DNA microarray analysis reveals differential gene expression in the soleus muscle between male and female rats exposed to a high fat diet

It is well recognized that diet-induced dysfunctions in skeletal muscle are closely related with many metabolic diseases, such as obesity and diabetes. In the present study, we identified global changes in gender-dependent gene expressions in the soleus muscle of lean and obese rats fed a high fat diet (HFD), using DNA microarray analysis. Prior to microarray analysis, the body weight gains were found to be higher in male HFD rats than the female HFD rats. To better understand the detailed phenotypic differences in response to HFD feeding, we identified differential gene expression in soleus muscle between the genders. To this end, we extracted and summarized the genes that were up- or down-regulated more than 1.5-fold between the genders in the microarray data. As expected, a greater number of genes encoding myofibrillar proteins and glycolytic proteins were expressed higher in males than females when exposed to HFD, reflecting greater muscular activity and higher capacity for utilizing glucose as an energy fuel. However, a series of genes involved in oxidative metabolism and cellular defenses were more up-regulated in females than males. These results allowed us to conclude that compared to males, females have greater fat clearing capacity in skeletal muscle through the activation of genes encoding enzymes for fat oxidation. In conclusion, our microarray data provide a better understanding of the molecular events underlying gender dimorphism in soleus muscle, and will provide valuable information in improving gender awareness in the health care system.     

Genome-wide profiling of gene expression in 29 normal human tissues with a cDNA microarray.

We have performed a comprehensive analysis of the expression profiles in 25 adult and 4 fetal human tissues by means of a cDNA microarray consisting of 23,040 human genes. This study revealed a number of genes that were expressed specifically in each of those tissues. Among the 29 tissues examined, 4,080 genes were highly expressed (at least a five-fold expression ratio) in one or only a few tissues and 1,163 of those were expressed exclusively (more than a ten-fold higher expression ratio) in a particular tissue. Expression of some of the genes in the latter category was confirmed by northern analysis. A hierarchical clustering analysis of gene-expression profiles in nerve tissues (adult brain, fetal brain, and spinal cord), lymphoid tissues (bone marrow, thymus, spleen, and lymph node), muscle tissues (heart and skeletal muscle), or adipose tissues (mesenteric adipose and mammary gland) identified a set of genes that were commonly expressed among related tissues. These data should provide useful information for medical research, especially for efforts to identify tissue-specific molecules as potential targets of novel drugs to treat human diseases.     

Technology for microarray analysis of gene expression

The past year has demonstrated the versatility of microarrays for the analysis of whole model-organism genomes and has seen the development of chips to measure the expression of 40,000 human genes. Microarray technology has also become considerably more robust and sensitive. Technology enhancements include the use of noncontact printing methods, improved 2-color sample preparation, and statistically based software for data analysis.     

Genome-wide gene expression analysis in mouse embryonic stem cells

Embryonic stem cell studies have generated great interest, due to their ability to form a wide variety of matured cells. However, there remains a poor understanding of mechanisms regulating the cell state of embryonic stem cells (ESCs) and of the genes they express during early differentiation. Gene expression analysis may be a valuable tool to elucidate either the molecular pathways involved in self-renewal and pluripotency, or early differentiation and to identify potential molecular therapy targets. The aim of this study was to characterize at the molecular level the undifferentiated mouse ESC state and the early development towards embryoid bodies. To attempt this issue, we performed CodeLink Mouse Uniset I 20K bioarrays in a well-characterized mouse ESC line, MES3, 3- and 7 day-old embryoid bodies and we compared our findings with those in adult tissue cells. Gene expression results were subsequently validated in a commercial stem cell line, CGR8 (ATCC). Significance Analysis of Microarrays (SAM) was used to identify statistically significant changes in microarray data. We identified 3664 genes expressed at significantly greater levels in MES3 stem cells than in adult tissue cells, which included 611 with 3-fold higher gene expression levels versus the adult cells. We also investigated the gene expression profile during early embryoid body formation, identifying 2040 and 2243 genes that were up-regulated in 3- and 7- day-old embryoid bodies, respectively. Our gene expression results in MES3 cells were partially confirmed in CGR8 cells, showing numerous genes that are expressed in both mouse stem cells. In conclusion, our results suggest that commonly expressed genes may be strong candidates for involvement in the maintenance of a pluripotent and undifferentiated phenotype and in early development.     

Endophyte-infected tall fescue diet alters gene expression in heifer luteal tissue as revealed by interspecies microarray analysis

Cattle consuming endophyte-infected tall fescue grass have an associated reduction in circulating progesterone and reduced reproductive rates. In this study, commercially available rat microarrays were used to analyze the gene expression in luteal tissues from heifers fed endophyte-free fescue, endophyte-infected fescue, or endophyte-infected fescue supplemented with the dopamine (DA) antagonist, domperidone. The number of hybridized spots represented approximately 40% of the total 10,000 rat genes/ESTs evaluated. Each luteal sample was analyzed in triplicate, resulting in within treatment correlation coefficients of >/=0.98. Median values of mRNA abundance from luteal tissue taken from the endophyte-infected fed heifers revealed 598 genes and ESTs that were down regulated and 56 genes and ESTs that were upregulated compared with luteal mRNA values from the endophyte-free treatment. There were fewer comparative differences between median values from luteal mRNA from the endophyte-free versus feeding endophyte-infected plus domperidone treated heifers. Only 19 genes and ESTs were upregulated and two were down-regulated.     

