Sex-biased gene expression in mammals
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Abstract:
Sex differences in gene expression start at puberty and vary across species and organsKeywords:
Expression (computer science)
Nutrients as external elements of gene expression has important effect in regulation of gene expression.The anthor introduce nutrimental regulation of gene expression on mechanisms and path,and regulation of several nutriments in gene expression.
Expression (computer science)
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Objective: To screen the differential expression genes of Kanglaite Injection in treating cancer cachexia. Methods: mRNA was extracted from the blood cells of T739 animal model of C.C., hybridizated respectively on 20S gene chip. Analysis discuss on differential expression genes was carried out. Results: 5 differential expression genes were obtained. Among these genes, 4 genes were up-regulated and 1 gene was down-regulated. Most of these genes were related with immunity and metabolism of tumor. Conclusion: cDNA microarray for analysis of gene expression patterns is a powerful method to identify associated genes of Kanglaite.
Cancer Cachexia
Gene chip analysis
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Understanding how gene expression systems influence biological outcomes is an important goal for diverse areas of research. Gene expression profiling allows for the simultaneous measurement of expression levels for thousands of genes and the opportunity to use this information to increase biological understanding. Yet, the best way to relate this immense amount of information to biological outcomes is far from clear. Here, a novel approach to gene expression systems research is presented that focuses on understanding gene expression systems at the level of gene expression program regulation. It is suggested that such an approach has important advantages over current techniques and may provide novel insights into how gene expression systems are regulated to shape biological outcomes such as the development of disease or response to treatment.
Expression (computer science)
Biological pathway
Gene regulatory network
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Regulation of gene expression levels is essential for all living systems and transcription factors (TFs) are the main regulators of gene expression through their ability to repress or induce transcription. A balance between synthesis and degradation rates controls gene expression levels. To determine which rate is dominant, we analyzed the correlation between expression levels of a TF and its regulated gene based on a mathematical model. We selected about 280,000 expression patterns of 355 TFs and 647 regulated genes using DNA microarray data from the Gene Expression Omnibus (GEO) data repository. Based on our model, correlation between the expressions of TF-regulated gene pairs corresponds to tuning of the synthesis rate, whereas no correlation indicates excessive synthesis and requires tuning of the degradation rate. The gene expression relationships between TF-regulated gene pairs were classified into four types that correspond to different gene regulatory mechanisms. It was surprising that fewer than 20% of these genes were governed by the familiar regulatory mechanism, i.e., through the synthesis rate. Moreover, we performed pathway analysis and found that each classification type corresponded to distinct gene functions: cellular regulation pathways were dominant in the type with synthesis rate regulation and terms associated with diseases such as cancer, Parkinson's disease, and Alzheimer's disease were dominant in the type with degradation rate regulation. Interestingly, these diseases are caused by the accumulation of proteins. These results indicated that gene expression is regulated structurally, not arbitrarily, according to the gene function. This funding is indicative of a systematic control of transcription processes at the whole-cell level.
Gene regulatory network
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ABSTRACT It is not understood what evolutionary factors drive some genes to be expressed at a higher level than others. Here, we hypothesized that a gene’s function plays an important role in setting expression level. First, we established that each S. cerevisiae gene is maintained at a specific expression level by analyzing RNA-seq data from multiple studies. Next, we found that mRNA and protein levels were maintained for the orthologous genes in S. pombe , showing that gene function, conserved in orthologs, is important in setting expression level. To further explore the role of gene function in setting expression level, we analyzed mRNA and protein levels of S. cerevisiae genes within gene ontology (GO) categories. The GO framework systematically defines gene function based on experimental evidence. We found that several GO categories contain genes with statistically significant expression extremes; for example, genes involved in translation or energy production are highly expressed while genes involved in chromosomal activities, such as replication and transcription, are weakly expressed. Finally, we were able to predict expression levels using GO information alone. We created and optimized a linear equation that predicted a gene’s expression based on the gene’s membership in 161 GO categories. The greater number of GO categories with which a gene is associated, the more accurately expression could be predicted. Taken together, our analysis systematically demonstrates that gene function is an important determinant of expression level.
