Since the sequencing of the genome and the development of high-throughput tools for the exploration of functional elements of the genome, the chicken has reached model organism status. Functional genomics focuses on understanding the function and regulation of genes and gene products on a global or genome-wide scale. Systems biology attempts to integrate functional information derived from multiple high-content data sets into a holistic view of all biological processes within a cell or organism. Generation of a large collection ( approximately 600K) of chicken expressed sequence tags, representing most tissues and developmental stages, has enabled the construction of high-density microarrays for transcriptional profiling. Comprehensive analysis of this large expressed sequence tag collection and a set of approximately 20K full-length cDNA sequences indicate that the transcriptome of the chicken represents approximately 20,000 genes. Furthermore, comparative analyses of these sequences have facilitated functional annotation of the genome and the creation of several bioinformatic resources for the chicken. Recently, about 20 papers have been published on transcriptional profiling with DNA microarrays in chicken tissues under various conditions. Proteomics is another powerful high-throughput tool currently used for examining the dynamics of protein expression in chicken tissues and fluids. Computational analyses of the chicken genome are providing new insight into the evolution of gene families in birds and other organisms. Abundant functional genomic resources now support large-scale analyses in the chicken and will facilitate identification of transcriptional mechanisms, gene networks, and metabolic or regulatory pathways that will ultimately determine the phenotype of the bird. New technologies such as marker-assisted selection, transgenics, and RNA interference offer the opportunity to modify the phenotype of the chicken to fit defined production goals. This review focuses on functional genomics in the chicken and provides a road map for large-scale exploration of the chicken genome.

The goal of our current consortium project is to launch a new era--functional genomics of poultry--by providing genomic resources [expressed sequence tags (EST) and DNA microarrays] and by examining global gene expression in target tissues of chickens. DNA microarray analysis has been a fruitful strategy for the identification of functional genes in several model organisms (i.e., human, rodents, fruit fly, etc.). We have constructed and normalized five tissue-specific or multiple-tissue chicken cDNA libraries [liver, fat, breast, and leg muscle/epiphyseal growth plate, pituitary/hypothalamus/pineal, and reproductive tract (oviduct/ovary/testes)] for high-throughput DNA sequencing of EST. DNA sequence clustering was used to build contigs of overlapping sequence and to identify unique, non-redundant EST clones (unigenes), which permitted printing of systems-wide chicken DNA microarrays. One of the most promising genetic resources for gene exploration and functional gene mapping is provided by two sets of experimental lines of broiler-type chickens developed at INRA, France, by divergent selection for extremes in growth traits (fast-growing versus slow-growing; fatness versus leanness at a similar growth rate). We are using DNA microarrays for global gene expression profiling to identify candidate genes and to map growth, metabolic, and regulatory pathways that control important production traits. Candidate genes will be used for functional gene mapping and QTL analysis of F2 progeny from intercrosses made between divergent genetic lines (fat x lean lines; fast-growing x slow-growing lines). Using our first chicken liver microarray, we have already identified several interesting differentially expressed genes in commercial broilers and in divergently selected broiler lines. Many of these candidate genes are involved in the lipogenic pathway and are controlled in part by the thyrotropic axis. Thus, genome-wide transcriptional profiling is a powerful tool used to visualize the cascade of genetic circuits that govern complex biological responses. Global gene expression profiling and QTL scans should enable us to functionally map the genetic pathways that control growth, development, and metabolism of chickens. This emerging technology will have broad applications for poultry breeding programs (i.e., use of molecular markers) and for future production systems (i.e., the health and welfare of birds and the quality of poultry products).

BackgroundThis descriptive study of the abdominal fat transcriptome takes advantage of two experimental lines of meat-type chickens (Gallus domesticus), which were selected over seven generations for a large difference in abdominal (visceral) fatness. At the age of selection (9 wk), the fat line (FL) and lean line (LL) chickens exhibit a 2.5-fold difference in abdominal fat weight, while their feed intake and body weight are similar. These unique avian models were originally created to unravel genetic and endocrine regulation of adiposity and lipogenesis in meat-type chickens. The Del-Mar 14K Chicken Integrated Systems microarray was used for a time-course analysis of gene expression in abdominal fat of FL and LL chickens during juvenile development (1–11 weeks of age).ResultsMicroarray analysis of abdominal fat in FL and LL chickens revealed 131 differentially expressed (DE) genes (FDR≤0.05) as the main effect of genotype, 254 DE genes as an interaction of age and genotype and 3,195 DE genes (FDR≤0.01) as the main effect of age. The most notable discoveries in the abdominal fat transcriptome were higher expression of many genes involved in blood coagulation in the LL and up-regulation of numerous adipogenic and lipogenic genes in FL chickens. Many of these DE genes belong to pathways controlling the synthesis, metabolism and transport of lipids or endocrine signaling pathways activated by adipokines, retinoid and thyroid hormones.ConclusionsThe present study provides a dynamic view of differential gene transcription in abdominal fat of chickens genetically selected for fatness (FL) or leanness (LL). Remarkably, the LL chickens over-express a large number of hemostatic genes that could be involved in proteolytic processing of adipokines and endocrine factors, which contribute to their higher lipolysis and export of stored lipids. Some of these changes are already present at 1 week of age before the divergence in fatness. In contrast, the FL chickens have enhanced expression of numerous lipogenic genes mainly after onset of divergence, presumably directed by multiple transcription factors. This transcriptional analysis shows that abdominal fat of the chicken serves a dual function as both an endocrine organ and an active metabolic tissue, which could play a more significant role in lipogenesis than previously thought.

In order to evaluate the role of insulin in chicken, an insulin immuno-neutralization was performed. Fed chickens received 1 or 3 i.v. injections of anti-insulin serum (2-h intervals), while fed or fasted controls received normal serum. Measurements included insulin signaling cascade (at 1 h in liver and muscle), metabolic or endocrine plasma parameters (at 1 and 5 h), and qRT-PCR analysis (at 5 h) of 23 genes involved in endocrine regulation, metabolisms, and transcription. Most plasma parameters and food intake were altered by insulin privation as early as 1 h and largely at 5 h. The initial steps of insulin signaling pathways including insulin receptor (IR), IR substrate-1 (IRS-1), and Src homology collagen and downstream elements: phosphatidylinositol 3-kinase (PI3K), Akt, GSK3, ERK2, and S6 ribosomal protein) were accordingly turned off in the liver. In the muscle, IR, IRS-1 tyrosine phosphorylation, and PI3K activity remained unchanged, whereas several subsequent steps were altered by insulin privation. In both tissues, AMPK was not altered. In the liver, insulin privation decreased Egr1, PPARg, SREBP1, THRSPa (spot14), D2-deiodinase, glucokinase (GK), and fatty acid synthase (whereas D3-deiodinase and IGF-binding protein1 transcripts were up-regulated. Liver SREBP1 and GK and plasma IGFBP1 proteins were accordingly downand up-regulated. In the muscle, PPARbd and atrogin-1 mRNA increased and Egr1 mRNA decreased. Changes in messengers were partly mimicked by fasting. Thus, insulin signaling in muscle is peculiar in chicken and is strictly dependent on insulin in fed status. The 'diabetic' status induced by insulin immuno-neutralization is accompanied by impairments of glucagon secretion, thyroid axis, and expression of several genes involved in regulatory pathways or metabolisms, evidencing pleiotropic effects of insulin in fed chicken.

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Background: Meat technological traits (i.e. meat pH, water retention and color) are important considerations for improving further processing of chicken meat. These quality traits were originally characterized in experimental lines selected for high (HG) and low (LG) growth. Presently, quantitative trait loci (QTL) for these traits were analyzed in an F 2 population issued from the HG × LG cross. A total of 698 animals in 50 full-sib families were genotyped for 108 microsatellite markers covering 21 linkage groups.

Quantitative trait loci (QTL) for metabolic and body composition traits were mapped at 7 and 9 wk, respectively, in an F(2) intercross between high-growth and low-growth chicken lines. These lines also diverged for abdominal fat percentage (AFP) and plasma insulin-like growth factor-I (IGF-I), insulin, and glucose levels. Genotypings were performed with 129 microsatellite markers covering 21 chromosomes. A total of 21 QTL with genomewide level of significance were detected by single-trait analyses for body weight (BW), breast muscle weight (BMW) and percentage (BMP), AF weight (AFW) and percentage (AFP), shank length (ShL) and diameter (ShD), fasting plasma glucose level (Gluc), and body temperature (T(b)). Other suggestive QTL were identified for these parameters and for plasma IGF-I and nonesterified fatty acid levels. QTL controlling adiposity and Gluc were colocalized on GGA3 and GGA5 and QTL for BW, ShL and ShD, adiposity, and T(b) on GGA4. Multitrait analyses revealed two QTL controlling Gluc and AFP on GGA5 and Gluc and T(b) on GGA26. Significant effects of the reciprocal cross were observed on BW, ShD, BMW, and Gluc, which may result from mtDNA and/or maternal effects. Most QTL regions for Gluc and adiposity harbor genes for which alleles have been associated with increased susceptibility to diabetes and/or obesity in humans. Identification of genes responsible for these metabolic QTL will increase our understanding of the constitutive "hyperglycemia" found in chickens. Furthermore, a comparative approach could provide new information on the genetic causes of diabetes and obesity in humans.

A preovulatory surge (PS) of luteinizing hormone (LH) and progesterone triggers follicle ovulation, which is the first step of egg production and is orchestrated by the hypothalamo-pituitary-gonadal (HPG) axis. In the HPG axis, hypothalamic peptides, gonadotropin releasing hormone, and gonadotropin inhibitory hormone, control the production of follicle stimulating hormone and LH by the pituitary, which subsequently regulate ovarian production of estradiol and progesterone, respectively. The goal of this study was to characterize the HPG axis function of average egg producing hens by assessing plasma hormone profiles and hypothalamic, pituitary, and follicle gene expression outside and during the PS (n = 3 per group). Results were analyzed by a one-way ANOVA using the mixed models procedure of SAS. Plasma estradiol was not affected by the PS (P > 0.05), but plasma progesterone levels increased 8-fold during the PS when compared to basal progesterone levels (P < 0.05). HPG axis gene expression related to ovulation stimulation (e.g., GNRH, GNRHR, and LHB) was down-regulated during the PS; whereas gene expression related to follicle development (e.g., FSHB) was up-regulated during the PS. Additionally, in the hypothalamus and pituitary, estradiol receptor expression was up-regulated during the PS, whereas progesterone receptor expression was down-regulated during the PS. In the follicle cells, gene expression pertaining to progesterone (e.g., STAR), androgen (e.g., HSD17B1), and estradiol (e.g., CYP19A1) production was up-regulated during the PS. Prior to this study, the HPG axis had yet to be characterized during the PS in the turkey hen. This study showed that the PS significantly impacted gene expression in the hypothalamus, pituitary, and ovarian follicles. These results provide a foundation for further research into the regulation of ovulation and egg production in turkey hens.