Laurence Liaubet

A muscle transcriptome analysis identifies positional candidate genes for a complex trait in pig

Muscle tenderness is an important complex trait for meat quality and thus for genetic improvement through animal breeding. However, the physiological or genetic control of tenderness development in muscle is still poorly understood. In this work, using transcriptome analysis, we found a relationship between gene expression variability and tenderness. Muscle (longissimus dorsi) samples from 30 F(2) pigs were characterized by Warner-Bratzler Shear Force (WBSF) on cooked meat as a measurement of muscle tenderness. Gene expression levels were measured using microarrays for 17 muscle samples selected to represent a range of WBSF values. Using a linear regression model, we determined that samples with WBSF values above 30 N could be effectively analysed for genes exhibiting a significant association of their expression level on shear force (false discovery rate <0.05). These genes were shown to be involved in three functional networks: cell cycle, energy metabolism and muscle development. Twenty-two genes were mapped on the pig genome and 12 were found to be located in regions previously reported to contain quantitative trait loci (QTL) affecting pig meat tenderness (chromosomes 2, 6 and 13). Some genes appear therefore as positional candidate genes for QTL.

Transcriptome profiling of sheep granulosa cells and oocytes during early follicular development obtained by Laser Capture Microdissection

BackgroundSuccessful achievement of early folliculogenesis is crucial for female reproductive function. The process is finely regulated by cell-cell interactions and by the coordinated expression of genes in both the oocyte and in granulosa cells. Despite many studies, little is known about the cell-specific gene expression driving early folliculogenesis. The very small size of these follicles and the mixture of types of follicles within the developing ovary make the experimental study of isolated follicular components very difficult.The recently developed laser capture microdissection (LCM) technique coupled with microarray experiments is a promising way to address the molecular profile of pure cell populations. However, one main challenge was to preserve the RNA quality during the isolation of single cells or groups of cells and also to obtain sufficient amounts of RNA.Using a new LCM method, we describe here the separate expression profiles of oocytes and follicular cells during the first stages of sheep folliculogenesis.ResultsWe developed a new tissue fixation protocol ensuring efficient single cell capture and RNA integrity during the microdissection procedure. Enrichment in specific cell types was controlled by qRT-PCR analysis of known genes: six oocyte-specific genes (SOHLH2, MAEL, MATER, VASA, GDF9, BMP15) and three granulosa cell-specific genes (KL, GATA4, AMH).A global gene expression profile for each follicular compartment during early developmental stages was identified here for the first time, using a bovine Affymetrix chip. Most notably, the granulosa cell dataset is unique to date. The comparison of oocyte vs. follicular cell transcriptomes revealed 1050 transcripts specific to the granulosa cell and 759 specific to the oocyte.Functional analyses allowed the characterization of the three main cellular events involved in early folliculogenesis and confirmed the relevance and potential of LCM-derived RNA.ConclusionsThe ovary is a complex mixture of different cell types. Distinct cell populations need therefore to be analyzed for a better understanding of their potential interactions. LCM and microarray analysis allowed us to identify novel gene expression patterns in follicular cells at different stages and in oocyte populations.

NEUROG2 Drives Cell Cycle Exit of Neuronal Precursors by Specifically Repressing a Subset of Cyclins Acting at the G1 and S Phases of the Cell Cycle

ABSTRACT Proneural NEUROG2 (neurogenin 2 [Ngn2]) is essential for neuronal commitment, cell cycle withdrawal, and neuronal differentiation. Although NEUROG2s influence on neuronal commitment and differentiation is beginning to be clarified, its role in cell cycle withdrawal remains unknown. We therefore set out to investigate the molecular mechanisms by which NEUROG2 induces cell cycle arrest during spinal neurogenesis. We developed a large-scale chicken embryo strategy, designed to find gene networks modified at the onset of NEUROG2 expression, and thereby we identified those involved in controlling the cell cycle. NEUROG2 activation leads to a rapid decrease of a subset of cell cycle regulators acting at G1 and S phases, including CCND1, CCNE1/2, and CCNA2 but not CCND2. The use of NEUROG2VP16 and NEUROG2EnR, acting as the constitutive activator and repressor, respectively, indicates that NEUROG2 indirectly represses CCND1 and CCNE2 but opens the possibility that CCNE2 is also repressed by a direct mechanism. We demonstrated by phenotypic analysis that this rapid repression of cyclins prevents S phase entry of neuronal precursors, thus favoring cell cycle exit. We also showed that cell cycle exit can be uncoupled from neuronal differentiation and that during normal development NEUROG2 is in charge of tightly coordinating these two processes.

