Decreased expression and DNA methylation levels of GATAD1 in preeclamptic placentas

Expression of syncytin-1, or the human endogenous retroviral family W member 1 (HERVWE1) in human placental trophoblasts is regulated by DNA methylation. Increased DNA methylation and decreased expression of syncytin-1 have been observed in preeclamptic placentas. The syncytin-1-mediated fusogenic as well as non-fusogenic activities, e.g., cell cycle promotion, anti-apoptosis, and immune suppression, are implicated in the pathogenic changes in preeclamptic placentas. It is noteworthy that in a close vicinity to syncytin-1 there are two genes, peroxisome biogenesis factor 1 (PEX1) and GATA zinc finger domain containing 1 (GATAD1), as well as multiple CpG islands around these genes. In this study we determined if these adjacent genes might, like syncytin-1, subject to epigenetic regulation in preeclamptic placentas. Data from quantitative real-time PCR and Western blotting indicated that while PEX1 expression remained stable, GATAD1 expression was significantly decreased in the third-trimester placentas associated with preeclampsia than those associated with normal pregnancy. Immunohistochemistry detected high GATAD1 expression in trophoblast linage, and confirmed its reduced levels in preeclamptic placentas. However, COBRA and bisulfate sequencing detected decreased DNA methylation in levels in the 3 [prime] region of GATAD1 gene in preeclamptic placentas. The positive correlation between 3 [prime] methylation and GATAD1 expression was confirmed by treatment of choriocarcinoma JAR cells with DNMT inhibitor. These data pointed to a potential role of GATAD1 for the syncytium deficiency often associated with preeclamptic placentas. The sharp contrast of the methylation alterations for the closely positioned GATAD1 and HERVWE1 may provide a useful model for studying the accurate control of DNA methylation as well as their positive and negative impact on gene expression in placental trophoblasts.     

Normalization and quantification of differential expression in gene expression microarrays

Array-based gene expression studies frequently serve to identify genes that are expressed differently under two or more conditions. The actual analysis of the data, however, may be hampered by a number of technical and statistical problems. Possible remedies on the level of computational analysis lie in appropriate preprocessing steps, proper normalization of the data and application of statistical testing procedures in the derivation of differentially expressed genes. This review summarizes methods that are available for these purposes and provides a brief overview of the available software tools.     

Discovery of glpC, an organic solvent tolerance-related gene in Escherichia coli, using gene expression profiles from DNA microarrays

Gene expression profiles were collected from Escherichia coli strains (OST3410, TK33, and TK31) before and after exposure to organic solvents, and the six genes that showed higher gene expression were selected. Among these genes, glpC encoding the anaerobic glycerol-3-phosphate dehydrogenase subunit C remarkably increased the organic solvent tolerance.     

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

MOTIVATION: An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. RESULTS: By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.     

Impairment of mitochondrial respiration in platelets and placentas: a pilot study in preeclamptic pregnancies

Molecular and Cellular Biochemistry - Preeclampsia (PE) is a major complication of pregnancy with partially elucidated pathophysiology. Placental mitochondrial dysfunction has been increasingly...     

Monitoring gene expression in mixed microbial communities by using DNA microarrays

A DNA microarray to monitor the expression of bacterial metabolic genes within mixed microbial communities was designed and tested. Total RNA was extracted from pure and mixed cultures containing the 2,4-dichlorophenoxyacetic acid (2,4-D)-degrading bacterium Ralstonia eutropha JMP134, and the inducing agent 2,4-D. Induction of the 2,4-D catabolic genes present in this organism was readily detected 4, 7, and 24 h after the addition of 2,4-D. This strain was diluted into a constructed mixed microbial community derived from a laboratory scale sequencing batch reactor. Induction of two of five 2,4-D catabolic genes (tfdA and tfdC) from populations of JMP134 as low as 10(5) cells/ml was clearly detected against a background of 10(8) cells/ml. Induction of two others (tfdB and tfdE) was detected from populations of 10(6) cells/ml in the same background; however, the last gene, tfdF, showed no significant induction due to high variability. In another experiment, the induction of resin acid degradative genes was statistically detectable in sludge-fed pulp mill effluent exposed to dehydroabietic acid in batch experiments. We conclude that microarrays will be useful tools for the detection of bacterial gene expression in wastewaters and other complex systems.     

Indirect measurements of differential gene expression with cDNA microarrays

The use of universal RNA reference sets is an increasingly common approach to molecular classification studies with cDNA microarrays. Here we evaluated the reliability of indirect measurements of fluorescence ratios with a common RNA reference as a means of identifying differentially expressed genes. Comparisons of direct and indirect measures of differential gene expression showed a strong overall correlation in fluorescence ratio measurements but also a high degree of false positives in our indirect measurements. These results indicated that the application of more stringent ratio filters may be required when assessing differential gene expression utilizing a common RNA reference in classification studies.     

