Annealing control primer system for identification of differentially expressed genes on agarose gels

We developed GeneFishing technology, an improved method for the identification of differentially expressed genes (DEGs) using our novel annealing control primer (ACP) system. Because of high annealing specificity during PCR using the ACP system, the application of the ACP to DEG discovery generates reproducible, authentic, and long (100 bp to 2 kb) PCR products that are detectable on agarose gels. To demonstrate this method for gene expression profiling, Gene-Fishing technology was used to detect genes that are differentially expressed during development using total RNAs isolated from mouse conceptus tissues at 4.5-18.5 days of gestation. Ten DEGs (DEG1-10) were isolated and confirmed by Northern blot hybridization. The sequence analysis of these DEGs showed that DEG6 and DEG10 are unknown genes.     

Identification of differentially expressed genes in three-pistil mutation in wheat using annealing control primer system

The common wheat line three-pistil (TP) is a valuable mutant for wheat breeding. The TP mutation has normal spike morphology; however, it only produces three pistils per floret. Therefore, it has potential to increase the grain number per spike. In order to determine the underlying molecular mechanism, an annealing control primer system was used to identify the different expressed genes in three-pistil mutation. Using 120 arbitrary ACP primers, we identified three differentially expressed genes in young spikes between two near-isogenic lines (i.e., Chuanmai 28 TP and Chinese Spring TP) and their recurrent parents. We tentatively designated the three differentially expressed genes as DETP-1, DETP-2, and DETP-3. DETP-1 showed similar function with maize cytoplasmic membrane protein, which is involved in cell division in bacteria. DETP-3 is homologous to maize endo-1, 4-beta-glucanase (EGases), which is associated with plant development, cell wall loosening, stem flowering, and root expansion. DETP-2 showed no significant hit with any sequence found in the database and translates unknown protein. These genes would likely play an important role in determining the three pistils trait in wheat.     

Suppression subtractive hybridization identifies differentially expressed genes in Brassica napus chlorophyll-reduced mutant

Suppressive subtraction hybridization (SSH) was used to identify differentially expressed genes caused by a chlorophyll-reduced mutation in B. napus. The cDNA fragments, derived from SSH positive subtractive library (tester: normal wild type, driver: mutant) were cloned into pMD18-T vector. Two hundred SSH cDNA clones were screened by dot blot array, and 151 clones were identified as differentially expressed cDNA fragments in Cr3529 line. Thirty-six positive clones which showed marked expression differences were selected and sequenced. After redundant cDNAs were removed, 33 differentially expressed unique cDNA section clones were obtained. Among the 33 clones, two clones possess different parts of the cDNA sequence of the same gene coding geranylgeranyl reductase, four clones belong to unknown proteins, and the rest share homology to genes of diverse class. Sequence analysis showed that at least 12 genes were discovered to be related to the photosynthesis, seven of them coded the proteins which belong to the subunit of photosystem 2. RNA gel blot analysis showed that compared with 3529, the gene expression of the chlorophyll a/b-binding protein Lhcb2 in photosystem 2 declined markedly in the cotyledons and seedling leaves of Cr3529, indicating that the reduced light-harvesting complex 2 accumulation in thylakoid membrane of Cr3529 was due to the decrease of the related gene mRNA level for translation.     

Annealing control primer system for improving specificity of PCR amplification

A novel primer designed to improve the specificity of PCR amplification, called the annealing control primer (ACP), comprises a tripartite structure with a polydeoxyinosine [poly(dI)] linker between the 3' end target core sequence and the 5' end nontarget universal sequence. We show that this ACP linker prevents annealing of the 5' end nontarget sequence to the template and facilitates primer hybridization at the 3' end to the target sequence at specific temperatures, resulting in a dramatic improvement of annealing specificity. The effect of this linker is demonstrated by the incorporation of ACP sequences as primers during the amplification of target nucleotide sequence and as hybridization probes in the genotyping of single nucleotide polymorphisms. This is the first report to show that a poly(dI) linker between two different sequences of ACP forms a bubble-like structure and disrupts or destabilizes DNA duplex formation at certain annealing temperatures.     

Detecting multivariate differentially expressed genes

Background  

Gene expression is governed by complex networks, and differences in expression patterns between distinct biological conditions may therefore be complex and multivariate in nature. Yet, current statistical methods for detecting differential expression merely consider the univariate difference in expression level of each gene in isolation, thus potentially neglecting many genes of biological importance.     

Microarray data quality control improves the detection of differentially expressed genes

Microarrays have become a routine tool for biomedical research. Data quality assessment is an essential part of the analysis, but it is still not easy to perform objectively or in an automated manner, and as a result it is often neglected. Here, we compared two strategies of array-level quality control using five publicly available microarray experiments: outlier removal and array weights. We also compared them against no outlier removal and random array removal. We find that removing outlier arrays can improve the signal-to-noise ratio and thus strengthen the power of detecting differentially expressed genes. Using array weights is similarly effective, but its applicability is more limited. The quality metrics presented here are implemented in the Bioconductor package arrayQualityMetrics.     

