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The lean-to-fat ratio is a major issue in the beef meat industry from both carcass and meat production perspectives. This industrial perspective has motivated meat physiologists to use transcriptomics technologies to decipher mechanisms behind fat deposition within muscle during the time course of muscle growth. However, synthetic biological information from this volume of data remains to be produced to identify mechanisms found in various breeds and rearing practices. We conducted a meta-analysis on 10 transcriptomic data sets stored in public databases, from the longissimus thoracis of five different bovine breeds divergent by age. We updated gene identifiers on the last version of the bovine genome (UCD1.2), and the 715 genes common to the 10 studies were subjected to the meta-analysis. Of the 238 genes differentially expressed (DEG), we identified a transcriptional signature of the dynamic regulation of glycolytic and oxidative metabolisms that agrees with a known shift between those two pathways from the animal puberty. We proposed some master genes of the myogenesis, namely MYOG and MAPK14, as probable regulators of the glycolytic and oxidative metabolisms. We also identified overexpressed genes related to lipid metabolism (APOE, LDLR, MXRA8, and HSP90AA1) that may contribute to the expected enhanced marbling as age increases. Lastly, we proposed a transcriptional signature related to the induction (YBX1) or repression (MAPK14, YWAH, ERBB2) of the commitment of myogenic progenitors into the adipogenic lineage. The relationships between the abundance of the identified mRNA and marbling values remain to be analyzed in a marbling biomarkers discovery perspectives.

Shadoo, encoded by Sprn, and PrP, encoded by Prnp, are related proteins whose biological functions are still incompletely understood. Although previous knockdown experiments have suggested the necessity of Shadoo in the absence of PrP during early mouse embryogenesis, little impact of the double-knockout of these two loci was reported. To further investigate this apparent discrepancy, we compared the transcriptome of WT, Prnp0/0 and Prnp0/0, Sprn0/0 E6.5 mouse embryos following inoculation by Sprn-ShRNA or Prnp-ShRNA lentiviral vectors at the one-cell stage. Our results highlighted a significant induction of an apoptotic pathway in Prnp0/0 E6.5 mouse embryos inoculated with Sprn-ShRNA vectors alongside interferon and to a lesser extent inflammatory responses, confirming previous reported experiments. On the contrary, ShRNA vector inoculation in Prnp0/0, Sprn0/0 embryos did not induce apoptosis and resulted in lower interferon responses. Finally, comparisons of the transcriptome of WT and Prnp0/0, Sprn0/0 embryos revealed only slight differences, which may in part explain the genetic robustness observed in the latter genotype.HighlightsSprn-ShRNA lentivirus vector inoculation in Prnp knockout one-cell mouse embryos results in the induction of an apoptosis pathway at E6.5, alongside interferon and to a lesser extent inflammatory responses.Sprn- or Prnp-ShRNA lentivirus vector inoculations in Prnp/Sprn knockout one-cell mouse embryos induce lower interferon responses and no apoptotic pathway at E6.5.Although wild type and Prnp/Sprn knockout E6.5 mouse embryos are transcriptomically similar, some differences might explain this apparent resilience of the double knockout genotype.

Importation of livestock genetic resources from industrialized countries for introgression of specific traits and other forms of crossbreeding is often indicative of a shift in production systems toward greater intensification and specialization. In developing countries, imported genetic resources are regarded as both a solution to improve the performance of local livestock and as one of the main threats to local populations. Using international databases, censuses and technical reports, we investigate ongoing trends and consequences of these two phenomena in 40 countries from Africa, Asia and Latin America. In these countries, the share of locally adapted breeds within species has decreased by an average of 0.76% per year over the last 20 years. The corresponding increase has been distributed between pure exotic breeds and crossbred animals, with differences across regions. In several countries, increased utilization of exotic cattle breeds and crossbreeding has been accompanied by a trend in increased milk yield per cow. The shift from local genetic resources to crossbred and exotic animals must be considered in the context of challenges such as food security, erosion of agrobiodiversity, interactions with other agricultural production, reduction of poverty and provision of ecosystem services, as well as resilience to and mitigation of climate change., Highlights • Share of locally adapted breeds decreased by 0.75% per year over the last 20 years. • Complementary increase is distributed between exotic breeds and crossbred animals. • Overall livestock numbers have increased, so many local populations remain stable. • Average milk yield at country scale increases with exotic introgression. • Breed replacement must be judged in the context of sustainability.

We have previously shown that dairy cows carrying the ‘fertil−’ haplotype for one quantitative trait locus affecting female fertility located on the bovine chromosome three (QTL-F-Fert-BTA3) have a significantly lower conception rate and body weight after calving than cows carrying the ‘fertil+’ haplotype. Here, we compared by Tiling Array the expression of genes included in the QTL-F-Fert-BTA3 in ‘fertil+’ and ‘fertil−’ adipose tissue one week after calving when plasma non-esterified fatty acid concentrations were greater in ‘fertil−’ animals. We observed that thirty-one genes were overexpressed whereas twelve were under-expressed in ‘fertil+’ as compared to ‘fertil−’ cows (P KIRREL mRNA and protein were significantly greater expressed in ‘fertil+’ than in ‘fertil−’. KIRREL mRNA is abundant in bovine kidney, adipose tissue, pituitary, and ovary and detectable in hypothalamus and mammary gland. Its expression (mRNA and protein) is greater in kidney of ‘fertil+’ than ‘fertil−’ cows (P P P in vitro oocyte maturation (P KIRREL could be a potential metabolic messenger linking body composition and fertility.

