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.

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.

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.

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.

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.

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.

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.

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

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.

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

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.

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.

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

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.

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/.

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.

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

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 ß-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.

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.

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.

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.

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

Numerous genes are overexpressed in the radioresistant bacterium Deinococcus radiodurans after exposure to radiation or prolonged desiccation. It was shown that the DdrO and IrrE proteins play a major role in regulating the expression of approximately twenty 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. At present, many questions remain, such as the number of genes regulated by DdrO. Here, we present the first ChIP-seq analysis performed at the genome level in Deinococcus species coupled with RNA-seq, which was achieved in the presence or not of DdrO. We also resequenced our laboratory stock strain of D. radiodurans R1 ATCC 13939 to obtain an accurate reference for read alignments and gene expression quantifications. We highlighted genes that are directly under the control of this transcriptional repressor and showed that the DdrO regulon in D. radiodurans includes numerous other genes than those previously described, including DNA and RNA metabolism proteins. These results thus pave the way to better understand the radioresistance pathways encoded by this bacterium and to compare the stress-induced responses mediated by this pair of proteins in diverse bacteria.

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.