Author: "Gaëlle Lelandais" - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Gaëlle Lelandais"' showing total 64 results

Start Over Author "Gaëlle Lelandais"

64 results on '"Gaëlle Lelandais"'

1. New insights into genome annotation in Podospora anserina through re-exploiting multiple RNA-seq data

Author: Gaëlle Lelandais, Damien Remy, Fabienne Malagnac, and Pierre Grognet
Subjects: Transcriptome, RNA-seq, Fungal genome, Functional annotation, Alternative splicing, nTARs, Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract: Abstract Background Publicly available RNA-seq datasets are often underused although being helpful to improve functional annotation of eukaryotic genomes. This is especially true for filamentous fungi genomes which structure differs from most well annotated yeast genomes. Podospora anserina is a filamentous fungal model, which genome has been sequenced and annotated in 2008. Still, the current annotation lacks information about cis-regulatory elements, including promoters, transcription starting sites and terminators, which are instrumental to integrate epigenomic features into global gene regulation strategies. Results Here we took advantage of 37 RNA-seq experiments that were obtained in contrasted developmental and physiological conditions, to complete the functional annotation of P. anserina genome. Out of the 10,800 previously annotated genes, 5’UTR and 3’UTR were defined for 7554, among which, 3328 showed differential transcriptional signal starts and/or transcriptional end sites. In addition, alternative splicing events were detected for 2350 genes, mostly due alternative 3’splice sites and 1732 novel transcriptionally active regions (nTARs) in unannotated regions were identified. Conclusions Our study provides a comprehensive genome-wide functional annotation of P. anserina genome, including chromatin features, cis-acting elements such as UTRs, alternative splicing events and transcription of non-coding regions. These new findings will likely improve our understanding of gene regulation strategies in compact genomes, such as those of filamentous fungi. Characterization of alternative transcripts and nTARs paves the way to the discovery of putative new genes, alternative peptides or regulatory non-coding RNAs.
Published: 2022
Full Text: View/download PDF

2. Additional insights into the organization of transcriptional regulatory modules based on a 3D model of the Saccharomyces cerevisiae genome

Author: Thibault Poinsignon, Mélina Gallopin, Jean-Michel Camadro, Pierre Poulain, and Gaëlle Lelandais
Subjects: Transcriptional regulations, Chromosome conformation capture, Yeast, 3D-Scere, Medicine, Biology (General), QH301-705.5, Science (General), Q1-390
Abstract: Abstract Objectives Transcriptional regulatory modules are usually modelled via a network, in which nodes correspond to genes and edges correspond to regulatory associations between them. In the model yeast Saccharomyces cerevisiae, the topological properties of such a network are well-described (distribution of degrees, hierarchical levels, organization in network motifs, etc.). To go further on this, our aim was to search for additional information resulting from the new combination of classical representations of transcriptional regulatory networks with more realistic models of the spatial organization of S. cerevisiae genome in the nucleus. Results Taking advantage of independent studies with high-quality datasets, i.e. lists of target genes for specific transcription factors and chromosome positions in a three dimensional space representing the nucleus, particular spatial co-localizations of genes that shared common regulatory mechanisms were searched. All transcriptional modules of S. cerevisiae, as described in the latest release of the YEASTRACT database were analyzed and significant biases toward co-localization for a few sets of target genes were observed. To help other researchers to reproduce such analysis with any list of genes of their interest, an interactive web tool called 3D-Scere ( https://3d-scere.ijm.fr/ ) is provided.
Published: 2022
Full Text: View/download PDF

3. Empowering the detection of ChIP-seq 'basic peaks' (bPeaks) in small eukaryotic genomes with a web user-interactive interface

Author: Thomas Denecker and Gaëlle Lelandais
Subjects: ChIP-seq, Peak calling, Protein DNA-binding sites, Small eukaryotic genomes, bPeaks, Medicine, Biology (General), QH301-705.5, Science (General), Q1-390
Abstract: Abstract Objective bPeaks is a peak calling program to detect protein DNA-binding sites from ChIPseq data in small eukaryotic genomes. The simplicity of the bPeaks method is well appreciated by users, but its use via an R package is challenging and time-consuming for people without programming skills. In addition, user feedback has highlighted the lack of a convenient way to carefully explore bPeaks result files. In this context, the development of a web user interface represents an important added value for expanding the bPeaks user community. Results We developed a new bPeaks application (bPeaks App). The application allows the user to perform all the peak-calling analysis steps with bPeaks in a few mouse clicks via a web browser. We added new features relative to the original R package, particularly the possibility to import personal annotation files to compare the location of the detected peaks with specific genomic elements of interest of the user, in any organism, and a new organization of the result files which are directly manageable via a user-interactive genome browser. This significantly improves the ability of the user to explore all detected basic peaks in detail.
Published: 2018
Full Text: View/download PDF

4. Label-free quantitative proteomics in Candida yeast species: technical and biological replicates to assess data reproducibility

Author: Gaëlle Lelandais, Thomas Denecker, Camille Garcia, Nicolas Danila, Thibaut Léger, and Jean-Michel Camadro
Subjects: Mass spectrometry, Label-free quantitative proteomics, Candida glabrata, Candida albicans, Alkaline pH, Medicine, Biology (General), QH301-705.5, Science (General), Q1-390
Abstract: Abstract Objective Label-free quantitative proteomics has emerged as a powerful strategy to obtain high quality quantitative measures of the proteome with only a very small quantity of total protein extract. Because our research projects were requiring the application of bottom-up shotgun mass spectrometry proteomics in the pathogenic yeasts Candida glabrata and Candida albicans, we performed preliminary experiments to (i) obtain a precise list of all the proteins for which measures of abundance could be obtained and (ii) assess the reproducibility of the results arising respectively from biological and technical replicates. Data description Three time-courses were performed in each Candida species, and an alkaline pH stress was induced for two of them. Cells were collected 10 and 60 min after stress induction and proteins were extracted. Samples were analysed two times by mass spectrometry. Our final dataset thus comprises label-free quantitative proteomics results for 24 samples (two species, three time-courses, two time points and two runs of mass spectrometry). Statistical procedures were applied to identify proteins with differential abundances between stressed and unstressed situations. Considering that C. glabrata and C. albicans are human pathogens, which face important pH fluctuations during a human host infection, this dataset has a potential value to other researchers in the field.
Published: 2019
Full Text: View/download PDF

5. The CCAAT-Binding Complex Controls Respiratory Gene Expression and Iron Homeostasis in Candida Glabrata

Author: Antonin Thiébaut, Thierry Delaveau, Médine Benchouaia, Julia Boeri, Mathilde Garcia, Gaëlle Lelandais, and Frédéric Devaux
Subjects: Medicine, Science
Abstract: Abstract The CCAAT-binding complex (CBC) is a heterotrimeric transcription factor which is widely conserved in eukaryotes. In the model yeast S. cerevisiae, CBC positively controls the expression of respiratory pathway genes. This role involves interactions with the regulatory subunit Hap4. In many pathogenic fungi, CBC interacts with the HapX regulatory subunit to control iron homeostasis. HapX is a bZIP protein which only shares with Hap4 the Hap4Like domain (Hap4L) required for its interaction with CBC. Here, we show that CBC has a dual role in the pathogenic yeast C. glabrata. It is required, along with Hap4, for the constitutive expression of respiratory genes and it is also essential for the iron stress response, which is mediated by the Yap5 bZIP transcription factor. Interestingly, Yap5 contains a vestigial Hap4L domain. The mutagenesis of this domain severely reduced Yap5 binding to its targets and compromised its interaction with Hap5. Hence, Yap5, like HapX in other species, acts as a CBC regulatory subunit in the regulation of iron stress response. This work reveals new aspects of iron homeostasis in C. glabrata and of the evolution of the role of CBC and Hap4L-bZIP proteins in this process.
Published: 2017
Full Text: View/download PDF

6. Pixel: a content management platform for quantitative omics data

Author: Thomas Denecker, William Durand, Julien Maupetit, Charles Hébert, Jean-Michel Camadro, Pierre Poulain, and Gaëlle Lelandais
Subjects: Data cycle analyses, Omics, Open source, Pixel Web App, Medicine, Biology (General), QH301-705.5
Abstract: Background In biology, high-throughput experimental technologies, also referred as “omics” technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal. We present here the Pixel web application (Pixel Web App), an original content management platform to help people involved in a multi-omics biological project. Methods The Pixel Web App is built with open source technologies and hosted on the collaborative development platform GitHub (https://github.com/Candihub/pixel). It is written in Python using the Django framework and stores all the data in a PostgreSQL database. It is developed in the open and licensed under the BSD 3-clause license. The Pixel Web App is also heavily tested with both unit and functional tests, a strong code coverage and continuous integration provided by CircleCI. To ease the development and the deployment of the Pixel Web App, Docker and Docker Compose are used to bundle the application as well as its dependencies. Results The Pixel Web App offers researchers an intuitive way to annotate, store, explore and mine their multi-omics results. It can be installed on a personal computer or on a server to fit the needs of many users. In addition, anyone can enhance the application to better suit their needs, either by contributing directly on GitHub (encouraged) or by extending Pixel on their own. The Pixel Web App does not provide any computational programs to analyze the data. Still, it helps to rapidly explore and mine existing results and holds a strategic position in the management of research data.
Published: 2019
Full Text: View/download PDF

7. Comparative Transcriptomics Highlights New Features of the Iron Starvation Response in the Human Pathogen Candida glabrata

Author: Médine Benchouaia, Hugues Ripoche, Mariam Sissoko, Antonin Thiébaut, Jawad Merhej, Thierry Delaveau, Laure Fasseu, Sabrina Benaissa, Geneviève Lorieux, Laurent Jourdren, Stéphane Le Crom, Gaëlle Lelandais, Eduardo Corel, and Frédéric Devaux
Subjects: yeast, Aft, ChIP-seq, NO GO decay, evolution, Microbiology, QR1-502
Abstract: In this work, we used comparative transcriptomics to identify regulatory outliers (ROs) in the human pathogen Candida glabrata. ROs are genes that have very different expression patterns compared to their orthologs in other species. From comparative transcriptome analyses of the response of eight yeast species to toxic doses of selenite, a pleiotropic stress inducer, we identified 38 ROs in C. glabrata. Using transcriptome analyses of C. glabrata response to five different stresses, we pointed out five ROs which were more particularly responsive to iron starvation, a process which is very important for C. glabrata virulence. Global chromatin Immunoprecipitation and gene profiling analyses showed that four of these genes are actually new targets of the iron starvation responsive Aft2 transcription factor in C. glabrata. Two of them (HBS1 and DOM34b) are required for C. glabrata optimal growth in iron limited conditions. In S. cerevisiae, the orthologs of these two genes are involved in ribosome rescue by the NO GO decay (NGD) pathway. Hence, our results suggest a specific contribution of NGD co-factors to the C. glabrata adaptation to iron starvation.
Published: 2018
Full Text: View/download PDF

8. Meet-U: Educating through research immersion.

Author: Nika Abdollahi, Alexandre Albani, Eric Anthony, Agnes Baud, Mélissa Cardon, Robert Clerc, Dariusz Czernecki, Romain Conte, Laurent David, Agathe Delaune, Samia Djerroud, Pauline Fourgoux, Nadège Guiglielmoni, Jeanne Laurentie, Nathalie Lehmann, Camille Lochard, Rémi Montagne, Vasiliki Myrodia, Vaitea Opuu, Elise Parey, Lélia Polit, Sylvain Privé, Chloé Quignot, Maria Ruiz-Cuevas, Mariam Sissoko, Nicolas Sompairac, Audrey Vallerix, Violaine Verrecchia, Marc Delarue, Raphael Guérois, Yann Ponty, Sophie Sacquin-Mora, Alessandra Carbone, Christine Froidevaux, Stéphane Le Crom, Olivier Lespinet, Martin Weigt, Samer Abboud, Juliana Bernardes, Guillaume Bouvier, Chloé Dequeker, Arnaud Ferré, Patrick Fuchs, Gaëlle Lelandais, Pierre Poulain, Hugues Richard, Hugo Schweke, Elodie Laine, and Anne Lopes
Subjects: Biology (General), QH301-705.5
Abstract: We present a new educational initiative called Meet-U that aims to train students for collaborative work in computational biology and to bridge the gap between education and research. Meet-U mimics the setup of collaborative research projects and takes advantage of the most popular tools for collaborative work and of cloud computing. Students are grouped in teams of 4-5 people and have to realize a project from A to Z that answers a challenging question in biology. Meet-U promotes "coopetition," as the students collaborate within and across the teams and are also in competition with each other to develop the best final product. Meet-U fosters interactions between different actors of education and research through the organization of a meeting day, open to everyone, where the students present their work to a jury of researchers and jury members give research seminars. This very unique combination of education and research is strongly motivating for the students and provides a formidable opportunity for a scientific community to unite and increase its visibility. We report on our experience with Meet-U in two French universities with master's students in bioinformatics and modeling, with protein-protein docking as the subject of the course. Meet-U is easy to implement and can be straightforwardly transferred to other fields and/or universities. All the information and data are available at www.meet-u.org.
Published: 2018
Full Text: View/download PDF

9. Transcriptomic analyses during the transition from biomass production to lipid accumulation in the oleaginous yeast Yarrowia lipolytica.

