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International audience; Different studies have shown that microbial communities living in animals (humans included), in or around plants have a significant impact on health and disease of their host and on various services, such as adaptation under stressing environment. The basic input data to study microbiomes is a matrix representing abundance data of micro-organisms across different sampling units. Such a matrix typically corresponds to taxonomic profiles derived from the high-throughput sequencing of environmental samples. Biclustering is one way to study the interactions between the structure of micro-organism communities and the environmental samples they come from.We propose a latent block model (LBM) and an associated inference procedure for the biclustering of rows and columns of abundance matrices. The LBM assumes that micro-organisms (rows) and environmental samples (columns) can both be clustered into groups characterizing preferential interaction or avoidance. We use the Poisson-Gamma distribution to model the overdispersion observed in microbial abundance data and introduce row and column effects to account for the sequencing effort in each sample and the mean abundance of each micro-organism. Because the latent variables are not independent conditionally on the observed ones, classical maximum likelihood inference is intractable. We then derive a variational-based inference algorithm and propose a strategy to select the number of biclusters.We illustrate the flexibility and performance of our approach both on a simulation study and on three ecological datasets. The model-based framework allows us to adapt to peculiarities of microbial ecological abundance data and allows us to explore relationships between entities of two different natures.We implemented our method in the cobiclust R package available on the CRAN and built a website with example of usage ().

Interactions between plants and phytophagous insects play an important part in shaping the biochemical composition of plants. Reciprocally plant metabolites can influence major life history traits in these insects and largely contribute to their fitness. Plant rhizospheric microorganisms are an important biotic factor modulating plant metabolites and adaptation to stress. While plant-insects or plant-microorganisms interactions and their consequences on the plant metabolite signature are well-documented, the impact of soil microbial communities on plant defenses against phytophagous insects remains poorly known. In this study, we used oilseed rape (Brassica napus) and the cabbage root fly (Delia radicum) as biological models to tackle this question. Even though D. radicum is a belowground herbivore as a larva, its adult life history traits depend on aboveground signals. We therefore tested whether soil microbial diversity influenced emergence rate and fitness but also fly oviposition behavior, and tried to link possible effects to modifications in leaf and root metabolites. Through a removal-recolonization experiment, 3 soil microbial modalities ("high," "medium," "low") were established and assessed through amplicon sequencing of 16S and 18S ribosomal RNA genes. The "medium" modality in the rhizosphere significantly improved insect development traits. Plant-microorganism interactions were marginally associated to modulations of root metabolites profiles, which could partly explain these results. We highlighted the potential role of plant-microbial interaction in plant defenses against Delia radicum. Rhizospheric microbial communities must be taken into account when analyzing plant defenses against herbivores, being either below or aboveground.

All organisms need to sense and respond to a range of stress conditions. In this study, we used transcriptional profiling to identify genes and cellular processes that are responsive during arsenite and tert-butyl hydroperoxide exposure in Kluyveromyces lactis. Many arsenite-responsive genes encode proteins involved in redox processes, protein folding and stabilization, and transmembrane transport. The majority of peroxide-responsive genes encode functions related to transcription, translation, redox processes, metabolism and transport. A substantial number of these stress-regulated genes contain binding motifs for the AP-1 like transcription factors KlYap1 and KlYap8. We demonstrate that KlYap8 binds to and regulates gene expression through a 13 base-pair promoter motif, and that KlYap8 provides protection against arsenite, antimonite, cadmium and peroxide toxicity. Direct transport assays show that Klyap8 Delta cells accumulate more arsenic and cadmium than wild type cells and that the Klyap8 Delta mutant is defective in arsenic and cadmium export. KlYap8 regulates gene expression in response to both arsenite and peroxide, and might cooperate with KlYap1 in regulation of specific gene targets. Comparison of KlYap8 with its Saccharomyces cerevisiae orthologue ScYap8 indicates that KlYap8 senses and responds to multiple stress signals whereas ScYap8 is only involved in the response to arsenite and antimonite. Thus, our data suggest that functional specialization of ScYap8 has occurred after the whole genome duplication event. This is the first genome-wide stress response analysis in K. lactis and the first demonstration of KlYap8 function.

