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A Network of Paralogous Stress Response Transcription Factors in the Human Pathogen Candida glabrata

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      The yeast Candida glabrata has become the second cause of systemic candidemia in humans. However, relatively few genome-wide studies have been conducted in this organism and our knowledge of its transcriptional regulatory network is quite limited. In the present work, we combined genome-wide chromatin immunoprecipitation (ChIP-seq), transcriptome analyses, and DNA binding motif predictions to describe the regulatory interactions of the seven Yap (Yeast AP1) transcription factors of C. glabrata. We described a transcriptional network containing 255 regulatory interactions and 309 potential target genes. We predicted with high confidence the preferred DNA binding sites for 5 of the 7 CgYaps and showed a strong conservation of the Yap DNA binding properties between S. cerevisiae and C. glabrata. We provided reliable functional annotation for 3 of the 7 Yaps and identified for Yap1 and Yap5 a core regulon which is conserved in S. cerevisiae, C. glabrata, and C. albicans. We uncovered new roles for CgYap7 in the regulation of iron-sulfur cluster biogenesis, for CgYap1 in the regulation of heme biosynthesis and for CgYap5 in the repression of GRX4 in response to iron starvation. These transcription factors define an interconnected transcriptional network at the cross-roads between redox homeostasis, oxygen consumption, and iron metabolism.

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      Most cited references 107

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      Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

      Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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        BEDTools: a flexible suite of utilities for comparing genomic features

        Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at Contact:; Supplementary information: Supplementary data are available at Bioinformatics online.
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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          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.

            Author and article information

            1Laboratoire de Biologie Computationnelle et Quantitative, Centre National de la Recherche Scientifique, Institut de Biologie Paris-Seine, UMR 7238, Sorbonne Universités, Université Pierre et Marie Curie Paris, France
            2École Normale Supérieure, Paris Sciences et Lettres Research University, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Institut de Biologie de l'École Normale Supérieure, Plateforme Génomique Paris, France
            3Centre National de la Recherche Scientifique, UMR 7592, Institut Jacques Monod, Université Paris Diderot, Sorbonne Paris Cité Paris, France
            4Évolution, Centre National de la Recherche Scientifique, Institut de Biologie Paris-Seine, UMR 7138, Sorbonne Universités, Université Pierre et Marie Curie Paris, France
            Author notes

            Edited by: Dominique Sanglard, University of Lausanne and University Hospital Center, Switzerland

            Reviewed by: Alejandro De Las Penas, Instituto Potosino de Investigacion Cientifica y Tecnologica, Mexico; Claudina Rodrigues-Pousadaj, Instituto de Tecnologia Química e Biológica Antonio Xavier, Portugal; Bernard Turcotte, McGill University, Canada

            *Correspondence: Gaelle Lelandais gaelle.lelandais@ ;

            This article was submitted to Fungi and Their Interactions, a section of the journal Frontiers in Microbiology

            Front Microbiol
            Front Microbiol
            Front. Microbiol.
            Frontiers in Microbiology
            Frontiers Media S.A.
            09 May 2016
            : 7
            Copyright © 2016 Merhej, Thiebaut, Blugeon, Pouch, Ali Chaouche, Camadro, Le Crom, Lelandais and Devaux.

            This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

            Figures: 7, Tables: 0, Equations: 0, References: 108, Pages: 16, Words: 12231
            Original Research

            Microbiology & Virology

            evolution, regulatory networks, transcriptome, chip-seq, yap, yeast


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