Author: Nicolas Morin, Julien Cescut, Athanasios Beopoulos, Gaëlle Lelandais, Veronique Le Berre, Jean-Louis Uribelarrea, Carole Molina-Jouve, and Jean-Marc Nicaud
Subjects: Medicine, Science
Abstract: We previously developed a fermentation protocol for lipid accumulation in the oleaginous yeast Y. lipolytica. This process was used to perform transcriptomic time-course analyses to explore gene expression in Y. lipolytica during the transition from biomass production to lipid accumulation. In this experiment, a biomass concentration of 54.6 g(CDW)/l, with 0.18 g/g(CDW) lipid was obtained in ca. 32 h, with low citric acid production. A transcriptomic profiling was performed on 11 samples throughout the fermentation. Through statistical analyses, 569 genes were highlighted as differentially expressed at one point during the time course of the experiment. These genes were classified into 9 clusters, according to their expression profiles. The combination of macroscopic and transcriptomic profiles highlighted 4 major steps in the culture: (i) a growth phase, (ii) a transition phase, (iii) an early lipid accumulation phase, characterized by an increase in nitrogen metabolism, together with strong repression of protein production and activity; (iv) a late lipid accumulation phase, characterized by the rerouting of carbon fluxes within cells. This study explores the potential of Y. lipolytica as an alternative oil producer, by identifying, at the transcriptomic level, the genes potentially involved in the metabolism of oleaginous species.
Published: 2011
Full Text: View/download PDF

10. The reconstruction of condition-specific transcriptional modules provides new insights in the evolution of yeast AP-1 proteins.

Author: Christel Goudot, Catherine Etchebest, Frédéric Devaux, and Gaëlle Lelandais
Subjects: Medicine, Science
Abstract: AP-1 proteins are transcription factors (TFs) that belong to the basic leucine zipper family, one of the largest families of TFs in eukaryotic cells. Despite high homology between their DNA binding domains, these proteins are able to recognize diverse DNA motifs. In yeasts, these motifs are referred as YRE (Yap Response Element) and are either seven (YRE-Overlap) or eight (YRE-Adjacent) base pair long. It has been proposed that the AP-1 DNA binding motif preference relies on a single change in the amino acid sequence of the yeast AP-1 TFs (an arginine in the YRE-O binding factors being replaced by a lysine in the YRE-A binding Yaps). We developed a computational approach to infer condition-specific transcriptional modules associated to the orthologous AP-1 protein Yap1p, Cgap1p and Cap1p, in three yeast species: the model yeast Saccharomyces cerevisiae and two pathogenic species Candida glabrata and Candida albicans. Exploitation of these modules in terms of predictions of the protein/DNA regulatory interactions changed our vision of AP-1 protein evolution. Cis-regulatory motif analyses revealed the presence of a conserved adenine in 5' position of the canonical YRE sites. While Yap1p, Cgap1p and Cap1p shared a remarkably low number of target genes, an impressive conservation was observed in the YRE sequences identified by Yap1p and Cap1p. In Candida glabrata, we found that Cgap1p, unlike Yap1p and Cap1p, recognizes YRE-O and YRE-A motifs. These findings were supported by structural data available for the transcription factor Pap1p (Schizosaccharomyces pombe). Thus, whereas arginine and lysine substitutions in Cgap1p and Yap1p proteins were reported as responsible for a specific YRE-O or YRE-A preference, our analyses rather suggest that the ancestral yeast AP-1 protein could recognize both YRE-O and YRE-A motifs and that the arginine/lysine exchange is not the only determinant of the specialization of modern Yaps for one motif or another.
Published: 2011
Full Text: View/download PDF

11. Spatio-temporal dynamics of yeast mitochondrial biogenesis: transcriptional and post-transcriptional mRNA oscillatory modules.

Author: Gaëlle Lelandais, Yann Saint-Georges, Colette Geneix, Liza Al-Shikhley, Geneviève Dujardin, and Claude Jacq
Subjects: Biology (General), QH301-705.5
Abstract: Examples of metabolic rhythms have recently emerged from studies of budding yeast. High density microarray analyses have produced a remarkably detailed picture of cycling gene expression that could be clustered according to metabolic functions. We developed a model-based approach for the decomposition of expression to analyze these data and to identify functional modules which, expressed sequentially and periodically, contribute to the complex and intricate mitochondrial architecture. This approach revealed that mitochondrial spatio-temporal modules are expressed during periodic spikes and specific cellular localizations, which cover the entire oscillatory period. For instance, assembly factors (32 genes) and translation regulators (47 genes) are expressed earlier than the components of the amino-acid synthesis pathways (31 genes). In addition, we could correlate the expression modules identified with particular post-transcriptional properties. Thus, mRNAs of modules expressed "early" are mostly translated in the vicinity of mitochondria under the control of the Puf3p mRNA-binding protein. This last spatio-temporal module concerns mostly mRNAs coding for basic elements of mitochondrial construction: assembly and regulatory factors. Prediction that unknown genes from this module code for important elements of mitochondrial biogenesis is supported by experimental evidence. More generally, these observations underscore the importance of post-transcriptional processes in mitochondrial biogenesis, highlighting close connections between nuclear transcription and cytoplasmic site-specific translation.
Published: 2009
Full Text: View/download PDF

12. Extending the Range of SLIM-Labeling Applications: From Human Cell Lines in Culture to Caenorhabditis elegans Whole-Organism Labeling

Author: Laurent Lignieres, Nicolas Sénécaut, Tien Dang, Laura Bellutti, Marion Hamon, Samuel Terrier, Véronique Legros, Guillaume Chevreux, Gaëlle Lelandais, René-Marc Mège, Julien Dumont, and Jean-Michel Camadro
Subjects: General Chemistry, Biochemistry
Published: 2023

13. Inter-generational nuclear crosstalk links the control of gene expression to programmed genome rearrangements during theParameciumsexual cycle

Author: Mélanie Bazin-Gélis, Evangelia Eleftheriou, Coralie Zangarelli, Gaëlle Lelandais, Linda Sperling, Olivier Arnaiz, and Mireille Bétermier
Abstract: Multinucleate cells are found in many eukaryotes, but how multiple nuclei coordinate their functions is still poorly understood. In the cytoplasm of the ciliateParamecium tetraurelia, two micronuclei (MIC) serving sexual reproduction coexist with a somatic macronucleus (MAC) dedicated to gene expression. During sexual processes, the MAC is progressively destroyed while still ensuring transcription and new MACs develop from copies of the zygotic MIC. Several gene clusters are successively induced and switched off before vegetative growth resumes. Concomitantly, programmed genome rearrangements (PGR) remove transposons and their relics from the new MACs. Development of the new MACs is controlled by the old MAC, since the latter expresses genes involved in PGR, including thePGMgene encoding the essential PiggyMac endonuclease that cleaves the ends of eliminated sequences. Using RNA deep sequencing and transcriptome analysis, we show that impairing PGR up-deregulates key PGR genes, together with ∼600 other genes possibly also involved in PGR. Among these genes, 42% are no longer induced when no new MACs are formed, including 180 genes that are co-expressed withPGMunder all tested conditions. We propose that bi-directional crosstalk between the two coexisting generations of MACs links gene expression to the progression of MAC development.
Published: 2023

14. Visual integration of omics data to improve 3D models of fungal chromosomes

Author: Thibault Poinsignon, Mélina Gallopin, Pierre Grognet, Fabienne Malagnac, Gaëlle Lelandais, and Pierre Poulain
Abstract: The functions of eukaryotic chromosomes and their spatial architecture in the nucleus are reciprocally dependent. Hi-C experiments are routinely used to study chromosome 3D organization by probing chromatin interactions. Standard representation of the data has relied on contact maps that show the frequency of interactions between parts of the genome. In parallel, it has become easier to build 3D models of the entire genome based on the same Hi-C data, and thus benefit from the methodology and visualization tools developed for structural biology. 3D modeling of entire genomes leverages the understanding of their spatial organization. However, this opportunity for original and insightful modeling is under exploited. In this paper, we show how seeing the spatial organization of chromosomes can bring new perspectives to Hi-C data analysis. We assembled state-of-the-art tools into a workflow that goes from Hi-C raw data to fully annotated 3D models and we re-analysed public Hi-C datasets available for three fungal species. Besides the well-described properties of the spatial organization of their chromosomes (Rabl conformation, hypercoiling and chromosome territories), our 3D models highlightedi)inSaccharomyces cerevisiae, the backbones of the cohesin anchor regions, which were aligned all along the chromosomes,ii)inSchizosaccharomyces pombe, the oscillations of the coiling of chromosome arms throughout the cell cycle andiii)inNeurospora crassa, the massive relocalization of histone marks in mutants of heterochromatin regulators. 3D modeling of the chromosomes brings new opportunities for visual integration. This holistic perspective supports intuition and lays the foundation for building new concepts.
Published: 2023

15. Omics Analyses: How to Navigate Through a Constant Data Deluge

Author: Thomas, Denecker and Gaëlle, Lelandais
Subjects: Databases, Factual, Computational Biology, Genomics, Software
Abstract: Omics data are very valuable for researchers in biology, but the work required to develop a solid expertise in their analysis contrasts with the rapidity with which the omics technologies evolve. Data accumulate in public databases, and despite significant advances in bioinformatics softwares to integrate them, data analysis remains a burden for those who perform experiments. Beyond the issue of dealing with a very large number of results, we believe that working with omics data requires a change in the way scientific problems are solved. In this chapter, we explain pitfalls and tips we found during our functional genomics projects in yeasts. Our main lesson is that, if applying a protocol does not guarantee a successful project, following simple rules can help to become strategic and intentional, thus avoiding an endless drift into an ocean of possibilities.
Published: 2022

16. Quantitative Proteomics in Yeast : From bSLIM and Proteome Discoverer Outputs to Graphical Assessment of the Significance of Protein Quantification Scores

Author: Nicolas, Sénécaut, Pierre, Poulain, Laurent, Lignières, Samuel, Terrier, Véronique, Legros, Guillaume, Chevreux, Gaëlle, Lelandais, and Jean-Michel, Camadro
Subjects: Proteomics, Proteome, Isotope Labeling, Saccharomyces cerevisiae, Peptides
Abstract: Simple light isotope metabolic labeling (bSLIM) is an innovative method to accurately quantify differences in protein abundance at the proteome level in standard bottom-up experiments. The quantification process requires computation of the ratio of intensity of several isotopologs in the isotopic cluster of every identified peptide. Thus, appropriate bioinformatic workflows are required to extract the signals from the instrument files and calculate the required ratio to infer peptide/protein abundance. In a previous study (Sénécaut et al., J Proteome Res 20:1476-1487, 2021), we developed original open-source workflows based on OpenMS nodes implemented in a KNIME working environment. Here, we extend the use of the bSLIM labeling strategy in quantitative proteomics by presenting an alternative procedure to extract isotopolog intensities and process them by taking advantage of new functionalities integrated into the Minora node of Proteome Discoverer 2.4 software. We also present a graphical strategy to evaluate the statistical robustness of protein quantification scores and calculate the associated false discovery rates (FDR). We validated these approaches in a case study in which we compared the differences between the proteomes of two closely related yeast strains.
Published: 2022

17. Quantitative Proteomics in Yeast: From bSLIM and Proteome Discoverer Outputs to Graphical Assessment of the Significance of Protein Quantification Scores

Author: Nicolas Sénécaut, Pierre Poulain, Laurent Lignières, Samuel Terrier, Véronique Legros, Guillaume Chevreux, Gaëlle Lelandais, Jean-Michel Camadro, Institut Jacques Monod (IJM (UMR_7592)), Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), ANR-18-CE44-0014,SLIM-labeling,Protéomique haute performance par réduction de la complexité isotopique in vivo(2018), and Frédéric Devaux
Subjects: Proteomics, Minora, MESH: Isotope Labeling, Mass spectrometry, MESH: Peptides, MESH: Proteomics, [SDV]Life Sciences [q-bio], KNIME, Proteome Discoverer, bSLIM, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], MESH: Saccharomyces cerevisiae, Yeast, Isotopic labeling, MESH: Proteome, Metabolism, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Quantification, [SDV.MP.MYC]Life Sciences [q-bio]/Microbiology and Parasitology/Mycology
Abstract: Simple light isotope metabolic labeling (bSLIM) is an innovative method to accurately quantify differences in protein abundance at the proteome level in standard bottom-up experiments. The quantification process requires computation of the ratio of intensity of several isotopologs in the isotopic cluster of every identified peptide. Thus, appropriate bioinformatic workflows are required to extract the signals from the instrument files and calculate the required ratio to infer peptide/protein abundance. In a previous study (Sénécaut et al., J Proteome Res 20:1476–1487, 2021), we developed original open-source workflows based on OpenMS nodes implemented in a KNIME working environment. Here, we extend the use of the bSLIM labeling strategy in quantitative proteomics by presenting an alternative procedure to extract isotopolog intensities and process them by taking advantage of new functionalities integrated into the Minora node of Proteome Discoverer 2.4 software. We also present a graphical strategy to evaluate the statistical robustness of protein quantification scores and calculate the associated false discovery rates (FDR). We validated these approaches in a case study in which we compared the differences between the proteomes of two closely related yeast strains.
Published: 2022

18. Novel Insights into Quantitative Proteomics from an Innovative Bottom-Up Simple Light Isotope Metabolic (bSLIM) Labeling Data Processing Strategy