Background High-protein diets (HPD) alter the large intestine microbiota composition in association with a metabolic shift towards protein degradation. Some amino acid-derived metabolites produced by the colon bacteria are beneficial for the mucosa while others are deleterious at high concentrations. The aim of the present work was to define the colonic epithelial response to an HPD. Transcriptome profiling was performed on colonocytes of rats fed an HPD or an isocaloric normal-protein diet (NPD) for 2 weeks. Results The HPD downregulated the expression of genes notably implicated in pathways related to cellular metabolism, NF-κB signaling, DNA repair, glutathione metabolism and cellular adhesion in colonocytes. In contrast, the HPD upregulated the expression of genes related to cell proliferation and chemical barrier function. These changes at the mRNA level in colonocytes were not associated with detrimental effects of the HPD on DNA integrity (comet assay), epithelium renewal (quantification of proliferation and apoptosis markers by immunohistochemistry and western blot) and colonic barrier integrity (Ussing chamber experiments). Conclusion The modifications of the luminal environment after an HPD were associated with maintenance of the colonic homeostasis that might be the result of adaptive processes in the epithelium related to the observed transcriptional regulations. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3514-z) contains supplementary material, which is available to authorized users.

Once-daily milking is known to modify cell number and activity in the bovine mammary gland. However, in goats, the effect of once-daily milking on mammary cell death is controversial. To assess the effect of once-daily milking on mammary transcriptome and cell turnover and the effect of the duration of once-daily milking, 10 goats producing 2.5 kg of milk per day at 100 DIM were divided into 2 groups. All goats were twice daily milked during a pre-experimental period of 2 wk. Then, the goats were once daily milked for the 3 following weeks. Mammary biopsy samples were collected at d −1 and 7 after the start of once-daily milking for one group of 5 goats and at d −1 and 21 for a second group of 5 goats. Cell apoptosis and proliferation rates were analyzed in mammary tissue by immunohistological analyses after TUNEL and PCNA staining, respectively. Ribonucleic acid was extracted from mammary tissues. A transcriptomic analysis using the Agilent Bovine 4x44k microarrays has been performed to compare the effect of once-daily milking on mammary transcript profiles. Data were normalized and statistical significant raw P-values were adjusted for multiple comparisons using the Benjamini–Hochberg procedure. A similar reduction in milk yield (−19%) was observed in both groups of goats during once-daily milking compared with twice-daily milking. Cell apoptosis was higher during once-daily milking than during twice-daily milking for both groups of goats (P < 0.05) whereas cell proliferation did not vary (P = 0.27). The transcriptomic analysis showed a differential gene expression of 4,039 transcripts, 2,238 and 1,801 transcripts up- and downregulated, respectively, by once-daily milking compared with twice-daily milking. More than 1,000 transcripts were commonly regulated between the 2 groups of goats. IPA analysis showed that these transcripts were part of networks associated with DNA replication, cellular growth and proliferation, and cell-to-cell signaling and interaction in both groups of goats. RT-qPCR analysis of 11 genes confirmed the differential gene expression with a downregulation of genes involved in milk synthesis and an upregulation of genes involved in cellular cycle and apoptosis. These results showed that once-daily milking induce cell turnover in goat mammary tissue, with a small impact of time.

Microarray analyses have become an important tool in animal genomics. While their use is becoming widespread, there is still a lot of ongoing research regarding the analysis of microarray data. In the context of a European Network of Excellence, 31 researchers representing 14 research groups from 10 countries performed and discussed the statistical analyses of real and simulated 2-colour microarray data that were distributed among participants. The real data consisted of 48 microarrays from a disease challenge experiment in dairy cattle, while the simulated data consisted of 10 microarrays from a direct comparison of two treatments (dye-balanced). While there was broader agreement with regards to methods of microarray normalisation and significance testing, there were major differences with regards to quality control. The quality control approaches varied from none, through using statistical weights, to omitting a large number of spots or omitting entire slides. Surprisingly, these very different approaches gave quite similar results when applied to the simulated data, although not all participating groups analysed both real and simulated data. The workshop was very successful in facilitating interaction between scientists with a diverse background but a common interest in microarray analyses.

The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC) or principal component analysis (PCA) to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus). The main result from these analyses was that HC and PCA were able to separate tissue samples taken at 24 h following E. coli infection from the other samples. The second approach identified groups of differentially co-expressed genes, by identifying clusters of genes highly correlated when animals were infected with E. coli but not correlated more than expected by chance when the infective pathogen was S. aureus. The third approach looked at differential expression of predefined gene sets. Gene sets were defined based on information retrieved from biological databases such as Gene Ontology. Based on these annotation sources the teams used either the GlobalTest or the Fisher exact test to identify differentially expressed gene sets. The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed.

Microarrays allow researchers to measure the expression of thousands of genes in a single experiment. Before statistical comparisons can be made, the data must be assessed for quality and normalisation procedures must be applied, of which many have been proposed. Methods of comparing the normalised data are also abundant, and no clear consensus has yet been reached. The purpose of this paper was to compare those methods used by the EADGENE network on a very noisy simulated data set. With the a priori knowledge of which genes are differentially expressed, it is possible to compare the success of each approach quantitatively. Use of an intensity-dependent normalisation procedure was common, as was correction for multiple testing. Most variety in performance resulted from differing approaches to data quality and the use of different statistical tests. Very few of the methods used any kind of background correction. A number of approaches achieved a success rate of 95% or above, with relatively small numbers of false positives and negatives. Applying stringent spot selection criteria and elimination of data did not improve the false positive rate and greatly increased the false negative rate. However, most approaches performed well, and it is encouraging that widely available techniques can achieve such good results on a very noisy data set.

