用基因表达谱芯片研究人正常肝和肝细胞癌中差异表达的基因

以基因表达谱芯片对人正常肝及肝癌组织基因表达的差异性进行了研究比较。奖４０９６条人ｃＤＮＡ用点样仪点在特制玻片上制备成表达谱芯片;利用肝和肝癌组织的ｍＲＮＡ通过逆转录方法,将Ｃｙ３和Ｃｙ５２种荧光分别标记到两种组织的ｃＤＮＡ上,制备成ｃＤＮＡ探针,并与表达谱芯片进行杂交及扫描,重复４次实验,通过计算机数据处理判定基因是否在上述２种组织中有表达差异,筛选出差异表达的基因共９０３条。基因芯片技术可同时     

Using Human Microarrays to Identify Differentially Expressed Genes Associated with Increased Steroidogenesis in Boars

Human microarrays are readily available, and it would be advantageous if they could be used to study gene expression in other species, such as pigs. The objectives of this research were to validate the use of human microarrays in the analysis of porcine gene expression, to assess the variability of the data generated, and to compare gene expression in boars with different levels of steroidogenesis. Cytochrome b5 (CYB5) expression was used to assess array detection sensitivity. Samples having high or low CYB5 RNA levels were hybridized to microarrays to determine if the known expression difference could be detected. Six hybridizations were conducted using human microarrays containing 3840 total spots representing 1718 characterized human ESTs. To analyze gene expression in boars with different levels of steroidogenesis, testis RNA from four boars with high levels of plasma estrone sulphate was hybridized to testis RNA from four boars with lower levels. Eight microarray hybridizations were conducted including fluor-flips. Self-self hybridizations were also conducted to assess the variability of array experiments. The Cy5 and Cy3 intensity values for each array were normalized using a locally weighted linear regression (LOESS). Statistical significance was assessed using a Student's t-test followed by the Benjamini and Hochberg multiple testing correction procedure. Quantitative real-time PCR (Q-RT-PCR) was used to verify select gene expression differences. The results show that CYB5 was significantly overexpressed in the high CYB5 sample by 1.8 fold (P < 0.05), verifying the known expression difference. The average log2 ratio of the majority of genes (1643) falls within one standard deviation of the mean, indicating the data were reproducible. In the high versus low steroidogenesis experiment, seven genes were significantly overexpressed in the high group (P < 0.05). Quantitative real-time PCR was used to validate five genes with the highest fold change, and the results corroborated those found by the microarray experiments. The results of the self-self hybridizations showed that no genes were significantly differentially expressed following the application of the Benjamini and Hochberg multiple testing correction procedure. The results presented in this report show that human arrays can be used for gene expression analysis in pigs.     

Identification of Differentially Expressed Genes in Distinct Skeletal Muscles in Cattle using cDNA Microarray

The 788-gene microarray was manufactured using selected elements from three different cDNA libraries in order to identify molecular processes that determine phenotypic characteristics between loin (M. longissimus thoracis) and round (M. semimembranosus) muscles. Microarray analyses identified 24 differentially expressed genes between the two muscles investigated. Five of the genes were verified by quantitative RT-PCR and three of them were mapped on bovine chromosomes using 5,000 rad bovine radiation hybrid (RH) panel. The map locations indicated that they were mapped in the same chromosomal regions where IMF and growth QTLs were located, suggesting that they are most possible positional candidate genes for the traits.     

