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Summer mortality of the Pacific oyster Crassostrea gigas is the result of a complex interaction between oysters, their environment, and pathogens. Heredity appears to be a major factor determining the sensitivity of oysters to summer mortality, allowing resistant (R) and susceptible (S) lines to be produced. We conducted genome-wide expression profiling of R and S gonads during the 3-month period preceding a summer mortality event, using a cDNA microarray that we designed. ANOVA analysis revealed that 34 genes were differentially expressed between R and S lines on four dates preceding the mortality event. Annotation of some of these genes highlights reproduction and its allocation and antioxidant defenses as the main pathways that operate differentially between R and S lines. This transcriptional analysis provides new indications to define markers for quantitative trait loci searches and functional studies and evaluate the potential role of each gene in the resistance to summer mortality.

In this paper, the relationship between codon usage and the physiological pattern of expression of a gene is investigated while considering a dataset of 815 nuclear genes of Arabidopsis thaliana. Factorial Correspondence Analysis, a commonly used multivariate statistical approach in codon usage analysis, was used in order to analyse codon usage bias gene by gene. The analysis reveals a single major trend in codon usage among genes in Arabidopsis. At one end of the trend lie genes with a highly G/C biased codon usage. This group contains mainly photosynthetic and housekeeping genes which are known to encode the most abundant proteins of the vegetal cell. At the other extreme lie genes with a weaker A/T-biased codon usage. This group contain genes with various functions which exhibits most of the time a strong tissue-specific pattern of expression in relation, for example, to stress conditions. These observations were confirmed by the detailed analysis of codon usage in the multigene family of tubulins and appear to be general in plant species, even as distant from Arabidopsis thaliana as a monocotyledonous plant such as maize.

International audience; Leukodystrophies (LD) are rare inherited disorders that primarily affect the white matter (WM) of the central nervous system. The large heterogeneity of LD results from the diversity of the genetically determined defects that interfere with glial cells functions. Astrocytes have been identified as the primary target of LD with cystic myelin breakdown including those related to mutations in the ubiquitous translation initiation factor eIF2B. EIF2B is involved in global protein synthesis and its regulation under normal and stress conditions. Little is known about how eIF2B mutations have a major effect on WM. We performed a transcriptomic analysis using fibroblasts of 10 eIF2B-mutated patients with a severe phenotype and 10 age matched patients with other types of LD in comparison to control fibroblasts. ANOVA was used to identify genes that were statistically significantly differentially expressed at basal state and after ER-stress. The pattern of differentially expressed genes between basal state and ER-stress did not differ significantly among each of the three conditions. However, 70 genes were specifically differentially expressed in eIF2B-mutated fibroblasts whatever the stress conditions tested compared to controls, 96% being under-expressed. Most of these genes were involved in mRNA regulation and mitochondrial metabolism. The 13 most representative genes, including genes belonging to the Heterogeneous Nuclear Ribonucleoprotein (HNRNP) family, described as regulators of splicing events and stability of mRNA, were dysregulated during the development of eIF2B-mutated brains. HNRNPH1, F and C mRNA were over-expressed in foetus but under-expressed in children and adult brains. The abnormal regulation of HNRNP expression in the brain of eIF2B-mutated patients was concomitant with splicing dysregulation of the main genes involved in glial maturation such as PLP1 for oligodendrocytes and GFAP in astrocytes. These findings demonstrate a developmental deregulation of splicing events in glial cells that is related to abnormal production of HNRNP, in eIF2B-mutated brains.

Background The processes of gene transcription, translation, as well as the reactions taking place between gene products, are subject to stochastic fluctuations. These stochastic events are being increasingly examined as it emerges that they can be crucial in the cell's survival. In a previous study we had examined the transcription patterns of two bacterial species (Escherichia coli and Bacillus subtilis) to elucidate the nucleoid's organization. The basic idea is that genes that share transcription patterns, must share some sort of spatial relationship, even if they are not close to each other on the chromosome. We had found that picking any gene at random, its transcription will be correlated with genes at well-defined short – as well as long-range distances, leaving the explanation of the latter an open question. In this paper we study the transcription correlations when the only transcription taking place is stochastic, in other words, no active or "deterministic" transcription takes place. To this purpose we use transcription data of Sinorhizobium meliloti. Results Even when only stochastic transcription takes place, the co-expression of genes varies as a function of the distance between genes: we observe again the short-range as well as the regular, long-range correlation patterns. Conclusion We explain these latter with a model based on the physical constraints acting on the DNA, forcing it into a conformation of groups of a few successive large and transcribed loops, which are evenly spaced along the chromosome and separated by small, non-transcribed loops. We discuss the question about the link between shared transcription patterns and physiological relationship and come to the conclusion that when genes are distantly placed along the chromosome, the transcription correlation does not imply a physiological relationship.

DNA microarrays of 86 genes from the yeasts Debaryomyces hansenii , Kluyveromyces marxianus , and Yarrowia lipolytica were developed to determine which genes were expressed in a medium mimicking a cheese-ripening environment. These genes were selected for potential involvement in lactose/lactate catabolism and the biosynthesis of sulfur-flavored compounds. Hybridization conditions to follow specifically the expression of homologous genes belonging to different species were set up. The microarray was first validated on pure cultures of each yeast; no interspecies cross-hybridization was observed. Expression patterns of targeted genes were studied in pure cultures of each yeast, as well as in coculture, and compared to biochemical data. As expected, a high expression of the LAC genes of K. marxianus was observed. This is a yeast that efficiently degrades lactose. Several lactate dehydrogenase-encoding genes were also expressed essentially in D. hansenii and K. marxianus , which are two efficient deacidifying yeasts in cheese ripening. A set of genes possibly involved in l -methionine catabolism was also used on the array. Y. lipolytica , which efficiently assimilates l -methionine, also exhibited a high expression of the Saccharomyces cerevisiae orthologs BAT2 and ARO8 , which are involved in the l -methionine degradation pathway. Our data provide the first evidence that the use of a multispecies microarray could be a powerful tool to investigate targeted metabolism and possible metabolic interactions between species within microbial cocultures.

Background Although the organisation of the bacterial chromosome is an area of active research, little is known yet on that subject. The difficulty lies in the fact that the system is dynamic and difficult to observe directly. The advent of massive hybridisation techniques opens the way to further studies of the chromosomal structure because the genes that are co-expressed, as identified by microarray experiments, probably share some spatial relationship. The use of several independent sets of gene expression data should make it possible to obtain an exhaustive view of the genes co-expression and thus a more accurate image of the structure of the chromosome. Results For both Bacillus subtilis and Escherichia coli the co-expression of genes varies as a function of the distance between the genes along the chromosome. The long-range correlations are surprising: the changes in the level of expression of any gene are correlated (positively or negatively) to the changes in the expression level of other genes located at well-defined long-range distances. This property is true for all the genes, regardless of their localisation on the chromosome. We also found short-range correlations, which suggest that the location of these co-expressed genes corresponds to DNA turns on the nucleoid surface (14–16 genes). Conclusion The long-range correlations do not correspond to the domains so far identified in the nucleoid. We explain our results by a model of the nucleoid solenoid structure based on two types of spirals (short and long). The long spirals are uncoiled expressed DNA while the short ones correspond to coiled unexpressed DNA.

The number of statistical tools used to analyze transcriptome data is continuously increasing and no one, definitive method has so far emerged. There is a need for comparison and a number of different approaches has been taken to evaluate the effectiveness of the different statistical tools available for microarray analyses. In this paper, we describe a simple and efficient protocol to compare the reliability of different statistical tools available for microarray analyses. It exploits the fact that genes within an operon exhibit the same expression patterns. In order to compare the tools, the genes are ranked according to the most relevant criterion for each tool; for each tool we look at the number of different operons represented within the first twenty genes detected. We then look at the size of the interval within which we find the most significant genes belonging to each operon in question. This allows us to define and estimate the sensitivity and accuracy of each statistical tool. We have compared four statistical tools using Bacillus subtilis expression data: the analysis of variance (ANOVA), the principal component analysis (PCA), the independent component analysis (ICA) and the partial least square regression (PLS). Our results show ICA to be the most sensitive and accurate of the tools tested. In this article, we have used the protocol to compare statistical tools applied to the analysis of differential gene expression. However, it can also be applied without modification to compare the statistical tools developed for other types of transcriptome analyses, like the study of gene co-expression.

International audience; We propose and study a new approach for the analysis of families of protein sequences. This method is related to the LogDet distances used in phylogenetic reconstructions; it can be viewed as an attempt to embed these distances into a multidimensional framework. The proposed method starts by associating a Markov matrix to each pairwise alignment deduced from a given multiple alignment. The central objects under consideration here are matrix-valued logarithms L of these Markov matrices, which exist under conditions that are compatible with fairly large divergence between the sequences. These logarithms allow us to compare data from a family of aligned proteins with simple models (in particular, continuous reversible Markov models) and to test the adequacy of such models. If one neglects fluctuations arising from the finite length of sequences, any continuous reversible Markov model with a single rate matrix Q over an arbitrary tree predicts that all the observed matrices L are multiples of Q. Our method exploits this fact, without relying on any tree estimation. We test this prediction on a family of proteins encoded by the mitochondrial genome of 26 multicellular animals, which include vertebrates, arthropods, echinoderms, molluscs, and nematodes. A principal component analysis of the observed matrices L shows that a single rate model can be used as a rough approximation to the data, but that systematic deviations from any such model are unmistakable and related to the evolutionary history of the species under consideration.

In the framework of genome annotation, scientific literature is obviously the major source of biological knowledge. The aim of the work described in this paper is to exploit this source of data for the model plant Arabidopsis thaliana. The first step has consisted in constituting a relevant bibliographic references dataset for plant genomic research. Genes co-citations have then been systematically annotated in this reference dataset, starting from the simple idea that if genes are cited in the same publication, they must probably share some related functional properties. In order to deal with the synonymous gene name problem, a gene name reference list has been constituted starting from A. thaliana SwissProt entries. This list was used to build clusters of co-cited genes by a single linkage procedure such that any gene in a given cluster possesses at least one co-cited partner in the same cluster. Analysis of the clusters demonstrate the biological consistency of this approach, with only very few fortuitous links. As an example, a cluster including genes related to flowering time is more deeply described in the paper. Finally, a graphical representation of each cluster was performed, which provides a convenient way to retrieve the genes (the nodes of the graphs) and the references in which they were co-cited (the edges of the graphs). All the results can be accessed at the URL http://chlora.Igi.infobiogen.fr:1234/bib_arath/.

