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.

Abstract 2664: Eradication of neuroblastoma by suppressing the expression of a single long noncoding RNA

Abstract: N-Myc gene amplification occurs in one quarter of human neuroblastoma tissues, and is a marker for poor patient prognosis. We performed RNA sequencing experiments, and identified 5 transcripts, including RP1NB1, which were most considerably differentially expressed between N-Myc gene amplified and nonamplified human neuroblastoma cell lines. Affymetrix microarray studies revealed that DEPD was one of the few genes considerably downregulated in neuroblastoma cells after RP1NB1 depletion. Chromatin immunoprecipitation assays showed that knocking down RP1NB1 expression reduced histone H3 lysine 4 trimethylation, a marker for active gene transcription, at the DEPD gene promoter. Luciferase assays demonstrated that knocking down RP1NB1 decreased DEPD gene promoter activity. Depletion of RP1NB1 or DEPD with two independent siRNAs or shRNAs significantly reduced ERK protein phosphorylation, N-Myc protein phosphorylation at Serine 62, N-Myc protein stabilization, neuroblastoma cell proliferation and survival. Clonogenic assays showed that knocking down RP1NB1 with doxycycline completely abolished colony formation capacity of neuroblastoma cells stably transfected with doxycycline-inducible RP1NB1 shRNAs. Importantly, treatment with doxycycline in mice xenografted with neuroblastoma cells stably transfected with doxycycline-inducible RP1NB1 shRNA led to tumor eradication. In human neuroblastoma tissues from 600 neuroblastoma patients, high levels of RP1NB1 gene expression correlated with DEPD gene expression and poor patient prognosis. In conclusion, this study identifies the novel long noncoding RNA RP1NB1 as an important regulator of N-Myc protein stability and neuroblastoma tumorigenesis. Citation Format: Andrew E. Tee, Pei Y. Liu, Giorgio Milazzo, Kate M. Hannan, Jesper Maag, Nenad Bartonicek, Renhua Song, Chen C. Jiang, Xu D. Zhang, Murray D. Norris, Michelle Haber, Glenn M. Marshall, Jinyan Li, Jo Vandesompele, John S. Mattick, Pieter Mestdagh, Giovanni Perini, Ross D. Hannan, Marcel E. Dinger, Tao Liu. Eradication of neuroblastoma by suppressing the expression of a single noncoding RNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2453.

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.