Angiogenesis gene expression profiling in xenograft models to study cellular interactions

The present study describes a method to simultaneously obtain the angiogenic expression profile in tumor cells and vascular cells of a single tumor. Human- and mouse-specific primers were used for quantitative real-time RT-PCR to determine the expression of vascular endothelial growth factors A, B, C, and D, vascular endothelial growth factor receptors 1, 2, and 3, neuropilin 1 and 2, angiopoietin 1, 2, 3/4, tyrosine kinase receptors 1 and 2, basic fibroblast growth factor (bFGF) in xenograft tumors obtained by injection of human ovarian carcinoma cells in nude mice. In addition, the effect of treatment with anginex and taxol on the expression profile was analyzed. Most factors were expressed higher in vascular cells as compared to tumor cells. In response to treatment, tumor cells significantly upregulated bFGF expression and downregulated VEGF receptor expression. This was accompanied by downregulation of VEGF-B and -D, and upregulation of angiopoietin-3 as well as angiopoetin receptors in nontumor cells. In conclusion, real-time qRT-PCR combined with xenograft tumor models presents a sensitive method to monitor angiogenesis and to analyze interactions between tumor cells and nontumor cells in vivo. The approach can be applied to different research fields in which xenograft models are used.     

Excess sterols disrupt plant cellular activity by inducing stress-responsive gene expression

Journal of Plant Research - Sterols are important lipid constituents of cellular membranes in plants and other organisms. Sterol homeostasis is under strict regulation in plants because excess...     

糖尿病肝脏基因表达谱代谢学分析

目的:探讨糖尿病患者肝脏与正常对照肝脏基因表达谱的变化,并进行代谢通路生物信息学分析.方法:应用Affymetrix的Human Genome U133A array芯片检测糖尿病肝脏组织(n=2)和正常对照肝脏组织(n=2)的基因表达谱变化,通过DAVID分析平台对芯片监测到的表达上调的基因进行KEGG通路分析,并用RT-PCR验证部分基因的表达情况.结果:以比值大于2或小于-2为准,共有492条基因明显上调,820条基因明显下调;在差异表达明显的前20位基因中,涉及氧化应激、免疫炎症反应和脂代谢.KEC-G代谢通路分析显示:该差异表达基因谱符合2型糖尿病的发病机制,其差异表达明显基因的代谢通路涉及胰岛素信号通路、脂代谢、补体和凝血系统、糖代谢、粘附功能和细胞因子和受体的相互作用.RT-PCR证实差异表达明显基因SOD2mRNA和CRPmRNA确实表达明显上调.结论:糖尿病肝脏与正常肝脏组织基因表达谱的变化在代谢通路上主要表现为在糖、脂代谢和胰岛素信号通路、补体和凝血系统等的改变,肝脏在糖尿病发病机制中的作用可能与其参与糖脂代谢和胰岛素的作用异常有关.     

Global expression profiling applied to plant development

Plant development is controlled by both endogenous genetic programs and responses to exogenous signals. Microarray experiments are being used to identify the genes involved in these developmental processes. Most of the analyses conducted to date have been conducted on whole organs. Although these studies have provided valuable information, they are limited by the composite nature of plant organs that consist of multiple cell types. Technical advances that have made it possible to study global patterns of gene expression in individual cell types promise to increase greatly the information revealed by microarray experiments.     

Microarray expression profiling resources for plant genomics

Large volumes of genomic data have been generated for several plant species over the past decade, including structural sequence data and functional annotation at the genome level. Various technologies such as expressed sequence tags (ESTs), massively parallel signature sequencing (MPSS) and microarrays have been used to study gene expression and to provide functional data for many genes simultaneously. This review focuses on recent advances in the application of microarrays in plant genomic research and in gene expression databases available for plants. Large sets of Arabidopsis microarray data are publicly available. Recently developed array platforms are currently being used to generate genome-wide expression profiles for several crop species. Coupled to these platforms are public databases that provide access to these large-scale expression data, which can be used to aid the functional discovery of gene function.     

Impact of RNA degradation on gene expression profiling

Background

Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose.

Method

To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment.

Result

We applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes.

Conclusions

We developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.     