多重检验法在基因芯片研究中鉴定差异表达基因的统计功效

鉴于基因芯片实验的造价,在基因芯片实验设计中,首要考虑的因素是需要多少重复才能检测出一个具有显著差异表达的基因。计算多重检验法要求的重复数（样本大小）或功效可为基因芯片实验设计提供重要的参考。为此,本文基于置换重抽样法构建了一种基因表达噪声混合分布模型。该方法适用各类基因表达数据,即无论是基因表达单噪声源或是多噪声源都可行。应用混合模型和多重检验法并给定统计功效。研究者能在基因芯片实验中获得所需要的最少生物学重复数：或者根据样本大小来确定测定一个显著差异表达的基因所具有的检验功效;或者根据样本大小和统计检验功效,选择最好的统计测验方法。本文以一组在老鼠中与中风有关的3000个基因的基因芯片实验所获得的数据为例,应用该方法拟和后组建了一个单分布模型（即表达单噪声源的分布模型）。根据该模型,我们计算了4种多重检验法在鉴定一个具有表达差异（D）值的基因中所需要的统计功效。结果表明。检测一个小的差异D值,4种多重检验法中B方法的统计功效最低,而BH方法最高。但是,对于鉴定一个具有最大表达差异的基因时,4种方法有相同的鉴定功效。与传统的单个检验法一样,BH方法检测一个小的变化所需要的效率不会随基因数目增加而改变,其他3种多重检验法的检测功效则随基因数目增加而降低。     

Powers of Multiple-Testing Procedures for Identification of Genes Significantly Differentially Expressed in Microarray Experiments

Because of the high operation costs involved in microarray experiments, the determination of the number of replicates required to detect a gene significantly differentially expressed in a given multiple-testing procedure is of considerable significance. Calculation of power/replicate numbers required in multiple-testing procedures provides design guidance for microarray experiments. Based on this model and by choice of a multiple-testing procedure, expression noises based on permutation resampling can be considerably minimized. The method for mixture distribution model is suitable to various microarray data types obtained from single noise sources, or from multiple noise sources. By using the biological replicate number required in microarray experiments for a given power or by determining the power required to detect a gene significantly differentially expressed, given the sample size, or the best multiple-testing method can be chosen. As an example, a single-distribution model of t-statistic was fitted to an observed microarray dataset of 3 000 genes responsive to stroke in rat, and then used to calculate powers of four popular multiple-testing procedures to detect a gene of an expression change D. The results show that the B-procedure had the lowest power to detect a gene of small change among the multiple-testing procedures, whereas the BH-procedure had the highest power. However, all multiple-testing procedures had the same power to identify a gene having the largest change. Similar to a single test, the power of the BH-procedure to detect a small change does not vary as the number of genes increases, but powers of the other three multiple-testing procedures decline as the number of genes increases.     

Oligonucleotide Microarray Identifies Genes Differentially Expressed during Tumorigenesis of DMBA-Induced Pancreatic Cancer in Rats

The extremely dismal prognosis of pancreatic cancer (PC) is attributed, at least in part, to lack of early diagnosis. Therefore, identifying differentially expressed genes in multiple steps of tumorigenesis of PC is of great interest. In the present study, a 7,12-dimethylbenzanthraene (DMBA)-induced PC model was established in male Sprague-Dawley rats. The gene expression profile was screened using an oligonucleotide microarray, followed by real-time quantitative polymerase chain reaction (qRT-PCR) and immunohistochemical staining validation. A total of 661 differentially expressed genes were identified in stages of pancreatic carcinogenesis. According to GO classification, these genes were involved in multiple molecular pathways. Using two-way hierarchical clustering analysis, normal pancreas, acute and chronic pancreatitis, PanIN, early and advanced pancreatic cancer were completely discriminated. Furthermore, 11 upregulated and 142 downregulated genes (probes) were found by Mann-Kendall trend Monotone test, indicating homologous genes of rat and human. The qRT-PCR and immunohistochemistry analysis of CXCR7 and UBe2c, two of the identified genes, confirmed the microarray results. In human PC cell lines, knockdown of CXCR7 resulted in decreased migration and invasion. Collectively, our data identified several promising markers and therapeutic targets of PC based on a comprehensive screening and systemic validation.     