Differential analysis of DNA microarray gene expression data

Here, we review briefly the sources of experimental and biological variance that affect the interpretation of high-dimensional DNA microarray experiments. We discuss methods using a regularized t-test based on a Bayesian statistical framework that allow the identification of differentially regulated genes with a higher level of confidence than a simple t-test when only a few experimental replicates are available. We also describe a computational method for calculating the global false-positive and false-negative levels inherent in a DNA microarray data set. This method provides a probability of differential expression for each gene based on experiment-wide false-positive and -negative levels driven by experimental error and biological variance.     

Weighted analysis of microarray gene expression using maximum-likelihood

MOTIVATION: The numerical values of gene expression measured using microarrays are usually presented to the biological end-user as summary statistics of spot pixel data, such as the spot mean, median and mode. Much of the subsequent data analysis reported in the literature, however, uses only one of these spot statistics. This results in sub-optimal estimates of gene expression levels and a need for improvement in quantitative spot variation surveillance. RESULTS: This paper develops a maximum-likelihood method for estimating gene expression using spot mean, variance and pixel number values available from typical microarray scanners. It employs a hierarchical model of variation between and within microarray spots. The hierarchical maximum-likelihood estimate (MLE) is shown to be a more efficient estimator of the mean than the 'conventional' estimate using solely the spot mean values (i.e. without spot variance data). Furthermore, under the assumptions of our model, the spot mean and spot variance are shown to be sufficient statistics that do not require the use of all pixel data.The hierarchical MLE method is applied to data from both Monte Carlo (MC) simulations and a two-channel dye-swapped spotted microarray experiment. The MC simulations show that the hierarchical MLE method leads to improved detection of differential gene expression particularly when 'outlier' spots are present on the arrays. Compared with the conventional method, the MLE method applied to data from the microarray experiment leads to an increase in the number of differentially expressed genes detected for low cut-off P-values of interest.     

Genome-wide analysis of gene expression in human embryonic tooth germ

While numerous genes that play important regulatory roles during tooth development in mice have been identified, little is known about gene expression profile and their function during human odontogenesis. To unveil expression profile of odontogenic genes in humans, we conducted genome-wide gene expression analysis by microarray assays to analyze differential gene expression between tooth germ and lip tissue from 11-week old human fetuses. We identified 167 genes that are strongly expressed in the cap stage tooth germ as compared to the lip tissue. Among them, 145 genes were further identified by gene ontology enrichment analysis that are highly represented in multiple gene ontology classes, include extracellular components, sequence-specific DNA binding proteins, Wnt-protein binding molecules, system development, organogenesis, and cell differentiation. Sixty-seven genes that are known to be associated with mammalian tooth development and tooth abnormalities were identified. Real-time PCR was further employed to validate microarray data. Moreover, in situ hybridization assay demonstrated tooth type specific expression of ISL1 and BARX1 in the incisor, canine, and molar respectively, consistent with microarray results. Our results represent a set of reliable data that could provide a solid base for future elaboration of molecular mechanisms underlying human tooth development.     

Quantitative trait associated microarray gene expression data analysis

Selection on phenotypes may cause genetic change. To understand the relationship between phenotype and gene expression from an evolutionary viewpoint, it is important to study the concordance between gene expression and profiles of phenotypes. In this study, we use a novel method of clustering to identify genes whose expression profiles are related to a quantitative phenotype. Cluster analysis of gene expression data aims at classifying genes into several different groups based on the similarity of their expression profiles across multiple conditions. The hope is that genes that are classified into the same clusters may share underlying regulatory elements or may be a part of the same metabolic pathways. Current methods for examining the association between phenotype and gene expression are limited to linear association measured by the correlation between individual gene expression values and phenotype. Genes may be associated with the phenotype in a nonlinear fashion. In addition, groups of genes that share a particular pattern in their relationship to phenotype may be of evolutionary interest. In this study, we develop a method to group genes based on orthogonal polynomials under a multivariate Gaussian mixture model. The effect of each expressed gene on the phenotype is partitioned into a cluster mean and a random deviation from the mean. Genes can also be clustered based on a time series. Parameters are estimated using the expectation-maximization algorithm and implemented in SAS. The method is verified with simulated data and demonstrated with experimental data from 2 studies, one clusters with respect to severity of disease in Alzheimer's patients and another clusters data for a rat fracture healing study over time. We find significant evidence of nonlinear associations in both studies and successfully describe these patterns with our method. We give detailed instructions and provide a working program that allows others to directly implement this method in their own analyses.     

Genome-wide profiling of gene expression in the epididymis of alpha-chlorohydrin-induced infertile rats using an oligonucleotide microarray

Background  

As one of the chlorinated antifertility compounds, alpha-chlorohydrin (ACH) can inhibit glyceraldehyde-3-phosphate dehydrogenase (G3PDH) activity in epididymal sperm and affect sperm energy metabolism, maturation and fertilization, eventually leading to male infertility. Further studies demonstrated that the inhibitory effect of ACH on G3PDH is not only confined to epididymal sperm but also to the epididymis. Moreover, little investigation on gene expression changes in the epididymis after ACH treatment has been conducted. Therefore, gene expression studies may indicate new epididymal targets related to sperm maturation and fertility through the analysis of ACH-treated infertile animals.