The RUNX1 transcription factor is expressed in serous epithelial ovarian carcinoma and contributes to cell proliferation,migration and invasion

Previously, we have identified the RUNX1 gene as hypomethylated and overexpressed in post-chemotherapy (CT) primary cultures derived from epithelial ovarian cancer (EOC) patients, when compared with primary cultures derived from matched primary (prior to CT) tumors. Here we show that RUNX1 displays a trend of hypomethylation, although not significant, in omental metastases compared with primary EOC tumors. Surprisingly, RUNX1 displayed significantly higher expression not only in metastatic tissue, but also in high-grade primary tumors and even in low malignant potential tumors. The RUNX1 expression levels were almost identical in primary tumors and omental metastases, suggesting that RUNX1 hypomethylation might have a limited impact on its overexpression in advanced (metastatic) stage of the disease.

Knockdown of the RUNX1 expression in EOC cells led to sharp decrease of cell proliferation and induced G₁ cell cycle arrest. Moreover, RUNX1 suppression significantly inhibited EOC cell migration and invasion. Gene expression profiling and consecutive network and pathway analyses confirmed these findings, as numerous genes and pathways known previously to be implicated in ovarian tumorigenesis, including EOC tumor invasion and metastasis, were found to be downregulated upon RUNX1 suppression, while a number of pro-apoptotic genes and some EOC tumor suppressor genes were induced.

Taken together, our data are indicative for a strong oncogenic potential of the RUNX1 gene in EOC progression and suggest that RUNX1 might be a novel EOC therapeutic target. Further studies are needed to more completely elucidate the functional implications of RUNX1 and other members of the RUNX gene family in ovarian tumorigenesis.     

Comparison and evaluation of RNA quantification methods using viral, prokaryotic, and eukaryotic RNA over a 10 concentration range

Quantification of RNA is essential for various molecular biology studies. In this work, three quantification methods were evaluated: ultraviolet (UV) absorbance, microcapillary electrophoresis (MCE), and fluorescence-based quantification. Viral, bacterial, and eukaryotic RNA were measured in the 500 to 0.05-ng μl⁻¹ range via an ND-1000 spectrophotometer (UV), Agilent RNA 6000 kits (MCE), and Quant-iT RiboGreen assay (fluorescence). The precision and accuracy of each method were assessed and compared with a concentration derived independently using inductively coupled plasma-optical emission spectroscopy (ICP-OES). Cost, operator time and skill, and required sample volumes were also considered in the evaluation. Results indicate an ideal concentration range for each quantification technique to optimize accuracy and precision. The ND-1000 spectrophotometer exhibits high precision and accurately quantifies a 1-μl sample in the 500 to 5-ng μl⁻¹ range. The Quant-iT RiboGreen assay demonstrates high precision in the 1 to 0.05-ng μl⁻¹ range but is limited to lower RNA concentrations and is more costly than the ND-1000 spectrophotometer. The Agilent kits exhibit less precision than the ND-1000 spectrophotometer and Quant-iT RiboGreen assays in the 500 to 0.05-ng μl⁻¹ range. However, the Agilent kits require 1 μl of sample and can determine the integrity of the RNA, a useful feature for verifying whether the isolation process was successful.     

Comparison of Commercially Available Target Enrichment Methods for Next-Generation Sequencing

Isolating high-priority segments of genomes greatly enhances the efficiency of next-generation sequencing (NGS) by allowing researchers to focus on their regions of interest. For the 2010–11 DNA Sequencing Research Group (DSRG) study, we compared outcomes from two leading companies, Agilent Technologies (Santa Clara, CA, USA) and Roche NimbleGen (Madison, WI, USA), which offer custom-targeted genomic enrichment methods. Both companies were provided with the same genomic sample and challenged to capture identical genomic locations for DNA NGS. The target region totaled 3.5 Mb and included 31 individual genes and a 2-Mb contiguous interval. Each company was asked to design its best assay, perform the capture in replicates, and return the captured material to the DSRG-participating laboratories. Sequencing was performed in two different laboratories on Genome Analyzer IIx systems (Illumina, San Diego, CA, USA). Sequencing data were analyzed for sensitivity, specificity, and coverage of the desired regions. The success of the enrichment was highly dependent on the design of the capture probes. Overall, coverage variability was higher for the Agilent samples. As variant discovery is the ultimate goal for a typical targeted sequencing project, we compared samples for their ability to sequence single-nucleotide polymorphisms (SNPs) as a test of the ability to capture both chromosomes from the sample. In the targeted regions, we detected 2546 SNPs with the NimbleGen samples and 2071 with Agilent''s. When limited to the regions that both companies included as baits, the number of SNPs was ∼1000 for each, with Agilent and NimbleGen finding a small number of unique SNPs not found by the other.     