The importance of variance modelling is now widely known for the analysis of microarray data. In particular the power and accuracy of statistical tests for differential gene expressions are highly dependent on variance modelling. The aim of this paper is to use a structural model on the variances, which includes a condition effect and a random gene effect, and to propose a simple estimation procedure for these parameters by working on the empirical variances. The proposed variance model was compared with various methods on both real and simulated data. It proved to be more powerful than the gene-by-gene analysis and more robust to the number of false positives than the homogeneous variance model. It performed well compared with recently proposed approaches such as SAM and VarMixt even for a small number of replicates, and performed similarly to Limma. The main advantage of the structural model is that, thanks to the use of a linear mixed model on the logarithm of the variances, various factors of variation can easily be incorporated in the model, which is not the case for previously proposed empirical Bayes methods. It is also very fast to compute and is adapted to the comparison of more than two conditions.

International audience; Lactation performance is dependent on both the genetic characteristics and the environmental conditions surrounding lactating cows. However, individual variations can still be observed within a given breed under similar environmental conditions. The role of the environment between birth and lactation could be better appreciated in cloned cows, which are presumed to be genetically identical, but differences in lactation performance between cloned and noncloned cows first need to be clearly evaluated. Conflicting results have been described in the literature, so our aim was to clarify this situation. Nine cloned Prim' Holstein cows were produced by the transfer of nuclei from a single fibroblast cell line after cell fusion with enucleated oocytes. The cloned cows and 9 noncloned counterparts were raised under similar conditions. Milk production and composition were recorded monthly from calving until 200 d in milk. At 67 d in milk, biopsies were sampled from the rear quarter of the udder, their mammary epithelial cell content was evaluated, and mammary cell renewal, RNA, and DNA were then analyzed in relevant samples. The results showed that milk production did not differ significantly between cloned and noncloned cows, but milk protein and fat contents were less variable in cloned cows. Furthermore, milk fat yield and contents were lower in cloned cows during early lactation. At around 67 DIM, milk fat and protein yields, as well as milk fat, protein, and lactose contents, were also lower in cloned cows. These lower yields could be linked to the higher apoptotic rate observed in cloned cows. Apoptosis is triggered by insulin-like factor growth binding protein 5 (IGFBP5) and plasminogen activator inhibitor (PAI), which both interact with CSN1S2. During our experiments, CSN1S2 transcript levels were lower in the mammary gland of cloned cows. The mammary cell apoptotic rate observed in cloned cows may have been related to the higher levels of DNA (cytosine-5-)-methyltransferase 1 (DNMT1) transcripts, coding for products that maintain the epigenetic status of cells. We conclude, therefore, that milk production in cloned cows differs slightly from that of noncloned cows. These differences may be due, in part, to a higher incidence of subclinical mastitis. They were associated with differences in cell apoptosis and linked to variations in DNMT1 mRNA. However, milk protein and fat contents were more similar among cloned cows than among noncloned cows.

In co-expression analyses of gene expression data, it is often of interest to interpret clusters of co-expressed genes with respect to a set of external information, such as a potentially incomplete list of functional properties for which a subset of genes may be annotated. Based on the framework of finite mixture models, we propose a model selection criterion that takes into account such external gene annotations, providing an efficient tool for selecting a relevant number of clusters and clustering model. This criterion, called the integrated completed annotated likelihood (ICAL), is defined by adding an entropy term to a penalized likelihood to measure the concordance between a clustering partition and the external annotation information. The ICAL leads to the choice of a model that is more easily interpretable with respect to the known functional gene annotations. We illustrate the interest of this model selection criterion in conjunction with Gaussian mixture models on simulated gene expression data and on real RNA-seq data.

The analysis of nonlinear function-valued characters is very important in genetic studies, especially for growth traits of agricultural and laboratory species. Inference in nonlinear mixed effects models is, however, quite complex and is usually based on likelihood approximations or Bayesian methods. The aim of this paper was to present an efficient stochastic EM procedure, namely the SAEM algorithm, which is much faster to converge than the classical Monte Carlo EM algorithm and Bayesian estimation procedures, does not require specification of prior distributions and is quite robust to the choice of starting values. The key idea is to recycle the simulated values from one iteration to the next in the EM algorithm, which considerably accelerates the convergence. A simulation study is presented which confirms the advantages of this estimation procedure in the case of a genetic analysis. The SAEM algorithm was applied to real data sets on growth measurements in beef cattle and in chickens. The proposed estimation procedure, as the classical Monte Carlo EM algorithm, provides significance tests on the parameters and likelihood based model comparison criteria to compare the nonlinear models with other longitudinal methods.

International audience; Model selection is an essential issue in longitudinal data analysis since many different models have been proposed to fit the covariance structure. The likelihood criterion is commonly used and allows to compare the fit of alternative models. Its value does not reflect, however, the potential improvement that can still be reached in fitting the data unless a reference model with the actual covariance structure is available. The score test approach does not require the knowledge of a reference model, and the score statistic has a meaningful interpretation in itself as a goodness-of-fit measure. The aim of this paper was to show how the score statistic may be separated into the genetic and environmental parts, which is difficult with the likelihood criterion, and how it can be used to check parametric assumptions made on variance and correlation parameters. Selection of models for genetic analysis was applied to a dairy cattle example for milk production.