Methods for interpreting lists of affected genes obtained in a DNA microarray experiment

BackgroundThe aim of this paper was to describe and compare the methods used and the results obtained by the participants in a joint EADGENE (European Animal Disease Genomic Network of Excellence) and SABRE (Cutting Edge Genomics for Sustainable Animal Breeding) workshop focusing on post analysis of microarray data. The participating groups were provided with identical lists of microarray probes, including test statistics for three different contrasts, and the normalised log-ratios for each array, to be used as the starting point for interpreting the affected probes. The data originated from a microarray experiment conducted to study the host reactions in broilers occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria.ResultsSeveral conceptually different analytical approaches, using both commercial and public available software, were applied by the participating groups. The following tools were used: Ingenuity Pathway Analysis, MAPPFinder, LIMMA, GOstats, GOEAST, GOTM, Globaltest, TopGO, ArrayUnlock, Pathway Studio, GIST and AnnotationDbi. The main focus of the approaches was to utilise the relation between probes/genes and their gene ontology and pathways to interpret the affected probes/genes. The lack of a well-annotated chicken genome did though limit the possibilities to fully explore the tools. The main results from these analyses showed that the biological interpretation is highly dependent on the statistical method used but that some common biological conclusions could be reached.ConclusionIt is highly recommended to test different analytical methods on the same data set and compare the results to obtain a reliable biological interpretation of the affected genes in a DNA microarray experiment.

Gene array and real time PCR analysis of the adrenal sensitivity to adrenocorticotropic hormone in pig

BackgroundVariability in hypothalamic-pituitary-adrenal (HPA) axis activity has been shown to be influenced by genetic factors and related to great metabolic differences such as obesity. The aim of this study was to investigate molecular bases of genetic variability of the adrenal sensitivity to ACTH, a major source of variability, in Meishan (MS) and Large White (LW) pigs, MS being reported to exhibit higher basal cortisol levels, response to ACTH and fatness than LW. A pig cDNA microarray was used to identify changes in gene expression in basal conditions and in response to ACTH stimulation.ResultsGenotype and/or ACTH affected the expression of 211 genes related to transcription, cell growth/maintenance, signal transduction, cell structure/adhesion/extra cellular matrix and protein kinase/phosphatase activity. No change in the expression of known key regulator proteins of the ACTH signaling pathway or of steroidogenic enzymes was found. However, Mdh2, Sdha, Suclg2, genes involved in the tricarboxylic acid (TCA) pathway, were over-expressed in MS pigs. Higher TCA cycle activity in MS than in LW may thus result in higher steroidogenic activity and thus explain the typically higher cortisol levels in MS compared to LW. Moreover, up-regulation of Star and Ldlr genes in MS and/or in response to ACTH suggest that differences in the adrenal function between MS and LW may also involve mechanisms requisite for cholesterol supply to steroidogenesis.ConclusionThe present study provides new potential candidate genes to explain genetic variations in the adrenal sensitivity to ACTH and better understand relationship between HPA axis activity and obesity.

Phenotypic prediction based on metabolomic data for growing pigs from three main European breeds.

Predicting phenotypes is a statistical and biotechnical challenge, both in medicine (predicting an illness) and animal breeding (predicting the carcass economical value on a young living animal). High-throughput fine phenotyping is possible using metabolomics, which describes the global metabolic status of an individual, and is the closest to the terminal phenotype. The purpose of this work was to quantify the prediction power of metabolomic profiles for commonly used production phenotypes from a single blood sample from growing pigs. Several statistical approaches were investigated and compared on the basis of cross validation: raw data vs. signal preprocessing (wavelet transformation), with a single-feature selection method. The best results in terms of prediction accuracy were obtained when data were preprocessed using wavelet transformations on the Daubechies basis. The phenotypes related to meat quality were not well predicted because the blood sample was taken some time before slaughter, and slaughter is known to have a strong influence on these traits. By contrast, phenotypes of potential economic interest (e.g., lean meat percentage and ADFI) were well predicted (R(2) = 0.7; P < 0.0001) using metabolomic data.

The Structure of a Gene Co-Expression Network Reveals Biological Functions Underlying eQTLs

What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology.

Pathway results from the chicken data set using GOTM, Pathway Studio and Ingenuity softwares

BackgroundAs presented in the introduction paper, three sets of differentially regulated genes were found after the analysis of the chicken infection data set from EADGENE. Different methods were used to interpret these results.ResultsGOTM, Pathway Studio and Ingenuity softwares were used to investigate the three lists of genes. The three softwares allowed the analysis of the data and highlighted different networks. However, only one set of genes, showing a differential expression between primary and secondary response gave significant biological interpretation.ConclusionCombining these databases that were developed independently on different annotation sources supplies a useful tool for a global biological interpretation of microarray data, even if they may contain some imperfections (e.g. gene not or not well annotated).

Genetic variability of transcript abundance in pig peri-mortem skeletal muscle: eQTL localized genes involved in stress response, cell death, muscle disorders and metabolism

BackgroundThe genetics of transcript-level variation is an exciting field that has recently given rise to many studies. Genetical genomics studies have mainly focused on cell lines, blood cells or adipose tissues, from human clinical samples or mice inbred lines. Few eQTL studies have focused on animal tissues sampled from outbred populations to reflect natural genetic variation of gene expression levels in animals. In this work, we analyzed gene expression in a whole tissue, pig skeletal muscle sampled from individuals from a half sib F2 family shortly after slaughtering.ResultsQTL detection on transcriptome measurements was performed on a family structured population. The analysis identified 335 eQTLs affecting the expression of 272 transcripts. The ontologic annotation of these eQTLs revealed an over-representation of genes encoding proteins involved in processes that are expected to be induced during muscle development and metabolism, cell morphology, assembly and organization and also in stress response and apoptosis. A gene functional network approach was used to evidence existing biological relationships between all the genes whose expression levels are influenced by eQTLs. eQTLs localization revealed a significant clustered organization of about half the genes located on segments of chromosome 1, 2, 10, 13, 16, and 18. Finally, the combined expression and genetic approaches pointed to putative cis-drivers of gene expression programs in skeletal muscle as COQ4 (SSC1), LOC100513192 (SSC18) where both the gene transcription unit and the eQTL affecting its expression level were shown to be localized in the same genomic region. This suggests cis-causing genetic polymorphims affecting gene expression levels, with (e.g. COQ4) or without (e.g. LOC100513192) potential pleiotropic effects that affect the expression of other genes (cluster of trans-eQTLs).ConclusionGenetic analysis of transcription levels revealed dependence among molecular phenotypes as being affected by variation at the same loci. We observed the genetic variation of molecular phenotypes in a specific situation of cellular stress thus contributing to a better description of muscle physiologic response. In turn, this suggests that large amounts of genetic variation, mediated through transcriptional networks, can drive transient cell response phenotypes and contribute to organismal adaptative potential.

Using microarrays to identify positional candidate genes for QTL: the case study of ACTH response in pigs.

BackgroundMicroarray studies can supplement QTL studies by suggesting potential candidate genes in the QTL regions, which by themselves are too large to provide a limited selection of candidate genes. Here we provide a case study where we explore ways to integrate QTL data and microarray data for the pig, which has only a partial genome sequence. We outline various procedures to localize differentially expressed genes on the pig genome and link this with information on published QTL. The starting point is a set of 237 differentially expressed cDNA clones in adrenal tissue from two pig breeds, before and after treatment with adrenocorticotropic hormone (ACTH).ResultsDifferent approaches to localize the differentially expressed (DE) genes to the pig genome showed different levels of success and a clear lack of concordance for some genes between the various approaches. For a focused analysis on 12 genes, overlapping QTL from the public domain were presented. Also, differentially expressed genes underlying QTL for ACTH response were described. Using the latest version of the draft sequence, the differentially expressed genes were mapped to the pig genome. This enabled co-location of DE genes and previously studied QTL regions, but the draft genome sequence is still incomplete and will contain many errors. A further step to explore links between DE genes and QTL at the pathway level was largely unsuccessful due to the lack of annotation of the pig genome. This could be improved by further comparative mapping analyses but this would be time consuming.ConclusionThis paper provides a case study for the integration of QTL data and microarray data for a species with limited genome sequence information and annotation. The results illustrate the challenges that must be addressed but also provide a roadmap for future work that is applicable to other non-model species.