利用基因芯片对一高保守基因进行肿瘤相关分析

本文对大规模人类cDNA 测序过程中获得的一条高保守基因进行了初步功能研究,生物信息学研究发现该基因在人类、小鼠、果蝇、拟南芥和裂殖酵母中都有很高的保守性,其他分析预测该基因可能具有肿瘤相关性。RT-PCR分析表明,该基因在成人和胎儿组织中广谱表达。利用基因芯片分析该基因在7例肝癌、5例胰腺癌、2例喉癌和2例肺癌中表达情况,结果证实了该基因的肿瘤相关性,并且提示该基因在不同肿瘤类型中可能处于不同的地位。 Abstract:Preliminary function research of a highly conserved human gene,which was cloned from human fetal cDNA library during large-scale cDNA sequencing,is illustrated in this article.Bioinformatics analysis indicates that this gene is highly conserved in human、mouse、fruit fly、thaliana and fission yeast.Other bioinformatics analysis implies its relevance with tumors.RT-PCR analysis shows its wide-ranging expression patterns.Its expression in 16 cancer cases(including 7 liver cancer cases、5 pancreas cancer cases、2 larynx cancer cases and 2 lung cancer cases) is studied by using gene microarray analysis.The result shows its relevance with tumors and implies it may have different status in different classification of tumors.     

利用基因芯片对一高保守基因进行肿瘤相关分析

本对大规模人类cDNA测序过程中获得的一条高保守基因进行了初步功能研究,生物信息学研究发现该基因在人类、小鼠、果蝇、拟南芥和裂殖酶母中都有很高的保守性,其他分析预测该基因可能具有肿瘤相关性。RT－PCR分析表明,该基因在成人和胎儿组织中广泛谱表达。利用基因芯片分析该基因在7例肝癌、5例胰腺癌、2例喉癌和2例肺癌中表达情况,结果证实了该基因的肿瘤相关性,并且提示该基因在不同类型中可能处于不同的地位。     

基因表达谱微阵列网络数据库在肿瘤研究中的应用

基因表达谱微阵列数据库是一类可提供存储、查询、下载分析的在线网络数据库,在肿瘤相关领域的研究中提供了大量的数据来源。由于微阵列分析对于无生物/医学信息学专业背景的研究人员仍然有较多困难,致使该数据库的使用尚未普及。本文从数据查询、下载分析和使用方法等方面对常用基因表达谱微阵列数据库进行概述,并对现阶段基因表达微阵列数据库的应用策略进行总结,旨在帮助该领域研究的初学工作者了解数据库的基本知识并推动其在科研工作中的应用。     

Rapid Production of Virus Protein Microarray Using Protein Microarray Fabrication through Gene Synthesis (PAGES)

The high genetic variability of RNA viruses is a significant factor limiting the discovery of effective biomarkers, the development of vaccines, and characterizations of the immune response during infection. Protein microarrays have been shown to be a powerful method in biomarker discovery and the identification of novel protein–protein interaction networks, suggesting that this technique could also be very useful in studies of infectious RNA viruses. However, to date, the amount of genetic material required to produce protein arrays, as well as the time- and labor-intensive procedures typically needed, have limited their more widespread application. Here, we introduce a method, protein microarray fabrication through gene synthesis (PAGES), for the rapid and efficient construction of protein microarrays particularly for RNA viruses. Using dengue virus as an example, we first identify consensus sequences from 3,604 different strains and then fabricate complete proteomic microarrays that are unique for each consensus sequence. To demonstrate their applicability, we show that these microarrays can differentiate sera from patients infected by dengue virus, related pathogens, or from uninfected patients. We anticipate that the microarray and expression library constructed in this study will find immediate use in further studies of dengue virus and that, more generally, PAGES will become a widely applied method in the clinical characterization of RNA viruses.     

Identifying Subspace Gene Clusters from Microarray Data Using Low-Rank Representation

Identifying subspace gene clusters from the gene expression data is useful for discovering novel functional gene interactions. In this paper, we propose to use low-rank representation (LRR) to identify the subspace gene clusters from microarray data. LRR seeks the lowest-rank representation among all the candidates that can represent the genes as linear combinations of the bases in the dataset. The clusters can be extracted based on the block diagonal representation matrix obtained using LRR, and they can well capture the intrinsic patterns of genes with similar functions. Meanwhile, the parameter of LRR can balance the effect of noise so that the method is capable of extracting useful information from the data with high level of background noise. Compared with traditional methods, our approach can identify genes with similar functions yet without similar expression profiles. Also, it could assign one gene into different clusters. Moreover, our method is robust to the noise and can identify more biologically relevant gene clusters. When applied to three public datasets, the results show that the LRR based method is superior to existing methods for identifying subspace gene clusters.     