In the analysis of longitudinal data, before assuming a parametric model, an idea of the shape of the variance and correlation functions for both the genetic and environmental parts should be known. When a small number of observations is available for each subject at a fixed set of times, it is possible to estimate unstructured covariance matrices, but not when the number of observations over time is large and when individuals are not measured at all times. The non-parametric approach, based on the variogram, presented by Diggle & Verbyla (1998), is specially adapted for exploratory analysis of such data. This paper presents a generalization of their approach to genetic analyses. The methodology is applied to daily records for milk production in dairy cattle and data on age-specific fertility in Drosophila.

International audience; Detection of footprints of selection has been a great research interest in population genetics over the past few years, both in Human and Animal populations. In this work we present two methodological improvements to increase the accuracy of detection of selection signature. First, we show how Principal Components Analysis (PCA) and Between-Groups Analysis (BGA), which are very computationally efficient methods to explore large SNP data sets and to characterize population genetic structures, can provide SNP typological values that are related to F-statistics. In a second step, we propose to use the fused Lasso approach to identify significant footprints of selection, taking into account the spatial organization of the SNPs along the chromosomes. Indeed, previously proposed methods are often based on empirical smoothing approaches and until now no clear recommendation was available for the choice of significant threshold for SNP detection. A simulation study both under a neutral model and under selection was performed to evaluate the performance of the proposed method in terms of detection power and false positive level. As an illustration of the approach, we analyzed human haplotypes sampled from three HapMap populations, and bovine data obtained from a 800K SNP chip.

Once daily milking (ODM) induces a reduction in milk production when compared to twice daily milking (TDM). Unilateral ODM of one udder half and TDM of the other half, enables the study of underlying mechanisms independently of inter-individual variability (same genetic background) and of environmental factors. Our results show that in first-calf heifers three CpG, located 10 kb upstream from the CSN1S1 gene were methylated to 33, 34 and 28%, respectively, after TDM but these levels were higher after ODM, 38, 38 and 33%, respectively. These methylation levels were much lower than those observed in the mammary gland during pregnancy (57, 59 and 50%, respectively) or in the liver (74, 78 and 61%, respectively). The methylation level of a fourth CpG (CpG4), located close by (29% during TDM) was not altered after ODM. CpG4 methylation reached 39.7% and 59.5%, during pregnancy or in the liver, respectively. CpG4 is located within a weak STAT5 binding element, arranged in tandem with a second high affinity STAT5 element. STAT5 binding is only marginally modulated by CpG4 methylation, but it may be altered by the methylation levels of the three other CpG nearby. Our results therefore shed light on mechanisms that help to explain how milk production is almost, but not fully, restored when TDM is resumed (15.1 ± 0.2 kg/day instead of 16.2 ± 0.2 kg/day, p

This article presents an extension of the methodology developed by Gilmour et al. [19], for ordered categorical data, taking into account the heterogeneity of residual variances of latent variables. Heterogeneity of residual variances is described via a structural linear model on log-variances. This method involves two main steps: i) a ’marginalization’ with respect to the random effects leading to quasi-score estimators; ii) an approximation of the variance-covariance matrix of the observations which leads to an analogue of the Henderson mixed model equations for continuous Gaussian data. This methodology is illustrated by a numerical example of footshape in sheep., Cet article présente une extension de la méthodologie développée par Gilmour et al. [19] dans le cas de variables qualitatives ordonnées, prenant en compte l’hétérogénéité des variances résiduelles des variables latentes. L’hétérogénéité des variances résiduelles est décrite par un modèle linéaire structurel sur les logarithmes des variances. Cette méthode comprend deux étapes principales : i) une « marginalisation » par rapport aux effets aléatoires qui conduit, grâce aux équations de quasi-score, à l’estimation des paramètres ; ii) une approximation de la matrice de variance-covariance des observations qui aboutit à un système analogue aux équations du modèle mixte d’Henderson dans le cas de variables continues gaussiennnes. Cette méthodologie est illustrée par un exemple sur la forme des pieds chez le mouton.

International audience; Gene network inference from transcriptomic data is an important methodological challenge and a key aspect of systems biology. Although several methods have been proposed to infer networks from microarray data, there is a need for inference methods able to model RNA-seq data, which are count-based and highly variable. In this work we propose a hierarchical Poisson log-normal model with a Lasso penalty to infer gene networks from RNA-seq data; this model has the advantage of directly modelling discrete data and accounting for inter-sample variance larger than the sample mean. Using real microRNA-seq data from breast cancer tumors and simulations, we compare this method to a regularized Gaussian graphical model on log-transformed data, and a Poisson log-linear graphical model with a Lasso penalty on power-transformed data. For data simulated with large inter-sample dispersion, the proposed model performs better than the other methods in terms of sensitivity, specificity and area under the ROC curve. These results show the necessity of methods specifically designed for gene network inference from RNA-seq data.

