Raluca Teusan

Testing the burden of rare variation in arrhythmia-susceptibility genes provides new insights into molecular diagnosis for Brugada syndrome

The Brugada syndrome (BrS) is a rare heritable cardiac arrhythmia disorder associated with ventricular fibrillation and sudden cardiac death. Mutations in the SCN5A gene have been causally related to BrS in 20-30% of cases. Twenty other genes have been described as involved in BrS, but their overall contribution to disease prevalence is still unclear. This study aims to estimate the burden of rare coding variation in arrhythmia-susceptibility genes among a large group of patients with BrS. We have developed a custom kit to capture and sequence the coding regions of 45 previously reported arrhythmia-susceptibility genes and applied this kit to 167 index cases presenting with a Brugada pattern on the electrocardiogram as well as 167 individuals aged over 65-year old and showing no history of cardiac arrhythmia. By applying burden tests, a significant enrichment in rare coding variation (with a minor allele frequency below 0.1%) was observed only for SCN5A, with rare coding variants carried by 20.4% of cases with BrS versus 2.4% of control individuals (P = 1.4 × 10(-7)). No significant enrichment was observed for any other arrhythmia-susceptibility gene, including SCN10A and CACNA1C. These results indicate that, except for SCN5A, rare coding variation in previously reported arrhythmia-susceptibility genes do not contribute significantly to the occurrence of BrS in a population with European ancestry. Extreme caution should thus be taken when interpreting genetic variation in molecular diagnostic setting, since rare coding variants were observed in a similar extent among cases versus controls, for most previously reported BrS-susceptibility genes.

Genomic assessment of human cumulus cell marker genes as predictors of oocyte developmental competence: impact of various experimental factors.

Background Single embryo transfer (SET) is the most successful way to reduce the frequency of multiple pregnancies following in vitro fertilisation. However, selecting the embryo for SET with the highest chances of pregnancy remains a difficult challenge since morphological and kinetics criteria provide poor prediction of both developmental and implantation ability. Partly through the expression of specific genes, the oocyte-cumulus interaction helps the oocyte to acquire its developmental competence. Our aim was therefore to identify at the level of cumulus cells (CCs) genes related to oocyte developmental competence. Methodology/Principal Findings 197 individual CCs were collected from 106 patients undergoing an intra-cytoplasmic sperm injection procedure. Gene expression of CCs was studied using microarray according to the nuclear maturity of the oocyte (immature vs. mature oocyte) and to the developmental competence of the oocyte (ability to reach the blastocyst stage after fertilisation). Microarray study was followed by a meta-analysis of the behaviour of these genes in other datasets available in Gene Expression Omnibus which showed the consistency of this list of genes. Finally, 8 genes were selected according to oocyte developmental competence from the 308 differentially expressed genes (p<0.0001) for further validation by quantitative PCR (qPCR). Three of these 8 selected genes were validated as potential biomarkers (PLIN2, RGS2 and ANG). Experimental factors such as inter-patient and qPCR series variability were then assessed using the Generalised Linear Mixed Model procedure, and only the expression level of RGS2 was confirmed to be related to oocyte developmental competence. The link between biomarkers and pregnancy was finally evaluated and level of RGS2 expression was also correlated with clinical pregnancy. Conclusion/Significance RGS2, known as a regulator of G protein signalling, was the only gene among our 8 selected candidates biomarkers of oocyte competence to cover many factors of variability, including inter-patient factors and experimental conditions.

Regulation of intestinal epithelial cells transcriptome by enteric glial cells: impact on intestinal epithelial barrier functions.

BackgroundEmerging evidences suggest that enteric glial cells (EGC), a major constituent of the enteric nervous system (ENS), are key regulators of intestinal epithelial barrier (IEB) functions. Indeed EGC inhibit intestinal epithelial cells (IEC) proliferation and increase IEB paracellular permeability. However, the role of EGC on other important barrier functions and the signalling pathways involved in their effects are currently unknown. To achieve this goal, we aimed at identifying the impact of EGC upon IEC transcriptome by performing microarray studies.ResultsEGC induced significant changes in gene expression profiling of proliferating IEC after 24 hours of co-culture. 116 genes were identified as differentially expressed (70 up-regulated and 46 down-regulated) in IEC cultured with EGC compared to IEC cultured alone. By performing functional analysis of the 116 identified genes using Ingenuity Pathway Analysis, we showed that EGC induced a significant regulation of genes favoring both cell-to-cell and cell-to-matrix adhesion as well as cell differentiation. Consistently, functional studies showed that EGC induced a significant increase in cell adhesion. EGC also regulated genes involved in cell motility towards an enhancement of cell motility. In addition, EGC profoundly modulated expression of genes involved in cell proliferation and cell survival, although no clear functional trend could be identified. Finally, important genes involved in lipid and protein metabolism of epithelial cells were shown to be differentially regulated by EGC.ConclusionThis study reinforces the emerging concept that EGC have major protective effects upon the IEB. EGC have a profound impact upon IEC transcriptome and induce a shift in IEC phenotype towards increased cell adhesion and cell differentiation. This concept needs to be further validated under both physiological and pathophysiological conditions.

MADGene: retrieval and processing of gene identifier lists for the analysis of heterogeneous microarray datasets

Summary: MADGene is a software environment comprising a web-based database and a java application. This platform aims at unifying gene identifiers (ids) and performing gene set analysis. MADGene allows the user to perform inter-conversion of clone and gene ids over a large range of nomenclatures relative to 17 species. We propose a set of 23 functions to facilitate the analysis of gene sets and we give two microarray applications to show how MADGene can be used to conduct meta-analyses. Availability: The MADGene resources are freely available online from http://www.madtools.org, a website dedicated to the analysis and annotation of DNA microarray data. Contact: gerard.ramstein@univ-nantes.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Large-scale mRNA analysis of female skeletal muscles during 60 days of bed rest with and without exercise or dietary protein supplementation as countermeasures

Microgravity has a dramatic impact on human physiology, illustrated in particular, with skeletal muscle impairment. A thorough understanding of the mechanisms leading to loss of muscle mass and structural disorders is necessary for defining efficient clinical and spaceflight countermeasures. We investigated the effects of long-term bed rest on the transcriptome of soleus (SOL) and vastus lateralis (VL) muscles in healthy women (BRC group, n = 8), and the potential beneficial impact of protein supplementation (BRN group, n = 8) and of a combined resistance and aerobic training (BRE group, n = 8). Gene expression profiles were obtained using a customized microarray containing 6,681 muscles-relevant genes. A two-class statistical analysis was applied on 2,103 genes with consolidated expression in BRC, BRN, and BRE groups. We identified 472 and 207 mRNAs whose expression was modified in SOL and VL from BRC group, respectively. Further clustering analysis, identifying relevant biological mechanisms and pathways, reported five main subclusters. Three are composed of upregulated mRNAs involved mainly in nucleic acid and protein metabolism, and two made up of downregulated transcripts encoding components involved in energy metabolism. Exercise countermeasure demonstrated drastic compensatory effects, decreasing the number of differentially expressed mRNAs by 89 and 96% in SOL and VL, respectively. In contrast, nutrition countermeasure had moderate effects and decreased the number of differentially-expressed transcripts by 40 and 25% in SOL and VL. Together, these data present a systematic, global and comprehensive view of the adaptive response of female muscle to long-term atrophy.

Meta-analysis of muscle transcriptome data using the MADMuscle database reveals biologically relevant gene patterns

BackgroundDNA microarray technology has had a great impact on muscle research and microarray gene expression data has been widely used to identify gene signatures characteristic of the studied conditions. With the rapid accumulation of muscle microarray data, it is of great interest to understand how to compare and combine data across multiple studies. Meta-analysis of transcriptome data is a valuable method to achieve it. It enables to highlight conserved gene signatures between multiple independent studies. However, using it is made difficult by the diversity of the available data: different microarray platforms, different gene nomenclature, different species studied, etc.DescriptionWe have developed a system tool dedicated to muscle transcriptome data. This system comprises a collection of microarray data as well as a query tool. This latter allows the user to extract similar clusters of co-expressed genes from the database, using an input gene list. Common and relevant gene signatures can thus be searched more easily. The dedicated database consists in a large compendium of public data (more than 500 data sets) related to muscle (skeletal and heart). These studies included seven different animal species from invertebrates (Drosophila melanogaster, Caenorhabditis elegans) and vertebrates (Homo sapiens, Mus musculus, Rattus norvegicus, Canis familiaris, Gallus gallus). After a renormalization step, clusters of co-expressed genes were identified in each dataset. The lists of co-expressed genes were annotated using a unified re-annotation procedure. These gene lists were compared to find significant overlaps between studies.ConclusionsApplied to this large compendium of data sets, meta-analyses demonstrated that conserved patterns between species could be identified. Focusing on a specific pathology (Duchenne Muscular Dystrophy) we validated results across independent studies and revealed robust biomarkers and new pathways of interest. The meta-analyses performed with MADMuscle show the usefulness of this approach. Our method can be applied to all public transcriptome data.

Molecular risk stratification in advanced heart failure patients

Risk stratification in advanced heart failure (HF) is crucial for the individualization of therapeutic strategy, in particular for heart transplantation and ventricular assist device implantation. We tested the hypothesis that cardiac gene expression profiling can distinguish between HF patients with different disease severity. We obtained tissue samples from both left (LV) and right (RV) ventricle of explanted hearts of 44 patients undergoing cardiac transplantation or ventricular assist device placement. Gene expression profiles were obtained using an in‐house microarray containing 4217 muscular organ‐relevant genes. Based on their clinical status, patients were classified into three HF‐severity groups: deteriorating (n= 12), intermediate (n= 19) and stable (n= 13). Two‐class statistical analysis of gene expression profiles of deteriorating and stable patients identified a 170‐gene and a 129‐gene predictor for LV and RV samples, respectively. The LV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 88% and 92%, and a specificity of 100% and 96%, respectively. The RV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 100% and 96%, and a specificity of 100% and 100%, respectively. The molecular prediction was reproducible across biological replicates in LV and RV samples. Gene expression profiling has the potential to reproducibly detect HF patients with highest HF severity with high sensitivity and specificity. In addition, not only LV but also RV samples could be used for molecular risk stratification with similar predictive power.

Efficient mitochondrial glutamine targeting prevails over glioblastoma metabolic plasticity.

Purpose: Glioblastoma (GBM) is the most common and malignant form of primary human brain tumor in adults, with an average survival at diagnosis of 18 months. Metabolism is a new attractive therapeutic target in cancer; however, little is known about metabolic heterogeneity and plasticity within GBM tumors. We therefore aimed to investigate metabolic phenotyping of primary cultures in the context of molecular tumor heterogeneity to provide a proof of concept for personalized metabolic targeting of GBM. Experimental Design: We have analyzed extensively several primary GBM cultures using transcriptomics, metabolic phenotyping assays, and mitochondrial respirometry. Results: We found that metabolic phenotyping clearly identifies 2 clusters, GLNHigh and GLNLow, mainly based on metabolic plasticity and glutamine (GLN) utilization. Inhibition of glutamine metabolism slows the in vitro and in vivo growth of GLNHigh GBM cultures despite metabolic adaptation to nutrient availability, in particular by increasing pyruvate shuttling into mitochondria. Furthermore, phenotypic and molecular analyses show that highly proliferative GLNHigh cultures are CD133neg and display a mesenchymal signature in contrast to CD133pos GLNLow GBM cells. Conclusions: Our results show that metabolic phenotyping identified an essential metabolic pathway in a GBM cell subtype, and provide a proof of concept for theranostic metabolic targeting. Clin Cancer Res; 23(20); 6292–304. ©2017 AACR.

Transcriptional orchestration of mitochondrial homeostasis in a cellular model of PGC-1-related coactivator-dependent thyroid tumor

The PGC-1 (Peroxisome proliferator-activated receptor Gamma Coactivator-1) family of coactivators (PGC-1α, PGC-1β, and PRC) plays a central role in the transcriptional control of mitochondrial biogenesis and oxidative phosphorylation (OXPHOS) processes. These coactivators integrate mitochondrial energy production into cell metabolism using complementary pathways. The XTC.UC1 cell line is a mitochondria-rich model of thyroid tumors whose biogenesis is almost exclusively dependent on PRC. Here we aim to propose an integrative view of the cellular pathways regulated by PRC through integration of cDNA and miRNA microarray data and chromatin immunoprecipitation results obtained from XTC.UC1 cells invalidated for PRC. This study showes that PRC induces a complex network of cellular functions interacting with at least one to five of the studied transcription factors (Estrogen Related Receptor alpha, ERR1; Nuclear-Respiratory Factors, NRF1 and NRF2; cAMP Response Element Binding, CREB; and Ying Yang, YY1). Our data confirm that ERR1 is a key partner of PRC in the regulation of mitochondrial functions and suggest a potential role of this complex in RNA processing. PRC is also involved in transcriptional regulatory complexes targeting 12 miRNAs, five of which are involved in the control of the OXPHOS process. Our findings demonstrate that the PRC coactivator can act in complex with several transcription factors and regulate miRNA expression to control the fine regulation of main metabolic functions in the cell. Therefore, in PGC-1α/β-associated pathologies, PRC, as a metabolic sensor, may ensure mitochondrial homeostasis.