A Non-transformation Method for Identifying Differentially Expressed Genes from cDNA Microarrays

cDNA microarray data are subject to many sources of variation that have to be removed before statistical tests can be applied for identifying genes that are expressed differentially. Background correction, log-ratio transformation, and normalization, referred as the log-ratio approach, have been widely used for this purpose. However, there are some problems associated with this procedure. In this study, we proposed an alternative approach that obviates the log-ratio transformation step and goes directly to normalization after background correction. The method can estimate the “noise” effect by utilizing the information more effectively. Simulation studies were carried out to compare the feasibility and efficiency of this approach for detecting the specifically and differentially expressed genes under various conditions with the log-ratio approach. The results showed that our approach worked well and was more robust and powerful than the log-ratio approach.     

cDNA芯片差异表达基因检测的非转换方法

cDNA微阵列数据中包含许多变异因素,用于检测差异表达基因和其它统计分析前,必须将这些“噪音”剔除。对数比法（背景校正、对数比转换和数据标准化）已经被广泛应用于cDNA微阵列数据分析中,然而这种方法却存在着一些亟待解决的缺陷。对此,该文提出一种非转换方法,它可免去对数比的转化过程,直接在背景校正后进行数据标准化,可以有效剔除实验“噪音”。研究结果表明：在检测差异表达基因的效率方面,非转换方法比常规的对数比法具有更好的稳健性和更高的检测功效,基因检出率和准确性大大提高。     

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes

Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates.     

Classification of Genes Differentially Expressed during Water-deficit Stress in Arabidopsis thaliana: an Analysis using Microarray and Differential Expression Data

Many changes in gene expression occur in response to water-deficitstress. A challenge is to determine which changes support plantadaptation to conditions of reduced soil water content and whichoccur in response to lesions in metabolic and cellular functions.Microarray methods are being employed to catalogue all of thechanges in gene expression that occur in response to specificwater-deficit conditions. Although these methods do not measurethe amount or activities of specific proteins that functionin the water-deficit response, they do target specific biochemicaland cellular events that should be detailed in further work.Potential functions of approx. 130 genes of Arabidopsis thalianathat have been shown to be up-regulated are tabulated here.These point to signalling events, detoxification and other functionsinvolved in the cellular response to water-deficit stress. Asmicroarray techniques are refined, plant stress biologists willbe able to characterize changes in gene expression within thewhole genome in specific organs and tissues subjected to differentlevels of water-deficit stress.     

Profiling Expression Patterns and Isolating Differentially Expressed Genes by cDNA Microarray System with Colorimetry Detection

A high-density cDNA microarray with colorimetry detection system to simultaneously monitor the expression of many genes on nylon membrane is described and characterized. To quantify the expression of genes and to isolate differentially expressed genes, the southern hybridization process on filter membranes was employed. The levels of gene expression were represented by color intensities generated by colorimetric reactions in place of hazardous radioisotopes or costly laser-induced fluorescence detection. The gene expression patterns on nylon membranes were digitized by devices such as an economical flatbed scanner or a digital camera. The quantitative information of gene expression was retrieved by image analysis software. Quantitative comparison of the northern dot-blotting method with the microarray system is described. Applications employing single-color detection as well as dual-color detection to isolate differentially expressed genes among thousands of genes are demonstrated.     

A Statistical Model to Identify Differentially Expressed Proteins in 2D PAGE Gels

Two dimensional polyacrylamide gel electrophoresis (2D PAGE) is used to identify differentially expressed proteins and may be applied to biomarker discovery. A limitation of this approach is the inability to detect a protein when its concentration falls below the limit of detection. Consequently, differential expression of proteins may be missed when the level of a protein in the cases or controls is below the limit of detection for 2D PAGE. Standard statistical techniques have difficulty dealing with undetected proteins. To address this issue, we propose a mixture model that takes into account both detected and non-detected proteins. Non-detected proteins are classified either as (a) proteins that are not expressed in at least one replicate, or (b) proteins that are expressed but are below the limit of detection. We obtain maximum likelihood estimates of the parameters of the mixture model, including the group-specific probability of expression and mean expression intensities. Differentially expressed proteins can be detected by using a Likelihood Ratio Test (LRT). Our simulation results, using data generated from biological experiments, show that the likelihood model has higher statistical power than standard statistical approaches to detect differentially expressed proteins. An R package, Slider (Statistical Likelihood model for Identifying Differential Expression in R), is freely available at http://www.cebl.auckland.ac.nz/slider.php.     

