Biomedical subjects

Tapan Mehta

Publications and source records attributed to Tapan Mehta.

6 recordsLinked to original sources

PubMed2006

Transcriptional coordination of the metabolic network in Arabidopsis.

Patterns of coexpression can reveal networks of functionally related genes and provide deeper understanding of processes requiring multiple gene products. We performed an analysis of coexpression networks for 1,330 genes from the AraCyc database of metabolic pathways in Arabidopsis (Arabidopsis thaliana). We found that genes associated with the same metabolic pathway are, on average, more highly coexpressed than genes from different pathways. Positively coexpressed genes within the same pathway tend to cluster close together in the pathway structure, while negatively correlated genes typically occupy more distant positions. The distribution of coexpression links per gene is highly skewed, with a small but significant number of genes having numerous coexpression partners but most having fewer than 10. Genes with multiple connections (hubs) tend to be single-copy genes, while genes with multiple paralogs are coexpressed with fewer genes, on average, than single-copy genes, suggesting that the network expands through gene duplication, followed by weakening of coexpression links involving duplicate nodes. Using a network-analysis algorithm based on coexpression with multiple pathway members (pathway-level coexpression), we identified and prioritized novel candidate pathway members, regulators, and cross pathway transcriptional control points for over 140 metabolic pathways. To facilitate exploration and analysis of the results, we provide a Web site (http://www.transvar.org/at_coexpress/analysis/web) listing analyzed pathways with links to regression and pathway-level coexpression results. These methods and results will aid in the prioritization of candidates for genetic analysis of metabolism in plants and contribute to the improvement of functional annotation of the Arabidopsis genome.

Arabidopsis↗

PubMed2006

Effects of secreted oligomers of amyloid beta-protein on hippocampal synaptic plasticity: a potent role for trimers.

The accumulation of amyloid beta-protein (Abeta) in brain regions serving memory and cognition is a central pathogenic feature of Alzheimer's disease (AD). We have shown that small soluble oligomers of human Abeta that are naturally secreted by cultured cells inhibit hippocampal long-term potentiation (LTP) in vitro and in vivo and transiently impair the recall of a complex learned behaviour in rats. These results support the hypothesis that diffusible oligomers of Abeta initiate a synaptic dysfunction that may be an early event in AD. We now report detailed electrophysiological analyses that define conditions under which acute application of soluble Abeta inhibits hippocampal synaptic plasticity in wild-type mice. To ascertain which Abeta assemblies contribute to the impairment of LTP, we fractionated oligomers by size-exclusion chromatography and found that Abeta trimers fully inhibit LTP, whereas dimers and tetramers have an intermediate potency. Natural Abeta oligomers are sensitive to heat denaturation, primarily inhibit the induction phase of LTP, and cause a sustained impairment of LTP even after extensive washout. We observed no effects of Abeta oligomers on presynaptic vesicle release. LTP in juvenile mice is resistant to the effects of Abeta oligomers, as is brain-derived-neurotrophic-factor-induced LTP in adult hippocampus. We conclude that specific assemblies, particularly timers, of naturally secreted Abeta oligomers are potent and selective inhibitors of certain forms of hippocampal LTP.

Amyloid beta-Peptides↗

PubMed2006

Orally available compound prevents deficits in memory caused by the Alzheimer amyloid-beta oligomers.

OBJECTIVE: Despite progress in defining a pathogenic role for amyloid beta protein (Abeta) in Alzheimer's disease, orally bioavailable compounds that prevent its effects on hippocampal synaptic plasticity and cognitive function have not yet emerged. A particularly attractive therapeutic strategy is to selectively neutralize small, soluble Abeta oligomers that have recently been shown to mediate synaptic dysfunction. METHODS: Using electrophysiological, biochemical, and behavioral assays, we studied how scyllo-inositol (AZD-103; molecular weight, 180) neutralizes the acutely toxic effects of Abeta on synaptic function and memory recall. RESULTS: Scyllo-inositol, but not its stereoisomer, chiro-inositol, dose-dependently rescued long-term potentiation in mouse hippocampus from the inhibitory effects of soluble oligomers of cell-derived human Abeta. Cerebroventricular injection into rats of the soluble Abeta oligomers interfered with learned performance on a complex lever-pressing task, but administration of scyllo-inositol via the drinking water fully prevented oligomer-induced errors. INTERPRETATION: A small, orally available natural product penetrates into the brain in vivo to rescue the memory impairment produced by soluble Abeta oligomers through a mechanism that restores hippocampal synaptic plasticity.

Administration, Oral↗

PubMed2005

Sources of variation in Affymetrix microarray experiments.

BACKGROUND: A typical microarray experiment has many sources of variation which can be attributed to biological and technical causes. Identifying sources of variation and assessing their magnitude, among other factors, are important for optimal experimental design. The objectives of this study were: (1) to estimate relative magnitudes of different sources of variation and (2) to evaluate agreement between biological and technical replicates. RESULTS: We performed a microarray experiment using a total of 24 Affymetrix GeneChip arrays. The study included 4th mammary gland samples from eight 21-day-old Sprague Dawley CD female rats exposed to genistein (soy isoflavone). RNA samples from each rat were split to assess variation arising at labeling and hybridization steps. A general linear model was used to estimate variance components. Pearson correlations were computed to evaluate agreement between technical and biological replicates. CONCLUSION: The greatest source of variation was biological variation, followed by residual error, and finally variation due to labeling when *.cel files were processed with dChip and RMA image processing algorithms. When MAS 5.0 or GCRMA-EB were used, the greatest source of variation was residual error, followed by biology and labeling. Correlations between technical replicates were consistently higher than between biological replicates.

Animals↗

PubMed2005

HDBStat!: a platform-independent software suite for statistical analysis of high dimensional biology data.

BACKGROUND: Many efforts in microarray data analysis are focused on providing tools and methods for the qualitative analysis of microarray data. HDBStat! (High-Dimensional Biology-Statistics) is a software package designed for analysis of high dimensional biology data such as microarray data. It was initially developed for the analysis of microarray gene expression data, but it can also be used for some applications in proteomics and other aspects of genomics. HDBStat! provides statisticians and biologists a flexible and easy-to-use interface to analyze complex microarray data using a variety of methods for data preprocessing, quality control analysis and hypothesis testing. RESULTS: Results generated from data preprocessing methods, quality control analysis and hypothesis testing methods are output in the form of Excel CSV tables, graphs and an Html report summarizing data analysis. CONCLUSION: HDBStat! is a platform-independent software that is freely available to academic institutions and non-profit organizations. It can be downloaded from our website http://www.soph.uab.edu/ssg_content.asp?id=1164.

Algorithms↗

PubMed2004

Towards sound epistemological foundations of statistical methods for high-dimensional biology.

A sound epistemological foundation for biological inquiry comes, in part, from application of valid statistical procedures. This tenet is widely appreciated by scientists studying the new realm of high-dimensional biology, or 'omic' research, which involves multiplicity at unprecedented scales. Many papers aimed at the high-dimensional biology community describe the development or application of statistical techniques. The validity of many of these is questionable, and a shared understanding about the epistemological foundations of the statistical methods themselves seems to be lacking. Here we offer a framework in which the epistemological foundation of proposed statistical methods can be evaluated.

Genetic Techniques↗