Mapping the ρ1 GABAC Receptor Agonist Binding Pocket

γ-Aminobutyric acid (GABA) is the major inhibitory neurotransmitter in the mammalian brain. The GABA receptor type C (GABAC) is a ligand-gated ion channel with pharmacological properties distinct from the GABAA receptor. To date, only three binding domains in the recombinant ρ1 GABAC receptor have been recognized among six potential regions. In this report, using the substituted cysteine accessibility method, we scanned three potential regions previously unexplored in the ρ1 GABAC receptor, corresponding to the binding loops A, E, and F in the structural model for ligand-gated ion channels. The cysteine accessibility scanning and agonist/antagonist protection tests have resulted in the identification of residues in loops A and E, but not F, involved in forming the GABAC receptor agonist binding pocket. Three of these newly identified residues are in a novel region corresponding to the extended stretch of loop E. In addition, the cysteine accessibility pattern suggests that part of loop A and part of loop E have a β-strand structure, whereas loop F is a random coil. Finally, when all of the identified ligand binding residues are mapped onto a three-dimensional homology model of the amino-terminal domain of the ρ1 GABAC receptor, they are facing toward the putative binding pocket. Combined with previous findings, a complete model of the GABAC receptor binding pocket was proposed and discussed in comparison with the GABAA receptor binding pocket

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 4(th )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

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

Epistemological Issues in Omics and High-Dimensional Biology: Give the People What They Want

Gene expression microarrays have been the vanguard of new analytic approaches in high-dimensional biology. Draft sequences of several genomes coupled with new technologies allow study of the influences and responses of entire genomes rather than isolated genes. This has opened a new realm of highly dimensional biology where questions involve multiplicity at unprecedented scales: thousands of genetic polymorphisms, gene expression levels, protein measurements, genetic sequences, or any combination of these and their interactions. Such situations demand creative approaches to the processes of inference, estimation, prediction, classification, and study design. Although bench scientists intuitively grasp the need for flexibility in the inferential process, the elaboration of formal supporting statistical frameworks is just at the very start. Here, we will discuss some of the unique statistical challenges facing investigators studying high-dimensional biology, describe some approaches being developed by statistical scientists, and offer an epistemological framework for the validation of proffered statistical procedures. A key theme will be the challenge in providing methods that a statistician judges to be sound and a biologist finds informative. The shift from family-wise error rate control to false discovery rate estimation and to assessment of ranking and other forms of stability will be portrayed as illustrative of approaches to this challenge

Urine Metabolomics Analysis for Kidney Cancer Detection and Biomarker Discovery*S⃞

Renal cell carcinoma (RCC) accounts for 11,000 deaths per year in the United States. When detected early, generally serendipitously by imaging conducted for other reasons, long term survival is generally excellent. When detected with symptoms, prognosis is poor. Under these circumstances, a screening biomarker has the potential for substantial public health benefit. The purpose of this study was to evaluate the utility of urine metabolomics analysis for metabolomic profiling, identification of biomarkers, and ultimately for devising a urine screening test for RCC. Fifty urine samples were obtained from RCC and control patients from two institutions, and in a separate study, urine samples were taken from 13 normal individuals. Hydrophilic interaction chromatography-mass spectrometry was performed to identify small molecule metabolites present in each sample. Cluster analysis, principal components analysis, linear discriminant analysis, differential analysis, and variance component analysis were used to analyze the data. Previous work is extended to confirm the effectiveness of urine metabolomics analysis using a larger and more diverse patient cohort. It is now shown that the utility of this technique is dependent on the site of urine collection and that there exist substantial sources of variation of the urinary metabolomic profile, although group variation is sufficient to yield viable biomarkers. Surprisingly there is a small degree of variation in the urinary metabolomic profile in normal patients due to time since the last meal, and there is little difference in the urinary metabolomic profile in a cohort of pre- and postnephrectomy (partial or radical) renal cell carcinoma patients, suggesting that metabolic changes associated with RCC persist after removal of the primary tumor. After further investigations relating to the discovery and identity of individual biomarkers and attenuation of residual sources of variation, our work shows that urine metabolomics analysis has potential to lead to a diagnostic assay for RCC

HDBStat!: A platform-independent software suite for statistical analysis of high dimensional biology data-2

<p><b>Copyright information:</b></p><p>Taken from "HDBStat!: A platform-independent software suite for statistical analysis of high dimensional biology data"</p><p>BMC Bioinformatics 2005;6():86-86.</p><p>Published online 6 Apr 2005</p><p>PMCID:PMC1087834.</p><p>Copyright © 2005 Trivedi et al; licensee BioMed Central Ltd.</p