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.

The mutational landscape of MYCN, Lin28b and ALKF1174L driven murine neuroblastoma mimics human disease.

Abstract: // Bram De Wilde 1, 2 , Anneleen Beckers 1 , Sven Lindner 3 , Althoff Kristina 3 , Katleen De Preter 1, 2 , Pauline Depuydt 1, 2 , Pieter Mestdagh 1, 2 , Tom Sante 1 , Steve Lefever 1, 2 , Falk Hertwig 4, 5 , Zhiyu Peng 6 , Le-Ming Shi 7 , Sangkyun Lee 8 , Elien Vandermarliere 9, 10 , Lennart Martens 9, 10 , Bjorn Menten 1 , Alexander Schramm 3 , Matthias Fischer 4, 5 , Johannes Schulte 11 , Jo Vandesompele 1, 2 and Frank Speleman 1, 2 1 Center for Medical Genetics, Ghent University, Ghent, Belgium 2 Cancer Research Institute Ghent, Ghent University, Ghent, Belgium 3 Department of Pediatric Oncology and Hematology, University Children’s Hospital, Essen, Germany 4 Department of Experimental Pediatric Oncology, University Children's Hospital of Cologne, Cologne, Germany 5 Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany 6 BGI-Shenzhen, Bei Shan Industrial Zone, Yantian District, Shenzhen, Guangdong, China 7 Center for Pharmacogenomics and Fudan-Zhangjiang Center for Clinical Genomics, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China 8 Department of Computer Science, Artificial Intelligence Group, TU Dortmund, Dortmund, Germany 9 Medical Biotechnology Center, VIB, Ghent, Belgium 10 Department of Biochemistry, Ghent University, Ghent, Belgium 11 Pediatric Oncology and Hematology, Charite University Medicine, Berlin, Germany Correspondence to: Frank Speleman, email: Franki.Speleman@UGent.be Keywords: neuroblastoma; mouse model; exome sequencing; array CGH Received: March 01, 2017 Accepted: October 28, 2017 Published: December 22, 2017 ABSTRACT Genetically engineered mouse models have proven to be essential tools for unraveling fundamental aspects of cancer biology and for testing novel therapeutic strategies. To optimally serve these goals, it is essential that the mouse model faithfully recapitulates the human disease. Recently, novel mouse models for neuroblastoma have been developed. Here, we report on the further genomic characterization through exome sequencing and DNA copy number analysis of four of the currently available murine neuroblastoma model systems ( ALK, Th- MYCN, Dbh- MYCN and Lin28b ). The murine tumors revealed a low number of genomic alterations – in keeping with human neuroblastoma - and a positive correlation of the number of genetic lesions with the time to onset of tumor formation was observed. Gene copy number alterations are the hallmark of both murine and human disease and frequently affect syntenic genomic regions. Despite low mutational load, the genes mutated in murine disease were found to be enriched for genes mutated in human disease. Taken together, our study further supports the validity of the tested mouse models for mechanistic and preclinical studies of human neuroblastoma.

Rapid systematic review of the sensitivity of SARS-CoV-2 molecular testing on saliva compared to nasopharyngeal swabs

Abstract: Background: Nasopharyngeal sampling has been the standard collection method for COVID-19 testing. Due to its invasive nature and risk of contamination for health care workers who collect the sample, non-invasive and safe sampling methods like saliva, can be used alternatively. Methods: A rapid systematic search was performed in PubMed and medRxiv, with the last retrieval on June 6th, 2020. Studies were included if they compared saliva with nasopharyngeal sampling for the detection of SARS-CoV-2 RNA using the same RT-qPCR applied on both types of samples. The primary outcome of interest was the relative sensitivity of SARS-CoV-2 testing on saliva versus nasopharyngeal samples (used as the comparator test). A secondary outcome was the proportion of nasopharyngeal-positive patients that tested also positive on a saliva sample. Results: Eight studies were included comprising 1070 saliva-nasopharyngeal sample pairs allowing assessment of the first outcome. The relative sensitivity of SARS-CoV-2 testing on saliva versus nasopharyngeal samples was 0.97 (95% CI=0.92-1.02). The second outcome incorporated patient data (n=257) from four other studies (n=97 patients) pooled with four studies from the first outcome (n=160 patients). This resulted in a pooled proportion of nasopharyngeal positive cases that was also positive on saliva of 86% (95% CI=77-93%). Discussion: Saliva could potentially be considered as an alternative sampling method when compared to nasopharyngeal swabs. However, studies included in this review often were small and involved inclusion of subjects with insufficient information on clinical covariates. Most studies included patients who were symptomatic (78%, 911/1167). Therefore, additional and larger studies should be performed to verify the relative performance of saliva in the context of screening of asymptomatic populations and contact-tracing.