Pair-rule gene
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Evolutionary rates provide important information about the pattern and mechanism of evolution. Although the rate of gene sequence evolution has been well studied, the rate of gene expression evolution is poorly understood. In particular, it is unclear whether the gene expression level and tissue specificity influence the divergence of expression profiles between orthologous genes. Here we address this question using a microarray data set comprising the expression signals of 10,607 pairs of orthologous human and mouse genes from over 60 tissues per species. We show that the level of gene expression and the degree of tissue specificity are generally conserved between the human and mouse orthologs. The rate of gene expression profile change during evolution is negatively correlated with the level of gene expression, measured by either the average or the highest level among all tissues examined. This is analogous to the observation that the rate of gene (or protein) sequence evolution is negatively correlated with the gene expression level. The impacts of the degree of tissue specificity on the evolutionary rate of gene sequence and that of expression profile, however, are opposite. Highly tissue-specific genes tend to evolve rapidly at the gene sequence level but slowly at the expression profile level. Thus, different forces and selective constraints must underlie the evolution of gene sequence and that of gene expression.
Molecular evolution
Sequence (biology)
Rate of evolution
Divergence (linguistics)
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To study the genes differentially expressed in the liver of Kkay diabetic and normal mice by genomic-scale gene expression analysis.cDNA microarray chips containing 8,192 cDNAs were used to explore the gene expression pattern of Kkay mouse liver.One hundred and fifty-four genes were screened out, including 68 complete cDNAs and expressed sequence tags, and among them 40 genes were up-regulated and 114 genes were down-regulated respectively.Most of the gene expression analysis results were consistent with previous study, and the gene expression pattern of Kkay mouse based on cDNA microarray could be used for high-throughout screening out the genes associated with type 2 diabetes.
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Objective To analyze the differential gene expression profiling of liver in rats subjected to hemorrhagic shock(HS) and sham hemorrhage shock(SHAM) by gene chip technology, thus to evaluate the possible molecular pathogenesis of HS. Method 20 male Wistar rats were randomly divided into a SHAM group and a HS group, with 10 rats in each group. Hepatic gene expression profiles were detected by oligonucleotide microarrays of 5705 mouse genes in two groups for three times. Genes with ratio(R) > 2 were identified as up-regulated and R < 0.5 were identified as down-regulated. Biological function of differentially expressed genes was analyzed and 9 genes were selected to undergo semi-quantitative RT-PCR. Results Among the total 5705 probes detected,86 genes showed differential expression in HS group comparison with SHAM group. The expression levels of 72 genes were up-regulated while those of 14 genes were down-regulated significantly. Differentially expressed genes were classified according to their biological function: transport genes, transcription regulator genes, signaling genes, response to stress genes, metabolic genes, development genes and cell adhesion genes. Conclusions cDNA microarray is an efficient and high-throughout method to survey gene expression profiles in HS.The variation of those gene expressions might be a potential pathogenic mechanism for HS that may offer a novel target for further study of therapeutic strategies of HS.
Key words:
Hemorrhagic shock; DNA chip; Gene expression; liver
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Therapeutic gene modulation
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Transcriptomes are known to organize themselves into gene co-expression clusters or modules where groups of genes display distinct patterns of coordinated or synchronous expression across independent biological samples. The functional significance of these co-expression clusters is suggested by the fact that highly coexpressed groups of genes tend to be enriched in genes involved in common functions and biological processes. While gene co-expression is widely assumed to reflect close regulatory proximity, the validity of this assumption remains unclear. Here we use a simple synthetic gene regulatory network (GRN) model and contrast the resulting co-expression structure produced by these networks with their known regulatory architecture and with the co-expression structure measured in available human expression data. Using randomization tests, we found that the levels of co-expression observed in simulated expression data were, just as with empirical data, significantly higher than expected by chance. When examining the source of correlated expression, we found that individual regulators, both in simulated and experimental data, fail, on average, to display correlated expression with their immediate targets. However, highly correlated gene pairs tend to share at least one common regulator, while most gene pairs sharing common regulators do not necessarily display correlated expression. Our results demonstrate that widespread co-expression naturally emerges in regulatory networks, and that it is a reliable and direct indicator of active co-regulation in a given cellular context.
Gene regulatory network
Expression (computer science)
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