Inter-generational nuclear crosstalk links the control of gene expression to programmed genome rearrangement during the Paramecium sexual cycle

Multinucleate cells are found in many eukaryotes, but how multiple nuclei coordinate their functions is still poorly understood. In the cytoplasm of the ciliate Paramecium 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 rearrangement (PGR) removes 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 the PGM gene encoding the essential PiggyMac endonuclease that cleaves the ends of eliminated sequences. Using RNA deep sequencing and transcriptome analysis, we show that impairing PGR upregulates key known 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 with PGM under 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.

3D models of fungal chromosomes to enhance visual integration of omics data

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 underexploited. In this paper, we show how seeing the spatial organization of chromosomes can bring new perspectives to omics data integration. 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 omics 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 results highlighted (i) in Saccharomyces cerevisiae, the backbones of the cohesin anchor regions, which were aligned all along the chromosomes, (ii) in Schizosaccharomyces pombe, the oscillations of the coiling of chromosome arms throughout the cell cycle and (iii) in Neurospora crassa, the massive relocalization of histone marks in mutants of heterochromatin regulators. 3D modeling of the chromosomes brings new opportunities for visual integration of omics data. This holistic perspective supports intuition and lays the foundation for building new concepts.

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

The simple light isotope metabolic-labeling technique relies on the in vivo biosynthesis of amino acids from U-[¹²C]-labeled molecules provided as the sole carbon source. The incorporation of the resulting U-[¹²C]-amino acids into proteins presents several key advantages for mass-spectrometry-based proteomics analysis, as it results in more intense monoisotopic ions, with a better signal-to-noise ratio in bottom-up analysis. In our initial studies, we developed the simple light isotope metabolic (SLIM)-labeling strategy using prototrophic eukaryotic microorganisms, the yeasts Candida albicans and Saccharomyces cerevisiae, as well as strains with genetic markers that lead to amino-acid auxotrophy. To extend the range of SLIM-labeling applications, we evaluated (i) the incorporation of U-[¹²C]-glucose into proteins of human cells grown in a complex RPMI-based medium containing the labeled molecule, considering that human cell lines require a large number of essential amino-acids to support their growth, and (ii) an indirect labeling strategy in which the nematode Caenorhabditis elegans grown on plates was fed U-[¹²C]-labeled bacteria (Escherichia coli) and the worm proteome analyzed for ¹²C incorporation into proteins. In both cases, we were able to demonstrate efficient incorporation of ¹²C into the newly synthesized proteins, opening the way for original approaches in quantitative proteomics.

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

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.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13104-022-05940-5.

Omics Analyses: How to Navigate Through a Constant Data Deluge

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.

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

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.

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

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-[12C] as the sole source of carbon synthesize U-[12C]-amino acids, which are incorporated into proteins, giving rise to U-[12C]-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 12C 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 12C abundance in peptides to follow the kinetics of protein labeling and the molar fraction of unlabeled and 12C-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 12C 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.

Functional networks of co-expressed genes to explore iron homeostasis processes in the pathogenic yeast Candida glabrata

Candida glabrata is a cause of life-threatening invasive infections especially in elderly and immunocompromised patients. Part of human digestive and urogenital microbiota, C. glabrata faces varying iron availability, low during infection or high in digestive and urogenital tracts. To maintain its homeostasis, C. glabrata must get enough iron for essential cellular processes and resist toxic iron excess. The response of this pathogen to both depletion and lethal excess of iron at 30°C have been described in the literature using different strains and iron sources. However, adaptation to iron variations at 37°C, the human body temperature and to gentle overload, is poorly known. In this study, we performed transcriptomic experiments at 30°C and 37°C with low and high but sub-lethal ferrous concentrations. We identified iron responsive genes and clarified the potential effect of temperature on iron homeostasis. Our exploration of the datasets was facilitated by the inference of functional networks of co-expressed genes, which can be accessed through a web interface. Relying on stringent selection and independently of existing knowledge, we characterized a list of 214 genes as key elements of C. glabrata iron homeostasis and interesting candidates for medical applications.

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

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.

Pixel: a content management platform for quantitative omics data

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.

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

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.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3802-y) contains supplementary material, which is available to authorized users.

Meet-U: Educating through research immersion

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.

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

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.

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

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 uncharacterized 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 dissociation 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 modeling and directed mutagenesis point to Tma108 as an original M1 metallopeptidase, which uses its putative 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.

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

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

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

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.

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

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.

The metacaspase (Mca1p) has a dual role in farnesol-induced apoptosis in Candida albicans

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.