Author: Jean-Michel Camadro, Samuel Terrier, Pierre Poulain, Lilian Yang-Crosson, Nicolas Sénécaut, Gelio Alves, Hendrik Weisser, Laurent Lignières, Gaëlle Lelandais, Yi-Kuo Yu, Institut Jacques Monod (IJM (UMR_7592)), Université de Paris (UP)-Centre National de la Recherche Scientifique (CNRS), National Center for Biotechnology Information, NLM, NIH, Bethesda, Maryland 20894, United States., STORM Therapeutics Limited, Cambridge CB22 3AT, U.K., Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), ANR-18-CE44-0014,SLIM-labeling,Protéomique haute performance par réduction de la complexité isotopique in vivo(2018), and Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)
Subjects: 0301 basic medicine, Proteomics, quantitative proteomics, MESH: Isotope Labeling, Proteome, Computer science, workflow, [SDV]Life Sciences [q-bio], Quantitative proteomics, Saccharomyces cerevisiae, KNIME, MESH: Amino Acid Sequence, Computational biology, yeast, Mass spectrometry, Biochemistry, Mass Spectrometry, Article, 03 medical and health sciences, Protein sequencing, data processing workflow, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Amino Acid Sequence, MESH: Mass Spectrometry, Data processing, light carbon isotope, In vivo metabolic labeling, 030102 biochemistry & molecular biology, biology, 12 C, MESH: Proteomics, OpenMS, Experimental data, General Chemistry, Metrics & More Article Recommendations In vivo metabolic labeling, biology.organism_classification, MESH: Proteome, 030104 developmental biology, Isotope Labeling, 12C, data processing
Abstract: International audience; Simple light isotope metabolic labeling (SLIM labeling) is an innovative method to quantify variations in the proteome based on an original in vivo labeling strategy. Heterotrophic cells grown in U-[ 12 C] as the sole source of carbon synthesize U-[ 12 C]-amino acids, which are incorporated into proteins, giving rise to U-[ 12 C]-proteins. This results in a large increase in the intensity of the monoisotope ion of peptides and proteins, thus allowing higher identification scores and protein sequence coverage in mass spectrometry experiments. This method, initially developed for signal processing and quantification of the incorporation rate of 12 C into peptides, was based on a multistep process that was difficult to implement for many laboratories. To overcome these limitations, we developed a new theoretical background to analyze bottom-up proteomics data using SLIM-labeling (bSLIM) and established simple procedures based on open-source software, using dedicated OpenMS modules, and embedded R scripts to process the bSLIM experimental data. These new tools allow computation of both the 12 C abundance in peptides to follow the kinetics of protein labeling and the molar fraction of unlabeled and 12 C-labeled peptides in multiplexing experiments to determine the relative abundance of proteins extracted under different biological conditions. They also make it possible to consider incomplete 12 C labeling, such as that observed in cells with nutritional requirements for nonlabeled amino acids. These tools were validated on an experimental dataset produced using various yeast strains of Saccharomyces cerevisiae and growth conditions. The workflows are built on the implementation of appropriate calculation modules in a KNIME working environment. These new integrated tools provide a convenient framework for the wider use of the SLIM-labeling strategy.
Published: 2021

19. Characterization of the Radiation Desiccation Response Regulon of the Radioresistant Bacterium Deinococcus radiodurans by Integrative Genomic Analyses

Author: Gaëlle Lelandais, C. Bouthier de la Tour, Esma Bentchikou, Nicolas Eugénie, Yvan Zivanovic, Geneviève Coste, Pascale Servant, Fabrice Confalonieri, Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and ANR-19-CE12-0010,NOVOREP,Réparation des dommages ou mort cellulaire chez les bactéries exposées aux stress(2019)
Subjects: QH301-705.5, RNA-Seq, Biology, Regulon, Genome, Article, 03 medical and health sciences, chemistry.chemical_compound, Bacterial Proteins, Deinococcus radiodurans, Radioresistance, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Gene expression, Homologous chromosome, Transcriptional regulation, Deinococcus, Biology (General), Gene, 030304 developmental biology, transcriptional regulator, Genetics, Metalloproteinase, 0303 health sciences, bioinformatic analyses, 030306 microbiology, Gene Expression Regulation, Bacterial, Genomics, General Medicine, biology.organism_classification, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, ChIP-seq, chemistry, RNA-seq, radioresistance/desiccation, Bacteria, DNA, DNA Damage, Transcription Factors
Abstract: Numerous genes are overexpressed in the radioresistant bacterium Deinococcus radiodurans after exposure to radiation or prolonged desiccation. The DdrO and IrrE proteins play a major role in regulating the expression of approximately predicted twenty of these genes. The transcriptional repressor DdrO blocks the expression of these genes under normal growth conditions. After exposure to genotoxic agents, the IrrE metalloprotease cleaves DdrO and relieves gene repression. Bioinformatic analyzes showed that this mechanism seems to be conserved in several species of Deinococcus, but many questions remain as such the number of genes regulated by DdrO. Here, by RNA-seq and CHiP-seq assays performed at a genome-wide scale coupled with bioinformatic analyses, we show that, the DdrO regulon in D. radiodurans includes many other genes than those previously described. These results thus pave the way to better understand the radioresistance mechanisms encoded by this bacterium.Author SummaryThe main response pathway to genotoxic conditions in the radioresistant bacterium Deinococcus radiodurans is regulated by the constitutively expressed metalloprotease IrrE that cleaves the transcriptional repressor DdrO, leading to the expression of the genes repressed by DdrO. One of the major goals to better understand how pathways involved in radioresistance are coordinated into this fascinating bacterium is to highlight genes regulated by DdrO. In this study, we mapped in vivo the DdrO regulon in D. radiodurans by using two genome-scale approaches, ChIP-seq and RNA-seq analyses, coupled with bioinformatic analyses. As homologs of these two proteins are also found in many other bacteria, these results also pave the way to compare the stress-induced responses mediated by this couple of proteins in diverse bacteria.
Published: 2021

20. Label-free quantitative proteomics in Candida yeast species: technical and biological replicates to assess data reproducibility

Author: Jean-Michel Camadro, Gaëlle Lelandais, Nicolas Danila, Camille Garcia, Thibaut Léger, Thomas Denecker, UMR-S 665, Dynamique des Structures et Interactions des Macromole´cules Biologiques, Université Paris Diderot - Paris 7 (UPD7), Institut Jacques Monod (IJM (UMR_7592)), and Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Proteomics, 0301 basic medicine, Proteome, Quantitative proteomics, lcsh:Medicine, Datasets as Topic, Candida glabrata, Computational biology, Data Note, Mass spectrometry, General Biochemistry, Genetics and Molecular Biology, Fungal Proteins, 03 medical and health sciences, 0302 clinical medicine, Stress, Physiological, Candida albicans, 030212 general & internal medicine, lcsh:Science (General), lcsh:QH301-705.5, Alkaline pH, 2. Zero hunger, Internet, biology, Information Dissemination, lcsh:R, General Medicine, Hydrogen-Ion Concentration, biology.organism_classification, Corpus albicans, Yeast, 030104 developmental biology, lcsh:Biology (General), Label-free quantitative proteomics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], lcsh:Q1-390
Abstract: International audience; Objective: Label-free quantitative proteomics has emerged as a powerful strategy to obtain high quality quantitative measures of the proteome with only a very small quantity of total protein extract. Because our research projects were requiring the application of bottom-up shotgun mass spectrometry proteomics in the pathogenic yeasts Candida glabrata and Candida albicans, we performed preliminary experiments to (i) obtain a precise list of all the proteins for which measures of abundance could be obtained and (ii) assess the reproducibility of the results arising respectively from biological and technical replicates. Data description: Three time-courses were performed in each Candida species, and an alkaline pH stress was induced for two of them. Cells were collected 10 and 60 min after stress induction and proteins were extracted. Samples were analysed two times by mass spectrometry. Our final dataset thus comprises label-free quantitative prot-eomics results for 24 samples (two species, three time-courses, two time points and two runs of mass spectrometry). Statistical procedures were applied to identify proteins with differential abundances between stressed and unstressed situations. Considering that C. glabrata and C. albicans are human pathogens, which face important pH fluctuations during a human host infection, this dataset has a potential value to other researchers in the field. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/ publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Objective Studying proteome dynamics is a key step in systems biology projects. In this context, label-free bottom-up shotgun MS-based proteomics produces quantitative analyses of proteomes. This technique has emerged from significant improvements achieved by mass spectrom-etry (MS) instrumentation, chromatographic separation systems and a stronger correlation between the relative measured ion intensity and the original molecule abundance in the electrospray ionization process [1-3]. Members of our research team were involved in functional genomics studies in pathogenic yeasts Candida glabrata and Candida albicans [4-8]. We observed how the experimental design is a critical step to empower the statistics used to assess the robustness of the results. "How many replicates is enough?" is certainly one of the most frequently asked questions in wet laboratories. This question is especially critical in situations where the experiments are expensive, and/or the preparation of the biological samples is challenging. Here, our objective was to assess the robustness of the results arising from label-free bottom-up shotgun MS-based proteom-ics performed in C. glabrata and C. albicans, in case of technical and biological replicates. If the importance of biological replicates was indisputable when we started
Published: 2019

21. Meet-U: educating through research immersion

Author: Anne Lopes, Laurent David, Sophie Sacquin-Mora, Alexandre Albani, Marc Delarue, Vasiliki Myrodia, Eric Anthony, Mélissa Cardon, Hugo Schweke, Nicolas Sompairac, Stéphane Le Crom, Christine Froidevaux, Patrick F.J. Fuchs, Pierre Poulain, Violaine Verrecchia, Jeanne Laurentie, Nadège Guiglielmoni, Raphael Guerois, Martin Weigt, Maria Ruiz-Cuevas, Juliana S Bernardes, Samer Abboud, Rémi Montagne, Nathalie Lehmann, Vaitea Opuu, Chloé Quignot, Agnes Baud, Camille Lochard, Chloé Dequeker, Samia Djerroud, Romain Conte, Elodie Laine, Dariusz Czernecki, Olivier Lespinet, Mariam Sissoko, Agathe Delaune, Guillaume Bouvier, Arnaud Ferré, Audrey Vallerix, Robert Clerc, Hugues Richard, Alessandra Carbone, Elise Parey, Sylvain Privé, Pauline Fourgoux, Nika Abdollahi, Yann Ponty, Lélia Polit, Gaëlle Lelandais, Sorbonne Universités, Université Paris-Saclay, Dynamique structurale des Macromolécules / Structural Dynamics of Macromolecules, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Intégrative de la Cellule (I2BC), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Assemblage moléculaire et intégrité du génome (AMIG), Département Biochimie, Biophysique et Biologie Structurale (B3S), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Intégrative de la Cellule (I2BC), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X), Algorithms and Models for Integrative BIOlogy (AMIBIO), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire de biochimie théorique [Paris] (LBT (UPR_9080)), Centre National de la Recherche Scientifique (CNRS)-Institut de biologie physico-chimique (IBPC), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Bioinformatique structurale, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de la Recherche Agronomique (INRA), Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB), Biologie Intégrée du Globule Rouge (BIGR (UMR_S_1134 / U1134)), Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pointe-à-Pitre/Abymes [Guadeloupe] -Université des Antilles (UA)-Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pointe-à-Pitre/Abymes [Guadeloupe] -Université des Antilles (UA), Institut de biologie physico-chimique (IBPC), Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot - Paris 7 (UPD7)-Institut de biologie physico-chimique (IBPC), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA)-CHU Pointe-à-Pitre/Abymes [Guadeloupe] -Université de La Réunion (UR)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Transfusion Sanguine [Paris] (INTS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA)-CHU Pointe-à-Pitre/Abymes [Guadeloupe] -Université de La Réunion (UR)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Transfusion Sanguine [Paris] (INTS), GenomiqueENS (Genomique ENS), Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Genetic Networks [LCQB] (LCQB-GN), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie physico-chimique (IBPC (FR_550)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Analyse des Données à Haut Débit en Génomique (ADHDG), Evolution Paris Seine, Université des Antilles et de la Guyane (UAG)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles et de la Guyane (UAG)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), Bioinformatique structurale - Structural Bioinformatics, Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA)-Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA), Institut Jacques Monod (IJM (UMR_7592)), Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Masters of Computer Science and of Molecular and Cellular Biology at Sorbonne Université / UPMC (Bioinformatics and Modeling specialty), Master of Analysis, Modeling and Engineering of Biological and Medical Information at Université Paris-Saclay, Groupement de Recherche BioInformatique Moléculaire, Société Française de BioInformatique, Ecole Doctorale Structure et Dynamique des Systèmes Vivants, Institut de Biologie Paris Seine, Institut Universitaire de France, Biochimie Structurale, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Institut de Biologie Intégrative de la Cellule ( I2BC ), Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Sud - Paris 11 ( UP11 ), Laboratoire d'informatique de l'École polytechnique [Palaiseau] ( LIX ), Centre National de la Recherche Scientifique ( CNRS ) -École polytechnique ( X ), Algorithms and Models for Integrative BIOlogy ( AMIBIO ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Laboratoire de biochimie théorique [Paris] ( LBT ), Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology ( LCQB ), Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ), Laboratoire de Recherche en Informatique ( LRI ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ), Institut de Biologie Paris Seine ( IBPS ), Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS ), Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] ( MaIAGE ), Institut National de la Recherche Agronomique ( INRA ), Dynamique des Structures et Interactions des Macromolécules Biologiques ( DSIMB ), Institut National de la Transfusion Sanguine [Paris] ( INTS ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Institut de biologie physico-chimique ( IBPC ), Centre National de la Recherche Scientifique ( CNRS ), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Institut de biologie physico-chimique (IBPC (FR_550)), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Université des Antilles et de la Guyane (UAG)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles et de la Guyane (UAG)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Nice Sophia Antipolis (... - 2019) (UNS), Université de Paris (UP)-Centre National de la Recherche Scientifique (CNRS), Laine, Elodie, and Lopes, Anne
Subjects: 0301 basic medicine, Research design, [SDV]Life Sciences [q-bio], Social Sciences, Cloud computing, Biochemistry, Computational biology, Database and Informatics Methods, Sociology, Jury, Nothing, ComputingMilieux_COMPUTERSANDEDUCATION, Macromolecular Structure Analysis, Biology (General), media_common, Schools, Ecology, 4. Education, Professions, Computational Theory and Mathematics, Research Design, Modeling and Simulation, Lectures, Engineering ethics, Computer and Information Sciences, Universities, QH301-705.5, Bioinformatics, media_common.quotation_subject, Research and Analysis Methods, Education, 03 medical and health sciences, Cellular and Molecular Neuroscience, [ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM], Genetics, Humans, Students, Molecular Biology, Ecology, Evolution, Behavior and Systematics, business.industry, Research, Biology and Life Sciences, Proteins, Teachers, Coopetition, Computing Methods, 030104 developmental biology, Protein structure prediction, People and Places, Protein structure, Population Groupings, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business
Abstract: This is a PLOS Computational Biology Education paper.; International audience; We present a new educational initiative, called Meet-U, that aims at training students to collaborativework in computational biology and at bridging the gap between education and research.Meet-U mimics the set up of collaborative research projects and takes advantage of the most populartools for collaborative work and of cloud computing. Students are grouped in teams of 4-5 peopleand have to realize a project from A to Z that answers a challenging question in Biology.Meet-U promotes "coopetition", as the students collaborate within and across the teams and are also incompetition with each other to develop the best final product.Meet-U fosters interactions between different actors of education and research through the organization of a meeting day, open to everyone, where the students present their work to a jury of researchers and jury members give researchseminars. This very unique combination of education and research is strongly motivating for thestudents and provides a formidable opportunity for a scientific community to unite and increase itsvisibility. We report on our experience with Meet-U in two French universities with master studentsin bioinformatics and modeling, with protein-protein docking as the subject of the course.Meet-U is easy to implement, virtually costs nothing and can be straightforwardly transferred to other fieldsand/or universities. All the information and data are available at http://www.meet-u.org.
Published: 2018

