Microarray analysis of peroxisome proliferator-activated receptor-gamma induced changes in gene expression in macrophages

We used a combination of expression microarray and Northern blot analyses to identify target genes for peroxisome proliferator-activated receptor (PPAR) gamma in RAW264.7 macrophages. PPARgamma natural ligand 15-deoxy-Delta(12,14) prostaglandin and synthetic ligands ciglitazone and rosiglitazone increased the expression of scavenger receptor CD36 and ATP-binding cassette transporter A1, as well as adipophilin (a lipid droplet coating protein involved in intracellular lipid storage and transport), calpain (a protease implicated in ABCA1 protein degradation), and ADAM8 (a disintegrin and metalloprotease protein involved in cell adhesion). These findings are relevant to understanding the effect of PPARgamma activation on gene expression and cognate pathways in macrophages.     

Comparison of commercial probe labeling kits for microarray: towards quality assurance and consistency of reactions

Microarray technology is readily available to scientists interested in gene expression. Commensurate with this availability is the growing market in accessory products offering convenience but potentially variable performance. Here we evaluate seven commercial kits for probe labeling against a human apoptosis oligonucleotide array. All kits were found to label probes successfully using the manufacturers' instructions. The Stratagene Fairplay Microarray Labeling Kit was the most sensitive, with an overall call rate of 74% and the lowest rate of indeterminant calls for the HEK and HepG2 cell lines. The Invitrogen SuperScript Indirect cDNA Labeling System showed the most reproducible gene expression pattern and the least technical variation, both in terms of signal strength and between replicates on each array. The Promega Pronto! Plus System showed the least dye bias however, a higher level of variation between replicates was observed. Pairwise comparisons revealed that the Promega Pronto! Plus System and Invitrogen SuperScript Indirect cDNA Labeling System had the most similarity in their patterns of gene expression. Results obtained suggest variability in the performance of commercial kits between different manufacturers. This study supports the need to conduct comparative evaluations of commercial microarray probe labeling kits and the need for validation prior to use.     

cRNA target preparation for microarrays: comparison of gene expression profiles generated with different amplification procedures

Microarray technology has become a standard tool for generation of gene expression profiles to explore human disease processes. Being able to start from minute amounts of RNA extends the fields of application to core needle biopsies, laser capture microdissected cells, and flow-sorted cells. Several RNA amplification methods have been developed, but no extensive comparability and concordance studies of gene expression profiles are available. Different amplification methods may produce differences in gene expression patterns. Therefore, we compared profiles processed by a standard microarray protocol with three different types of RNA amplification: (i) two rounds of linear target amplification, (ii) random amplification, and (iii) amplification based on a template switching mechanism. The latter two methods accomplish target amplification in a nonlinear way using PCR technology. Starting from as little as 50 ng of total RNA, the yield of labeled cRNA was sufficient for hybridization to Affymetrix HG-U133A GeneChip array using the respective methods. Replicate experiments were highly reproducible for each method. In comparison with the standard protocol, all three approaches are less sensitive and introduced a minor but clearly detectable bias of the detection call. In conclusion, the three amplification protocols used are applicable for GeneChip analysis of small tissue samples.     

Outlier sums for differential gene expression analysis

We propose a method for detecting genes that, in a disease group, exhibit unusually high gene expression in some but not all samples. This can be particularly useful in cancer studies, where mutations that can amplify or turn off gene expression often occur in only a minority of samples. In real and simulated examples, the new method often exhibits lower false discovery rates than simple t-statistic thresholding. We also compare our approach to the recent cancer profile outlier analysis proposal of Tomlins and others (2005).     

Dynamic models of gene expression and classification

Powerful new methods, like expression profiles using cDNA arrays, have been used to monitor changes in gene expression levels as a result of a variety of metabolic, xenobiotic or pathogenic challenges. This potentially vast quantity of data enables, in principle, the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Here we present a general approach to developing dynamic models for analyzing time series of whole genome expression. In this approach, a self-consistent calculation is performed that involves both linear and non-linear response terms for interrelating gene expression levels. This calculation uses singular value decomposition (SVD) not as a statistical tool but as a means of inverting noisy and near-singular matrices. The linear transition matrix that is determined from this calculation can be used to calculate the underlying network reflected in the data. This suggests a direct method of classifying genes according to their place in the resulting network. In addition to providing a means to model such a large multivariate system this approach can be used to reduce the dimensionality of the problem in a rational and consistent way, and suppress the strong noise amplification effects often encountered with expression profile data. Non-linear and higher-order Markov behavior of the network are also determined in this self-consistent method. In data sets from yeast, we calculate the Markov matrix and the gene classes based on the linear-Markov network. These results compare favorably with previously used methods like cluster analysis. Our dynamic method appears to give a broad and general framework for data analysis and modeling of gene expression arrays. Electronic Publication     

