by miRNA networks and hence it is important to discover the biologically significant correlations among co-regulated miRNAs that have a substantial role in the progression of AD. The NF-kappa-B complex is inhibited by I-kappa-B proteins (NFKBIA), which inactivate NF-kappa-B by trapping it in the cytoplasm. MiRNA-146a (an NF-kappa-B-sensitive gene) is found in increased amounts in stressed human brain cells and in AD, and that it plays a crucial role in the regulation of inflammation. In the previous work it was observed that miRNA-146a appears to reduce the amount and bioavailability of CFH, promoting the inflammation of brain cells and contributing to the development of AD. During this reporting period, we have investigated into the DNA microarray gene expression data from AD hippocampal tissue of diseased and age-matched controls by specifically concentrating to ?nd discriminatory NF-kappa-B-sensitive patterns with other co-regulated genes using an information theory approach. This Mutual Information (MI) based approach can capture the non-linear dependencies amongst the genes by ?nding the most discriminatory gene vector. We developed a clique-based method using variable and fixed thresholds to retain the significant gene-gene correlations and will consequently identify co-regulated gene clusters in the data that show the same pattern of changing tendencies in the diseased samples. We performed a biomedical literature search to support the effectiveness of our results. Our future work comprises in identifying the regulation of these genes in control versus the AD samples and carrying out the pathway analysis.
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