BackgroundAnimal’s efficiency in converting feed into lean gain is a critical issue for the profitability of meat industries. This study aimed to describe shared and specific molecular responses in different tissues of pigs divergently selected over eight generations for residual feed intake (RFI).ResultsPigs from the low RFI line had an improved gain-to-feed ratio during the test period and displayed higher leanness but similar adiposity when compared with pigs from the high RFI line at 132 days of age. Transcriptomics data were generated from longissimus muscle, liver and two adipose tissues using a porcine microarray and analyzed for the line effect (n = 24 pigs per line). The most apparent effect of the line was seen in muscle, whereas subcutaneous adipose tissue was the less affected tissue. Molecular data were analyzed by bioinformatics and subjected to multidimensional statistics to identify common biological processes across tissues and key genes participating to differences in the genetics of feed efficiency. Immune response, response to oxidative stress and protein metabolism were the main biological pathways shared by the four tissues that distinguished pigs from the low or high RFI lines. Many immune genes were under-expressed in the four tissues of the most efficient pigs. The main genes contributing to difference between pigs from the low vs high RFI lines were CD40, CTSC and NTN1. Different genes associated with energy use were modulated in a tissue-specific manner between the two lines. The gene expression program related to glycogen utilization was specifically up-regulated in muscle of pigs from the low RFI line (more efficient). Genes involved in fatty acid oxidation were down-regulated in muscle but were promoted in adipose tissues of the same pigs when compared with pigs from the high RFI line (less efficient). This underlined opposite line-associated strategies for energy use in skeletal muscle and adipose tissue. Genes related to cholesterol synthesis and efflux in liver and perirenal fat were also differentially regulated in pigs from the low vs high RFI lines.ConclusionsNon-productive functions such as immunity, defense against pathogens and oxidative stress contribute likely to inter-individual variations in feed efficiency.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3639-0) contains supplementary material, which is available to authorized users.

BackgroundBecause the cost of cereals is unstable and represents a large part of production charges for meat-type chicken, there is an urge to formulate alternative diets from more cost-effective feedstuff. We have recently shown that meat-type chicken source is prone to adapt to dietary starch substitution with fat and fiber. The aim of this study was to better understand the molecular mechanisms of this adaptation to changes in dietary energy sources through the fine characterization of transcriptomic changes occurring in three major metabolic tissues – liver, adipose tissue and muscle – as well as in circulating blood cells.ResultsWe revealed the fine-tuned regulation of many hepatic genes encoding key enzymes driving glycogenesis and de novo fatty acid synthesis pathways and of some genes participating in oxidation. Among the genes expressed upon consumption of a high-fat, high-fiber diet, we highlighted CPT1A, which encodes a key enzyme in the regulation of fatty acid oxidation. Conversely, the repression of lipogenic genes by the high-fat diet was clearly associated with the down-regulation of SREBF1 transcripts but was not associated with the transcript regulation of MLXIPL and NR1H3, which are both transcription factors. This result suggests a pivotal role for SREBF1 in lipogenesis regulation in response to a decrease in dietary starch and an increase in dietary PUFA. Other prospective regulators of de novo hepatic lipogenesis were suggested, such as PPARD, JUN, TADA2A and KAT2B, the last two genes belonging to the lysine acetyl transferase (KAT) complex family regulating histone and non-histone protein acetylation. Hepatic glycogenic genes were also down-regulated in chickens fed a high-fat, high-fiber diet compared to those in chickens fed a starch-based diet. No significant dietary-associated variations in gene expression profiles was observed in the other studied tissues, suggesting that the liver mainly contributed to the adaptation of birds to changes in energy source and nutrients in their diets, at least at the transcriptional level. Moreover, we showed that PUFA deposition observed in the different tissues may not rely on transcriptional changes.ConclusionWe showed the major role of the liver, at the gene expression level, in the adaptive response of chicken to dietary starch substitution with fat and fiber.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4520-5) contains supplementary material, which is available to authorized users.

BackgroundDuck species are known to have different susceptibility to fatty liver production in response to overfeeding. In order to better describe mechanisms involved in the development of hepatic steatosis and differences between species, transcriptome analyses were conducted on RNAs extracted from the livers of Pekin and Muscovy duck species and of their reciprocal hybrids, Mule and Hinny ducks fed ad libitum or overfed to identify differentially expressed genes and associated functions.ResultsAfter extraction from the liver of ducks from the four genetic types, RNAs were sequenced and sequencing data were analyzed. Hierarchic clustering and principal component analyses of genes expression levels indicated that differences between individuals lie primarily in feeding effect, differences between genetic types being less important. However, Muscovy ducks fed ad libitum and overfed were clustered together. Interestingly, Hinny and Mule hybrid ducks could not be differentiated from each other, according to feeding. Many genes with expression differences between overfed and ad libitum fed ducks were identified in each genetic type. Functional annotation analyses of these differentially expressed genes highlighted some expected functions (carbohydrate and lipid metabolisms) but also some unexpected ones (cell proliferation and immunity).ConclusionsThese analyses evidence differences in response to overfeeding between different genetic types and help to better characterize functions involved in hepatic steatosis in ducks.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-5415-1) contains supplementary material, which is available to authorized users.

The R package FAMT (factor analysis for multiple testing) provides a powerful method for large-scale significance testing under dependence. It is especially designed to select differentially expressed genes in microarray data when the correlation structure among gene expressions is strong. Indeed, this method reduces the negative impact of dependence on the multiple testing procedures by modeling the common information shared by all the variables using a factor analysis structure. New test statistics for general linear contrasts are deduced, taking advantage of the common factor structure to reduce correlation and consequently the variance of error rates. Thus, the FAMT method shows improvements with respect to most of the usual methods regarding the non discovery rate and the control of the false discovery rate (FDR). The steps of this procedure, each of them corresponding to R functions, are illustrated in this paper by two microarray data analyses. We first present how to import the gene expression data, the covariates and gene annotations. The second step includes the choice of the optimal number of factors, the factor model fitting, and provides a list of selected genes according to a preset FDR control level. Finally, diagnostic plots are provided to help the user interpret the factors using available external information on either genes or arrays.

Microarray analysis was used to identify genes whose expression in the mammary gland of Holstein-Friesian dairy cows was affected by the nonconservative Ala to Lys amino acid substitution at position 232 in exon VIII of the diacylglycerol-O-transferase 1 (DGAT1) gene. Mammary gland biopsies of 9 homozygous Ala cows, 13 heterozygous cows (Ala/Lys), and 4 homozygous Lys cows in midlactation were taken. Microarray ANOVA and factor analysis for multiple testing methods were used as statistical methods to associate the expression level of the genes present on Affymetrix bovine genome arrays (Affymetrix Inc., Santa Clara, CA) with the DGAT1 gene polymorphism. The data was also analyzed at the level of functional modules by gene set enrichment analysis. In this small-scale experimental setting, DGAT1 gene polymorphism did not modify milk yield and composition significantly, although expected changes occurred in the yields of C14:0, cis-9 C16:1, and long-chain fatty acids. Diacylglycerol-O-transferase 1 gene polymorphism affected the expression of 30 annotated genes related to cell growth, proliferation, and development, remodeling of the tissue, cell signaling and immune system response. Furthermore, the main affected functional modules were related to energy metabolism (lipid biosynthesis, oxidative phosphorylation, electron transport chain, citrate cycle, and propanoate metabolism), protein degradation (proteosome-ubiquitin pathways), and the immune system. We hypothesize that the observed differences in transcriptional activity reflect counter mechanisms of mammary gland tissue to respond to changes in milk fatty acid concentration or composition, or both.

BackgroundChanging the energy and nutrient source for growing animals may be an effective way of limiting adipose tissue expansion, a response which may depend on the genetic background of the animals. This study aims to describe the transcriptional modulations present in the adipose tissues of two pig lines divergently selected for residual feed intake which were either fed a high-fat high-fiber (HF) diet or an isocaloric low-fat high-starch diet (LF).ResultsTranscriptomic analysis using a porcine microarray was performed on 48 pigs (n = 12 per diet and per line) in both perirenal (PRAT) and subcutaneous (SCAT) adipose tissues. There was no interaction between diet and line on either adiposity or transcriptional profiles, so that the diet effect was inferred independently of the line. Irrespective of line, the relative weights of the two fat depots were lower in HF pigs than in LF pigs after 58 days on dietary treatment. In the two adipose tissues, the most apparent effect of the HF diet was the down-regulation of several genes associated with the ubiquitin-proteasome system, which therefore may be associated with dietary-induced modulations in genes acting in apoptotic and cell cycle regulatory pathways. Genes involved in glucose metabolic processes were also down-regulated by the HF diet, with no significant variation or decreased expression of important lipid-related genes such as the low-density lipoprotein receptor and leptin in the two fat pads. The master regulators of glucose and fatty acid homeostasis SREBF1 and MLXIPL, and peroxisome proliferator-activated receptor (PPAR)δ and its heterodimeric partner RXRA were down-regulated by the HF diet. PPARγ which has pleiotropic functions including lipid metabolism and adipocyte differentiation, was however up-regulated by this diet in PRAT and SCAT. Dietary-related modulations in the expression of genes associated with immunity and inflammation were mainly revealed in PRAT.ConclusionA high-fat high-fiber diet depressed glucose and lipid anabolic molecular pathways, thus counteracting adipose tissue expansion. Interaction effects between dietary intake of fiber and lipids on gene expression may modulate innate immunity and inflammation, a response which is of interest with regard to chronic inflammation and its adverse effects on health and performance.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2438-3) contains supplementary material, which is available to authorized users.

GeneGeneInteR is an R package dedicated to the detection of an association between a case-control phenotype and the interaction between two sets of biallelic markers (single nucleotide polymorphisms or SNPs) in case-control genome-wide associations studies. The development of statistical procedures for searching gene-gene interaction at the SNP-set level has indeed recently grown in popularity as these methods confer advantage in both statistical power and biological interpretation. However, all these methods have been implemented in home made softwares that are for most of them available only on request to the authors and at best have a web interface. Since the implementation of these methods is not straightforward, there is a need for a user-friendly tool to perform gene-based genegene interaction. The purpose of GeneGeneInteR is to propose a collection of tools for all the steps involved in gene-based gene-gene interaction testing in case-control association studies. Illustrated by an example of a dataset related to rheumatoid arthritis, this paper details the implementation of the functions available in GeneGeneInteR to perform an analysis of a collection of SNP sets. Such an analysis aims at addressing the complete statistical pipeline going from data importation to the visualization of the results through data manipulation and statistical analysis.

Inference on gene regulatory networks from high-throughput expression data turns out to be one of the main current challenges in systems biology. Such networks can be very insightful for the deep understanding of interactions between genes. Because genes-gene interactions is often viewed as joint contributions to known biological mechanisms, inference on the dependence among gene expressions is expected to be consistent to some extent with the functional characterization of genes which can be derived from ontologies (GO, KEGG, …). The present paper introduces a sparse factor model as a general framework either to account for a prior knowledge on joint contributions of modules of genes to latent biological processes or to infer on the corresponding co-expression network. We propose an ℓ1 - regularized EM algorithm to fit a sparse factor model for correlation. We demonstrate how it helps extracting modules of genes and more generally improves the gene clustering performance. The method is compared to alternative estimation procedures for sparse factor models of relevance networks in a simulation study. The integration of a biological knowledge based on the gene ontology (GO) is also illustrated on a liver expression data generated to understand adiposity variability in chicken.