22. Fermentation and alternative respiration compensate for NADH dehydrogenase deficiency in a prokaryotic model of DJ-1-associated Parkinsonism

Author: Gaëlle Lelandais, Julien Dairou, Gilbert Richarme, Mouhad Mihoub, Valérie Gautier, Philippe Bouloc, Ahmed Landoulsi, Nadia Messaoudi, Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Unité de Biologie Fonctionnelle et Adaptative (BFA (UMR_8251 / U1133)), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de génétique et microbiologie [Orsay] (IGM), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Biochimie et Biologie Moléculaire - 03/UR/0902, Faculté des Sciences de Bizerte [Université de Carthage], Université de Carthage - University of Carthage-Université de Carthage - University of Carthage, EGIDE, and Faculté des Sciences de Bizerte
Subjects: Ribosomal Proteins, Alternative respiration, Molecular Sequence Data, Dehydrogenase, Oxidative phosphorylation, Models, Biological, Microbiology, NADH dehydrogenase activity, 03 medical and health sciences, 0302 clinical medicine, Parkinsonian Disorders, Escherichia coli, Humans, Glycolysis, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, biology, Escherichia coli Proteins, Gene Expression Profiling, NADH dehydrogenase, NADH Dehydrogenase, Sequence Analysis, DNA, Microarray Analysis, Pyruvate dehydrogenase complex, Aerobiosis, Metabolic Flux Analysis, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, Biochemistry, Fermentation, biology.protein, Branched-chain alpha-keto acid dehydrogenase complex, 030217 neurology & neurosurgery
Abstract: YajL is the closest prokaryotic homologue of Parkinson's disease-associated DJ-1, a protein of undefined function involved in the oxidative stress response. We reported recently that YajL and DJ-1 protect cells against oxidative stress-induced protein aggregation by acting as covalent chaperones for the thiol proteome, including the NuoG subunit of NADH dehydrogenase 1, and that NADH dehydrogenase 1 activity is negligible in the yajL mutant. We report here that this mutant compensates for low NADH dehydrogenase activity by utilizing NADH-independent alternative dehydrogenases, including pyruvate oxidase PoxB and D-amino acid dehydrogenase DadA, and mixed acid aerobic fermentations characterized by acetate, lactate, succinate and ethanol excretion. The yajL mutant has a low adenylate energy charge favouring glycolytic flux, and a high NADH/NAD ratio favouring fermentations over pyruvate dehydrogenase and the Krebs cycle. DNA array analysis showed upregulation of genes involved in glycolytic and pentose phosphate pathways and alternative respiratory pathways. Moreover, the yajL mutant preferentially catabolized pyruvate-forming amino acids over Krebs cycle-related amino acids, and thus the yajL mutant utilizes pyruvate-centred respiro-fermentative metabolism to compensate for the NADH dehydrogenase 1 defect and constitutes an interesting model for studying eukaryotic respiratory complex I deficiencies, especially those associated with Alzheimer's and Parkinson's diseases.
Published: 2015

23. Yap7 is a transcriptional repressor of nitric oxide oxidase in yeasts, which arose from neofunctionalization after whole genome duplication

Author: Thierry Delaveau, Mathilde Garcia, Benoit Palancade, Jawad Merhej, Frédéric Devaux, Gaëlle Lelandais, Juliette Guitard, and Christophe Hennequin
Subjects: Genetics, 0303 health sciences, Candida glabrata, biology, 030302 biochemistry & molecular biology, Mutant, Saccharomyces cerevisiae, biology.organism_classification, Microbiology, Yeast, 03 medical and health sciences, Neofunctionalization, Molecular Biology, Gene, Transcription factor, Psychological repression, 030304 developmental biology
Abstract: Flavohemoglobins are the main detoxifiers of nitric oxide (NO) in bacteria and fungi and are induced in response to nitrosative stress. In fungi, the flavohemoglobin encoding gene YHB1 is positively regulated by transcription factors which are activated upon NO exposure. In this study, we show that in the model yeast Saccharomyces cerevisiae and in the human pathogen Candida glabrata, the transcription factor Yap7 constitutively represses YHB1 by binding its promoter. Consequently, YAP7 deletion conferred high NO resistance to the cells. Co-immunoprecipitation experiments and mutant analyses indicated that Yap7 represses YHB1 by recruiting the transcriptional repressor Tup1. In S. cerevisiae, YHB1 repression also involves interaction of Yap7 with the Hap2/3/5 complex through a conserved Hap4-like-bZIP domain, but this interaction has been lost in C. glabrata. The evolutionary origin of this regulation was investigated by functional analyses of Yap7 and of its paralogue Yap5 in different yeast species. These analyses indicated that the negative regulation of YHB1 by Yap7 arose by neofunctionalization after the whole genome duplication which led to the C. glabrata and S. cerevisiae extant species. This work describes a new aspect of the regulation of fungal nitric oxidase and provides detailed insights into its functioning and evolution.
Published: 2015

24. The Metacaspase (Mca1p) has a Dual Role in Farnesol-induced Apoptosis in Candida albicans

Author: Gaëlle Lelandais, Camille Garcia, Thibaut Léger, Marwa Ounissi, Jean-Michel Camadro, Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), and CNRS
Subjects: Programmed cell death, Proteome, Apoptosis, Mitochondrion, Biochemistry, Analytical Chemistry, Fungal Proteins, chemistry.chemical_compound, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Candida albicans, Molecular Biology, Caspase, biology, Research, Serine Endopeptidases, Intrinsic apoptosis, Farnesol, biology.organism_classification, Metacaspase, Cell biology, chemistry, Caspases, biology.protein
Abstract: International audience; Manipulating the apoptotic response of Candida albicans may help in the control of this opportunistic pathogen. The metacaspase Mca1p has been described as a key protease for apoptosis in C. albicans but little is known about its cleavage specificity and substrates. We therefore initiated a series of studies to describe its function. We used a strain disrupted for the MCA1 gene (mca1Δ/Δ) and compared its proteome to that of a wild-type isogenic strain, in the presence and absence of a known inducer of apoptosis, the quorum-sensing molecule farnesol. Label-free and TMT labeling quantitative proteomic analyses showed that both mca1 disruption and farnesol treatment significantly affected the proteome of the cells. The combination of both conditions led to an unexpected biological response: the strong overexpression of proteins implicated in the general stress. We studied sites cleaved by Mca1p using native peptidomic techniques, and a bottom-up approach involving GluC endoprotease: there appeared to be a "K/R" substrate specificity in P1 and a "D/E" specificity in P2. We also found 77 potential substrates of Mca1p, 13 of which validated using the most stringent filters, implicated in protein folding, protein aggregate resolubilization, glycolysis, and a number of mitochondrial functions. An immunoblot assay confirmed the cleavage of Ssb1p, a member of the HSP70 family of heat-shock proteins, in conditions where the metacaspase is activated. These various results indicate that Mca1p is involved in a limited and specific proteolysis program triggered by apoptosis. One of the main functions of Mca1p appears to be the degradation of several major heat-shock proteins, thereby contributing to weakening cellular defenses and amplifying the cell death process. Finally, Mca1p appears to contribute significantly to the control of mitochondria biogenesis and degradation. Consequently, Mca1p may be a link between the extrinsic and the intrinsic programmed cell death pathways in C. albicans.
Published: 2015

25. bPeaks: a bioinformatics tool to detect transcription factor binding sites from ChIPseq data in yeasts and other organisms with small genomes

Author: Jawad Merhej, Gaëlle Lelandais, Jean-Michel Camadro, Stéphane Le Crom, Amandine Frigo, and Frédéric Devaux
Subjects: 0303 health sciences, biology, 030302 biochemistry & molecular biology, Saccharomyces cerevisiae, Bioengineering, Bioinformatics, biology.organism_classification, Applied Microbiology and Biotechnology, Biochemistry, Genome, DNA sequencing, DNA binding site, 03 medical and health sciences, chemistry.chemical_compound, chemistry, Genetics, Binding site, Transcription factor, Peak calling, DNA, 030304 developmental biology, Biotechnology
Abstract: Peak calling is a critical step in ChIPseq data analysis. Choosing the correct algorithm as well as optimized parameters for a specific biological system is an essential task. In this article, we present an original peak-calling method (bPeaks) specifically designed to detect transcription factor (TF) binding sites in small eukaryotic genomes, such as in yeasts. As TF interactions with DNA are strong and generate high binding signals, bPeaks uses simple parameters to compare the sequences (reads) obtained from the immunoprecipitation (IP) with those from the control DNA (input). Because yeasts have small genomes (
Published: 2014

26. Ostreococcus tauri is a new model green alga for studying iron metabolism in eukaryotic phytoplankton

Author: Robert Sutak, Hugo Botebol, Jana Pilátová, Ivo F. Scheiber, Thibaut Léger, Jean-Michel Camadro, Emmanuel Lesuisse, Javier Paz-Yepes, Gaëlle Lelandais, François-Yves Bouget, Vojtěch Žárský, Pierre-Louis Blaiseau, Jean-Claude Lozano, Chris Bowler, Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Dpt of Parasitology [Prague], Charles University [Prague] (CU), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Océanographie Microbienne (LOMIC), Observatoire océanologique de Banyuls (OOB), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (IBENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Observatoire océanologique de Banyuls (OOB), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), HAL-UPMC, Gestionnaire, École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL)
Subjects: 0301 basic medicine, [SDE] Environmental Sciences, Photoperiod, Iron, [SDV]Life Sciences [q-bio], Adaptation, Biological, Iron assimilation, Ostreococcus tauri, Ostreococcus, Transcriptome, 03 medical and health sciences, Chlorophyta, Stress, Physiological, Genetics, Cluster Analysis, Homeostasis, 14. Life underwater, Gene, Phylogeny, Plant Proteins, Marine phytoplankton, Regulation of gene expression, biology, Gene Expression Profiling, Eukaryota, High-Throughput Nucleotide Sequencing, biology.organism_classification, Cell biology, Ferritin, [SDV] Life Sciences [q-bio], 030104 developmental biology, Regulon, Gene Expression Regulation, Phytoplankton, [SDE]Environmental Sciences, biology.protein, RNA-seq analysis, Oxidation-Reduction, Copper, Iron Compounds, Signal Transduction, Research Article, Biotechnology
Abstract: Background Low iron bioavailability is a common feature of ocean surface water and therefore micro-algae developed original strategies to optimize iron uptake and metabolism. The marine picoeukaryotic green alga Ostreococcus tauri is a very good model for studying physiological and genetic aspects of the adaptation of the green algal lineage to the marine environment: it has a very compact genome, is easy to culture in laboratory conditions, and can be genetically manipulated by efficient homologous recombination. In this study, we aimed at characterizing the mechanisms of iron assimilation in O. tauri by combining genetics and physiological tools. Specifically, we wanted to identify and functionally characterize groups of genes displaying tightly orchestrated temporal expression patterns following the exposure of cells to iron deprivation and day/night cycles, and to highlight unique features of iron metabolism in O. tauri, as compared to the freshwater model alga Chalamydomonas reinhardtii. Results We used RNA sequencing to investigated the transcriptional responses to iron limitation in O. tauri and found that most of the genes involved in iron uptake and metabolism in O. tauri are regulated by day/night cycles, regardless of iron status. O. tauri lacks the classical components of a reductive iron uptake system, and has no obvious iron regulon. Iron uptake appears to be copper-independent, but is regulated by zinc. Conversely, iron deprivation resulted in the transcriptional activation of numerous genes encoding zinc-containing regulation factors. Iron uptake is likely mediated by a ZIP-family protein (Ot-Irt1) and by a new Fea1-related protein (Ot-Fea1) containing duplicated Fea1 domains. The adaptation of cells to iron limitation involved an iron-sparing response tightly coordinated with diurnal cycles to optimize cell functions and synchronize these functions with the day/night redistribution of iron orchestrated by ferritin, and a stress response based on the induction of thioredoxin-like proteins, of peroxiredoxin and of tesmin-like methallothionein rather than ascorbate. We briefly surveyed the metabolic remodeling resulting from iron deprivation. Conclusions The mechanisms of iron uptake and utilization by O. tauri differ fundamentally from those described in C. reinhardtii. We propose this species as a new model for investigation of iron metabolism in marine microalgae. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2666-6) contains supplementary material, which is available to authorized users.
Published: 2016

