Biomedical subjects

Stanislav O Zakharkin

Publications and source records attributed to Stanislav O Zakharkin.

9 recordsLinked to original sources

PubMed2006

Stoichiometry of a pore mutation that abolishes picrotoxin-mediated antagonism of the GABAA receptor.

Picrotoxin, a potent antagonist of the inhibitory central nervous system GABAA and glycine receptors, is believed to interact with residues that line the central ion pore. These pore-lining residues are in the second transmembrane domain (TM2) of each of the five constituent subunits. One of these amino acids, a threonine at the 6' location, when mutated to phenylalanine, abolishes picrotoxin sensitivity. It has been suggested that this threonine, via hydrogen bonding, directly interacts with the picrotoxin molecule. We previously demonstrated that this mutation, in the alpha, beta or gamma subunit, can impart picrotoxin resistance to the GABA receptor. Since the functional pentameric GABA receptor contains two alpha subunits, two beta subunits and one gamma subunit, it is not clear how many alpha and beta subunits must carry this mutation to impart the resistant phenotype. In this study, by coexpression of mutant alpha or beta subunits with their wild-type counterparts in various defined ratios, we demonstrate that any single subunit carrying the 6' mutation imparts picrotoxin resistance. Implications of this finding in terms of the mechanism of antagonism are considered.

Animals↗

PubMed2006

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.

Computational Biology↗

PubMed2006

Optimal allocation of replicates for measurement evaluation studies.

Optimal experimental design is important for the efficient use of modern high-throughput technologies such as microarrays and proteomics. Multiple factors including the reliability of measurement system, which itself must be estimated from prior experimental work, could influence design decisions. In this study, we describe how the optimal number of replicate measures (technical replicates) for each biological sample (biological replicate) can be determined. Different allocations of biological and technical replicates were evaluated by minimizing the variance of the ratio of technical variance (measurement error) to the total variance (sum of sampling error and measurement error). We demonstrate that if the number of biological replicates and the number of technical replicates per biological sample are variable, while the total number of available measures is fixed, then the optimal allocation of replicates for measurement evaluation experiments requires two technical replicates for each biological replicate. Therefore, it is recommended to use two technical replicates for each biological replicate if the goal is to evaluate the reproducibility of measurements.

Computational Biology↗

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↗

PubMed2005

Catechol-O-methyl-transferase functional polymorphism and nicotine dependence: an evaluation of nonreplicated results.

Review articles have focused attention on and cited possible reasons for the nonreplication of genetic association studies. Herein, we illustrate how one might work through these possible reasons to make a judgment about the most plausible reason(s) when faced with two or more studies which yield seemingly inconsistent results. In the first study, 342 treatment-seeking smokers were genotyped for the Val108Met polymorphism in the functional catechol-O-methyl-transferase (COMT) locus. Alleles coding Val at codon 108 are denoted as H and those coding Met are denoted as L. An association between presence of the "H" (high activity) allele and pretreatment level of nicotine dependence level using the Fagerstrom Test for Nicotine Dependence was detected (P = 0.0072), after controlling for baseline body mass index (BMI, kg/m2), depression symptoms, and age. To validate this initial finding, 443 treatment-seeking smokers from an independent smoking cessation clinical trial were genotyped for the COMT polymorphism. Within the second study, no association between presence of the "H" allele and nicotine dependence was detected (P = 0.6418) after controlling for baseline BMI, depression symptoms, and age. We critically reviewed both studies with regard to often cited reasons for nonreplication, including type I error, population stratification, low statistical power, and imprecise measures of phenotype. Although in our opinion the failure to replicate the initial association in the second study is likely either the result of low statistical power to detect a small effect or effect heterogeneity, thorough analyses failed to definitively identify the reason for nonreplication.

Adult↗

PubMed2004

Mapping the rho1 GABA(C) receptor agonist binding pocket. Constructing a complete model.

Gamma-aminobutyric acid (GABA) is the major inhibitory neurotransmitter in the mammalian brain. The GABA receptor type C (GABA(C)) is a ligand-gated ion channel with pharmacological properties distinct from the GABA(A) receptor. To date, only three binding domains in the recombinant rho1 GABA(C) 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 rho1 GABA(C) 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 GABA(C) 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 beta-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 rho1 GABA(C) receptor, they are facing toward the putative binding pocket. Combined with previous findings, a complete model of the GABA(C) receptor binding pocket was proposed and discussed in comparison with the GABA(A) receptor binding pocket.

Amino Acid Sequence↗

PubMed2004

Identification of two mariner-like elements in the genome of the mosquito Ochlerotatus atropalpus.

Two distinct mariner-like elements, Atmar-1 and Atmar-2, were isolated from the genome of the mosquito Ochlerotatus atropalpus. Full-sized Atmar-1 elements, obtained by screening a genomic library, have a 1293-bp consensus sequence with 27-bp inverted terminal repeats and a 1047-bp open reading frame (ORF) encoding the transposase. The Atmar-2 elements were amplified by polymerase chain reaction from genomic DNA and contain the central part of the transposase ORF. Individual clones of both mariner elements contain deletions, frameshifts, and stop codons. The Atmar-1 elements are present in 370-1200 copies, while the Atmar-2 elements are present in approximately 100-300 copies per haploid genome. One of the Atmar-1 elements, Atmar-1.33, could be mobilized, suggesting the presence of functional Atmar-1 elements elsewhere in the genome. Phylogenetic analysis demonstrated that Atmar-1 elements belong to the irritans subfamily and Atmar-2 elements to the cecropia subfamily of mariner elements.

Amino Acid Sequence↗

PubMed2004

Applications of Bayesian statistical methods in microarray data analysis.

Microarray technology allows one to measure gene expression levels simultaneously on the whole-genome scale. The rapid progress generates both a great wealth of information and challenges in making inferences from such massive data sets. Bayesian statistical modeling offers an alternative approach to frequentist methodologies, and has several features that make these methods advantageous for the analysis of microarray data. These include the incorporation of prior information, flexible exploration of arbitrarily complex hypotheses, easy inclusion of nuisance parameters, and relatively well developed methods to handle missing data. Recent developments in Bayesian methodology generated a variety of techniques for the identification of differentially expressed genes, finding genes with similar expression profiles, and uncovering underlying gene regulatory networks. Bayesian methods will undoubtedly become more common in the future because of their great utility in microarray analysis.

Bayes Theorem↗