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

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 (<20 Mb), our program has the advantage of using ChIPseq information at the single nucleotide level and can explore, in a reasonable computational time, results obtained with different sets of parameter values. Graphical outputs and text files are provided to rapidly assess the relevance of the detected peaks. Taking advantage of the simple promoter structure in yeasts, additional functions were implemented in bPeaks to automatically assign the peaks to promoter regions and retrieve peak coordinates on the DNA sequence for further predictions of regulatory motifs, enriched in the list of peaks. Applications of the bPeaks program to three different ChIPseq datasets from Saccharomyces cerevisiae, Candida albicans and Candida glabrata are presented. Each time, bPeaks allowed us to correctly predict the DNA binding sequence of the studied TF and provided relevant lists of peaks. The bioinformatics tool bPeaks is freely distributed to academic users. Supplementary data, together with detailed tutorials, are available online: http://bpeaks.gene-networks.net.

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

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.

The evolution of gene expression regulatory networks in yeasts

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.

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

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.

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

Background

Comparative 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 other Populus species.

Results

Using 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.

Conclusions

In 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.

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

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.

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

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/.

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

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.

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

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.

In bacterial and eukaryotic cells, gene expression is regulated at both the transcriptional and translational levels. In eukaryotes these two processes cannot be directly coupled because the nuclear membrane separates the chromosomes from the ribosomes. Although the transcription levels in different cellular conditions have been widely examined, genome-wide post-transcriptional mechanisms are poorly documented and therefore, the connections between the two processes are difficult to explain. In this work, the time-regulated expression of the genes involved in the construction of the mitochondrion, an important organelle present in nearly all the eukaryotic cells, was scrutinized both at transcriptional and post-transcriptional levels. We observed that temporal transcriptional profiles coincide with groups of genes which are translated at specific cellular loci. The description of these relationships is functionally relevant since the genes which are transcribed early in mitochondria cycles are those which are translated to the vicinity of mitochondria. In addition, these early genes code for essential assembling factors or core elements of the protein complexes whereas the peripheral proteins are translated later in the cytoplasm. Also, these observations support the concerted action of important regulatory factors which control either the gene transcription level (transcription factors) or the mRNA localization (mRNA-binding proteins).

Genome adaptation to chemical stress: clues from comparative transcriptomics in Saccharomyces cerevisiae and Candida glabrata

Comparative transcriptomics of Saccharomyces cerevisiae and Candida glabrata revealed a remarkable conservation of response to drug-induced stress, despite underlying differences in the regulatory networks.

Background

Recent technical and methodological advances have placed microbial models at the forefront of evolutionary and environmental genomics. To better understand the logic of genetic network evolution, we combined comparative transcriptomics, a differential clustering algorithm and promoter analyses in a study of the evolution of transcriptional networks responding to an antifungal agent in two yeast species: the free-living model organism Saccharomyces cerevisiae and the human pathogen Candida glabrata.

Results

We found that although the gene expression patterns characterizing the response to drugs were remarkably conserved between the two species, part of the underlying regulatory networks differed. In particular, the roles of the oxidative stress response transcription factors ScYap1p (in S. cerevisiae) and Cgap1p (in C. glabrata) had diverged. The sets of genes whose benomyl response depends on these factors are significantly different. Also, the DNA motifs targeted by ScYap1p and Cgap1p are differently represented in the promoters of these genes, suggesting that the DNA binding properties of the two proteins are slightly different. Experimental assays of ScYap1p and Cgap1p activities in vivo were in accordance with this last observation.

Conclusions

Based on these results and recently published data, we suggest that the robustness of environmental stress responses among related species contrasts with the rapid evolution of regulatory sequences, and depends on both the coevolution of transcription factor binding properties and the versatility of regulatory associations within transcriptional networks.

Clustering gene expression data using graph separators

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.

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

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.

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

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.

Early expression of yeast genes affected by chemical stress

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.

MiCoViTo: a tool for gene-centric comparison and visualization of yeast transcriptome states

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 .

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.

yMGV: a cross-species expression data mining tool

The yeast Microarray Global Viewer (yMGV @ http://transcriptome.ens.fr/ymgv) was created 3 years ago as a database that houses a collection of Saccharomyces cerevisiae and Schizosaccharo myces pombe microarray data sets published in 82 different articles. yMGV couples data mining tools with a user-friendly web interface so that, with a few mouse clicks, one can identify the conditions that affect the expression of a gene or list of genes regulated in a set of experiments. One of the major new features we present here is a set of tools that allows for inter-organism comparisons. This should enable the fission yeast community to take advantage of the large amount of available information on budding yeast transcriptome. New tools and ongoing developments are also presented here.

Competitive Promoter Occupancy by Two Yeast Paralogous Transcription Factors Controlling the Multidrug Resistance Phenomenon

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.