"Chantier qualité spécifique "Auteurs Externes" département de Génétique animale : uniquement liaison auteur au référentiel HR-Access "; International audience; BackgroundHigh-throughput sequencing is now regularly used for studies of the transcriptome (RNA-seq), particularly for comparisons among experimental conditions. For the time being, a limited number of biological replicates are typically considered in such experiments, leading to low detection power for differential expression. As their cost continues to decrease, it is likely that additional follow-up studies will be conducted to re-address the same biological question.ResultsWe demonstrate how p-value combination techniques previously used for microarray meta-analyses can be used for the differential analysis of RNA-seq data from multiple related studies. These techniques are compared to a negative binomial generalized linear model (GLM) including a fixed study effect on simulated data and real data on human melanoma cell lines. The GLM with fixed study effect performed well for low inter-study variation and small numbers of studies, but was outperformed by the meta-analysis methods for moderate to large inter-study variability and larger numbers of studies.ConclusionsThe p-value combination techniques illustrated here are a valuable tool to perform differential meta-analyses of RNA-seq data by appropriately accounting for biological and technical variability within studies as well as additional study-specific effects. An R package metaRNASeq is available on the CRAN (http://cran.r-project.org/web/packages/metaRNASeq).

Background: Inference of gene regulatory networks from transcriptomic data has been a wide research area in recent years. Proposed methods are mainly based on the use of graphical Gaussian models for observational wild-type data and provide undirected graphs that are not able to accurately highlight the causal relationships among genes. In the present work, we seek to improve estimation of causal effects among genes by jointly modeling observational transcriptomic data with intervention data obtained by performing knock-outs or knock-downs on a subset of genes. By examining the impact of such expression perturbations on other genes, a more accurate reflection of regulatory relationships may be obtained than through the use of wild-type data alone. Results: Using the framework of Gaussian Bayesian networks, we propose a Markov chain Monte Carlo algorithm with a Mallows model and an analytical likelihood maximization to sample from the posterior distribution of causal node orderings, and in turn, to estimate causal effects. The main advantage of the proposed algorithm over previously proposed methods is that it has the flexibility to accommodate any kind of intervention design, including partial or multiple knock-out experiments. Methods were compared on simulated data as well as data from the DREAM 2007 challenge. Conclusions: The simulation study confirmed the impossibility of estimating causal orderings of genes with observation data only. The proposed algorithm was found, in most cases, to perform better than the previously proposed methods in terms of accuracy for the estimation of causal effects. In addition, multiple knock-outs proved to bring valuable additional information compared to single knock-outs. The choice of optimal intervention design therefore appears to be a crucial aspect for causal inference and an interesting challenge for future research., Comment: 18 pages

Gene regulatory networks are collections of genes that interact with one other and with other substances in the cell. By measuring gene expression over time using high-throughput technologies, it may be possible to reverse engineer, or infer, the structure of the gene network involved in a particular cellular process. These gene expression data typically have a high dimensionality and a limited number of biological replicates and time points. Due to these issues and the complexity of biological systems, the problem of reverse engineering networks from gene expression data demands a specialized suite of statistical tools and methodologies. We propose a non-standard adaptation of a simulation-based approach known as Approximate Bayesian Computing based on Markov chain Monte Carlo sampling. This approach is particularly well suited for the inference of gene regulatory networks from longitudinal data. The performance of this approach is investigated via simulations and using longitudinal expression data from a genetic repair system in Escherichia coli., 16 pages, 11 figures

The potential requirement of either the Prion or Shadoo protein for early mouse embryogenesis was recently suggested. However, the current data did not allow to precise the developmental process that was affected in the absence of both proteins and that led to the observed early lethal phenotype. In the present study, using various Prnp transgenic mouse lines and lentiviral vectors expressing shRNAs that target the Shadoo-encoding mRNA, we further demonstrate the specific requirement of at least one of these two PrP-related proteins at early developmental stages. Histological analysis reveals developmental defect of the ectoplacental cone and important hemorrhage surrounding the Prnp-knockout-Sprn-knockdown E7.5 embryos. By restricting the RNA interference to the trophoblastic cell lineages, the observed lethal phenotype could be attributed to the sole role of these proteins in this trophectoderm-derived compartment. RNAseq analysis performed on early embryos of various Prnp and Sprn genotypes indicated that the simultaneous down-regulation of these two proteins affects cell-adhesion and inflammatory pathways as well as the expression of ectoplacental-specific genes. Overall, our data provide biological clues in favor of a crucial and complementary embryonic role of the prion protein family in Eutherians and emphasizes the need to further evaluate its implication in normal and pathological human placenta biology.

International audience; Somatic cell nuclear transfer (SCNT) is the most efficient cell reprogramming technique available, especially when working with bovine species. Although SCNT blastocysts performed equally well or better than controls in the weeks following embryo transfer at Day 7, elongation and gastrulation defects were observed prior to implantation. To understand the developmental implications of embryonic/extra-embryonic interactions, the morphological and molecular features of elongating and gastrulating tissues were analysed. At Day 18, 30 SCNT conceptuses were compared to 20 controls (AI and IVP: 10 conceptuses each); one-half of the SCNT conceptuses appeared normal while the other half showed signs of atypical elongation and gastrulation. SCNT was also associated with a high incidence of discordance in embryonic and extra-embryonic patterns, as evidenced by morphological and molecular ''uncoupling''. Elongation appeared to be secondarily affected; only 3 of 30 conceptuses had abnormally elongated shapes and there were very few differences in gene expression when they were compared to the controls. However, some of these differences could be linked to defects in microvilli formation or extracellular matrix composition and could thus impact extra-embryonic functions. In contrast to elongation, gastrulation stages included embryonic defects that likely affected the hypoblast, the epiblast, or the early stages of their differentiation. When taking into account SCNT conceptus somatic origin, i.e. the reprogramming efficiency of each bovine ear fibroblast (Low: 0029, Med: 7711, High: 5538), we found that embryonic abnormalities or severe embryonic/extra-embryonic uncoupling were more tightly correlated to embryo loss at implantation than were elongation defects. Alternatively, extra-embryonic differences between SCNT and control conceptuses at Day 18 were related to molecular plasticity (high efficiency/high plasticity) and subsequent pregnancy loss. Finally, because it alters re-differentiation processes in vivo, SCNT reprogramming highlights temporally and spatially restricted interactions among cells and tissues in a unique way.

The French StatOmique Consortium gathers more than 40 statisticians and bioinformaticians involved in high-throughput transcriptome data analysis in France. The objective of this group, created in 2008, is to share among researchers and practitioners knowledge of the statistical analysis of high-throughput data. Chantier qualité GA; International audience; During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis. In this work, we focus on a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of RNA-seq data, with an emphasis on the use of varied real and simulated datasets involving different species and experimental designs to represent data characteristics commonly observed in practice. Based on this comparison study, we propose practical recommendations on the appropriate normalization method to be used and its impact on the differential analysis of RNA-seq data.

Chantier qualité GA; The biological function of the Prion protein remains largely unknown but recent data revealed its implication in early zebrafish and mammalian embryogenesis. To gain further insight into its biological function, comparative transcriptomic analysis between FVB/N and FVB/N Prnp knockout mice was performed at early embryonic stages. RNAseq analysis revealed the differential expression of 73 and 263 genes at E6.5 and E7.5, respectively. The related metabolic pathways identified in this analysis partially overlap with those described in PrP1 and PrP2 knockdown zebrafish embryos and prion-infected mammalian brains and emphasize a potentially important role for the PrP family genes in early developmental processes.

International audience; Coccidiosis is a parasitic disease with major economic impact, one of whose main causative agents is Eimeria tenella. Chicken breeds display variable natural resistance to this disease. Unravelling the genetic bases of such variations could provide new clues for protection strategies. Transcriptomic experiments were conducted comparing resistant (Fayoumi) and susceptible (Leghorn) lines. Caecum and caecal tonsils were analysed. A global increase in differential gene expression following infection was observed for caecum comparisons, whereas a global decrease following infection was observed for caecal tonsils. Gene lists for infected tissues display 40 genes in common across breeds, 20 of which were specific to infected tissues. Among these specific genes, 9 belong to the 100 more differentially expressed genes of the infected caecum comparison. Gene expression networks were constructed in parallel, identifying highly connected genes. Comparing information from differential gene lists and gene network analysis allows one to highlight potential pivotal genes in the infection process, one of which was located in a putative significant QTL region for infection associated lesions.

MicroRNAs are small non-coding RNAs approximately 22 nt long that modulate gene expression in animals and plants. It has been recently demonstrated that herpesviruses encode miRNAs to control the post-transcriptional regulation of expression from their own genomes and possibly that of their host, thus adding an additional layer of complexity to the physiological cross-talk between host and pathogen. The present study focussed on the interactions between porcine dendritic cells (DCs) and the Pseudorabies virus (PRV), an alpha-herpesvirus causing Aujeszky's disease in pigs. A catalogue of porcine and viral miRNAs, expressed eight hours post-infection, was established by deep sequencing. An average of 2 million reads per sample with a size of 21-24 nucleotides was obtained from six libraries representing three biological replicates of infected and mock-infected DCs. Almost 95% of reads mapped to the draft pig genome sequence and pig miRNAs previously annotated in dedicated databases were detected by sequence alignment. In silico prediction allowed the identification of unknown porcine as well as of five miRNAs transcribed by the Large Latency Transcript (LLT) of PRV. The gene target prediction of the viral miRNAs and the Ingenuity Pathway Analysis of differentially expressed pig miRNAs were conducted to contextualize the identified small RNA molecules and functionally characterize their involvement in the post-transcriptional regulation of gene expression. The results support a role for PRV miRNAs in the maintenance of the host cell latency state through the down-regulation of immediate-early viral genes which is similar to other herpesviruses. The differentially expressed swine miRNAs identified a unique network of target genes with highly significant functions in the development and function of the nervous system and in infectious mechanisms, suggesting that the modulation of both host and viral miRNAs is necessary for the establishment of PRV latency.

Genes are known to interact with one another through proteins by regulating the rate at which gene transcription takes place. As such, identifying these gene-to-gene interactions is essential to improving our knowledge of how complex biological systems work. In recent years, a growing body of work has focused on methods for reverse-engineering these so-called gene regula-tory networks from time-course gene expression data. However, reconstruction of these networks is often complicated by the large number of genes potentially involved in a given network and the limited number of time points and biological replicates typically measured. Bayesian methods are particularly well-suited for dealing with problems of this nature, as they provide a systematic way to deal with different sources of variation and allow for a measure of uncertainty in parameter esti-mates through posterior distributions, rather than point estimates. Our current work examines the application of approximate Bayesian methodology for the purpose of reverse engineering regula-tory networks from time-course gene expression data. We demonstrate the advantages of our pro-posed approximate Bayesian approaches by comparing their performance on a well-characterized pathway in [i]Escherichia coli[/i].

Dairy cattle breeds have been subjected over the last fifty years to intense artificial selection towards improvement of milk production traits. In this study, we performed a whole genome scan for differentiation using 42,486 SNPs in the three major French dairy cattle breeds (Holstein, Normande and Montbéliarde) to identify the main physiological pathways and regions which were affected by this selection. After analyzing the population structure, we estimated F(ST) within and across the three breeds for each SNP under a pure drift model. We further considered two different strategies to evaluate the effect of selection at the genome level. First, smoothing F(ST) values over each chromosome with a local variable bandwidth kernel estimator allowed identifying 13 highly significant regions subjected to strong and/or recent positive selection. Some of them contained genes within which causal variants with strong effect on milk production traits (GHR) or coloration (MC1R) have already been reported. To go further in the interpretation of the observed signatures of selection we subsequently concentrated on the annotation of differentiated genes defined according to the F(ST) value of SNPs localized close or within them. To that end we performed a comprehensive network analysis which suggested a central role of somatotropic and gonadotropic axes in the response to selection. Altogether, these observations shed light on the antagonism, at the genome level, between milk production and reproduction traits in highly producing dairy cows.

Background Mammalians gamete production takes place in the testis but when they exit this organ, although spermatozoa have acquired a specialized and distinct morphology, they are immotile and infertile. It is only after their travel in the epididymis that sperm gain their motility and fertility. Epididymis is a crescent shaped organ adjacent to the testis that can be divided in three gross morphological regions, head (caput), body (corpus) and tail (cauda). It contains a long and unique convoluted tubule connected to the testis via the efferent ducts and finished by joining the vas deferens in its caudal part. Results In this study, the testis, the efferent ducts (vas efferens, VE), nine distinct successive epididymal segments and the deferent duct (vas deferens, VD) of four adult boars of known fertility were isolated and their mRNA extracted. The gene expression of each of these samples was analyzed using a pig generic 9 K nylon microarray (AGENAE program; GEO accession number: GPL3729) spotted with 8931 clones derived from normalized cDNA banks from different pig tissues including testis and epididymis. Differentially expressed transcripts were obtained with moderated t-tests and F-tests and two data clustering algorithms based either on partitioning around medoid (top down PAM) or hierarchical clustering (bottom up HCL) were combined for class discovery and gene expression analysis. Tissue clustering defined seven transcriptomic units: testis, vas efferens and five epididymal transcriptomic units. Meanwhile transcripts formed only four clusters related to the tissues. We have then used a specific statistical method to sort out genes specifically over-expressed (markers) in testis, VE or in each of the five transcriptomic units of the epididymis (including VD). The specific regional expression of some of these genes was further validated by PCR and Q-PCR. We also searched for specific pathways and functions using available gene ontology information. Conclusion This study described for the first time the complete transcriptomes of the testis, the epididymis, the vas efferens and the vas deferens on the same species. It described new genes or genes not yet reported over-expressed in these boar tissues, as well as new control mechanisms. It emphasizes and fulfilled the gap between studies done in rodents and human, and provides tools that will be useful for further studies on the biochemical processes responsible for the formation and maintain of the epididymal regionalization and the development of a fertile spermatozoa.

Background The recent settlement of cattle in West Africa after several waves of migration from remote centres of domestication has imposed dramatic changes in their environmental conditions, in particular through exposure to new pathogens. West African cattle populations thus represent an appealing model to unravel the genome response to adaptation to tropical conditions. The purpose of this study was to identify footprints of adaptive selection at the whole genome level in a newly collected data set comprising 36,320 SNPs genotyped in 9 West African cattle populations. Results After a detailed analysis of population structure, we performed a scan for SNP differentiation via a previously proposed Bayesian procedure including extensions to improve the detection of loci under selection. Based on these results we identified 53 genomic regions and 42 strong candidate genes. Their physiological functions were mainly related to immune response (MHC region which was found under strong balancing selection, CD79A, CXCR4, DLK1, RFX3, SEMA4A, TICAM1 and TRIM21), nervous system (NEUROD6, OLFM2, MAGI1, SEMA4A and HTR4) and skin and hair properties (EDNRB, TRSP1 and KRTAP8-1). Conclusion The main possible underlying selective pressures may be related to climatic conditions but also to the host response to pathogens such as Trypanosoma(sp). Overall, these results might open the way towards the identification of important variants involved in adaptation to tropical conditions and in particular to resistance to tropical infectious diseases.

Background Often, software available for biological pathways reconstruction rely on literature search to find links between genes. The aim of this study is to reconstruct gene networks from microarray data, using Graphical Gaussian models. Results The GeneNet R package was applied to the Eadgene chicken infection data set. No significant edges were found for the list of differentially expressed genes between conditions MM8 and MA8. On the other hand, a large number of significant edges were found among 85 differentially expressed genes between conditions MM8 and MM24. Conclusion Many edges were inferred from the microarray data. Most of them could, however, not be validated using other pathway reconstruction software. This was partly due to the fact that a quite large proportion of the differentially expressed genes were not annotated. Further biological validation is therefore needed for these networks, using for example in vitro invalidation of genes.