The objective was to examine if IVF/ICSI repeated implantation failures (IF) or recurrent miscarriages (RM) could be related to preconceptional endometrial deregulations. IF was defined as the absence of pregnancy despite the transfer of at least ten IVF/ICSI good quality embryos, and RM as having at least three unexplained miscarriages. Fertile controls (FC) were women who had given birth at least once. Endometrial biopsy was performed in the mild luteal phase of a non-conceptual cycle (five women were selected in each group). Affymetrix chips (GeneChip Human Genome U133 Plus2.0 Array) were used for hybridization. Data were normalized by the gcRMA method, and raw p values adjusted by the Bonferroni procedure (1%). Differential expression of selected genes was analysed using real-time PCR. Gene networks and biological functions were explored using the Ingenuity Pathways Analysis software. Endometrial gene expression profiles at the time of uterine receptivity differ dramatically in the endometrium among FC, RM, and IF patients. Compared to FC, 2126 and 2477 genes are differentially expressed in IF and RM groups, respectively, and 2363 between IF and RM. In both conditions, differential gene expression referred mainly to DNA transcription and expression. Other main cellular functions deregulated in IF conditions correspond to cell morphology, cellular development, cell cycle, and cellular assembly, while in RM conditions, deregulated cellular functions relate to cell signalling (degradation of cyclic AMP and calcium metabolism) and cellular maintenance. In both conditions, there is an over-representation of deregulations related to the haematological system. In the IF condition, cell-mediated immune response and nervous system development and function are highly deregulated, while in RM patients, main deregulations are in organ and tissue development, humoral immune response, and muscular system development and function. Extensive endometrial deregulations are present before conception in patients who experienced IF or RM with both distinct and common deregulation.

In the introduction, we briefly recall old but classic evidence that there is no tolerance to paternal alloantigens in a first pregnancy. Therefore, we performed small- and large-scale microarrays in CBA × DBA/2 and CBA × BALB/c combinations, recently described as a murine model for preeclampsia. Our results are in line with other data suggesting a very early deregulation of local immune vascular events rather than a break of immune tolerance. Other data presented at the Tioman 2010 Preeclampsia Workshop supporting this hypothesis are briefly summarised, as well as indications and caveats from a recent human microarray on implantation failure and recurrent pregnancy loss.

Motivation: In cDNA microarray experiments all samples are labeled with either Cy3 or Cy5. Systematic and gene-specific dye bias effects have been observed in dual-color experiments. In contrast to systematic effects which can be corrected by a normalization method, the gene-specific dye bias is not completely suppressed and may alter the conclusions about the differentially expressed genes. Methods: The gene-specific dye bias is taken into account using an analysis of variance model. We propose an index, named label bias index, to measure the gene-specific dye bias. It requires at least two self--self hybridization cDNA microarrays. Results: After lowess normalization we have found that the gene-specific dye bias is the major source of experimental variability between replicates. The ratio (R/G) may exceed 2. As a consequence false positive genes may be found in direct comparison without dye-swap. The stability of this artifact and its consequences on gene variance and on direct or indirect comparisons are addressed. Availability: http://www.inapg.inra.fr/ens_rech/mathinfo/recherche/mathematique Contact: mlmartin@inapg.fr