27. Tma108, a putative M1 aminopeptidase, is a specific nascent chain-associated protein in Saccharomyces cerevisiae

Author: Camille Garcia, Thibaut Léger, Dimitri Davoine, Mathilde Garcia, Athenaïs Gerber, Benoit Palancade, Ariane Jolly, Roxane Roquigny, Antoine Vallot, Frédéric Devaux, Gaëlle Lelandais, Elodie Laine, Jérôme O. Rouvière, Thierry Delaveau, Marion Plessis, Jawad Merhej, Sandra Brochet, Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0301 basic medicine, Saccharomyces cerevisiae Proteins, Saccharomyces cerevisiae, Peptide Chain Elongation, Translational, RNA-binding protein, Ribosome, Aminopeptidases, RNA Transport, 03 medical and health sciences, Adenosine Triphosphate, Genetics, Protein biosynthesis, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, RNA, Messenger, Molecular Biology, In Situ Hybridization, Fluorescence, biology, RNA, RNA-Binding Proteins, Translation (biology), Translocon, biology.organism_classification, Mitochondria, Proton-Translocating ATPases, Zinc, 030104 developmental biology, Directed mutagenesis, Biochemistry, Protein Biosynthesis, Ribosomes, Protein Binding
Abstract: International audience; The discovery of novel specific ribosome-associated factors challenges the assumption that translation relies on standardized molecular machinery. In this work, we demonstrate that Tma108, an uncharac-terized translation machinery-associated factor in yeast, defines a subpopulation of cellular ribosomes specifically involved in the translation of less than 200 mRNAs encoding proteins with ATP or Zinc binding domains. Using ribonucleoparticle dissoci-ation experiments we established that Tma108 directly interacts with the nascent protein chain. Additionally , we have shown that translation of the first 35 amino acids of Asn1, one of the Tma108 targets, is necessary and sufficient to recruit Tma108, suggesting that it is loaded early during translation. Comparative genomic analyses, molecular model-ing and directed mutagenesis point to Tma108 as an original M1 metallopeptidase, which uses its pu-tative catalytic peptide-binding pocket to bind the N-terminus of its targets. The involvement of Tma108 in co-translational regulation is attested by a drastic change in the subcellular localization of ATP2 mRNA upon Tma108 inactivation. Tma108 is a unique example of a nascent chain-associated factor with high selectivity and its study illustrates the existence of other specific translation-associated factors besides RNA binding proteins.
Published: 2016

28. ChIPseq in Yeast Species: From Chromatin Immunoprecipitation to High-Throughput Sequencing and Bioinformatics Data Analyses

Author: Gaëlle, Lelandais, Corinne, Blugeon, Jawad, Merhej, Institut Jacques Monod ( IJM ), Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Plateforme Génomique de l'IBENS, Institut de biologie de l'ENS Paris (UMR 8197/1024) ( IBENS ), Département de Biologie - ENS Paris, École normale supérieure - Paris ( ENS Paris ) -École normale supérieure - Paris ( ENS Paris ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ) -Département de Biologie - ENS Paris, École normale supérieure - Paris ( ENS Paris ) -École normale supérieure - Paris ( ENS Paris ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology ( LCQB ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS ), Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (IBENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), GenomiqueENS (Genomique ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), and Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Binding Sites, High-throughput sequencing, Bioinformatics, Computational Biology, High-Throughput Nucleotide Sequencing, Saccharomyces cerevisiae, Chromatin immunoprecipitation, DNA-Binding Proteins, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, [ SDV.MP ] Life Sciences [q-bio]/Microbiology and Parasitology, Yeasts, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, [ SDV.BBM ] Life Sciences [q-bio]/Biochemistry, Molecular Biology, DNA binding site s of proteins, Transcription Factors
Abstract: International audience; Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIPseq) is a powerful technique for the genome-wide location of protein DNA-binding sites. The ChIP experiment consists in treating living cells with a cross-linking agent to bind proteins to their DNA substrates. After fragmentation of DNA, specific fractions associated with a particular protein of interest are purified by immunoaffinity. They are next sequenced and identified on the reference genome using dedicated bioinformatics programs. Several technical aspects are important to obtain high-quality ChIPseq results. This includes the quality of antibodies, the sequencing protocols, the use of accurate controls and the careful choice of bioinformatics tools. We present here a general protocol to perform ChIPseq analyses in yeast species. This protocol has been optimized to identify target genes of specific transcription factors but can be used for any other DNA binding proteins.
Published: 2016

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

Author: Mohammed El Amine Ali Chaouche, Jean-Michel Camadro, Corinne Blugeon, Antonin Thiébaut, Jawad Merhej, Stéphane Le Crom, Gaëlle Lelandais, Juliette Pouch, Frédéric Devaux, Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), GenomiqueENS (Genomique ENS), Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Analyse des Données à Haut Débit en Génomique (ADHDG), Evolution Paris Seine, Université des Antilles et de la Guyane (UAG)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles et de la Guyane (UAG)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), HAL UPMC, Gestionnaire, Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Plateforme Génomique de l'IBENS, Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles et de la Guyane (UAG)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles et de la Guyane (UAG)-Université Pierre et Marie Curie - Paris 6 (UPMC), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Département de Biologie - ENS Paris
Subjects: 0301 basic medicine, Microbiology (medical), Yap proteins, 030106 microbiology, lcsh:QR1-502, yeast, Microbiology, lcsh:Microbiology, Transcriptome, 03 medical and health sciences, regulatory networks, evolution, transciptome, Gene, Transcription factor, [SDV.MP.MYC]Life Sciences [q-bio]/Microbiology and Parasitology/Mycology, Original Research, 2. Zero hunger, YAP1, Genetics, Candida glabrata, biology, biology.organism_classification, [SDV.MP.MYC] Life Sciences [q-bio]/Microbiology and Parasitology/Mycology, DNA binding site, ChIP-seq, Regulon, Yap, Chromatin immunoprecipitation, transcriptome
Abstract: International audience; 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 crossroads between redox homeostasis, oxygen consumption, and iron metabolism.
Published: 2016

30. Comparative Functional Genomics of Stress Responses in Yeasts

Author: Gaëlle Lelandais and Frédéric Devaux
Subjects: Systems biology, Saccharomyces cerevisiae, Gene regulatory network, Genomics, Computational biology, Models, Biological, Biochemistry, Fungal Proteins, Stress, Physiological, Gene Expression Regulation, Fungal, Yeasts, Genetics, Gene Regulatory Networks, Molecular Biology, Phylogeny, biology, business.industry, Gene Expression Profiling, biology.organism_classification, Yeast, Biotechnology, Multigene Family, Molecular Medicine, Genome, Fungal, Adaptation, business, Functional genomics, Biological network
Abstract: Systems biology proposes a multidisciplinary approach of biological systems, in order to obtain global and quantitative models of their functioning. This review presents an overview of 10 years of systemic analyses of the cellular responses to environmental stresses, using one of the favorite postgenomics models: the budding yeast Saccharomyces cerevisiae. These analyses include the global reconstruction and the dynamic modeling of the regulatory networks involved in the adaptation of the cells to environmental fluctuations and the extension of these approaches to other yeast species, to provide insights in the mechanisms that drive the evolution of the biological network properties.
Published: 2010

31. MitoGenesisDB: an expression data mining tool to explore spatio-temporal dynamics of mitochondrial biogenesis

Author: Mickael Orgeur, Gaëlle Lelandais, Claude Jacq, and Jean-Christophe Gelly
Subjects: Saccharomyces cerevisiae Proteins, Transcription, Genetic, Operon, RNA Stability, ved/biology.organism_classification_rank.species, Respiratory chain, Saccharomyces cerevisiae, Computational biology, Mitochondrion, Biology, Electron Transport, Mitochondrial Proteins, Genetics, Protein biosynthesis, Data Mining, RNA, Messenger, Databases, Protein, Model organism, Gene, Ribonucleoprotein, ved/biology, Articles, Mitochondria, Genes, Mitochondrial, Mitochondrial biogenesis, Protein Biosynthesis, Software
Abstract: Mitochondria constitute complex and flexible cellular entities, which play crucial roles in normal and pathological cell conditions. The database MitoGenesisDB focuses on the dynamic of mitochondrial protein formation through global mRNA analyses. Three main parameters confer a global view of mitochondrial biogenesis: (i) time-course of mRNA production in highly synchronized yeast cell cultures, (ii) microarray analyses of mRNA localization that define translation sites and (iii) mRNA transcription rate and stability which characterize genes that are more dependent on post-transcriptional regulation processes. MitoGenesisDB integrates and establishes cross-comparisons between these data. Several model organisms can be analyzed via orthologous relationships between interspecies genes. More generally this database supports the 'post-transcriptional operon' model, which postulates that eukaryotes co-regulate related mRNAs based on their functional organization in ribonucleoprotein complexes. MitoGenesisDB allows identifying such groups of post-trancriptionally regulated genes and is thus a useful tool to analyze the complex relationships between transcriptional and post-transcriptional regulation processes. The case of respiratory chain assembly factors illustrates this point. The MitoGenesisDB interface is available at http://www.dsimb.inserm.fr/dsimb_tools/mitgene/.
Published: 2010

32. Comparing gene expression networks in a multi-dimensional space to extract similarities and differences between organisms

Author: Pierre Vincens, Gaëlle Lelandais, Claude Jacq, S. Hazout, Stéphane Vialette, Anne Badel-Chagnon, Régulation de l'expression génétique (REG), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), and Duponchelle, Martine
Subjects: Statistics and Probability, Source code, media_common.quotation_subject, Saccharomyces cerevisiae, Computational biology, Space (commercial competition), Biology, Biochemistry, Evolution, Molecular, Fungal Proteins, 03 medical and health sciences, 0302 clinical medicine, Species Specificity, Molecular evolution, Gene Expression Regulation, Fungal, Schizosaccharomyces, Gene expression, [SDV.BBM] Life Sciences [q-bio]/Biochemistry, Molecular Biology, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Molecular Biology, Gene, 030304 developmental biology, media_common, Genetics, 0303 health sciences, Genome, Models, Statistical, Gene Expression Profiling, Computational Biology, Expression (mathematics), Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, Multi dimensional, Software, 030217 neurology & neurosurgery, Orthologous Gene
Abstract: Motivation: Molecular evolution, which is classically assessed by comparison of individual proteins or genes between species, can now be studied by comparing co-expressed functional groups of genes. This approach, which better reflects the functional constraints on the evolution of organisms, can exploit the large amount of data generated by genome-wide expression analyses. However, it requires new methodologies to represent the data in a more accessible way for cross-species comparisons. Results: In this work, we present an approach based on Multi-dimensional Scaling techniques, to compare the conformation of two gene expression networks, represented in a multi-dimensional space. The expression networks are optimally superimposed, taking into account two criteria: (1) inter-organism orthologous gene pairs have to be nearby points in the final multi-dimensional space and (2) the distortion of the gene expression networks, the organization of which reflects the similarities between the gene expression measurements, has to be circumscribed. Using this approach, we compared the transcriptional programs that drive sporulation in budding and fission yeasts, extracting some common properties and differences between the two species. Availability: The source code is freely distributed to academic users upon request to the authors. More information can be found online at . Contact: lelandais@biologie.ens.fr Supplementary information: Supplementary data are available at
Published: 2006

33. Global stress response in a prokaryotic model of DJ-1-associated Parkinsonism

Author: Kazuyuki Tao, Mouadh Mihoub, Philippe Bouloc, Gilbert Richarme, Danièle Joseleau-Petit, Chantal Bohn, Gaëlle Lelandais, Leah Tolosa, Ahmed Landoulsi, Valérie Gautier, Nadia Messaoudi, Govind Rao, Teresa Caldas, Fatoum Kthiri, Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Biochimie et Biologie Moléculaire - 03/UR/0902, Faculté des Sciences de Bizerte, Faculté des Sciences de Bizerte, Institut de génétique et microbiologie [Orsay] (IGM), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Department of Chemical and Biochemical Engineering, University of Maryland [Baltimore County] (UMBC), University of Maryland System-University of Maryland System-Center for Advanced Technology, Radioisotope Center, The University of Tokyo, program grant PHC-Utique 10G0803, Fondation pour la Recherche Médicale, EMBO short-term fellowship, Faculté des Sciences de Bizerte [Université de Carthage], Université de Carthage - University of Carthage-Université de Carthage - University of Carthage, and The University of Tokyo (UTokyo)
Subjects: DNA, Bacterial, Ribosomal Proteins, DNA Repair, Iron, Protein Deglycase DJ-1, Protein aggregation, Microbiology, 03 medical and health sciences, 0302 clinical medicine, Parkinsonian Disorders, Sigma factor, Heat shock protein, Escherichia coli, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Molecular Biology, Gene, Heat-Shock Proteins, 030304 developmental biology, Oncogene Proteins, 0303 health sciences, biology, Escherichia coli Proteins, Gene Expression Profiling, Intracellular Signaling Peptides and Proteins, Articles, Gene Expression Regulation, Bacterial, [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, Oxidative Stress, Biochemistry, Chaperone (protein), Mutation, biology.protein, Thioredoxin, Chemical chaperone, Oxidation-Reduction, 030217 neurology & neurosurgery, Molecular Chaperones
Abstract: International audience; YajL is the most closely related Escherichia coli homolog of Parkinsonism-associated protein DJ-1, a protein with a yet-undefined function in the oxidative-stress response. YajL protects cells against oxidative-stress-induced protein aggregation and functions as a covalent chaperone for the thiol proteome, including FeS proteins. To clarify the cellular responses to YajL deficiency, transcriptional profiling of the yajL mutant was performed. Compared to the parental strain, the yajL mutant overexpressed genes coding for chaperones, proteases, chemical chaperone transporters, superoxide dismutases, catalases, peroxidases, components of thioredoxin and glutaredoxin systems, iron transporters, ferritins and FeS cluster biogenesis enzymes, DNA repair proteins, RNA chaperones, and small regulatory RNAs. It also overexpressed the RNA polymerase stress sigma factors sigma S (multiple stresses) and sigma 32 (protein stress) and activated the OxyR and SoxRS oxidative-stress transcriptional regulators, which together trigger the global stress response. The yajL mutant also overexpressed genes involved in septation and adopted a shorter and rounder shape characteristic of stressed bacteria. Biochemical experiments showed that this upregulation of many stress genes resulted in increased expression of stress proteins and improved biochemical function. Thus, protein defects resulting from the yajL mutation trigger the onset of a robust and global stress response in a prokaryotic model of DJ-1-associated Parkinsonism.
Published: 2013