Summary: GeneANOVA is an ANOVA-based software devoted to the analysis of gene expression data. Availability: GeneANOVA is freely available on request for non-commercial use. Contact: didier@genopole.cnrs.fr

Previous analyses of retroviral nucleotide sequences, suggest a so-called "scrambled duplicative stepwise molecular evolution" (many sectors with successive duplications/deletions of short and longer motifs) that could have stemmed from one or several starter tandemly repeated short sequence(s). In the present report, we tested this hypothesis by focusing on the long terminal repeats (LTRs) (and flanking sequences) of 24 human and 3 simian immunodeficiency viruses. By using a calculation strategy applicable to short sequences, we found consensus overrepresented motifs (often containing CTG or CAG) that were congruent with the previously defined "retroviral signature." We also show many local repetition patterns that are significant when compared with simply shuffled sequences. First- and second-order Markov chain analyses demonstrate that a major portion of the overrepresented oligonucleotides can be predicted from the dinucleotide compositions of the sequences, but by no means can biological mechanisms be deduced from these results: some of the listed local repetitions remain significant against dinucleotide-conserving shuffled sequences; together with previous results, this suggests that interspersed and/or local mononucleotide and oligonucleotide repetitions could have biased the dinucleotide compositions of the sequences. We searched for suggestive evolutionary patterns by scrutinizing a reliable multiple alignment of the 27 sequences. A manually constructed alignment based on homology blocks was in good agreement with the polypeptide alignment in the coding sectors and has been exhaustively assessed by using a multiplied alphabet obtained by the promising mathematical strategy called the N-block presentation (taking into account the environment of each nucleotide in a sequence). Sector by sector, we hypothesize many successive duplication/deletion scenarios that fit our previous evolutionary hypotheses. This suggests an important duplication/deletion role for the reverse transcriptase, particularly in inducing stuttering cryptic simplicity patterns.

In global gene expression profiling experiments, variation in the expression of genes of interest can often be hidden by general noise. To determine how biologically significant variation can be distinguished under such conditions we have analyzed the differences in gene expression when Bacillus subtilis is grown either on methionine or on methylthioribose as sulfur source. An unexpected link between arginine metabolism and sulfur metabolism was discovered, enabling us to identify a high-affinity arginine transport system encoded by the yqiXYZ genes. In addition, we tentatively identified a methionine/methionine sulfoxide transport system which is encoded by the operon ytmIJKLMhisP and is presumably used in the degradation of methionine sulfoxide to methane sulfonate for sulfur recycling. Experimental parameters resulting in systematic biases in gene expression were also uncovered. In particular, we found that the late competence operons comE, comF and comG were associated with subtle variations in growth conditions. Using variance analysis it is possible to distinguish between systematic biases and relevant gene-expression variation in transcriptome experiments. Co-variation of metabolic gene expression pathways was thus uncovered linking nitrogen and sulfur metabolism in B. subtilis.

BackgroundCurrent histo-pathological prognostic factors are not very helpful in predicting the clinical outcome of breast cancer due to the disease's heterogeneity. Molecular profiling using a large panel of genes could help to classify breast tumours and to define signatures which are predictive of their clinical behaviour.MethodsTo this aim, quantitative RT-PCR amplification was used to study the RNA expression levels of 47 genes in 199 primary breast tumours and 6 normal breast tissues. Genes were selected on the basis of their potential implication in hormonal sensitivity of breast tumours. Normalized RT-PCR data were analysed in an unsupervised manner by pairwise hierarchical clustering, and the statistical relevance of the defined subclasses was assessed by Chi2 analysis. The robustness of the selected subgroups was evaluated by classifying an external and independent set of tumours using these Chi2-defined molecular signatures.ResultsHierarchical clustering of gene expression data allowed us to define a series of tumour subgroups that were either reminiscent of previously reported classifications, or represented putative new subtypes. The Chi2 analysis of these subgroups allowed us to define specific molecular signatures for some of them whose reliability was further demonstrated by using the validation data set. A new breast cancer subclass, called subgroup 7, that we defined in that way, was particularly interesting as it gathered tumours with specific bioclinical features including a low rate of recurrence during a 5 year follow-up.ConclusionThe analysis of the expression of 47 genes in 199 primary breast tumours allowed classifying them into a series of molecular subgroups. The subgroup 7, which has been highlighted by our study, was remarkable as it gathered tumours with specific bioclinical features including a low rate of recurrence. Although this finding should be confirmed by using a larger tumour cohort, it suggests that gene expression profiling using a minimal set of genes may allow the discovery of new subclasses of breast cancer that are characterized by specific molecular signatures and exhibit specific bioclinical features.