Jo Vandesompele

Bio: Jo Vandesompele is an academic researcher from Ghent University. The author has contributed to research in topics: Neuroblastoma & microRNA. The author has an hindex of 88, co-authored 383 publications receiving 59368 citations. Previous affiliations of Jo Vandesompele include Washington University in St. Louis & Ghent University Hospital.

Genome-wide promoter methylation analysis in neuroblastoma identifies prognostic methylation biomarkers.

Abstract: Background: Accurate outcome prediction in neuroblastoma, which is necessary to enable the optimal choice of risk-related therapy, remains a challenge. To improve neuroblastoma patient stratification, this study aimed to identify prognostic tumor DNA methylation biomarkers. Results: To identify genes silenced by promoter methylation, we first applied two independent genome-wide methylation screening methodologies to eight neuroblastoma cell lines. Specifically, we used re-expression profiling upon 5-aza-2’-deoxycytidine (DAC) treatment and massively parallel sequencing after capturing with a methyl-CpG-binding domain (MBD-seq). Putative methylation markers were selected from DAC-upregulated genes through a literature search and an upfront methylation-specific PCR on 20 primary neuroblastoma tumors, as well as through MBD- seq in combination with publicly available neuroblastoma tumor gene expression data. This yielded 43 candidate biomarkers that were subsequently tested by high-throughput methylation-specific PCR on an independent cohort of 89 primary neuroblastoma tumors that had been selected for risk classification and survival. Based on this analysis, methylation of KRT19, FAS, PRPH, CNR1, QPCT, HIST1H3C, ACSS3 and GRB10 was found to be associated with at least one of the classical risk factors, namely age, stage or MYCN status. Importantly, HIST1H3C and GNAS methylation was associated with overall and/or event-free survival. Conclusions: This study combines two genome-wide methylation discovery methodologies and is the most extensive validation study in neuroblastoma performed thus far. We identified several novel prognostic DNA methylation markers and provide a basis for the development of a DNA methylation-based prognostic classifier in neuroblastoma.

Primer Sequence Disclosure: A Clarification of the MIQE Guidelines

Abstract: To the Editor: The publication of the minimum information for the publication of real-time quantitative PCR experiments (MIQE)1 guidelines (1) has turned out to be a defining event in the maturing of quantitative real-time PCR (qPCR) technology. The response from instrument and reagent manufacturers has been universally positive. There has been extensive publicity in print, online, and at scientific meetings, and scientific journals are beginning to take note (2). Citations of the MIQE paper are accelerating, with 63 of the 169 citations (as of the end of January 2011) having appeared since September 2010. There is an enormous amount of good will toward this initiative, with many researchers keen to implement the different parameters within their own experimental protocols. MIQE was never conceived with the intent of imposing an immutable edict, as in the spirit of a regulatory agency. The aim was to provide commonsense guidelines for enhancing the reproducibility and transparency of qPCR assays. MIQE, however, has become a marketing and selling argument (“MIQE compliance”), and this practice places a responsibility on the authors of the guidelines to assess whether the rapidly evolving technology demands refinement of the guidelines to acknowledge researchers' uncertainty. Most discussion has concerned the stipulation of primer sequence disclosure. Many commercial qPCR assays are not supplied with the primer/probe sequences because most vendors consider such information commercially sensitive. In addition, there usually …

Identification and expression analysis of genes associated with bovine blastocyst formation

Abstract: Background Normal preimplantation embryo development encompasses a series of events including first cleavage division, activation of the embryonic genome, compaction and blastocyst formation. First lineage differentiation starts at the blastocyst stage with the formation of the trophectoderm and the inner cell mass. The main objective of this study was the detection, identification and expression analysis of genes associated with blastocyst formation in order to help us better understand this process. This information could lead to improvements of in vitro embryo production procedures.

Functional analysis of the p53 pathway in neuroblastoma cells using the small-molecule MDM2 antagonist nutlin-3.

Abstract: Suppression of p53 activity is essential for proliferation and survival of tumor cells. A direct p53-activating compound, nutlin-3, was used in this study, together with p53 mutation analysis, to characterize p53 pathway defects in a set of 34 human neuroblastoma cell lines. We identified 9 cell lines (26%) with a p53 loss-of-function mutation, including 6 missense mutations, 1 nonsense mutation, 1 in-frame deletion, and 1 homozygous deletion of the 3' end of the p53 gene. Sensitivity to nutlin-3 was highly predictive of absence of p53 mutation. Signaling pathways downstream of p53 were functionally intact in 23 of 25 cell lines with wild-type p53. Knockdown and overexpression experiments revealed a potentiating effect of p14(ARF) expression on the response of neuroblastoma cells to nutlin-3. Our findings shed light on the spectrum of p53 pathway lesions in neuroblastoma cells, indicate that defects in effector molecules downstream of p53 are remarkably rare in neuroblastoma, and identify p14(ARF) as a determinant of the outcome of the response to MDM2 inhibition. These insights may prove useful for the clinical translation of evolving strategies aimed at p53 reactivation and for the development of new therapeutic approaches.

limma powers differential expression analyses for RNA-sequencing and microarray studies

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

Cutadapt removes adapter sequences from high-throughput sequencing reads

Abstract: When small RNA is sequenced on current sequencing machines, the resulting reads are usually longer than the RNA and therefore contain parts of the 3' adapter. That adapter must be found and removed error-tolerantly from each read before read mapping. Previous solutions are either hard to use or do not offer required features, in particular support for color space data. As an easy to use alternative, we developed the command-line tool cutadapt, which supports 454, Illumina and SOLiD (color space) data, offers two adapter trimming algorithms, and has other useful features. Cutadapt, including its MIT-licensed source code, is available for download at http://code.google.com/p/cutadapt/

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes

Abstract: Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences.