BackgroundBiological 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.MethodsTo 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.).ResultsWe 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.ConclusionsARTIVA 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.

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 other Populus species.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.

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 environ-

Background: Stress responses provide valuable models for deciphering the transcriptional networks controlling the adaptation of the cell to its environment. We analyzed the transcriptome response of yeast to toxic concentrations of selenite. We used gene network mapping tools to identify functional pathways and transcription factors involved in this response. We then used chromatin immunoprecipitation and knock-out experiments to investigate the role of some of these regulators and the regulatory connections between them.

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 gCDW/l, with 0.18 g/gCDW 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 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.All living organisms have developed complex transcriptional responses for rapidly adapting genome expression to the presence of toxic compounds in the environment. These responses involve various types of cellular pathway. Genome-wide studies of drug responses in microorganisms have revealed that these responses comprise both specific effects depending on the precise chemical nature and cellular targets of the toxic compound and a general stress response (environmental stress response (ESR) 5 in the yeast Saccharomyces cerevisiae), reflecting cell adaptation to growth defects and cellular damages, regardless of the type of stress encountered by the cell (1). Inbetween these very specific and very general responses, prokaryotic and eukaryotic cells have evolved multidrug resistance (MDR) pathways, which confer resistance to a broad spectrum of unrelated chemicals, but which are restricted to the stress responses associated with organic drugs. From bacteria to humans, MDR is essentially based on the overexpression of membrane transporters able to export a large number of chemically different compounds (2-4). MDR is a major concern for human health, as it leads to antibiotic resistance in pathogens and enables cancer cells to survive chemotherapy.In the model yeast S. cerevisiae, MDR is referred to as PDR (pleiotropic drug response). The PDR network currently comprise 10 transcription factors regulating about 70 different target genes reviewed in Ref. 18). In this network, the Pdr1p transcription factor has the largest set of potential targets (about 50). Pdr1p and its functional homologue, Pdr3p, were identified in the early 1990s as regulators of the basal level of drug resistance in yeast cells (19,20). Gain-or loss-of-function alleles of PDR1 and PDR3 confer resistance or sensitivit...

SummaryFlavohemoglobins 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. Coimmunoprecipitation 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.

BackgroundMicroarray 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.ResultsWe 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.ConclusionsMore 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.