A methodology for global validation of microarray experiments

BACKGROUND: DNA microarrays are popular tools for measuring gene expression of biological samples. This ever increasing popularity is ensuring that a large number of microarray studies are conducted, many of which with data publicly available for mining by other investigators. Under most circumstances, validation of differential expression of genes is performed on a gene to gene basis. Thus, it is not possible to generalize validation results to the remaining majority of non-validated genes or to evaluate the overall quality of these studies. RESULTS: We present an approach for the global validation of DNA microarray experiments that will allow researchers to evaluate the general quality of their experiment and to extrapolate validation results of a subset of genes to the remaining non-validated genes. We illustrate why the popular strategy of selecting only the most differentially expressed genes for validation generally fails as a global validation strategy and propose random-stratified sampling as a better gene selection method. We also illustrate shortcomings of often-used validation indices such as overlap of significant effects and the correlation coefficient and recommend the concordance correlation coefficient (CCC) as an alternative. CONCLUSION: We provide recommendations that will enhance validity checks of microarray experiments while minimizing the need to run a large number of labour-intensive individual validation assays

A Monte Carlo simulation of ion transport at finite temperatures

We have developed a Monte Carlo simulation for ion transport in hot background gases, which is an alternative way of solving the corresponding Boltzmann equation that determines the distribution function of ions. We consider the limit of low ion densities when the distribution function of the background gas remains unchanged due to collision with ions. A special attention has been paid to properly treat the thermal motion of the host gas particles and their influence on ions, which is very important at low electric fields, when the mean ion energy is comparable to the thermal energy of the host gas. We found the conditional probability distribution of gas velocities that correspond to an ion of specific velocity which collides with a gas particle. Also, we have derived exact analytical formulas for piecewise calculation of the collision frequency integrals. We address the cases when the background gas is monocomponent and when it is a mixture of different gases. The developed techniques described here are required for Monte Carlo simulations of ion transport and for hybrid models of non-equilibrium plasmas. The range of energies where it is necessary to apply the technique has been defined. The results we obtained are in excellent agreement with the existing ones obtained by complementary methods. Having verified our algorithm, we were able to produce calculations for Ar$^+$ ions in Ar and propose them as a new benchmark for thermal effects. The developed method is widely applicable for solving the Boltzmann equation that appears in many different contexts in physics.Comment: 14 page

Role of Esrrg in the Fibrate-Mediated Regulation of Lipid Metabolism Genes in Human ApoA-I Transgenic Mice

We have used a new ApoA-I transgenic mouse model to identify by global gene expression profiling, candidate genes that affect lipid and lipoprotein metabolism in response to fenofibrate treatment. Multilevel bioinformatical analysis and stringent selection criteria (2-fold change, 0% false discovery rate) identified 267 significantly changed genes involved in several molecular pathways. The fenofibrate-treated group did not have significantly altered levels of hepatic human APOA-I mRNA and plasma ApoA-I compared with the control group. However, the treatment increased cholesterol levels to 1.95-fold mainly due to the increase in high-density lipoprotein (HDL) cholesterol. The observed changes in HDL are associated with the upregulation of genes involved in phospholipid biosynthesis and lipid hydrolysis, as well as phospholipid transfer protein. Significant upregulation was observed in genes involved in fatty acid transport and β-oxidation, but not in those of fatty acid and cholesterol biosynthesis, Krebs cycle and gluconeogenesis. Fenofibrate changed significantly the expression of seven transcription factors. The estrogen receptor-related gamma gene was upregulated 2.36-fold and had a significant positive correlation with genes of lipid and lipoprotein metabolism and mitochondrial functions, indicating an important role of this orphan receptor in mediating the fenofibrate-induced activation of a specific subset of its target genes.National Institutes of Health (HL48739 and HL68216); European Union (LSHM-CT-2006-0376331, LSHG-CT-2006-037277); the Biomedical Research Foundation of the Academy of Athens; the Hellenic Cardiological Society; the John F Kostopoulos Foundatio

Dynamic Epitope Expression from Static Cytometry Data: Principles and Reproducibility

Background: An imprecise quantitative sense for the oscillating levels of proteins and their modifications, interactions, and translocations as a function of the cell cycle is fundamentally important for a cartoon/narrative understanding for how the cell cycle works. Mathematical modeling of the same cartoon/narrative models would be greatly enhanced by an openended methodology providing precise quantification of many proteins and their modifications, etc. Here we present methodology that fulfills these features. Methodology: Multiparametric flow cytometry was performed on Molt4 cells to measure cyclins A2 and B1, phospho-S10histone H3, DNA content, and light scatter (cell size). The resulting 5 dimensional data were analyzed as a series of bivariate plots to isolate the data as segments of an N-dimensional ‘‘worm’ ’ through the data space. Sequential, unidirectional regions of the data were used to assemble expression profiles for each parameter as a function of cell frequency. Results: Analysis of synthesized data in which the true values where known validated the approach. Triplicate experiments demonstrated exceptional reproducibility. Comparison of three triplicate experiments stained by two methods (single cyclin or dual cyclin measurements with common DNA and phospho-histone H3 measurements) supported the feasibility of combining an unlimited number of epitopes through this methodology. The sequential degradations of cyclin A2 followed by cyclin B1 followed by de-phosphorylation of histone H3 were precisely mapped. Finally, a two phase expression rat

