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

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)

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

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

IL-6 up-regulates mcl-1 in human myeloma cells through JAK / STAT rather than ras / MAP kinase pathway

International audienceMcl-1 is an anti-apoptotic member of the Bcl-2 family which is tightly regulated during myeloid and B cell differentiation. We have recently reported that Mcl-1 is expressed in human myeloma cells and that Mcl-1 and Bcl-x(L) expression are correlated. In the current study, we demonstrate that IL-6, a survival factor for the human myeloma cell line MDN, rapidly up-regulates Mcl-1 whereas it has no effect on Bcl-2 protein level. In MDN cells, IL-6 induces both extracellular signal-regulated protein kinase (ERK)1,2 and STAT3 activation whereas STAT1 and STAT5 activation remains undetectable. Furthermore, while investigating the IL-6 signaling pathway leading to Mcl-1 up-regulation, we show that a janus kinase (JAK)-2 inhibitor is able to inhibit both STAT3 activation and Mcl-1 up-regulation whereas an MAP/ERK kinase (MEK) inhibitor has no effect. In conclusion, our data suggest the involvement of the JAK / STAT pathway but not of the Ras / mitogen-activated protein (MAP) kinase pathway in IL-6-induced Mcl-1 up-regulation

OLOGRAM-MODL: mining enriched n-wise combinations of genomic features with Monte Carlo and dictionary learning

International audienceMost epigenetic marks, such as Transcriptional Regulators or histone marks, are biological objects known to work together in n-wise complexes. A suitable way to infer such functional associations between them is to study the overlaps of the corresponding genomic regions. However, the problem of the statistical significance of n-wise overlaps of genomic features is seldom tackled, which prevent rigorous studies of n-wise interactions. We introduce OLOGRAM-MODL, which considers overlaps between n ≥ 2 sets of genomic regions, and computes their statistical mutual enrichment by Monte Carlo fitting of a Negative Binomial distribution, resulting in more resolutive P-values. An optional machine learning method is proposed to find complexes of interest, using a new itemset mining algorithm based on dictionary learning which is resistant to noise inherent to biological assays. The overall approach is implemented through an easy-to-use CLI interface for workflow integration, and a visual treebased representation of the results suited for explicability. The viability of the method is experimentally studied using both artificial and biological data. This approach is accessible through the command line interface of the pygtftk toolkit, available on Bioconda and from https://github.com/dputhier/pygtft

Cross-species analysis of differential transcript usage in humans and chickens with fatty liver disease

Background and Aim: Fatty liver disease is a common condition, characterized by excess fat accumulation in the liver. It can contribute to more severe liver-related health issues, making it a critical concern in avian and human medicine. Apart from modifying the gene expression of liver cells, the disease also alters the expression of specific transcript isoforms, which might serve as new biological markers for both species. This study aimed to identify cross species genes displaying differential expressions in their transcript isoforms in humans and chickens with fatty liver disease. Materials and Methods: We performed differential gene expression and differential transcript usage (DTU) analyses on messenger RNA datasets from the livers of both chickens and humans with fatty liver disease. Using appropriate cross-species gene identification methods, we reviewed the acquired candidate genes and their transcript isoforms to determine their potential role in fatty liver disease's pathogenesis. Results: We identified seven genes-ALG5, BRD7, DIABLO, RSU1, SFXN5, STIMATE, TJP3, and VDAC2-and their corresponding transcript isoforms as potential candidates (false discovery rate ≤0.05). Our findings showed that these genes most likely contribute to fatt

Inspection of C-type lectin superfamily expression profile in chicken and mouse dendritic cells

International audienceC-type lectin superfamily is a cell surface molecule family well-recognized for its role in inducing and tailoring dendritic cell (DC) immune response. Despite its importance in DC targeting and activation, knowledge about C-type lectin family expression in chicken DC is still limited, and thus hinders its utilization for poultry DC vaccine development. In this study, expressions of C-type lectin orthologous gene among available chicken and mouse DC subsets were determined for expression levels and differential expression patterns. Leukocyte microarray datasets and bone marrow-derived DC mRAN-sequencing datasets were pre-processed prior to observation and comparison. Data virtualization revealed variation in C-type lectin gene expressions among the DC subsets, while the differential and relative expression results manifested PLA2R1 and CLEC16A as candidate genes for their future exploration at molecular and functional levels (FDR= 0). Of note, a step-by-step procedure to extract novel C-type lectin gene candidates in chicken DC by cross-species comparative expression profiling analysis was illustrated in this study. Based on the same process, a future study is planned to apply the same procedures to investigate other gene families' potential for DC targeting and activation in chicken

A demonstration of the H3 trimethylation ChIP-seq analysis of galline follicular mesenchymal cells and male germ cells

International audienceTrimethylation of histone 3 (H3) at 4th lysine N-termini (H3K4me3) in gene promoter region was the universal marker of active genes specific to cell lineage. On the contrary, coexistence of trimethylation at 27th lysine (H3K27me3) in the same loci-the bivalent H3K4m3/H3K27me3 was known to suspend the gene transcription in germ cells, and could also be inherited to the developed stem cell. In galline species, throughout example of H3K4m3 and H3K27me3 ChIP-seq analysis was still not provided. We therefore designed and demonstrated such procedures using ChIP-seq and mRNA-seq data of chicken follicular mesenchymal cells and male germ cells