应用Agilent 2100 Bioanalyzer分析限制性显示技术制备的HIV片段库

使用Agilent 2 10 0Bioanalyzer分析限制性显示技术 (restrictiondisplay ,RD)制备的HIV片段库 .利用合适的限制酶从质粒上获得HIVB亚型代表株U2 6 94 2全基因cDNA ,然后将目的片段进行Sau3AⅠ消化 ,在消化得到的片段两端加接头 ,利用通用引物进行PCR扩增 ,扩增结果通过琼脂糖凝胶电泳以及Agilent 2 10 0Bioanalyzer两种方法分析 .结果显示 ,Agilent 2 10 0Bioanalyzer比琼脂糖凝胶电泳能更快速、直接和客观地反映RD技术制备的DNA片段的大小以及含量 ,并能对RD PCR过程中片段自身连接以及优势扩增的现象进行直接的监控作用 .     

Bayesian Modeling of ChIP‐chip Data Through a High‐Order Ising Model

Summary ChIP‐chip experiments are procedures that combine chromatin immunoprecipitation (ChIP) and DNA microarray (chip) technology to study a variety of biological problems, including protein–DNA interaction, histone modification, and DNA methylation. The most important feature of ChIP‐chip data is that the intensity measurements of probes are spatially correlated because the DNA fragments are hybridized to neighboring probes in the experiments. We propose a simple, but powerful Bayesian hierarchical approach to ChIP‐chip data through an Ising model with high‐order interactions. The proposed method naturally takes into account the intrinsic spatial structure of the data and can be used to analyze data from multiple platforms with different genomic resolutions. The model parameters are estimated using the Gibbs sampler. The proposed method is illustrated using two publicly available data sets from Affymetrix and Agilent platforms, and compared with three alternative Bayesian methods, namely, Bayesian hierarchical model, hierarchical gamma mixture model, and Tilemap hidden Markov model. The numerical results indicate that the proposed method performs as well as the other three methods for the data from Affymetrix tiling arrays, but significantly outperforms the other three methods for the data from Agilent promoter arrays. In addition, we find that the proposed method has better operating characteristics in terms of sensitivities and false discovery rates under various scenarios.     

用聚丙烯酰胺凝胶电泳和微流体芯片电泳鉴别6种液态奶

目的:研究6种液态奶制品蛋白电泳图谱的区别,建立奶制品的蛋白质学鉴别方法。方法:以5种纯牛奶、羊奶、水牛奶、骆驼奶、牦牛奶、黄豆浆为研究对象,通过SDS-PAGE和Agilent 2100微流体芯片电泳法进行分析比较。结果:骆驼奶、黄豆浆与其他研究对象的图谱有明显区别,而牛奶、羊奶、水牛奶、牦牛奶的差异却不是很大;采用微流体芯片电泳可有效地对豆奶、骆驼奶进行区分,还可在一定程度上鉴别牛奶、羊奶、水牛奶和牦牛奶。结论:Agilent2100系统作为一种新型半自动微流体芯片技术,可以快速、高效、准确地应用于液态奶制品的蛋白成分分析及鉴别。     

Comparative analyses of Saccharomyces cerevisiae RNAs using Agilent RNA 6000 Nano Assay and agarose gel electrophoresis

Precise quantification and quality characterisation of isolated RNAs are prerequisites for their further exploitation in genome-wide microarrays, Northern blots, cDNA library preparation and others. Our data indicate that RNA analyses using Agilent RNA Nano Assay exhibit several advantages when compared with those performed on ethidium bromide-stained agarose gel electrophoresis or on a spectrophotometer. The RNA Nano Assay makes it possible to estimate RNA concentrations in the range from 1000 ng microl(-1) to 17 ng microl(-1). The presence of impurities including traces of DNA within RNA samples does not influence the concentration measurements. Like agarose gel electrophoresis, RNA Nano Assay allows to analyse RNAs dissolved in formamide and therefore protected against RNase action. Moreover, it allows a clearer distinction of partially degraded samples. The limitation of RNA Nano Assay is the impossibility to detect and to analyse double-stranded RNAs.