Cell Cycle-Related Cyclin B1 Quantification

To obtain non-relative measures of cell proteins, purified preparations of the same proteins are used as standards in Western blots. We have previously quantified SV40 large T antigen expressed over a several fold range in different cell lines and correlated the average number of molecules to average fluorescence obtained by cytometry and determined cell cycle phase related expression by calculation from multi-parametric cytometry data. Using a modified approach, we report quantification of endogenous cyclin B1 and generation of the cell cycle time related expression profile.Recombinant cyclin B1 was purified from a baculovirus lysate using an antibody affinity column and concentrated. We created fixed cell preparations from nocodazole-treated (high cyclin B1) and serum starved (low cyclin B1) PC3 cells that were either lyophilized (for preservation) or solubilized. The lysates and purified cyclin B1 were subjected to Western blotting; the cell preparations were subjected to cytometry, and fluorescence was correlated to molecules. Three untreated cell lines (K562, HeLa, and RKO) were prepared for cytometry without lyophilization and also prepared for Western blotting. These were quantified by Western blotting and by cytometry using the standard cell preparations.The standard cell preparations had 1.5 x 10(5) to 2.5 x 10(6) molecules of cyclin B1 per cell on average (i.e., 16-fold range). The average coefficient of variation was 24%. Fluorescence varied 12-fold. The relationship between molecules/cell (Western blot) and immunofluorescence (cytometry) was linear (r(2) = 0.87). Average cyclin B1 levels for the three untreated cell lines determined by Western blotting and cytometry agreed within a factor of 2. The non-linear rise in cyclin B1 in S phase was quantified from correlated plots of cyclin B1 and DNA content. The peak levels achieved in G2 were similar despite differences in lineage, growth conditions, and rates of increase through the cell cycle (range: 1.6-2.2 x 10(6) molecules per cell).Net cyclin B1 expression begins in G1 in human somatic cells lines; increases non-linearly with variation in rates of accumulation, but peaks at similar peak values in different cell lines growing under different conditions. This suggests tight quantitative end point control

Characterizing Protein-Protein Interactions with the Fragment Molecular Orbital Method

Proteins are vital components of living systems, serving as building blocks, molecular machines, enzymes, receptors, ion channels, sensors, and transporters. Protein-protein interactions (PPIs) are a key part of their function. There are more than 645,000 reported disease-relevant PPIs in the human interactome, but drugs have been developed for only 2% of these targets. The advances in PPI-focused drug discovery are highly dependent on the availability of structural data and accurate computational tools for analysis of this data. Quantum mechanical approaches are often too expensive computationally, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed and the ability to reveal key interactions that would otherwise be hard to detect. FMO provides essential information for PPI drug discovery, namely, identification of key interactions formed between residues of two proteins, including their strength (in kcal/mol) and their chemical nature (electrostatic or hydrophobic). In this chapter, we have demonstrated how three different FMO-based approaches (pair interaction energy analysis (PIE analysis), subsystem analysis (SA) and analysis of protein residue networks (PRNs)) have been applied to study PPI in three protein-protein complexes

MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS

The genome-wide association study (GWAS) approach has discovered hundreds of genetic variants associated with diseases and quantitative traits. However, despite clinical overlap and statistical correlation between many phenotypes, GWAS are generally performed one-phenotype-at-a-time. Here we compare the performance of modelling multiple phenotypes jointly with that of the standard univariate approach. We introduce a new method and software, MultiPhen, that models multiple phenotypes simultaneously in a fast and interpretable way. By performing ordinal regression, MultiPhen tests the linear combination of phenotypes most associated with the genotypes at each SNP, and thus potentially captures effects hidden to single phenotype GWAS. We demonstrate via simulation that this approach provides a dramatic increase in power in many scenarios. There is a boost in power for variants that affect multiple phenotypes and for those that affect only one phenotype. While other multivariate methods have similar power gains, we describe several benefits of MultiPhen over these. In particular, we demonstrate that other multivariate methods that assume the genotypes are normally distributed, such as canonical correlation analysis (CCA) and MANOVA, can have highly inflated type-1 error rates when testing case-control or non-normal continuous phenotypes, while MultiPhen produces no such inflation. To test the performance of MultiPhen on real data we applied it to lipid traits in the Northern Finland Birth Cohort 1966 (NFBC1966). In these data MultiPhen discovers 21% more independent SNPs with known associations than the standard univariate GWAS approach, while applying MultiPhen in addition to the standard approach provides 37% increased discovery. The most associated linear combinations of the lipids estimated by MultiPhen at the leading SNPs accurately reflect the Friedewald Formula, suggesting that MultiPhen could be used to refine the definition of existing phenotypes or uncover novel heritable phenotypes

Differential inflammatory microRNA and cytokine expression in pulmonary sarcoidosis

Sarcoidosis is a granulomatous disease of unknown etiology. The disease has an important inflammatory and immune component; however, its immunopathogenesis is not completely understood. Recently, the role of microRNAs (miRNAs), the small non-coding RNAs, has attracted attention as both being involved in pathogenesis and serving as disease markers. Accordingly, changes in the expression of some miRNAs have been also associated with different autoimmune pathologies. However, not much is known about the role of miRNAs in sarcoidosis. Therefore, the aim of this study was to compare the level of expression of selected miRNAs in healthy individuals and patients with sarcoidosis. We detected significantly increased level of miR-34a in peripheral blood mononuclear cells isolated from sarcoidosis patients. Moreover, significantly up-regulated levels of interferon (IFN)-γ, IFN-γ inducible protein (IP-10) and vascular endothelial growth factor were detected in sera of patients when compared to healthy subjects. Our results add to a known inflammatory component in sarcoidosis. Changes in the levels of miR-34a may suggest its involvement in the pathology of this disease