The application of DNA microarrays in gene expression analysis

DNA microarray technology is a new and powerful technology that will substantially increase the speed of molecular biological research. This paper gives a survey of DNA microarray technology and its use in gene expression studies. The technical aspects and their potential improvements are discussed. These comprise array manufacturing and design, array hybridisation, scanning, and data handling. Furthermore, it is discussed how DNA microarrays can be applied in the working fields of: safety, functionality and health of food and gene discovery and pathway engineering in plants.     

Detection of coregulation in differential gene expression profiles

Genomics and proteomics approaches generate distinct gene expression and protein profiles, listing individual genes embedded in broad functional terms as gene ontologies. However, interpretation of gene profiles in a regulatory and functional context remains a major issue. Elucidation of regulatory mechanisms at the gene expression level via analysis of promoter regions is a prominent procedure to decipher such gene regulatory networks. We propose a novel genetic algorithm (GA) to extract joint promoter modules in a set of coexpressed genes as resulting from differential gene expression experiments. Algorithm design has focused on the following constraints: (I) identification of the major promoter modules, which are (II) characterized by a maximum number of joint motifs and (III) are found in a maximum number of coexpressed genes. The capability of the GA in detecting multiple modules was evaluated on various test data sets, analyzing the impact of the number of motifs per promoter module, the number of genes associated with a module, as well as the total number of distinct promoter modules encoded in a sequence set. In addition to the test data sets, the GA was evaluated on two biological examples, namely a muscle-specific data set and the upstream sequences of the beta-actin gene (ACTB) derived from different species, complemented by a comparison to alternative promoter module identification routines.     

Application of Bayesian networks for inferring cause-effect relations from gene expression profiles of cancer versus normal cells

The paper is devoted to two questions: whether distinction of causes versus effects of neoplasia leaves a signature in the cancer versus normal gene expression profiles and whether roles of genes, "causes" or "effects", can be inferred from repeated measurements of gene expressions. We model joint probability distributions of logarithms of gene expressions with the use of Bayesian networks (BN). Fitting our models to real data confirms that our BN models have the ability to explain some aspects of observational evidence from DNA microarray experiments. Effects of neoplastic transformation are well seen among genes with the highest power to differentiate between normal and cancer cells. Likelihoods of BNs depend on the biological role of selected genes, defined by Gene Ontology. Also predictions of our BN models are coherent with the set of putative causes and effects constructed based on our data set of papillary thyroid cancer.     

Simultaneous measurement of gene expression for hepatotoxicity in thioacetamide-administered rats by DNA microarrays

DNA microarray technology was developed as a tool for simultaneously measuring a number of gene expression changes, and has been applied for investigations of toxicity assessments of chemicals. In this study, we used a typical hepatotoxicant, thioacetamide (TA), to find correlations between the extent of hepatotoxicity and certain gene expression patterns or specific gene expression profiles. TA was intraperitoneally administered at high (400 mg/kg), medium (150 mg/kg) or low (50 mg/kg) dose (four rats per group) and then the serum and liver were collected at the indicated time (6, 12, 24, 36 and 48 h). Serum biochemical markers were measured and hepatic mRNA expression profiles were analyzed by a DNA microarray. Serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were increased by TA-administration in a dose-dependent manner and reached the maximum at 24h. Hierarchical clustering analysis of all dosage groups revealed in 2 major clusters, distinguished by an early (6 and 12h) and a late (24, 36 and 48 h) phase. The early and late phase clusters were sorted in time- and dose-dependent manners. The major gene expression profile obtained by quality-threshold (QT) clustering analysis showed the same maximal toxic time as that estimated by the serum biochemical markers. The individual expression profiles of the candidate genes selected in our previous studies and the simultaneous gene expression patterns measured by five typical hepatotoxicants including TA also reflected the hepatotoxicity of TA. These findings suggest that the potential toxic effects appearing as gene expression changes are independent of the dosage of TA. This study suggested that the major gene expression profile estimated by QT clustering would be a sensitive marker of hepatotoxicity.     

Exploiting microarrays to reveal differential gene expression in the nervous system

Microarrays have been used in a wide variety of experimental systems, but realizing their full potential is contingent on sophisticated and rigorous experimental design and data analysis. This article highlights what is needed to get the most out of microarrays in terms of accurately and effectively revealing differential gene expression and regulation in the nervous system.     

Normal uniform mixture differential gene expression detection for cDNA microarrays

Background  

One of the primary tasks in analysing gene expression data is finding genes that are differentially expressed in different samples. Multiple testing issues due to the thousands of tests run make some of the more popular methods for doing this problematic.     

DNA microarrays and papillary thyroid carcinoma gene expression profile

The paper presents gene expression profile analysis with DNA microarrays and compares two core technological platforms used for this purpose - high density oligonucleotide microarrays and cDNA microarrays. With this background recent results of papillary thyroid carcinoma analysis with DNA microarrays are presented.