The objective was to examine if IVF/ICSI repeated implantation failures (IF) or recurrent miscarriages (RM) could be related to preconceptional endometrial deregulations. IF was defined as the absence of pregnancy despite the transfer of at least ten IVF/ICSI good quality embryos, and RM as having at least three unexplained miscarriages. Fertile controls (FC) were women who had given birth at least once. Endometrial biopsy was performed in the mild luteal phase of a non-conceptual cycle (five women were selected in each group). Affymetrix chips (GeneChip Human Genome U133 Plus2.0 Array) were used for hybridization. Data were normalized by the gcRMA method, and raw p values adjusted by the Bonferroni procedure (1%). Differential expression of selected genes was analysed using real-time PCR. Gene networks and biological functions were explored using the Ingenuity Pathways Analysis software. Endometrial gene expression profiles at the time of uterine receptivity differ dramatically in the endometrium among FC, RM, and IF patients. Compared to FC, 2126 and 2477 genes are differentially expressed in IF and RM groups, respectively, and 2363 between IF and RM. In both conditions, differential gene expression referred mainly to DNA transcription and expression. Other main cellular functions deregulated in IF conditions correspond to cell morphology, cellular development, cell cycle, and cellular assembly, while in RM conditions, deregulated cellular functions relate to cell signalling (degradation of cyclic AMP and calcium metabolism) and cellular maintenance. In both conditions, there is an over-representation of deregulations related to the haematological system. In the IF condition, cell-mediated immune response and nervous system development and function are highly deregulated, while in RM patients, main deregulations are in organ and tissue development, humoral immune response, and muscular system development and function. Extensive endometrial deregulations are present before conception in patients who experienced IF or RM with both distinct and common deregulation.

We would like to thank K. Dobbin, J. Shih and R. Simon for their comments about Martin-Magniette et al. (2005). Their remarks relate to the design of microarray experiments and notably about the use of dye-swaps. We, however, want to make it clear that our manuscript primarily focuses on the detection, quantification and correction of the gene-specific dye-bias introduced in dual-color microarray experiments. The most important point in Martin-Magniette et al. (2005) is the following: by dint of studying technical errors, we will be able to identify and then to remove most of them. For the first time, in Martin-Magniette et al. (2005), we were able to quantify the genespecific dye bias by calculating the Labelling Bias Index, (LBI). The LBI measured on different array types shows that this artifact seems to be very low in some cases and could be thus neglected. However, it is high in other cases and it cannot thus be neglected for data issued from these array types. This measure is of crucial importance as it allows users to evaluate the impact of this bias on their data. Moreover, we think that each platform should at least know in what class it belongs for each array types. Although this artifact is not lowered nor understood, it is simply dangerous to underestimate it. The gene-specific dye bias is not an inevitability and can be well controlled, as we point out in our paper. Recently, in another paper, Dobbin et al. (2005) studied the genespecific dye bias. Although they reached the same conclusions, some of their remarks are explained in Martin-Magniette et al. (2005): e.g. Dobbin et al. (2005) have found that ‘(the gene-specific) dye bias appears to have masked the true differential expression’. This is explained in Martin-Magniette et al. (2005): the variance associated to a gene is overestimated by the dye bias effect in model (2) of Martin-Magniette et al. Dye-swaps constitute a simple and effective design to remove gene-specific dye bias when it is high. Nevertheless, we agree with Dobbin et al. (2005), that a balanced block design may be better than dye-swaps in some situations. As the former designs allow the use of more biological samples, the estimation of the biological variability will be more precise. Even if balanced block designs are statistically more efficient, the following considerations should be taken into account before choosing the experimental design

Thousands of genes in a genomewide data set are tested against some null hypothesis, for detecting differentially expressed genes in microarray experiments. The expected proportion of false positive genes in a set of genes, called the False Discovery Rate (FDR), has been proposed to measure the statistical significance of this set. Various procedures exist for controlling the FDR. However the threshold (generally 5%) is arbitrary and a specific measure associated with each gene would be worthwhile. Using process intensity estimation methods, we define and give estimates of the local FDR, which may be considered as the probability for a gene to be a false positive. After a global assessment rule controlling the false positive error, the local FDR is a valuable guideline for deciding wether a gene is differentially expressed. The interest of the method is illustrated on three well known data sets. A R routine for computing local FDR estimates from p-values is available at http://www.inapg.fr/ens_rech/mathinfo/recherche/mathematique/outil.html . The local FDR associated with each gene measures the probability that it is a false positive. It gives the opportunity to compute the FDR of any given group of clones (of the same gene) or genes pertaining to the same regulation network or the same chromosomic region.