Identification of differentially expressed genes in a porcine in vivo model of adipogenesis using suppression subtractive hybridization

Although they provide valuable information, in vitro models of adipocyte development often require high doses of hormones and growth factors, which may influence gene expression and adipocyte differentiation patterns. To overcome these problems, a novel in vivo model of adipose tissue development was used to characterize genes involved in adipogenesis. The suppression subtractive hybridization technique was used to identify genes showing differential expression between the adipose tissue of a day 90 gestating sow, which is enriched in adipocytes, and day 90 fetal adipose tissue, which is enriched in preadipocytes. A total of 149 expressed sequence tags corresponding to identified genes and tentative consensus sequences emerged. Thirty-seven clones matched expressed sequence tags or genomic DNA sequences and six novel sequences were also identified. Adipogenesis-related genes were identified, many of which have never been reported to be expressed in mammalian adipose tissue, and may play a role in regulation of adipose tissue differentiation. Validation of differentially expressed genes was confirmed for perilipin, monocyte to macrophage differentiation-associated, myocilin, paraoxonase 3, stearoyl-CoA desaturase, angiotensinogen and adiponectin genes using real-time RT-PCR.     

差异表达基因的分离策略

生命活动的各个进程伴随着不同基因的选择性开启和关闭。如何有效地分离克隆各种差异表达的基因,成为分子生物学研究的一个努力方向,大量差异基因分离策略因之问世。本文扼要介绍了近年来发展的几种主要分离策略,并详细介绍一种新的差异基因分离方法--抑制差减杂交。     

Ranking analysis for identifying differentially expressed genes

Microarrays allow researchers to examine the expression of thousands of genes simultaneously. However, identification of genes differentially expressed in microarray experiments is challenging. With an optimal test statistic, we rank genes and estimate a threshold above which genes are considered to be differentially expressed genes (DE). This overcomes the embarrassing shortcoming of many statistical methods to determine the cut-off values in ranking analysis. Experiments demonstrate that our method is a good performance and avoids the problems with graphical examination and multiple hypotheses testing that affect alternative approaches. Comparing to those well known methods, our method is more sensitive to data sets with small differentially expressed values and not biased in favor of data sets based on certain distribution models.     

Finding differentially expressed genes for pattern generation

MOTIVATION: It is important to consider finding differentially expressed genes in a dataset of microarray experiments for pattern generation. RESULTS: We developed two methods which are mainly based on the q-values approach; the first is a direct extension of the q-values approach, while the second uses two approaches: q-values and maximum-likelihood. We present two algorithms for the second method, one for error minimization and the other for confidence bounding. Also, we show how the method called Patterns from Gene Expression (PaGE) (Grant et al., 2000) can benefit from q-values. Finally, we conducted some experiments to demonstrate the effectiveness of the proposed methods; experimental results on a selected dataset (BRCA1 vs BRCA2 tumor types) are provided. CONTACT: alhajj@cpsc.ucalgary.ca.     

Selecting differentially expressed genes from microarray experiments

High throughput technologies, such as gene expression arrays and protein mass spectrometry, allow one to simultaneously evaluate thousands of potential biomarkers that could distinguish different tissue types. Of particular interest here is distinguishing between cancerous and normal organ tissues. We consider statistical methods to rank genes (or proteins) in regards to differential expression between tissues. Various statistical measures are considered, and we argue that two measures related to the Receiver Operating Characteristic Curve are particularly suitable for this purpose. We also propose that sampling variability in the gene rankings be quantified, and suggest using the "selection probability function," the probability distribution of rankings for each gene. This is estimated via the bootstrap. A real dataset, derived from gene expression arrays of 23 normal and 30 ovarian cancer tissues, is analyzed. Simulation studies are also used to assess the relative performance of different statistical gene ranking measures and our quantification of sampling variability. Our approach leads naturally to a procedure for sample-size calculations, appropriate for exploratory studies that seek to identify differentially expressed genes.     

Statistical methods for ranking differentially expressed genes

In the analysis of microarray data the identification of differential expression is paramount. Here I outline a method for finding an optimal test statistic with which to rank genes with respect to differential expression. Tests of the method show that it allows generation of top gene lists that give few false positives and few false negatives. Estimation of the false-negative as well as the false-positive rate lies at the heart of the method.     

Detecting differentially expressed genes by relative entropy

DNA microarray experiments have generated large amount of gene expression measurements across different conditions. One crucial step in the analysis of these data is to detect differentially expressed genes. Some parametric methods, including the two-sample t-test (T-test) and variations of it, have been used. Alternatively, a class of non-parametric algorithms, such as the Wilcoxon rank sum test (WRST), significance analysis of microarrays (SAM) of Tusher et al. (2001), the empirical Bayesian (EB) method of Efron et al. (2001), etc., have been proposed. Most available popular methods are based on t-statistic. Due to the quality of the statistic that they used to describe the difference between groups of data, there are situations when these methods are inefficient, especially when the data follows multi-modal distributions. For example, some genes may display different expression patterns in the same cell type, say, tumor or normal, to form some subtypes. Most available methods are likely to miss these genes. We developed a new non-parametric method for selecting differentially expressed genes by relative entropy, called SDEGRE, to detect differentially expressed genes by combining relative entropy and kernel density estimation, which can detect all types of differences between two groups of samples. The significance of whether a gene is differentially expressed or not can be estimated by resampling-based permutations. We illustrate our method on two data sets from Golub et al. (1999) and Alon et al. (1999). Comparing the results with those of the T-test, the WRST and the SAM, we identified novel differentially expressed genes which are of biological significance through previous biological studies while they were not detected by the other three methods. The results also show that the genes selected by SDEGRE have a better capability to distinguish the two cell types.