应用基因芯片技术检测非综合征型耳聋基因突变

目的:应用遗传性耳聋基因芯片对散发性聋患者进行分子病因学检测,评估其在遗传性耳聋快速基因诊断中的可靠性。方法:门诊收集散发性聋患者10例,取外周血,提取基因组DNA,用遗传性耳聋基因芯片检测4个中国人中常见的耳聋相关基因中的9个热点突变,包括GJB2(35delG、176del16bp、235delC及299delAT)、GJB3(C538T)、SLC26A4(IVS7-2AG、A2168G)和线粒体DNA 12S rRNA(A1555G、C1494T)。同时,PCR扩增GJB2、线粒体12S rRNA基因全序列,DNA测序,以验证基因芯片检测结果的准确性。结果:在10名耳聋患者中,基因芯片方法检出1例携带线粒体DNA 12S rRNA C1494T突变;2例GJB2基因235delC纯合突变;2例235delC杂合突变;SLC26A4基因和GJB3基因未检出突变。基因芯片的结果与测序结果完全一致。结论:遗传性耳聋基因芯片技术对中国人常见耳聋相关基因热点突变的检出率高,结果准确、可靠,具有快速、高通量、高准确性、低成本等特点,能够满足临床耳聋基因检测的要求,同时结合产前诊断技术能有效预防耳聋患儿的出生,因而具有广阔的临床应用前景。     

应用寡核苷酸芯片分析水稻花序相关基因在花序发育中的表达谱

从Internet、国内外文献中查询了50个水稻花序的相关基因,制备成水稻花序相关基因的寡核苷酸芯片。对3个不同生长阶段的水稻花序材料进行了表达谱检测,用ScanArray3000对杂交结果进行扫描,得到了不同的基因表达谱。用ImaGene 4.0软件对获得的表达谱进行分析,获得基因表达差异的散点图及饼图。 图像分析表明,候选基因在水稻花序3个不同发育阶段的材料中,表达水平有显著差异。这些结果将有助于研究水稻花序的发育机理。 Abstract:In this paper we chose 50 rice inflorescence genes from Internet,references.Rice oligonucleotide microarray was prepared by printing the target rice inflorescence genes oligonucleotide.Expression patterns of 50 genes from rice inflorescence in three different development phase were obtained by scanning using ScanArray3000 after array hybridization.The scatter plots and scale maps of the images were acquired after the acquired gene expression patterns were analyzed by ImaGene4.0 software.The scatter plots and scale maps show that there existed a significant difference in the expression of these candidate genes in rice inflorescences with different development phase.Further analysis of those candidate gene expression patterns will be helpful to understand the developmental mechanism of rice inflorescence.     

Detection of Gene Expression in an Individual Cell Type within a Cell Mixture Using Microarray Analysis

Background

A central issue in the design of microarray-based analysis of global gene expression is the choice between using cells of single type and a mixture of cells. This study quantified the proportion of lipopolysaccharide (LPS) induced differentially expressed monocyte genes that could be measured in peripheral blood mononuclear cells (PBMC), and determined the extent to which gene expression in the non-monocyte cell fraction diluted or obscured fold changes that could be detected in the cell mixture.

Methodology/Principal Findings

Human PBMC were stimulated with LPS, and monocytes were then isolated by positive (Mono+) or negative (Mono−) selection. The non-monocyte cell fraction (MonoD) remaining after positive selection of monocytes was used to determine the effect of non-monocyte cells on overall expression. RNA from LPS-stimulated PBMC, Mono+, Mono− and MonoD samples was co-hybridised with unstimulated RNA for each cell type on oligonucleotide microarrays. There was a positive correlation in gene expression between PBMC and both Mono+ (0.77) and Mono− (0.61–0.67) samples. Analysis of individual genes that were differentially expressed in Mono+ and Mono− samples showed that the ability to detect expression of some genes was similar when analysing PBMC, but for others, differential expression was either not detected or changed in the opposite direction. As a result of the dilutional or obscuring effect of gene expression in non-monocyte cells, overall about half of the statistically significant LPS-induced changes in gene expression in monocytes were not detected in PBMC. However, 97% of genes with a four fold or greater change in expression in monocytes after LPS stimulation, and almost all (96–100%) of the top 100 most differentially expressed monocyte genes were detected in PBMC.

Conclusions/Significance

The effect of non-responding cells in a mixture dilutes or obscures the detection of subtle changes in gene expression in an individual cell type. However, for studies in which only the most highly differentially expressed genes are of interest, separating and analysing individual cell types may be unnecessary.     