34. RNA sequencing revealed novel actors of the acquisition of drug resistance in Candida albicans

Author: Jean-Yves Coppée, Gaëlle Lelandais, Odile Sismeiro, Sophie Lemoine, Frédéric Devaux, Stéphane Le Crom, Rajendra Prasad, Maria Bernard, Sanjiveeni Dhamgaye, Membrane Biology Laboratory, Jawaharlal Nehru University (JNU), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Dynamique des Structures et Interactions des Macromolécules Biologiques - Pôle de La Réunion (DSIMB Réunion), Biologie Intégrée du Globule Rouge (BIGR (UMR_S_1134 / U1134)), Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA)-Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA), Transcriptome et Epigénome (PF2), Institut Pasteur [Paris], Génomique des Microorganismes (LGM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), The work presented in this paper has been supported in part by grants to R.P. from Indo-French Center for the Promotion of Advanced Scientific Research (IFC-3403-2/2006) and DST INT/SWISS/P-31/2009., Institut de biologie de l'ENS Paris (IBENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Institut Pasteur [Paris] (IP), BMC, Ed., Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Université des Antilles (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Université des Antilles (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: Candida albicans, beta-Glucans, lcsh:QH426-470, lcsh:Biotechnology, Repressor, RNA-Seq, Drug resistance, Multidrug resistance, Fungal Proteins, 03 medical and health sciences, Drug Resistance, Fungal, lcsh:TP248.13-248.65, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Transcriptional regulation, Genetics, CZF1, Gene, 030304 developmental biology, 0303 health sciences, biology, 030306 microbiology, Sequence Analysis, RNA, Gene Expression Profiling, biology.organism_classification, Yeast, 3. Good health, Multiple drug resistance, Gene expression profiling, lcsh:Genetics, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], RNA-seq, Biotechnology, Transcription Factors, Research Article
Abstract: Background Drug susceptible clinical isolates of Candida albicans frequently become highly tolerant to drugs during chemotherapy, with dreadful consequences to patient health. We used RNA sequencing (RNA-seq) to analyze the transcriptomes of a CDR (Candida Drug Resistance) strain and its isogenic drug sensitive counterpart. Results RNA-seq unveiled differential expression of 228 genes including a) genes previously identified as involved in CDR, b) genes not previously associated to the CDR phenotype, and c) novel transcripts whose function as a gene is uncharacterized. In particular, we show for the first time that CDR acquisition is correlated with an overexpression of the transcription factor encoding gene CZF1. CZF1 null mutants were susceptible to many drugs, independently of known multidrug resistance mechanisms. We show that CZF1 acts as a repressor of β-glucan synthesis, thus negatively regulating cell wall integrity. Finally, our RNA-seq data allowed us to identify a new transcribed region, upstream of the TAC1 gene, which encodes the major CDR transcriptional regulator. Conclusion Our results open new perspectives of the role of Czf1 and of our understanding of the transcriptional and post-transcriptional mechanisms that lead to the acquisition of drug resistance in C. albicans, with potential for future improvements of therapeutic strategies.
Published: 2012

35. Role of the Yap Family in the Transcriptional Response to Oxidative Stress in Yeasts

Author: Christel Goudot, Frédéric Devaux, and Gaëlle Lelandais
Subjects: chemistry.chemical_classification, Reactive oxygen species, Radical, Cell, medicine.disease_cause, Metabolic pathway, chemistry.chemical_compound, medicine.anatomical_structure, Membrane, chemistry, Ageing, medicine, Biophysics, DNA, Oxidative stress
Abstract: Oxidative stress can be defined as physiological changes that arise in a living organism, in reaction to an abnormal level of cytotoxic oxidants and free radicals in the environment. Because they have unpaired valence shell electrons, free radicals are very unstable and constantly seek to bond to other molecules in order to increase their stability. Within the cell, free radicals can therefore cause considerable damages in different components such as DNA (Storz et al., 1987), lipid membranes (Davies, 1985) or proteins (Smith et al., 1984). Free radicals are implicated in the ageing process (Wickens, 2001), in some autoimmune diseases (Blake et al., 1987) and in the development of cancer (Trush and Kensler, 1991). Notably, the generation of free radicals is a natural process in cell functioning and for instance, reactive oxygen species (ROS) are continuously produced as side products of aerobic metabolic pathways. To neutralize them, living systems have developed specific strategies. In normal conditions, equilibrium thus exists between the generation and the degradation of free radicals, whereas in case of oxidative stress conditions a persistent imbalance is observed.
Published: 2012

36. Recovering Genetic Network from Continuous Data with Dynamic Bayesian Networks

Author: Sophie Lèbre and Gaëlle Lelandais
Subjects: Engineering, business.industry, Genetic network, Artificial intelligence, business, Machine learning, computer.software_genre, computer, Dynamic Bayesian network, Continuous data
Published: 2011

37. Transcriptomic analyses during the transition from biomass production to lipid accumulation in the oleaginous yeast yarrowia lipolytica

Author: Jean-Louis Uribelarrea, Athanasios Beopoulos, Carole Molina-Jouve, Veronique Le Berre, Julien Cescut, Jean-Marc Nicaud, Gaëlle Lelandais, Nicolas Morin, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), UMR-S 665, Dynamique des Structures et Interactions des Macromole´cules Biologiques, Université Paris Diderot - Paris 7 (UPD7), Plateforme Biopuces, Génopôle Toulouse Midi-Pyrénées, This work was labellized by Aerospace Valley (a non-profit association) and financially supported in part by CALIN French national research program (DGCIS), by the Lipicaero French national research program (ANR-PNRB) No. 0701C0089 and by the EU in the 7th Framework Programme for Research and Technological Development, under ALFA-BIRD (Alternative Fuels and Biofuels for Aircraft Development) project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript., Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), MICrobiologie de l'ALImentation au Service de la Santé humaine (MICALIS), Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB), Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM), Biochip and Bionanotechnogy, Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), and Nicaud, Jean-Marc
Subjects: multidisciplinary sciences, [SDV]Life Sciences [q-bio], lcsh:Medicine, Yarrowia, Nitrogen Metabolism, Gene Expression, Yeast and Fungal Models, 7. Clean energy, Biochemistry, Gene Expression Regulation, Fungal, Cluster Analysis, Biomass, lcsh:Science, fermentation, ComputingMilieux_MISCELLANEOUS, 2. Zero hunger, 0303 health sciences, Fungal protein, Multidisciplinary, biology, oléagineux, Fungal genetics, Up-Regulation, Batch Cell Culture Techniques, Research Article, science and technology, fungal proteins, Lipoproteins, Saccharomyces cerevisiae, Genes, Fungal, Down-Regulation, Mycology, Microbiology, lipids, 03 medical and health sciences, Model Organisms, transcription factors, Genetics, Protein Interactions, Biology, 030304 developmental biology, 030306 microbiology, Gene Expression Profiling, lcsh:R, Proteins, Lipid metabolism, Metabolism, biology.organism_classification, Lipid Metabolism, Yeast, Regulatory Proteins, Kinetics, Fermentation, lcsh:Q, Protein Translation, Energy Metabolism, Transcriptome
Abstract: We previously developed a fermentation protocol for lipid accumulation in the oleaginous yeast Y. lipolytica. This process was used to perform transcriptomic time-course analyses to explore gene expression in Y. lipolytica during the transition from biomass production to lipid accumulation. In this experiment, a biomass concentration of 54.6 g(CDW)/l, with 0.18 g/g(CDW) lipid was obtained in ca. 32 h, with low citric acid production. A transcriptomic profiling was performed on 11 samples throughout the fermentation. Through statistical analyses, 569 genes were highlighted as differentially expressed at one point during the time course of the experiment. These genes were classified into 9 clusters, according to their expression profiles. The combination of macroscopic and transcriptomic profiles highlighted 4 major steps in the culture: (i) a growth phase, (ii) a transition phase, (iii) an early lipid accumulation phase, characterized by an increase in nitrogen metabolism, together with strong repression of protein production and activity; (iv) a late lipid accumulation phase, characterized by the rerouting of carbon fluxes within cells. This study explores the potential of Y. lipolytica as an alternative oil producer, by identifying, at the transcriptomic level, the genes potentially involved in the metabolism of oleaginous species.
Published: 2011

38. Comparative transcriptomics of drought responses in Populus: a meta-analysis of genome-wide expression profiling in mature leaves and root apices across two genotypes

Author: Nathalie Ningre, Sandrine Balzergue, David Cohen, Jean-Pierre Renou, Marie-Laure Martin-Magniette, Emilie Tisserant, Didier Le Thiec, Irène Hummel, Marie-Béatrice Bogeat-Triboulot, Jean-Philippe Tamby, Gaëlle Lelandais, Ecologie et Ecophysiologie Forestières [devient SILVA en 2018] (EEF), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Interactions Arbres-Microorganismes (IAM), Unité de recherche en génomique végétale (URGV), Institut National de la Recherche Agronomique (INRA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Mathématiques et Informatique Appliquées (MIA-Paris), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Dynamique des Structures et Interactions des Macromolécules Biologiques - Pôle de La Réunion (DSIMB Réunion), Biologie Intégrée du Globule Rouge (BIGR (UMR_S_1134 / U1134)), Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA)-Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA), This work was funded and DC was supported by the project ANR GENOPLANTE 'POPSEC' (GPLA06028G). Part of this work was also funded by a Region Lorraine grant (# 12000160)., ANR-06-GPLA-0007,POPSEC,POPSEC : Molecular bases of acclimation and adaptation to water deficit in poplar(2006), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, BMC, Ed., Réseau de Génomique végétale - GENOPLANTE 2010 - POPSEC : Molecular bases of acclimation and adaptation to water deficit in poplar - - POPSEC2006 - ANR-06-GPLA-0007 - GPLA - VALID, Université de Lorraine (UL)-Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE)-Institut National de la Recherche Agronomique (INRA), Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pointe-à-Pitre/Abymes [Guadeloupe] -Université des Antilles (UA)-Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pointe-à-Pitre/Abymes [Guadeloupe] -Université des Antilles (UA), ANR-06-GPLA-0007,GPLA06028G,POPSEC : Molecular bases of acclimation and adaptation to water deficit in poplar(2006), and Bogeat-Triboulot, Marie-Béatrice
Subjects: MESH: Genome, Plant, 0106 biological sciences, Transcription, Genetic, genotype, Plant genetics, MESH: Genetic Markers, 01 natural sciences, Genome, genomic, MESH: Genotype, Transcriptome, Gene Expression Regulation, Plant, MESH: Genes, Plant, Cluster Analysis, Gene Regulatory Networks, MESH: Ecosystem, MESH: Organ Specificity, MESH: Meristem, MESH: Gene Regulatory Networks, 2. Zero hunger, Genetics, 0303 health sciences, food and beverages, Droughts, MESH: Plant Leaves, Populus, Organ Specificity, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Genome, Plant, Research Article, Biotechnology, Genetic Markers, lcsh:QH426-470, lcsh:Biotechnology, Meristem, MESH: Droughts, Drought tolerance, Genomics, MESH: Molecular Sequence Annotation, Biology, Genes, Plant, MESH: Gene Expression Profiling, 03 medical and health sciences, Meta-Analysis as Topic, comparative transcriptomics, lcsh:TP248.13-248.65, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Botany, MESH: Meta-Analysis as Topic, MESH: Gene Expression Regulation, Plant, Ecosystem, 030304 developmental biology, Comparative genomics, Gene Expression Profiling, MESH: Transcription, Genetic, fungi, Molecular Sequence Annotation, MESH: Cluster Analysis, Plant Leaves, meta-analysis, Gene expression profiling, lcsh:Genetics, MESH: Populus, 010606 plant biology & botany
Abstract: BackgroundComparative genomics has emerged as a promising means of unravelling the molecular networks underlying complex traits such as drought tolerance. Here we assess the genotype-dependent component of the drought-induced transcriptome response in two poplar genotypes differing in drought tolerance. Drought-induced responses were analysed in leaves and root apices and were compared with available transcriptome data from otherPopulusspecies.ResultsUsing a multi-species designed microarray, a genomic DNA-based selection of probesets provided an unambiguous between-genotype comparison. Analyses of functional group enrichment enabled the extraction of processes physiologically relevant to drought response. The drought-driven changes in gene expression occurring in root apices were consistent across treatments and genotypes. For mature leaves, the transcriptome response varied weakly but in accordance with the duration of water deficit. A differential clustering algorithm revealed similar and divergent gene co-expression patterns among the two genotypes. Since moderate stress levels induced similar physiological responses in both genotypes, the genotype-dependent transcriptional responses could be considered as intrinsic divergences in genome functioning. Our meta-analysis detected several candidate genes and processes that are differentially regulated in root and leaf, potentially under developmental control, and preferentially involved in early and long-term responses to drought.ConclusionsIn poplar, the well-known drought-induced activation of sensing and signalling cascades was specific to the early response in leaves but was found to be general in root apices. Comparing our results to what is known in arabidopsis, we found that transcriptional remodelling included signalling and a response to energy deficit in roots in parallel with transcriptional indices of hampered assimilation in leaves, particularly in the drought-sensitive poplar genotype.
Published: 2010

