Tet-on诱导表达c-myc和SV40Tag的转基因小鼠肿瘤模型

目的研究Tet-on诱导表达c-myc和SV40Tag小鼠肿瘤模型的肿瘤发生和基因表达情况,探讨c-myc基因的作用。方法用pTRE2-c-myc单阳性转基因小鼠和Tet-on、pTRE2-SV40Tag双阳性转基因小鼠交配,后代检测得到Tet-onp、TRE2-SV40Tag、pTRE2-c-myc三阳性转基因小鼠,经强力霉素诱导一段时间以后,观察肿瘤的发生;通过RT-PCR、病理组织切片和磁共振等方法对肿瘤的发生部位和时相进行研究。结果Tet-on、pTRE2-SV40Tag、pTRE2-c-myc三阳性转基因小鼠①经诱导后发生肿瘤,且发瘤率和发瘤时间高于和短于Tet-on、pTRE2-SV40Tag双阳性转基因小鼠;②c-myc和SV40Tag基因在表达部位上有所不同。结论c-myc和SV40Tag基因同时表达与SV40Tag基因单独表达时相比,肿瘤发生明显增强,提示c-myc基因与肿瘤的发生有着密切关系。     

Microarray analysis of novel cell lines representing two stages of metanephric mesenchyme differentiation

Clonal cell lines representing different developmental stages of the metanephric mesenchyme were made from transgenic mice with the Simian Virus 40 T-antigen (SV40 Tag) gene driven by the Hoxa 11 promoter. The resulting mK3 cell line represented early metanephric mesenchyme, prior to induction by the ureteric bud. These cells showed a spindle-shaped, fibroblast morphology. They expressed genes characteristic of early mesenchyme, including Hoxa 11, Hoxd 11, collagen I, and vimentin. Moreover, the mK3 cells displayed early metanephric mesenchyme biological function. In organ co-culture experiments they were able to induce growth and branching of the ureteric bud. Another cell line, mK4, represented later, induced metanephric mesenchyme undergoing epithelial conversion. These cells were more polygonal, or epithelial in shape, and expressed genes diagnostic of late mesenchyme, including Pax-2, Pax-8, Wnt-4, Cadherin-6, Collagen IV, and LFB3. To better define the gene expression patterns of kidney metanephric mesenchyme cells at these two stages of development, RNAs from the mK3 and mK4 cells were hybridized to Affymetrix GeneChip probe arrays. Over 4000 expressed genes were identified and thereby implicated in kidney formation. Comparison of the mK3 and mK4 gene expression profiles revealed 121 genes showing greater than a ten-fold difference in expression level. Several are known to be expressed during metanephric mesenchyme differentiation, but most had not been previously associated with this process. In situ hybridizations were used to confirm that selected novel genes were expressed in the developing kidney.     

A verification protocol for the probe sequences of Affymetrix genome arrays reveals high probe accuracy for studies in mouse,human and rat

Background  

The Affymetrix GeneChip technology uses multiple probes per gene to measure its expression level. Individual probe signals can vary widely, which hampers proper interpretation. This variation can be caused by probes that do not properly match their target gene or that match multiple genes. To determine the accuracy of Affymetrix arrays, we developed an extensive verification protocol, for mouse arrays incorporating the NCBI RefSeq, NCBI UniGene Unique, NIA Mouse Gene Index, and UCSC mouse genome databases.     

Summaries of Affymetrix GeneChip probe level data

High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11–20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated.     

Gsh-1, an orphan Hox gene, is required for normal pituitary development.

The anterior pituitary regulates the function of multiple organ systems as well as body growth, and in turn is controlled by peptides released by the hypothalamus. We find that mutation of the Gsh-1 homeobox gene results in pleiotropic effects on pituitary development and function. Homozygous mutants exhibit extreme dwarfism, sexual infantilism and significant perinatal mortality. The mutant pituitary is small in size and hypocellular, with severely reduced numbers of growth hormone- and prolactin-producing cells. Moreover, the pituitary content of a subset of pituitary hormones, including growth hormone, prolactin and luteinizing hormone, is significantly decreased. The hypothalamus, although morphologically normal, is also perturbed in mutants. The gsh-1 gene is shown to be essential for growth hormone-releasing hormone (GHRH) gene expression in the arcuate nucleus of the hypothalamus. Further, sequence and electrophoretic mobility shift data suggest the Gsh-1 and GHRH genes as potential targets regulated by the Gsh-1-encoded protein. The mutant phenotype indicates a critical role for Gsh-1 in the genetic hierarchy of the formation and function of the hypothalamic-pituitary axis.     

Enteroendocrine cell expression of a cholecystokinin gene construct in transgenic mice and cultured cells

CCK is predominantly expressed in subsets of endocrine cells in the intestine and neurons in the brain. We evaluated the expression of a CCK gene construct in transgenic mice and cultured cells to identify a genomic region that directs correct tissue- and cell-specific expression in enteroendocrine cells. The CCKL1 transgene contained 6.4 kb of mouse Cck fused to lacZ. Expression was evaluated in three transgenic lines (J11, J12, J14) by measurement of beta-galactosidase in tissue homogenates and frozen sections. Correct tissue-specific expression was observed, with beta-galactosidase activity detected in intestine and brain. However, there were differences seen in cell-specific expression in the intestine. Line J14 exhibited expression in CCK-endocrine cells, with expressing cells arising at the normal time during fetal development. However, transgene expression in line J12 intestine was limited to neurons of the enteric nervous system, which reflect an early fetal expression pattern for CCK. Analysis of an additional 15 transgenic founder mice demonstrated intestinal expression in 40% of transgenics, with expressing mice following either an endocrine cell pattern or a neuronal pattern in approximately equal numbers. CCKL1 transfection analysis in cultured cells also demonstrated enteroendocrine cell expression, with 100-fold enhanced activity in the enteroendocrine cell line STC-1 compared with nonendocrine cell lines. The results suggest that the minimal cis-regulatory DNA elements necessary for appropriate CCK expression in enteroendocrine cells reside within the 6.4-kb mouse genomic fragment.     