High Level Secretion of Calf Chymosin Using a Glucoamylase-prochymosin Fusion Gene in Aspergillus oryzae

A recombinant chymosin was secreted at high levels using fusion genes with A. oryzae glucoamylase gene (glaA) and a wheat bran solid-state culture system. Two portions of the A. oryzae glucoamylase, one with almost the entire glucoamylase (GA1–603) lacking 9 amino acids at the carboxyl terminal, and the other (GA1–511) lacking the starch binding-domain, were fused in frame with prochymosin cDNA. Western blot analysis indicated that the mature chymosin was released from the secreted fusion protein by autocatalytic processing. The transformant harboring the GA1-511-prochymosin construct showed about 5-fold chymosin production of the transformant in which the chymosin gene was directly expressed under the control of the glaA promoter in submerged culture. Moreover, wheat bran solid-state culture gave about 500-fold higher yield of the chymosin (approximately 150 mg/kg wheat bran) compared with the submerged culture.     

Converting a Microarray Signature into a Diagnostic Test: A Trial of Custom 74 Gene Array for Clarification and Prediction the Prognosis of Gastric Cancer

Background

Gastric cancer (GC) is associated with high mortality rates and an unfavorable prognosis at advanced stages. In addition, there are no effective methods for diagnosing gastric cancer at an early stage or for predicting the outcome for the purpose of selecting patient-specific treatment options. Therefore, it is important to investigate new methods for GC diagnosis.

Methodology/Principal Findings

To facilitate its use in a diagnostic setting, a group of 74 genes with diagnostic and prognostic information was translated into a customized microarray containing a reduced set of 1,042 probes suitable for high throughput processing. In this report, we demonstrate for the first time that the custom mini-array can be used as a reliable diagnostic tool in gastric cancer. With an AUC value of 0.565 (95% CI 0.305-0.825) indicating a perfect test, the sensitivity and specificity of diagnosis from the ROC curve were calculated to be 70% and 80%, respectively.

Conclusions/Significance

The data clearly demonstrate the reproducibility and robustness of the small custom-made microarray. The array is an excellent tool for classifying and predicting the outcome of disease in gastric cancer patients.     

DNA芯片技术中利用内标对数据归一化后检测基因的表达变化

在DNA芯片技术中 ,通过反转录反应 ,由mRNA合成带有荧光标记物的cDNA的过程中 ,往往要参入已知质量的poly(A) + RNA ,以对DNA芯片的检测灵敏度进行归一化处理 .通过体外转录的方法 ,以真核生物的cDNA克隆中的DNA片段为模板合成poly(A) +RNA ,对之定量后 ,以不同的质量比参入到样品的反转录体系中 ,代表不同的RNA拷贝丰度 ,从而对DNA芯片检测的灵敏度进行了定量 ,并得到DNA芯片上杂交点的荧光信号强度与基因表达的RNA拷贝数成正相关的关系 .利用含有内标的DNA芯片检测了热击反应后酵母细胞的基因表达变化 ,结果与Northern印迹方法检测结果是相符的     

A Measure of the Signal-to-Noise Ratio of Microarray Samples and Studies Using Gene Correlations

Background

The quality of gene expression data can vary dramatically from platform to platform, study to study, and sample to sample. As reliable statistical analysis rests on reliable data, determining such quality is of the utmost importance. Quality measures to spot problematic samples exist, but they are platform-specific, and cannot be used to compare studies.

Results

As a proxy for quality, we propose a signal-to-noise ratio for microarray data, the “Signal-to-Noise Applied to Gene Expression Experiments”, or SNAGEE. SNAGEE is based on the consistency of gene-gene correlations. We applied SNAGEE to a compendium of 80 large datasets on 37 platforms, for a total of 24,380 samples, and assessed the signal-to-noise ratio of studies and samples. This allowed us to discover serious issues with three studies. We show that signal-to-noise ratios of both studies and samples are linked to the statistical significance of the biological results.

Conclusions

We showed that SNAGEE is an effective way to measure data quality for most types of gene expression studies, and that it often outperforms existing techniques. Furthermore, SNAGEE is platform-independent and does not require raw data files. The SNAGEE R package is available in BioConductor.     

基因芯片表达谱数据的预处理分析

基因芯片数据的预处理是一个十分关键的步骤,通过数据过滤获取需要的数据、数据转换满足正态分布的分析要求、缺失值的估计弥补不完整的数据、数据归一化纠正系统误差等处理为后续分析工作做准备,预处理分析的重要性并不亚于基因芯片的后续分析,它将直接影响后续分析是否能得到预期的结果.本文重点综述了cDNA芯片的数据预处理,简要地概述寡核苷酸芯片的数据预处理.