39. The evolution of gene expression regulatory networks in yeasts

Author: Frédéric Devaux, Christel Goudot, Gaëlle Lelandais, Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Immunité et cancer (U932), Université Paris Descartes - Paris 5 (UPD5)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université Paris Descartes - Paris 5 (UPD5)-Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut Jacques Monod ( IJM ), Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Biologie Intégrative de la Cellule ( I2BC ), Université Paris-Sud - Paris 11 ( UP11 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ), Immunité et cancer ( U932 ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Institut Curie, Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology ( LCQB ), and Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS )
Subjects: [SDV]Life Sciences [q-bio], Computational biology, Biology, Genome, General Biochemistry, Genetics and Molecular Biology, Evolution, Molecular, 03 medical and health sciences, 0302 clinical medicine, Gene Expression Regulation, Fungal, Yeasts, Gene expression, Gene Regulatory Networks, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Whole genome sequencing, Regulation of gene expression, Comparative genomics, Genetics, 0303 health sciences, General Immunology and Microbiology, [ SDV ] Life Sciences [q-bio], Gene Expression Profiling, General Medicine, Phenotype, Gene expression profiling, Genome, Fungal, General Agricultural and Biological Sciences, Functional genomics, 030217 neurology & neurosurgery
Abstract: Gene regulation is a major source of phenotypic diversity between and within species. This aspect of evolution has long been addressed from the sole point of view of the genome sequence. The incredible development of transcriptomics approaches now allows one to actually study the topology and the properties of regulatory networks on an evolutionary perspective. This new discipline is called comparative functional genomics or comparative transcriptomics. This article reviews some of the main advances made in this field, using yeast species, and especially the species sequenced in the frame of the Genolevures program, as a model.
Published: 2010

40. Posttranscriptional control of mitochondrial biogenesis: spatio-temporal regulation of the protein import process

Author: Gaëlle Lelandais, Frédéric Devaux, Claude Jacq, Mathilde Garcia, and Sebastien Goussard
Subjects: Biophysics, Respiratory chain, RNA-binding protein, Biology, Mitochondrion, Biochemistry, Models, Biological, Mitochondrial Proteins, Structural Biology, TOR pathway, Genetics, Protein biosynthesis, Animals, Humans, RNA regulon, Post-genomics, RNA, Messenger, RNA Processing, Post-Transcriptional, Molecular Biology, Messenger RNA, Binding protein, RNA-Binding Proteins, Cell Biology, Transport protein, Cell biology, Mitochondria, Protein Transport, Mitochondrial biogenesis, Protein Biosynthesis
Abstract: This review focuses on the posttranscriptional processes which govern mitochondrial biogenesis, with a special emphasis on the asymmetric localization–translation of nuclear-encoded mRNAs as an important regulatory step of the protein import process. We review how spatio-temporal mRNA regulons help to elicit timely, versatile, and coordinated intracellular processes to assemble mitochondrial structures. Our current knowledge on the mitochondrial import of respiratory chain assembly factors and the role of the ribonucleic acid (RNA) binding protein Puf3 are presented. A connection with the target of rapamycine signalling pathway may explain how respiratory chain assembly senses environmental conditions via the protein import machinery.
Published: 2010

41. Statistical inference of the time-varying structure of gene-regulation networks

Author: Jennifer Becq, Gaëlle Lelandais, Frédéric Devaux, Sophie Lèbre, Michael P. H. Stumpf, Center for Bioinformatics, Imperial College London, Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection (LSIIT), Centre National de la Recherche Scientifique (CNRS), Dynamique des Structures et Interactions des Macromolécules Biologiques - Pôle de La Réunion (DSIMB Réunion), Biologie Intégrée du Globule Rouge (BIGR (UMR_S_1134 / U1134)), Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA)-Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA), Génomique des microorganismes, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Institute for Mathematical Sciences [London] (IMS), SL and MPHS gratefully acknowledge support from the BBSRC. JB and GL gratefully acknowledge support from the Institut National de la Transfusion Sanguine. MPHS is a Royal Society Wolfson Research Merit Award holder., Génomique des Microorganismes (LGM), and BMC, Ed.
Subjects: Gene regulatory network, computer.software_genre, 01 natural sciences, MESH: Regression Analysis, 010104 statistics & probability, Structural Biology, Statistical inference, Gene Regulatory Networks, MESH: Animals, MESH: Models, Genetic, lcsh:QH301-705.5, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], MESH: Gene Regulatory Networks, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Methodology Article, Applied Mathematics, MESH: Transcription Factors, MESH: Saccharomyces cerevisiae, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Markov Chains, Computer Science Applications, MESH: Reproducibility of Results, Drosophila melanogaster, Modeling and Simulation, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Regression Analysis, Graph (abstract data type), Benomyl, Monte Carlo Method, Algorithms, Systems biology, MESH: Algorithms, Saccharomyces cerevisiae, Biology, MESH: Monte Carlo Method, Machine learning, MESH: Drosophila melanogaster, 03 medical and health sciences, MESH: Markov Chains, Modelling and Simulation, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Animals, 0101 mathematics, Molecular Biology, 030304 developmental biology, Models, Genetic, Markov chain, business.industry, Reproducibility of Results, Statistical model, Biological process, lcsh:Biology (General), MESH: Benomyl, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, Biological network, Transcription Factors
Abstract: Background Biological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems and their underlying molecular interactions. Methods To overcome these limitations, we propose here a new statistical modelling framework, the ARTIVA formalism (Auto Regressive TIme VArying models), and an associated inferential procedure that allows us to learn temporally varying gene-regulation networks from biological time-course expression data. ARTIVA simultaneously infers the topology of a regulatory network and how it changes over time. It allows us to recover the chronology of regulatory associations for individual genes involved in a specific biological process (development, stress response, etc.). Results We demonstrate that the ARTIVA approach generates detailed insights into the function and dynamics of complex biological systems and exploits efficiently time-course data in systems biology. In particular, two biological scenarios are analyzed: the developmental stages of Drosophila melanogaster and the response of Saccharomyces cerevisiae to benomyl poisoning. Conclusions ARTIVA does recover essential temporal dependencies in biological systems from transcriptional data, and provide a natural starting point to learn and investigate their dynamics in greater detail.
Published: 2010

42. Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments

Author: Gaëlle Lelandais, Magalie Celton, Alexandre G. de Brevern, Alain Malpertuy, Sciences Pour l'Oenologie (SPO), Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Bioinformatique génomique et moléculaire ((U 726)), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM), Atragene Informatics, Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB), Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM), This work was supported by grants from the Ministère de la Recherche, from French Institute for Health and Medical Research (INSERM), Université Paris Diderot - Paris 7, Institut National de Transfusion Sanguine (INTS) and Genopole®., Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Diderot - Paris 7 (UPD7), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université Montpellier 1 (UM1)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA), Université Montpellier 1 (UM1)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and BMC, Ed.
Subjects: lcsh:QH426-470, lcsh:Biotechnology, MESH: Algorithms, Biotechnologies, Saccharomyces cerevisiae, Biology, computer.software_genre, 03 medical and health sciences, MESH: Gene Expression Profiling, 0302 clinical medicine, lcsh:TP248.13-248.65, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Gene cluster, Genetics, Cluster Analysis, Humans, Imputation (statistics), Cluster analysis, 030304 developmental biology, Oligonucleotide Array Sequence Analysis, 0303 health sciences, MESH: Humans, Gene Expression Profiling, Computational Biology, cluster de gènes, Maximization, Missing data, MESH: Cluster Analysis, MESH: Saccharomyces cerevisiae, Hierarchical clustering, Gene expression profiling, lcsh:Genetics, biopuce, 030220 oncology & carcinogenesis, MESH: Oligonucleotide Array Sequence Analysis, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Data mining, DNA microarray, computer, Algorithms, Biotechnology, MESH: Computational Biology, Research Article
Abstract: BMC Genomics ISI Document Delivery No.: 565GZ Times Cited: 11 Cited Reference Count: 84 Celton, Magalie Malpertuy, Alain Lelandais, Gaelle de Brevern, Alexandre G. Ministere de la Recherche; French Institute for Health and Medical Research (INSERM); Universite - Paris Diderot Paris 7; Institut National de Transfusion Sanguine (INTS) and Genopole(R); Clustering Agreement Ration (CAR) We would like to thanks all the scientists who have deposited their experiments and make them freely available to the scientific community. In the same way, we would like to thanks all the scientists who have developed and distributed missing value replacement methods. This work was supported by grants from the Ministere de la Recherche, from French Institute for Health and Medical Research (INSERM), Universite - Paris Diderot Paris 7, Institut National de Transfusion Sanguine (INTS) and Genopole (R). Clustering Agreement Ration (CAR) was proposed by late Pr. Serge Hazout. Biomed central ltd London; International audience; BACKGROUND: Microarray technologies produced large amount of data. In a previous study, we have shown the interest of k-Nearest Neighbour approach for restoring the missing gene expression values, and its positive impact of the gene clustering by hierarchical algorithm. Since, numerous replacement methods have been proposed to impute missing values (MVs) for microarray data. In this study, we have evaluated twelve different usable methods, and their influence on the quality of gene clustering. Interestingly we have used several datasets, both kinetic and non kinetic experiments from yeast and human. RESULTS: We underline the excellent efficiency of approaches proposed and implemented by Bo and co-workers and especially one based on expected maximization (EM_array). These improvements have been observed also on the imputation of extreme values, the most difficult predictable values. We showed that the imputed MVs have still important effects on the stability of the gene clusters. The improvement on the clustering obtained by hierarchical clustering remains limited and, not sufficient to restore completely the correct gene associations. However, a common tendency can be found between the quality of the imputation method and the gene cluster stability. Even if the comparison between clustering algorithms is a complex task, we observed that k-means approach is more efficient to conserve gene associations. CONCLUSIONS: More than 6.000.000 independent simulations have assessed the quality of 12 imputation methods on five very different biological datasets. Important improvements have so been done since our last study. The EM_array approach constitutes one efficient method for restoring the missing expression gene values, with a lower estimation error level. Nonetheless, the presence of MVs even at a low rate is a major factor of gene cluster instability. Our study highlights the need for a systematic assessment of imputation methods and so of dedicated benchmarks. A noticeable point is the specific influence of some biological dataset.
Published: 2009

43. Modeling a Regulatory Network Using Temporal Gene Expression Data: Why and How?

Author: Gaëlle Lelandais and Sophie Lèbre
Subjects: Computer science, Gene expression, Data mining, computer.software_genre, computer, Hierarchical clustering
Published: 2009

44. Clustering gene expression data using graph separators

Author: Bangaly, Kaba, Nicolas, Pinet, Gaëlle, Lelandais, Alain, Sigayret, and Anne, Berry
Subjects: Computer Graphics, Cluster Analysis, Computational Biology, Gene Expression, Algorithms, Oligonucleotide Array Sequence Analysis
Abstract: Recent work has used graphs to modelize expression data from microarray experiments, in view of partitioning the genes into clusters. In this paper, we introduce the use of a decomposition by clique separators. Our aim is to improve the classical clustering methods in two ways: first we want to allow an overlap between clusters, as this seems biologically sound, and second we want to be guided by the structure of the graph to define the number of clusters. We test this approach with a well-known yeast database (Saccharomyces cerevisiae). Our results are good, as the expression profiles of the clusters we find are very coherent. Moreover, we are able to organize into another graph the clusters we find, and order them in a fashion which turns out to respect the chronological order defined by the the sporulation process.
Published: 2008

45. Responses of pathogenic and nonpathogenic yeast species to steroids reveal the functioning and evolution of multidrug resistance transcriptional networks

Author: Dibyendu Banerjee, Sudhanshu Shukla, Rajendra Prasad, Gaëlle Lelandais, Claude Jacq, Gauranga Mukhopadhyay, and Frédéric Devaux
Subjects: Saccharomyces cerevisiae Proteins, Saccharomyces cerevisiae, Microbiology, Fungal Proteins, 03 medical and health sciences, Drug Resistance, Multiple, Fungal, Ergosterol, Gene Expression Regulation, Fungal, Candida albicans, Gene Regulatory Networks, Molecular Biology, Gene, Progesterone, 030304 developmental biology, Oligonucleotide Array Sequence Analysis, Regulation of gene expression, Genetics, 0303 health sciences, Fungal protein, biology, 030306 microbiology, Reverse Transcriptase Polymerase Chain Reaction, Gene Expression Profiling, Fungal genetics, General Medicine, Articles, biology.organism_classification, Blotting, Northern, Biological Evolution, Corpus albicans, 3. Good health, Gene expression profiling, DNA-Binding Proteins, Trans-Activators, Steroids, Progestins, Transcription Factors
Abstract: Steroids are known to induce pleiotropic drug resistance states in hemiascomycetes, with tremendous potential consequences for human fungal infections. Our analysis of gene expression in Saccharomyces cerevisiae and Candida albicans cells subjected to three different concentrations of progesterone revealed that their pleiotropic drug resistance (PDR) networks were strikingly sensitive to steroids. In S. cerevisiae , 20 of the Pdr1p/Pdr3p target genes, including PDR3 itself, were rapidly induced by progesterone, which mimics the effects of PDR1 gain-of-function alleles. This unique property allowed us to decipher the respective roles of Pdr1p and Pdr3p in PDR induction and to define functional modules among their target genes. Although the expression profiles of the major PDR transporters encoding genes Sc PDR5 and Ca CDR1 were similar, the S. cerevisiae global PDR response to progesterone was only partly conserved in C. albicans . In particular, the role of Tac1p, the main C. albicans PDR regulator, in the progesterone response was apparently restricted to five genes. These results suggest that the C. albicans and S. cerevisiae PDR networks, although sharing a conserved core regarding the regulation of membrane properties, have different structures and properties. Additionally, our data indicate that other as yet undiscovered regulators may second Tac1p in the C. albicans drug response.
Published: 2007

