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.

lncRNA discovery in the HIV replication cycle adds new layer in HIV-host interplay

Abstract: Studying the effects of HIV infection on the host transcriptome has typically focused on protein-coding genes. However, recent advances in the field of RNA sequencing revealed that long non-coding RNAs (lncRNAs) add an extensive additional layer to the cell’s molecular network and are crucial for normal cellular function. lncRNAs exert the ability to control a wide range of (post-)transcriptional processes and offer unique possibilities for pathogens like HIV to hijack the cellular machinery and reshape gene expression in their favor. Therefore, lncRNA discovery can result in new insights into the HIV-host interplay. We performed transcriptome profiling throughout a characterized primary HIV infection in vitro to investigate lncRNA expression at the different HIV replication cycle processes (reverse transcription, integration and particle production). Subsequently, guilt-by-association, transcription factor and co-expression analysis were performed to infer biological roles for the lncRNAs identified in the HIV-host interplay. Throughout the HIV replication cycle we identified 387 lncRNAs that were differentially expressed with the majority observed at the viral integration phase. Many of these lncRNAs (173) were suggested to play a role in mechanisms at the heart of HIV-host interplay that rely on proteasomal and ubiquitination pathways (113), apoptosis inhibition (12), BRCA1/2 DNA damage responses and ATR cell cycle regulation (12). Through transcription factor binding analysis, we found that lncRNAs display a distinct transcriptional regulation profile (ao. TAF1/3/7, CHD1 and AT3) as compared to protein coding mRNAs (ao. KLF4, SUZ12 and SOX2), suggesting that mRNAs and lncRNAs are independently modulated during HIV replication. In addition, we identified five differentially expressed lncRNA-mRNA pairs with mRNA involvement in HIV pathogenesis with possible cis regulatory lncRNAs that control nearby mRNA expression and function (ao. lnc-HES5-1 and TNFRSF14). Altogether, the present study demonstrates that lncRNAs add a new dimension to the HIV-host interplay and exhibit an independent transcriptionally regulated response. These identified lncRNAs are involved in viral and antiviral response pathways and should be further investigated as they may represent possible biomarkers or targets for controlling HIV replication.

Chromosome 3p microsatellite allelotyping in neuroblastoma: a report on the technical hurdles

Abstract: Pinpointing critical regions of recurrent loss may help localize tumor suppressor genes. To determine the regions of loss on chromosome 3p in neuroblastoma, we performed loss of heterozygosity analysis using 16 microsatellite markers in a series of 65 primary tumors and 29 neuroblastoma cell lines. In this study, we report the results and discuss the technical hurdles that we encountered during data generation and interpretation that are of relevance for current studies or tests employing microsatellites. To provide functional support for the implication of 3p tumor suppressor genes in this childhood malignancy, we performed a microcell-mediated chromosome 3 transfer in neuroblastoma cells.

Equivalence of saliva RT-qPCR testing to nasal-throat/nasopharyngeal swab testing in the general practitioner's setting to detect SARS-CoV-2

Abstract: Study design Saliva has been proposed as valid alternative for nasopharyngeal swab for RT-qPCR detection of SARS-CoV-2. The sensitivity is generally equivalent, and it comes with much less discomfort for the patient. While there is an overall good performance in the literature for adults, there is much less information on the use of saliva in children or in the general practitioner’s setting. Methods We tested a novel commercially available saliva collection kit with a virus inactivating and RNA stabilizing buffer (InActiv Blue®) in matched saliva and swab samples from 245 individuals, including 216 children, collected by general practitioners. Results Blind RT-qPCR testing of the saliva samples confirmed all 23 positives identified by swab testing (100% concordance), irrespective of age, presence of symptoms, or high-risk status. One child’s saliva sample was found low positive while negative on the nasopharyngeal swab, resulting in an overall relative sensitivity of RT-qPCR saliva testing of 104.3%. Conclusion Saliva collected in InActiv Blue® can be a valid alternative for SARS-CoV-2 RT-qPCR testing in the general practitioner’s setting, including children.

Probabilistic index models for testing differential expression in single cell RNA sequencing data

Abstract: Single-cell RNA sequencing (scRNA-seq) technologies profile gene expression patterns in individual cells. It is often of interest to test for differential expression (DE) between conditions, e.g. treatment and control or between cell types. Simulation studies have shown that non-parametric tests, such as the Wilcoxon-rank sum test, can robustly detect significant DE, with better performance than many parametric tools specifically developed for scRNA-seq data analysis. However, these classical rank tests cannot be used for complex experimental designs involving multiple groups, multiple factors and confounding variables. Further, rank based tests do not provide an interpretable measure of the effect size. We propose a semi-parametric approach based on probabilistic index models (PIM) that form a flexible class of models that generalize classical rank tests. Our method does not rely on strong distributional assumptions and it allows accounting for confounding factors. Moreover, our method allows for the estimation of the effect size in terms of a probabilistic index. Real data analysis demonstrated that PIM is capable of identifying biologically meaningful DE. Our simulation studies also show that tests for DE succeed well in controlling the false discovery rate at its nominal level, while maintaining good sensitivity as compared to competing methods.

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.