A Robust Statistical Method for Detecting Differentially Expressed Genes

DNA microarray technology allows researchers to monitor the expressions of thousands of genes under different conditions, and to measure the levels of thousands of different DNA molecules at a given point in the life of an organism, tissue or cell. A wide variety of different diseases that are characterised by unregulated gene expression, DNA replication, cell division and cell death, can be detected early using microarrays. One of the major objectives of microarray experiments is to identify differentially expressed genes under various conditions. The detection of differential gene expression under two different conditions is very important in biological studies, and allows us to identify experimental variables that affect different biological processes. Most of the tests available in the literature are based on the assumption of normal distribution. However, the assumption of normality may not be true in real-life data, particularly with respect to microarray data.A test is proposed for the identification of differentially expressed genes in replicated microarray experiments conducted under two different conditions. The proposed test does not assume the distribution of the parent population; thus, the proposed test is strictly nonparametric in nature. We calculate the p-value and the asymptotic power function of the proposed test statistic. The proposed test statistic is compared with some of its competitors under normal, gamma and exponential population setup using the Monte Carlo simulation technique. The application of the proposed test statistic is presented using microarray data. The proposed test is robust and highly efficient when populations are non-normal.     

Prokaryotic Suppression Subtractive Hybridization PCR cDNA Subtraction, a Targeted Method To Identify Differentially Expressed Genes

Molecular biology tools can be used to monitor and optimize biological treatment systems, but the application of nucleic acid-based tools has been hindered by the lack of available sequences for environmentally relevant biodegradation genes. The objective of our work was to extend an existing molecular method for eukaryotes to prokaryotes, allowing us to rapidly identify differentially expressed genes for subsequent sequencing. Suppression subtractive hybridization (SSH) PCR cDNA subtraction is a technique that can be used to identify genes that are expressed under specific conditions (e.g., growth on a given pollutant). While excellent methods for eukaryotic SSH PCR cDNA subtraction are available, to our knowledge, no methods previously existed for prokaryotes. This work describes our methodology for prokaryotic SSH PCR cDNA subtraction, which we validated using a model system: Pseudomonas putida mt-2 degrading toluene. cDNA from P. putida mt-2 grown on toluene (model pollutant) or acetate (control substrate) was subjected to our prokaryotic SSH PCR cDNA subtraction protocol to generate subtraction clone libraries. Over 90% of the sequenced clones contained gene fragments encoding toluene-related enzymes, and 20 distinct toluene-related genes from three key operons were sequenced. Based on these results, prokaryotic SSH PCR cDNA subtraction shows promise as a targeted method for gene identification.     

Differentially Expressed RNA from Public Microarray Data Identifies Serum Protein Biomarkers for Cross-Organ Transplant Rejection and Other Conditions

Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers.     

应用寡核苷酸芯片筛选维甲酸诱导神经母细胞瘤细胞系SH-SY5Y分化成神经元过程中的差异表达基因

目的:应用寡核苷酸芯片筛选维甲酸(RA)诱导神经母细胞瘤细胞系SH-SY5Y分化成神经元过程中的差异表达基因。方法:从人胎脑及不同类型神经系统肿瘤组织中获取目的基因,查询相应基因mRNA序列,设计并合成探针,制备了含218种基因的神经功能相关的寡核苷酸芯片。应用RA诱导SH-SY5Y8d分化成成熟神经元,提取对照组和实验组每天的总RNA,通过逆转录荧光标记cDNA探针并与芯片杂交,洗片后扫描获取图像,数据分析获得差异表达基因,并通过RT-PCR进行验证。结果:发现13种基因表达上调,没有得到下调基因。RT-PCR验证结果基本与芯片结果一致。结论:SH-SY5Y经RA诱导分化成神经元存在一些差异表达的基因,寡核苷酸芯片技术可为研究SH-SY5Y诱导分化成神经元的分子作用机理提供技术平台。