Microarray analysis of ageing-related signatures and their expression in tumors based on a computational biology approach

Ageing and cancer have been associated with genetic and genomic changes.The identification of common signatures between ageing and cancer can reveal shared molecular mechanisms underlying them.In this study,we collected ageing-related gene signatures from ten published studies involved in six different human tissues and an online resource.We found that most of these gene signatures were tissuespecific and a few were related to multiple tissues.We performed a genome-wide examination of the expression of these signatures in various human tumor types,and found that a large proportion of these signatures were universally differentially expressed among normal vs.tumor phenotypes.Functional analyses of the highly-overlapping genes between ageing and cancer using DAVID tools have identified important functional categories and pathways linking ageing with cancer.The convergent and divergent mechanisms between ageing and cancer are discussed.This study provides insights into the biology of ageing and cancer,suggesting the possibility of potential interventions aimed at postponing ageing and preventing cancer.     

RNA expression analysis from formalin fixed paraffin embedded tissues

Formalin fixation and paraffin embedding (FFPE) is the most commonly used method worldwide for tissue storage. This method preserves the tissue integrity but causes extensive damage to nucleic acids stored within the tissue. As methods for measuring gene expression such as RT-PCR and microarray are adopted into clinical practice there is an increasing necessity to access the wealth of information locked in the Formalin fixation and paraffin embedding archives. This paper reviews the progress in this field and discusses the unique opportunities that exist for the application of these techniques in the development of personalized medicine.     

DNA array profiling of gene expression changes during maize embryo development

We are using DNA microarray-based gene expression profiling to classify temporal patterns of gene expression during the development of maize embryos, to understand mRNA-level control of embryogenesis and to dissect metabolic pathways and their interactions in the maize embryo. Genes involved in carbohydrate, fatty acid, and amino acid metabolism, the tricarboxylic acid (TCA) cycle, glycolysis, the pentose phosphate pathway, embryogenesis, membrane transport, signal transduction, cofactor biosynthesis, photosynthesis, oxidative phosphorylation and electron transfer, as well as 600 random complementary DNA (cDNA) clones from maize embryos, were arrayed on glass slides. DNA arrays were hybridized with fluorescent dye-labeled cDNA probes synthesized from kernel and embryo poly(A)⁺RNA from different stages of maize seed development. Several characteristic developmental patterns of expression were identified and correlated with gene function. Patterns of coordinated gene expression in the TCA cycle and glycolysis were analyzed in detail. The steady state level of poly(A)⁺ RNA for many genes varies dramatically during maize embryo development. Expression patterns of genes coding for enzymes of fatty acid biosynthesis and glycolysis are coordinately regulated during development. Genes of unknown function may by assigned a hypothetical role based on their patterns of expression resembling well characterized genes. Electronic supplementary material to this paper can be obtained by using the Springer LINK server located at http://dx.doi.org/10.1007/s10142-002-0046-6. Electronic Publication     

Chromosome-specific spatial periodicities in gene expression revealed by spectral analysis

Recent years have seen an unprecedented surge of research activity in studies of gene expression. This extensive work, however, has been almost uniformly focused on genome-wide gene expression and has largely ignored the fundamental fact that every gene has a specific chromosome location. We propose a novel method of spectral analysis for detecting hidden periodicities in gene expression signals ordered along the length of each chromosome. Using this method, we have discovered that each chromosome in rodents and humans has a unique periodic pattern of gene expression. The uncovered spatial periodicities in gene expression are tissue-specific in the sense that the largest differences in humans were observed between two normal tissues (brain and mammary gland) as well as between their tumor counterparts (glioma and breast cancer). The smallest differences resulted from the comparison of tumors (glioma and breast cancer) with their normal counterparts. All such effects do not extend to all chromosomes but are limited to only some of them. The estimated periods and amplitudes are identical for the genes located on the positive and negative DNA strands. While precise molecular mechanisms of chromosome-specific periodicities in gene expression have yet to be unraveled, their universal presence in different tissues adds another dimension to the current understanding of the genome organization.