Global functional profiling of gene expression

The typical result of a microarray experiment is a list of tens or hundreds of genes found to be differentially regulated in the condition under study. Independent of the methods used to select these genes, the common task faced by any researcher is to translate these lists of genes into a better understanding of the biological phenomena involved. Currently, this is done through a tedious combination of searches through the literature and a number of public databases. We developed Onto-Express (OE) as a novel tool able to automatically translate such lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function, and chromosome location. Statistical significance values are calculated for each category. We demonstrate the validity and the utility of this comprehensive global analysis of gene function by analyzing two breast cancer datasets from two separate laboratories. OE was able to identify correctly all biological processes postulated by the original authors, as well as discover novel relevant mechanisms.     

Analysis of pathway activity in primary tumors and NCI60 cell lines using gene expression profiling data

To determine cancer pathway activities in nine types of primary tumors and NCI60 cell lines, we applied an in silico approach by examining gene signatures reflective of consequent pathway activation using gene expression data. Supervised learning approaches predicted that the Ras pathway is active in ～70% of lung adenocarcinomas but inactive in most squamous cell carcinomas, pulmonary carcinoids, and small cell lung carcinomas. In contrast, the TGF-β, TNF-α, Src, Myc, E2F3, and β-catenin pathways are inactive in lung adenocarcinomas. We predicted an active Ras, Myc, Src, and/or E2F3 pathway in significant percentages of breast cancer, colorectal carcinoma, and gliomas. Our results also suggest that Ras may be the most prevailing oncogenic pathway. Additionally, many NCI60 cell lines exhibited a gene signature indicative of an active Ras, Myc, and/or Src, but not E2F3, β-catenin, TNF-α, or TGF-β pathway. To our knowledge, this is the first comprehensive survey of cancer pathway activities in nine major tumor types and the most widely used NCI60 cell lines. The "gene expression pathway signatures" we have defined could facilitate the understanding of molecular mechanisms in cancer development and provide guidance to the selection of appropriate cell lines for cancer research and pharmaceutical compound screening.     

阿维链霉菌基因表达谱芯片的初步研究

用表达谱芯片分析阿维链霉菌在发酵过程中转录组表达情况,优化微生物转录组分析条件。提取RNA,进行荧光探针的合成,杂交,扫描对数据进行分析。RNA的提取要注意破壁方法的选择以及茵体量;合成cDNA时,RNA的加入量一般在10μg左右;当cDNA用逆转录的方法得到时,用专门的纯化试剂盒要比传统的异丙醇纯化效果好;杂交温度对于合成基因来说一般在42％,对于比较长的PCR产物来说,温度相比较要高点;对于杂交时间来说,杂交12h之上,效果要明显比6h的好,所以杂交时间一般选择12h之上。对芯片上的197个合成基因进行分析：大约有60个基因进行了表达,其中包括18个双组分或激酶基因;10个糖酵解或者三羧酸循环途径基因;3个阿维基因簇;7个调节基因;5个细胞分化或孢子形;4个运输或固定基因;两个伴侣分子;5个RNA聚合酶σ因子;3个转录基因;两个脂肪酸合成基因;1个RNA聚合酶β亚基。     

The application of gene co-expression network reconstruction based on CNVs and gene expression microarray data in breast cancer

Copy number variations (CNVs) are one type of the human genetic variations and are pervasive in the human genome. It has been confirmed that they can play a causal role in complex diseases. Previous studies of CNVs focused more on identifying the disease-specific CNV regions or candidate genes on these CNV regions, but less on the synergistic actions between genes on CNV regions and other genes. Our research combined the CNVs with related gene co-expression to reconstruct gene co-expression network by using single nucleotide polymorphism microarray datasets and gene microarray datasets of breast cancer, and then extracted the modules which connected densely inside and analyzed the functions of modules. Interestingly, all of these modules’ functions were related to breast cancer according to our enrichment analysis, and most of the genes in these modules have been reported to be involved in breast cancer. Our findings suggested that integrating CNVs and gene co-expressed relations was an available way to analyze the roles of CNV genes and their synergistic genes in breast cancer, and provided a novel insight into the pathological mechanism of breast cancer.