Motivation: With the proliferation of microarray experiments and their availability in the public domain, the use of meta-analysis methods to combine results from different studies increases. In microarray experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably increase the statistical power and give more accurate results. Results: A moderated effect size combination method was proposed and compared with other meta-analysis approaches. All methods were applied to real publicly available datasets on prostate cancer, and were compared in an extensive simulation study for various amounts of inter-study variability. Although the proposed moderated effect size combination improved already existing effect size approaches, the P-value combination was found to provide a better sensitivity and a better gene ranking than the other meta-analysis methods, while effect size methods were more conservative. Availability: An R package metaMA is available on the CRAN. Contact: guillemette.marot@jouy.inra.fr

International audience; Time-course microarray studies require a particular modelling of covariance matrices when measures are repeated on the same individuals. Taking into account the within-subject correlation in the test statistics for differential gene expression, however, requires a large number of parameters when a gene-specific approach is used, which often results in a lack of power due to the small number of individuals usually considered in microarray experiments. Shrinkage approaches can improve this detection power in differential gene expression studies by reducing the number of parameters, while offering a good flexibility and a small rate of false positives. A natural extension of the shrinkage approach based on a structural mixed model to variance–covariance matrices is proposed. The structural model was used in three configurations to shrink (i) the eigenvalues in an eigenvalue/eigenvector decomposition, (ii) the innovation variances in a Cholesky decomposition, (iii) both the variances and correlation parameters of a gene-by-gene covariance matrix using a Fisher transformation. The proposed methods were applied both to a publicly available data set and to simulated data. They were found to perform well, compared to previously proposed empirical Bayesian approaches, and outperformed the gene-specific or common-covariance methods in many cases.

A longitudinal approach is proposed to map QTL affecting function-valued traits and to estimate their effect over time. The method is based on fitting mixed random regression models. The QTL allelic effects are modelled with random coefficient parametric curves and using a gametic relationship matrix. A simulation study was conducted in order to assess the ability of the approach to fit different patterns of QTL over time. It was found that this longitudinal approach was able to adequately fit the simulated variance functions and considerably improved the power of detection of time-varying QTL effects compared to the traditional univariate model. This was confirmed by an analysis of protein yield data in dairy cattle, where the model was able to detect QTL with high effect either at the beginning or the end of the lactation, that were not detected with a simple 305 day model. A longitudinal approach is proposed to map QTL affecting function-valued traits and to estimate their effect over time. The method is based on fitting mixed random regression models. The QTL allelic effects are modelled with random coefficient parametric curves and using a gametic relationship matrix. A simulation study was conducted in order to assess the ability of the approach to fit different patterns of QTL over time. It was found that this longitudinal approach was able to adequately fit the simulated variance functions and considerably improved the power of detection of time-varying QTL effects compared to the traditional univariate model. This was confirmed by an analysis of protein yield data in dairy cattle, where the model was able to detect QTL with high effect either at the beginning or the end of the lactation, that were not detected with a simple 305 day model.

International audience; Generalized linear mixed models (GLMM) form a very general class of random effects models for discrete and continuous responses in the exponential family. They are useful in a variety of applications. The traditional likelihood approach for GLMM usually involves high dimensional integrations which are computationally intensive. In this work, we investigate the case of binary outcomes analyzed under a two stage probit normal model with random effects. First, it is shown how ML estimates of the fixed effects and variance components can be computed using a stochastic approximation of the EM algorithm (SAEM). The SAEM algorithm can be applied directly, or in conjunction with a parameter expansion version of EM to speed up the convergence. A procedure is also proposed to obtain REML estimates of variance components and REML-based estimates of fixed effects. Finally an application to a real data set involving a clinical trial is presented, in which these techniques are compared to other procedures (penalized quasi-likelihood, maximum likelihood, Bayesian inference) already available in classical softwares (SAS Glimmix, SAS Nlmixed, WinBUGS), as well as to a Monte Carlo EM (MCEM) algorithm.

A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE (European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two different mastitis causing bacteria: Escherichia coli and Staphylococcus aureus. It was reassuring to see that most of the teams found the same main biological results. In fact, most of the differentially expressed genes were found for infection by E. coli between uninfected and 24 h challenged udder quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised a biological problem of cross-talk between infected and uninfected quarters which will have to be dealt with for further microarray studies.

The objective of the research was to estimate genetic parameters, such as heritabilities and genetic correlations, using daily test day data for milk yield (MY), milking speed (MS), dry matter intake (DMI), and body weight (BW) using random regression methodology. Data were from first lactation dairy cows (n = 320) from the Chamau research farm of the Swiss Federal Institute of Technology, Switzerland over the period from April 1994 to 2004. All traits were recorded daily using automated machines. Estimated heritabilities (h(2)) varied from 0.18 to 0.30 (mean h(2) = 0.24) for MY, 0.003 to 0.098 (mean h(2) = 0.03) for MS, 0.22 to 0.53 (mean h(2) = 0.43) for BW, and 0.12 to 0.34 (mean h(2) = 0.23) for DMI. A permanent environmental effect was included in both the univariate and bivariate models, but was assumed constant in estimating some genetic correlations because of convergence problems. Estimated genetic correlations varied from 0.31 to 0.41 between MY and MS, from -0.47 to 0.29 between MY and DMI, from -0.60 to 0.54 between MY and BW, from 0.17 to 0.26 between MS and DMI, from -0.18 to 0.25 between MS and BW, and from -0.89 to 0.29 between DMI and BW. Genetic correlations for MY, MS, DMI, and BW from calving to midlactation decreased similarly to 0.40, 0.36, 0.14, and 0.36 and, at the end of the lactation, decreased to -0.06, 0.23, -0.07, and 0.09, respectively. Daily genetic variance-covariance of many functional traits are reported for the first time and will be useful when constructing selection indexes for more than one trait based on longitudinal genetic parameters.