Exploration,normalization, and summaries of high density oligonucleotide array probe level data

In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.     

A comparison of cDNA, oligonucleotide, and Affymetrix GeneChip gene expression microarray platforms.

We have conducted a study to compare the variability in measured gene expression levels associated with three types of microarray platforms. Total RNA samples were obtained from liver tissue of four male mice, two each from inbred strains A/J and C57BL/6J. The same four samples were assayed on Affymetrix Mouse Genome Expression Set 430 GeneChips (MOE430A and MOE430B), spotted cDNA microarrays, and spotted oligonucleotide microarrays using eight arrays of each type. Variances associated with measurement error were observed to be comparable across all microarray platforms. The MOE430A GeneChips and cDNA arrays had higher precision across technical replicates than the MOE430B GeneChips and oligonucleotide arrays. The Affymetrix platform showed the greatest range in the magnitude of expression levels followed by the oligonucleotide arrays. We observed good concordance in both estimated expression level and statistical significance of common genes between the Affymetrix MOE430A GeneChip and the oligonucleotide arrays. Despite their apparently high precision, cDNA arrays showed poor concordance with other platforms.     

Signal deconvolution based expression-detection and background adjustment for microarray data.

Background adjustment is an essential stage in analyzing DNA microarrays. Discriminating expressed genes from unexpressed ones (expression detection), and estimating the expression levels of weakly expressed genes, critically depend on accurate treatment of the background intensity. Current methods for background adjustment either do not deal with nonspecific hybridization or strongly depend on the reliability of control probes. Existing model-based methods have limited accuracy. A new platform-independent background adjustment algorithm is presented. The algorithm relies on the deconvoluted experimental signal distribution for evaluating the expression probability and adjusting the background of each probe. Considering expression detection, it is shown, for two-channels cDNA arrays and for the Affymetrix GeneChip platform, that the algorithm performs at least as good or better than control-probes-based algorithms. For the Affymetrix GeneChip arrays, it is further shown that the algorithm outperforms the robust multiarray (RMA) expression measure in estimating genomewide expression levels.     

BGX: a fully Bayesian integrated approach to the analysis of Affymetrix GeneChip data

We present Bayesian hierarchical models for the analysis of Affymetrix GeneChip data. The approach we take differs from other available approaches in two fundamental aspects. Firstly, we aim to integrate all processing steps of the raw data in a common statistically coherent framework, allowing all components and thus associated errors to be considered simultaneously. Secondly, inference is based on the full posterior distribution of gene expression indices and derived quantities, such as fold changes or ranks, rather than on single point estimates. Measures of uncertainty on these quantities are thus available. The models presented represent the first building block for integrated Bayesian Analysis of Affymetrix GeneChip data: the models take into account additive as well as multiplicative error, gene expression levels are estimated using perfect match and a fraction of mismatch probes and are modeled on the log scale. Background correction is incorporated by modeling true signal and cross-hybridization explicitly, and a need for further normalization is considerably reduced by allowing for array-specific distributions of nonspecific hybridization. When replicate arrays are available for a condition, posterior distributions of condition-specific gene expression indices are estimated directly, by a simultaneous consideration of replicate probe sets, avoiding averaging over estimates obtained from individual replicate arrays. The performance of the Bayesian model is compared to that of standard available point estimate methods on subsets of the well known GeneLogic and Affymetrix spike-in data. The Bayesian model is found to perform well and the integrated procedure presented appears to hold considerable promise for further development.     

Quality assessment of the Affymetrix U133A&B probesets by target sequence mapping and expression data analysis

Careful analysis of microarray probe design should be an obligatory component of MicroArray Quality Control (MACQ) project [Patterson et al., 2006; Shi et al., 2006] initiated by the FDA (USA) in order to provide quality control tools to researchers of gene expression profiles and to translate the microarray technology from bench to bedside. The identification and filtering of unreliable probesets are important preprocessing steps before analysis of microarray data. These steps may result in an essential improvement in the selection of differentially expressed genes, gene clustering and construction of co-regulatory expression networks. We revised genome localization of the Affymetrix U133A&B GeneChip initial (target) probe sequences, and evaluated the impact of erroneous and poorly annotated target sequences on the quality of gene expression data. We found about 25% of Affymetrix target sequences overlapping with interspersed repeats that could cause cross-hybridization effects. In total, discrepancies in target sequence annotation account for up to approximately 30% of 44692 Affymetrix probesets. We introduce a novel quality control algorithm based on target sequence mapping onto genome and GeneChip expression data analysis. To validate the quality of probesets we used expression data from large, clinically and genetically distinct groups of breast cancers (249 samples). For the first time, we quantitatively evaluated the effect of repeats and other sources of inadequate probe design on the specificity, reliability and discrimination ability of Affymetrix probesets. We propose that only functionally reliable Affymetrix probesets that passed our quality control algorithm (approximately 86%) for gene expression analysis should be utilized. The target sequence annotation and filtering is available upon request.