46. The central role of PDR1 in the foundation of yeast drug resistance

Author: Héle`ne Salin, Gaëlle Lelandais, Andrew J. Oldfield, Frédéric Devaux, Véronique Tanty, Vivienne Fardeau, Sophie Lemoine, Claude Jacq, Stéphane Le Crom, Mathilde Garcia, Régulation de l'expression génétique (REG), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'Ecole Normale Supérieure (IBENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Plate-forme Transcriptome, Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (IBENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Duponchelle, Martine
Subjects: Saccharomyces cerevisiae Proteins, endocrine system diseases, Transcription, Genetic, Regulator, Drug resistance, Saccharomyces cerevisiae, Biology, [SDV.BBM.BM] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, Biochemistry, 03 medical and health sciences, chemistry.chemical_compound, Drug Resistance, Fungal, Gene Expression Regulation, Fungal, Fluphenazine, Molecular Biology, Gene, Transcription factor, 030304 developmental biology, Genetics, 0303 health sciences, 030306 microbiology, [SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, Cell Biology, Phenotype, eye diseases, Yeast, 3. Good health, Fungicides, Industrial, DNA-Binding Proteins, chemistry, Trans-Activators, sense organs, Efflux, Benomyl, DNA, Antipsychotic Agents, Transcription Factors
Abstract: The widespread pleiotropic drug resistance (PDR) phenomenon is well described as the long term selection of genetic variants expressing constitutively high levels of membrane transporters involved in drug efflux. However, the transcriptional cascades leading to the PDR phenotype in wild-type cells are largely unknown, and the first steps of this phenomenon are poorly understood. We investigated the transcriptional mechanisms underlying the establishment of an efficient PDR response in budding yeast. We show that within a few minutes of drug sensing yeast elicits an effective PDR response, involving tens of PDR genes. This early PDR response (ePDR) is highly dependent on the Pdr1p transcription factor, which is also one of the major genetic determinants of long term PDR acquisition. The activity of Pdr1p in early drug response is not drug-specific, as two chemically unrelated drugs, benomyl and fluphenazine, elicit identical, Pdr1p-dependent, ePDR patterns. Our data also demonstrate that Pdr1p is an original stress response factor, the DNA binding properties of which do not depend on the presence of drugs. Thus, Pdr1p is a promoter-resident regulator involved in both basal expression and rapid drug-dependent induction of PDR genes.
Published: 2007

47. Early Expression of Yeast Genes Affected by Chemical Stress†

Author: Frédéric Devaux, Z. Kozovska, Gaëlle Lelandais, Ancuta Lucau-Danila, Claude Jacq, Thierry Delaveau, and Véronique Tanty
Subjects: Saccharomyces cerevisiae Proteins, Saccharomyces cerevisiae, Gene Expression, Down-Regulation, Computational biology, Biology, Gene Expression Regulation, Fungal, Gene expression, Molecular Biology, Gene, Transcription factor, Oligonucleotide Array Sequence Analysis, Sequence Deletion, Genetics, YAP1, Gene Expression Profiling, Promoter, Cell Biology, biology.organism_classification, Yeast, Stimulation, Chemical, Up-Regulation, Gene expression profiling, DNA-Binding Proteins, Trans-Activators, Benomyl, Genome, Fungal, Transcription Factors
Abstract: The variety of environmental stresses is probably the major challenge imposed on transcription activators and the transcriptional machinery. To precisely describe the very early genomic response developed by yeast to accommodate a chemical stress, we performed time course analyses of the modifications of the yeast gene expression program which immediately follows the addition of the antimitotic drug benomyl. Similar analyses were conducted with different isogenic yeast strains in which genes coding for relevant transcription factors were deleted and coupled with efficient bioinformatics tools. Yap1 and Pdr1, two well-known key mediators of stress tolerance, appeared to be responsible for the very rapid establishment of a transient transcriptional response encompassing 119 genes. Yap1, which plays a predominant role in this response, binds, in vivo, promoters of genes which are not automatically up-regulated. We proposed that Yap1 nuclear localization and DNA binding are necessary but not sufficient to elicit the specificity of the chemical stress response. Cellular organisms develop a myriad of strategies to maintain specific internal conditions constantly challenged by the varying drug environment. The complexity of the yeast cell system for detecting and responding to environmental variations is only beginning to come to light. It has been reported previously (13) that a large set of yeast genes (about 900) showed a similar drastic response to a large variety of environmental changes including temperature shocks, hydrogen peroxide, menadione, diamide, dithiothreitol, hyper- or hypoosmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase. Since these pioneering studies were reported, many observations of the global effects of a large variety of drugs on gene expression have been made. In most of these studies, a binary comparison (i.e., control versus stress-exposed cells) was carried out, whereas in some cases, time course experiments over rather long periods (several hours) were conducted. Although much valuable information has been collected in these studies, the heterogeneity in the protocols followed precludes a simple comparison between the different drug responses. In particular, it is extremely difficult to identify the different regulatory networks and to establish their chronological relationships. Time series experiments soon appeared and were much more informative than simple binary experiments. Such approaches were a particularly valuable source of information in the case of cell cycle analyses (24, 27); however, they were less suitable to describe the chronology of transcriptional events in the case of environmental stress responses of the cell. This is probably due to the natural complexity of the genomic response which has to cope
Published: 2005

48. Competitive promoter occupancy by two yeast paralogous transcription factors controlling the multidrug resistance phenomenon

Author: Ancuta Lucau-Danila, Thierry Delaveau, Gaëlle Lelandais, Claude Jacq, and Frédéric Devaux
Subjects: Saccharomyces cerevisiae Proteins, Time Factors, Transcription, Genetic, Response element, Drug Resistance, E-box, Transcription coregulator, Saccharomyces cerevisiae, Biology, Biochemistry, Models, Biological, Promoter Regions, Genetic, Molecular Biology, Alleles, Oligonucleotide Array Sequence Analysis, Genetics, Zinc finger transcription factor, Sp1 transcription factor, General transcription factor, Pioneer factor, Cell Membrane, Promoter, Zinc Fingers, Cell Biology, DNA, Blotting, Northern, Precipitin Tests, Chromatin, Up-Regulation, Mutation, Genome, Fungal, Plasmids, Transcription Factors
Abstract: Highly flexible gene expression programs are required to allow cell growth in the presence of a wide variety of chemicals. We used genome-wide expression analyses coupled with chromatin immunoprecipitation experiments to study the regulatory relationships between two very similar yeast transcription factors involved in the control of the multidrug resistance phenomenon. Yrm1 (Yor172w) is a new zinc finger transcription factor, the overproduction of which decreases the level of transcription of the target genes of Yrr1, a zinc finger transcription factor controlling the expression of several membrane transporter-encoding genes. Surprisingly, the absence of YRR1 releases the transcriptional activity of Yrm1, which then up-regulates 23 genes, 14 of which are also direct target genes of Yrr1. Chromatin immunoprecipitation experiments confirmed that Yrm1 binds to the promoters of the up-regulated genes only in yeast strains from which YRR1 has been deleted. This sophisticated regulatory program can be associated with drug resistance phenotypes of the cell. The program-specific distribution of paired transcription factors throughout the genome may be a general mechanism by which similar transcription factors regulate overlapping gene expression programs in response to chemical stress.
Published: 2003

49. The Reconstruction of Condition-Specific Transcriptional Modules Provides New Insights in the Evolution of Yeast AP-1 Proteins

Author: Frédéric Devaux, Catherine Etchebest, Christel Goudot, Gaëlle Lelandais, Institut Curie, Biologie Intégrée du Globule Rouge ( BIGR ), Institut National de la Transfusion Sanguine [Paris] ( INTS ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Université de la Réunion ( UR ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -CHU Pointe-à-Pitre/Abymes [Guadeloupe] -Université des Antilles ( UA ), Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology ( LCQB ), Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ), Institut Jacques Monod ( IJM ), Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Biologie Intégrative de la Cellule ( I2BC ), Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Sud - Paris 11 ( UP11 ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS ), Université Paris-Sud - Paris 11 ( UP11 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ), Institut Curie [Paris], Biologie Intégrée du Globule Rouge (BIGR (UMR_S_1134 / U1134)), Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA), Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Biologie intégrée du globule rouge (BIGR), Université des Antilles et de la Guyane (UAG)-Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay, Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Université des Antilles (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Models, Molecular, Transcription, Genetic, Base pair, Molecular Sequence Data, Saccharomyces cerevisiae, lcsh:Medicine, Candida glabrata, Yeast and Fungal Models, Response Elements, DNA-binding protein, Evolution, Molecular, Fungal Proteins, 03 medical and health sciences, Model Organisms, Protein structure, Species Specificity, Sequence Homology, Nucleic Acid, Candida albicans, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Amino Acid Sequence, lcsh:Science, Biology, [ SDV.BBM ] Life Sciences [q-bio]/Biochemistry, Molecular Biology, Gene, 030304 developmental biology, Regulatory Networks, Genetics, 0303 health sciences, Fungal protein, Multidisciplinary, Base Sequence, biology, lcsh:R, 030302 biochemistry & molecular biology, Computational Biology, Genomics, DNA-binding domain, Comparative Genomics, biology.organism_classification, Protein Structure, Tertiary, Functional Genomics, Transcription Factor AP-1, Schizosaccharomyces pombe, lcsh:Q, Benomyl, Schizosaccharomyces pombe Proteins, Genome Expression Analysis, Research Article
Abstract: International audience; AP-1 proteins are transcription factors (TFs) that belong to the basic leucine zipper family, one of the largest families of TFs in eukaryotic cells. Despite high homology between their DNA binding domains, these proteins are able to recognize diverse DNA motifs. In yeasts, these motifs are referred as YRE (Yap Response Element) and are either seven (YRE-Overlap) or eight (YRE-Adjacent) base pair long. It has been proposed that the AP-1 DNA binding motif preference relies on a single change in the amino acid sequence of the yeast AP-1 TFs (an arginine in the YRE-O binding factors being replaced by a lysine in the YRE-A binding Yaps). We developed a computational approach to infer condition-specific transcriptional modules associated to the orthologous AP-1 protein Yap1p, Cgap1p and Cap1p, in three yeast species: the model yeast Saccharomyces cerevisiae and two pathogenic species Candida glabrata and Candida albicans. Exploitation of these modules in terms of predictions of the protein/DNA regulatory interactions changed our vision of AP-1 protein evolution. Cis-regulatory motif analyses revealed the presence of a conserved adenine in 59 position of the canonical YRE sites. While Yap1p, Cgap1p and Cap1p shared a remarkably low number of target genes, an impressive conservation was observed in the YRE sequences identified by Yap1p and Cap1p. In Candida glabrata, we found that Cgap1p, unlike Yap1p and Cap1p, recognizes YRE-O and YRE-A motifs. These findings were supported by structural data available for the transcription factor Pap1p (Schizosaccharomyces pombe). Thus, whereas arginine and lysine substitutions in Cgap1p and Yap1p proteins were reported as responsible for a specific YRE-O or YRE-A preference, our analyses rather suggest that the ancestral yeast AP-1 protein could recognize both YRE-O and YRE-A motifs and that the arginine/lysine exchange is not the only determinant of the specialization of modern Yaps for one motif or another.
Published: 2011

50. [Untitled]

Author: Claude Jacq, Pierre Vincens, Gaëlle Lelandais, Philippe Marc, and Stéphane Vialette
Subjects: Genetics, 0303 health sciences, education.field_of_study, Microarray analysis techniques, Relational database, Applied Mathematics, Population, Computational biology, Biology, Biochemistry, Genome, Computer Science Applications, Gene expression profiling, Transcriptome, 03 medical and health sciences, ComputingMethodologies_PATTERNRECOGNITION, 0302 clinical medicine, Structural Biology, Microarray databases, DNA microarray, education, Molecular Biology, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract: Background: Information obtained by DNA microarray technology gives a rough snapshot of the transcriptome state, i.e., the expression level of all the genes expressed in a cell population at any given time. One of the challenging questions raised by the tremendous amount of microarray data is to identify groups of co-regulated genes and to understand their role in cell functions. Results: MiCoViTo (Microarray Comparison Visualization Tool) is a set of biologists' tools for exploring, comparing and visualizing changes in the yeast transcriptome by a gene-centric approach. A relational database includes data linked to genome expression and graphical output makes it easy to visualize clusters of co-expressed genes in the context of available biological information. To this aim, upload of personal data is possible and microarray data from fifty publications dedicated to S. cerevisiae are provided on-line. A web interface guides the biologist during the usage of this tool and is freely accessible at http://www.transcriptome.ens.fr/micovito/. Conclusions: MiCoViTo offers an easy-to-read picture of local transcriptional changes connected to current biological knowledge. This should help biologists to mine yeast microarray data and better understand the underlying biology. We plan to add functional annotations from other organisms. That would allow inter-species comparison of transcriptomes via orthology tables.
Published: 2004

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Publication Type

Journal

Database

Publisher

64 results on '"Gaëlle Lelandais"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources