Entropy Measures Quantify Global Splicing Disorders in Cancer

Most mammalian genes are able to express several splice variants in a phenomenon known as alternative splicing. Serious alterations of alternative splicing occur in cancer tissues, leading to expression of multiple aberrant splice forms. Most studies of alternative splicing defects have focused on the identification of cancer-specific splice variants as potential therapeutic targets. Here, we examine instead the bulk of non-specific transcript isoforms and analyze their level of disorder using a measure of uncertainty called Shannon's entropy. We compare isoform expression entropy in normal and cancer tissues from the same anatomical site for different classes of transcript variations: alternative splicing, polyadenylation, and transcription initiation. Whereas alternative initiation and polyadenylation show no significant gain or loss of entropy between normal and cancer tissues, alternative splicing shows highly significant entropy gains for 13 of the 27 cancers studied. This entropy gain is characterized by a flattening in the expression profile of normal isoforms and is correlated to the level of estimated cellular proliferation in the cancer tissue. Interestingly, the genes that present the highest entropy gain are enriched in splicing factors. We provide here the first quantitative estimate of splicing disruption in cancer. The expression of normal splice variants is widely and significantly disrupted in at least half of the cancers studied. We postulate that such splicing disorders may develop in part from splicing alteration in key splice factors, which in turn significantly impact multiple target genes

Platelets Alter Gene Expression Profile in Human Brain Endothelial Cells in an In Vitro Model of Cerebral Malaria

Platelet adhesion to the brain microvasculature has been associated with cerebral malaria (CM) in humans, suggesting that platelets play a role in the pathogenesis of this syndrome. In vitro co-cultures have shown that platelets can act as a bridge between Plasmodium falciparum-infected red blood cells (pRBC) and human brain microvascular endothelial cells (HBEC) and potentiate HBEC apoptosis. Using cDNA microarray technology, we analyzed transcriptional changes of HBEC in response to platelets in the presence or the absence of tumor necrosis factor (TNF) and pRBC, which have been reported to alter gene expression in endothelial cells. Using a rigorous statistical approach with multiple test corrections, we showed a significant effect of platelets on gene expression in HBEC. We also detected a strong effect of TNF, whereas there was no transcriptional change induced specifically by pRBC. Nevertheless, a global ANOVA and a two-way ANOVA suggested that pRBC acted in interaction with platelets and TNF to alter gene expression in HBEC. The expression of selected genes was validated by RT-qPCR. The analysis of gene functional annotation indicated that platelets induce the expression of genes involved in inflammation and apoptosis, such as genes involved in chemokine-, TREM1-, cytokine-, IL10-, TGFβ-, death-receptor-, and apoptosis-signaling. Overall, our results support the hypothesis that platelets play a pathogenic role in CM

TranscriptomeBrowser: A Powerful and Flexible Toolbox to Explore Productively the Transcriptional Landscape of the Gene Expression Omnibus Database

International audienceAs public microarray repositories are constantly growing, we are facing the challenge of designing strategies to provide productive access to the available data.\ We used a modified version of the Markov clustering algorithm to systematically extract clusters of co-regulated genes from hundreds of microarray datasets stored in the Gene Expression Omnibus database (n = 1,484). This approach led to the definition of 18,250 transcriptional signatures (TS) that were tested for functional enrichment using the DAVID knowledgebase. Over-representation of functional terms was found in a large proportion of these TS (84%). We developed a JAVA application, TBrowser that comes with an open plug-in architecture and whose interface implements a highly sophisticated search engine supporting several Boolean operators (http://tagc.univ-mrs.fr/tbrowser/). User can search and analyze TS containing a list of identifiers (gene symbols or AffyIDs) or associated with a set of functional terms.\ As proof of principle, TBrowser was used to define breast cancer cell specific genes and to detect chromosomal abnormalities in tumors. Finally, taking advantage of our large collection of transcriptional signatures, we constructed a comprehensive map that summarizes gene-gene co-regulations observed through all the experiments performed on HGU133A Affymetrix platform. We provide evidences that this map can extend our knowledge of cellular signaling pathways

Identifying common and specific microRNAs expressed in peripheral blood mononuclear cell of type 1, type 2, and gestational diabetes mellitus patients

Abstract\ud \ud \ud \ud Background\ud Regardless the regulatory function of microRNAs (miRNA), their differential expression pattern has been used to define miRNA signatures and to disclose disease biomarkers. To address the question of whether patients presenting the different types of diabetes mellitus could be distinguished on the basis of their miRNA and mRNA expression profiling, we obtained peripheral blood mononuclear cell (PBMC) RNAs from 7 type 1 (T1D), 7 type 2 (T2D), and 6 gestational diabetes (GDM) patients, which were hybridized to Agilent miRNA and mRNA microarrays. Data quantification and quality control were obtained using the Feature Extraction software, and data distribution was normalized using quantile function implemented in the Aroma light package. Differentially expressed miRNAs/mRNAs were identified using Rank products, comparing T1DxGDM, T2DxGDM and T1DxT2D. Hierarchical clustering was performed using the average linkage criterion with Pearson uncentered distance as metrics.\ud \ud \ud \ud Results\ud The use of the same microarrays platform permitted the identification of sets of shared or specific miRNAs/mRNA interaction for each type of diabetes. Nine miRNAs (hsa-miR-126, hsa-miR-1307, hsa-miR-142-3p, hsa-miR-142-5p, hsa-miR-144, hsa-miR-199a-5p, hsa-miR-27a, hsa-miR-29b, and hsa-miR-342-3p) were shared among T1D, T2D and GDM, and additional specific miRNAs were identified for T1D (20 miRNAs), T2D (14) and GDM (19) patients. ROC curves allowed the identification of specific and relevant (greater AUC values) miRNAs for each type of diabetes, including: i) hsa-miR-1274a, hsa-miR-1274b and hsa-let-7f for T1D; ii) hsa-miR-222, hsa-miR-30e and hsa-miR-140-3p for T2D, and iii) hsa-miR-181a and hsa-miR-1268 for GDM. Many of these miRNAs targeted mRNAs associated with diabetes pathogenesis.\ud \ud \ud \ud Conclusions\ud These results indicate that PBMC can be used as reporter cells to characterize the miRNA expression profiling disclosed by the different diabetes mellitus manifestations. Shared miRNAs may characterize diabetes as a metabolic and inflammatory disorder, whereas specific miRNAs may represent biological markers for each type of diabetes, deserving further attention.This study was funded by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP - (FAPESP #2008/56594-8, FAPESP #2010/05622-1, FAPESP #210/00932-2, FAPESP #2010/12069-7), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq # 563731/2010-9), and NAP-DIN (Núcleo de Apoio à Pesquisa em Doenças Inflamatórias)

Scigenex datasets

These datasets (which are subsets of larger datasets) can be dowloaded through the Scigenex R library and are used to showcase the use of available functions and classes. The scigenex_example_spatial_file.R & scigenex_example_pbmc3k.R provide the code that was used to create these example datasets

Xenium_Mouse_Brain_Coronal_Sub.txt.gz

<p>A subset of the Xenium_V1_FF_Mouse_Brain_Coronal_Subset_CTX_HP_outs 10X Genomics dataset (Spatial Transcriptomics). For demonstration purpose.</p&gt

SAOD- Statistical Analysis of Omics Data

<p>Some datasets for the SAOD (Statistical Analysis of Omics Data) course (Aix-Marseille Université, D. Puthier).</p> <p>The Homo_sapiens.GRCh38.110.chr.tsv was produced using the following command:</p> <p>            gtftk retrieve -r 110</p> <p>            gtftk convert_ensembl -i  Homo_sapiens.GRCh38.110.chr.gtf.gz | gtftk nb_exons | gtftk feature_size -t mature_rna | gtftk feature_size -t transcript -k tx_genomic_size | gtftk exon_sizes | gtftk intron_sizes | gtftk select_by_key -t | gtftk tabulate -k  '*' -u -x  > Homo_sapiens.GRCh38.110.chr.tsv</p> <p> </p&gt

Xenium_Mouse_Brain_Coronal_Sub.txt.gz

<p>A subset of the Xenium_V1_FF_Mouse_Brain_Coronal_Subset_CTX_HP_outs 10X Genomics dataset (Spatial Transcriptomics). For demonstration purpose.</p&gt

Xenium_Mouse_Brain_Coronal_Sub.txt.gz

<p>A subset of the Xenium_V1_FF_Mouse_Brain_Coronal_Subset_CTX_HP_outs 10X Genomics dataset (Spatial Transcriptomics). For demonstration purpose.</p&gt

Xenium_Mouse_Brain_Coronal_Sub.txt.gz

<p>A subset of the Xenium_V1_FF_Mouse_Brain_Coronal_Subset_CTX_HP_outs 10X Genomics dataset (Spatial Transcriptomics). For demonstration purpose.</p&gt