Validation of Circular RNAs Using RT-qPCR After Effective Removal of Linear RNAs by Ribonuclease R

Abstract: Circular RNAs (circRNAs) are a class of endogenous noncoding RNAs that have been shown to play a role in normal development, homeostasis, and disease, including cancer. CircRNAs are formed through a process called back-splicing, which results in a covalently closed loop with a nonlinear back-spliced junction (BSJ). In general, circRNA BSJs are predicted in RNA sequencing data using one of numerous circRNA detection algorithms. Selected circRNAs are then typically validated using an orthogonal method such as reverse transcription quantitative PCR (RT-qPCR) with circRNA-specific primers. However, linear transcripts originating from endogenous trans-splicing can lead to false-positive signals both in RNA sequencing and in RT-qPCR experiments. Therefore, it is essential to perform the RT-qPCR validation step only after linear RNAs have been degraded using an exonuclease such as ribonuclease R (RNase R). Several RNase R protocols are available for circRNA detection using RNA sequencing or RT-qPCR. These protocols-which vary in enzyme concentration, RNA input amount, incubation times, and cleanup steps-typically lack a detailed validated standard protocol and fail to provide a range of conditions that deliver accurate results. As such, some protocols use RNase R concentrations that are too high, resulting in partial degradation of the target circRNAs. Here, we describe an optimized workflow for circRNA validation, combining RNase R treatment and RT-qPCR. First, we outline the steps for circRNA primer design and qPCR assay validation. Then, we describe RNase R treatment of total RNA and, importantly, a subsequent essential buffer cleanup step. Lastly, we outline the steps to perform the RT-qPCR and discuss the downstream data analyses. © 2021 Wiley Periodicals LLC. Basic Protocol 1: CircRNA primer design and qPCR assay validation Basic Protocol 2: RNase R treatment, cleanup, and RT-qPCR.

Lack of association between MDM2 promoter SNP309 and clinical outcome in patients with neuroblastoma.

Abstract: While a polymorphism located within the promoter region of the MDM2 proto-oncogene, SNP309 (T > G), has previously been associated with increased risk and aggressiveness of neuroblastoma and other tumor entities, a protective effect has also been reported in certain other cancers. In this study, we evaluated the association of MDM2 SNP309 with outcome in 496 patients with neuroblastoma and its effect on MDM2 expression. No significant difference in overall or event-free survival was observed among patients with neuroblastoma with or without MDM2 SNP309. The presence of SNP309 does not affect MDM2 expression in neuroblastoma. Pediatr Blood Cancer 2014; 61:1867–1870. © 2014 Wiley Periodicals, Inc.

Zipper plot : visualizing transcriptional activity of genomic regions

Abstract: Reconstructing transcript models from RNA-sequencing (RNA-seq) data and establishing these as independent transcriptional units can be a challenging task. Current state-of-the-art tools for long non-coding RNA (lncRNA) annotation are mainly based on evolutionary constraints, which may result in false negatives due to the overall limited conservation of lncRNAs. To tackle this problem we have developed the Zipper plot, a novel visualization and analysis method that enables users to simultaneously interrogate thousands of human putative transcription start sites (TSSs) in relation to various features that are indicative for transcriptional activity. These include publicly available CAGE-sequencing, ChIPsequencing and DNase-sequencing datasets. Our method only requires three tab-separated fields (chromosome, genomic coordinate of the TSS and strand) as input and generates a report that includes a detailed summary table, a Zipper plot and several statistics derived from this plot. Using the Zipper plot, we found evidence of transcription for a set of well-characterized lncRNAs and observed that fewer mono-exonic lncRNAs have CAGE peaks overlapping with their TSSs compared to multi-exonic lncRNAs. Our method is implemented using the statistical programming language R and is freely available as a webtool.

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.