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A recently developed mechanistic mathematical model of the bovine estrous cycle was parameterized to fit empirical data sets collected during one estrous cycle of 31 individual cows, with the main objective to further validate the model. The a priori criteria for validation were (1) the resulting model can simulate the measured data correctly (i.e. goodness of fit), and (2) this is achieved without needing extreme, probably non-physiological parameter values. We used a least squares optimization procedure to identify parameter configurations for the mathematical model to fit the empirical in vivo measurements of follicle and corpus luteum sizes, and the plasma concentrations of progesterone, estradiol, FSH and LH for each cow. The model was capable of accommodating normal variation in estrous cycle characteristics of individual cows. With the parameter sets estimated for the individual cows, the model behavior changed for 21 cows, with improved fit of the simulated output curves for 18 of these 21 cows. Moreover, the number of follicular waves was predicted correctly for 18 of the 25 two-wave and three-wave cows, without extreme parameter value changes. Estimation of specific parameters confirmed results of previous model simulations indicating that parameters involved in luteolytic signaling are very important for regulation of general estrous cycle characteristics, and are likely responsible for differences in estrous cycle characteristics between cows. [ABSTRACT FROM PUBLISHER]

The expression of oestrous behaviour in Holstein Friesian dairy cows has progressively decreased over the past 50 years. Reduced oestrus expression is one of the factors contributing to the current suboptimal reproductive efficiency in dairy farming. Variation between and within cows in the expression of oestrous behaviour is associated with variation in peripheral blood oestradiol concentrations during oestrus. In addition, there is evidence for a priming role of progesterone for the full display of oestrous behaviour. A higher rate of metabolic clearance of ovarian steroids could be one of the factors leading to lower peripheral blood concentrations of oestradiol and progesterone in high-producing dairy cows. Oestradiol acts on the brain by genomic, non-genomic and growth factor-dependent mechanisms. A firm base of understanding of the ovarian steroid-driven central genomic regulation of female sexual behaviour has been obtained from studies on rodents. These studies have resulted in the definition of five modules of oestradiol-activated genes in the brain, referred to as the GAPPS modules. In a recent series of studies, gene expression in the anterior pituitary and four brain areas (amygdala, hippocampus, dorsal hypothalamus and ventral hypothalamus) in oestrous and luteal phase cows, respectively, has been measured, and the relation with oestrous behaviour of these cows was analysed. These studies identified a number of genes of which the expression was associated with the intensity of oestrous behaviour. These genes could be grouped according to the GAPPS modules, suggesting close similarity of the regulation of oestrous behaviour in cows and female sexual behaviour in rodents. A better understanding of the central genomic regulation of the expression of oestrous behaviour in dairy cows may in due time contribute to improved (genomic) selection strategies for appropriate oestrus expression in high-producing dairy cows. [ABSTRACT FROM PUBLISHER]

The expression of estrous (sexually receptive) behavior (EB), a key fertility trait in dairy cows, has been declining over the past few decades both in intensity and duration. Improved knowledge of the genomic factors underlying EB, which is currently lacking, may lead to novel applications to enhance fertility. Our objective was to identify genes and biological processes shared among the bovine anterior pituitary (AP) and four brain areas that act together to regulate EB by investigating networks of coexpressed genes between these tissues. We used a systems biology approach called weighted gene coexpression network analysis for defining gene coexpression networks using gene expression data from the following tissues collected from 14 cows at estrus: AP, dorsal hypothalamus (DH), ventral hypothalamus (VH), amygdala (AM), and hippocampus (HC). Consensus modules of coexpressed genes were identified between the networks for the AM-DH, HC-DH, VH-DH, AP-DH, and AM-HC tissue pairs. The correlation between the module's eigengene (weighted average gene expression profile) and levels of EB exhibited by the experimental cows were tested. Estrous behavior-correlated modules were found enriched for gene ontology terms like glial cell development and regulation of neural projection development as well as for Kyoto Encyclopedia of Genes and Genomes pathway terms related to brain degenerative diseases. General cellular processes like oxidative phosphorylation and ribosome and biosynthetic processes were found enriched in several correlated modules, indicating increased transcription and protein synthesis. Stimulation of ribosomal RNA synthesis is known from rodent studies to be a primary event in the activation of neuronal cells and pathways involved in female reproductive behavior and this precedes the estrogen-driven expansion of dendrites and synapses. Similar processes also operate in cows to affect EB. Hub genes within EB-correlated modules (e.g. NEFL, NDRG2, GAP43, THY1, and TCF7L2, among others) are strong candidates among genes regulating EB expression. The study improved our understanding of the genomic regulation of EB in dairy cows by providing new insights into genes and biological processes shared among the bovine AP and brain areas acting together to regulate EB. The new knowledge could lead to the development of novel management strategies to monitor and improve reproductive performance in dairy cows (for example, biomarkers for estrus detection). [ABSTRACT FROM AUTHOR]

Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed ‘omics’ technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A ‘system’ approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with ‘system approaches’ in animal sciences, providing exciting opportunities to predict and modulate animal traits. [ABSTRACT FROM AUTHOR]

Intensive selection for high milk yield in dairy cows has raised production levels substantially but at the cost of reduced fertility, which manifests in different ways including reduced expression of oestrous behaviour. The genomic regulation of oestrous behaviour in bovines remains largely unknown. Here, we aimed to identify and study those genes that were associated with oestrous behaviour among genes expressed in the bovine anterior pituitary either at the start of oestrous cycle or at the mid-cycle (around day 12 of cycle), or regardless of the phase of cycle. Oestrous behaviour was recorded in each of 28 primiparous cows from 30 days in milk onwards till the day of their sacrifice (between 77 and 139 days in milk) and quantified as heat scores. An average heat score value was calculated for each cow from heat scores observed during consecutive oestrous cycles excluding the cycle on the day of sacrifice. A microarray experiment was designed to measure gene expression in the anterior pituitary of these cows, 14 of which were sacrificed at the start of oestrous cycle (day 0) and 14 around day 12 of cycle (day 12). Gene expression was modelled as a function of the orthogonally transformed average heat score values using a Bayesian hierarchical mixed model on data from day 0 cows alone (analysis 1), day 12 cows alone (analysis 2) and the combined data from day 0 and day 12 cows (analysis 3). Genes whose expression patterns showed significant linear or non-linear relationships with average heat scores were identified in all three analyses (177, 142 and 118 genes, respectively). Gene ontology terms enriched among genes identified in analysis 1 revealed processes associated with expression of oestrous behaviour whereas the terms enriched among genes identified in analysis 2 and 3 were general processes which may facilitate proper expression of oestrous behaviour at the subsequent oestrus. Studying these genes will help to improve our understanding of the genomic regulation of oestrous behaviour, ultimately leading to better management strategies and tools to improve or monitor reproductive performance in bovines. [ABSTRACT FROM AUTHOR]

Describes fluctuations in leptin concentrations during late pregnancy and lactation in dairy cows. Leptin concentrations and reproductive measures; Influence of feed intake on the production of leptin during the lactation period; Recovery of leptin concentrations after the nadir at parturition.

Chicken skeletal muscle satellite cells are located between the basement membrane and the sarcolemma of mature muscle fibers. Avian broilers have been genetically selected based on their high growth velocity and large muscle mass. The Wuding chicken is a famous local chicken in Yunnan Province that undergoes non-selection breeding and is slow growing. In this study, we aimed to explore differences in the proliferation and differentiation properties of satellite cells isolated from the two chicken breeds. Using immunofluorescence, hematoxylin-eosin staining and real-time polymerase chain reaction analysis, we analyzed the in vitro characteristics of proliferating and differentiating satellite cells isolated from the two chicken breeds. The growth curve of satellite cells was S-shaped, and cells from Wuding chickens entered the logarithmic phase and plateau phase 1 day later than those from Avian chicken. The results also showed that the two skeletal muscle satellite cell lines were positive for Pax7, MyoD and IGF-1. The expression of Pax7 followed a downward trend, whereas that of MyoD and IGF-1 first increased and subsequently decreased in cells isolated from the two chickens. These data indicated that the skeletal muscle satellite cells of Avian chicken grow and differentiate faster than did those of Wuding chickens. We suggest that the methods of breeding selection applied to these breeds regulate the characteristics of skeletal muscle satellite cells to influence muscle growth.

Selective breeding is an effective tool to improve livestock. Several selection experiments have been conducted to study direct selection responses as well as correlated responses in traits of skeletal muscle growth and function. Moreover, comparisons of domestic with wild-type species and of extreme breeds provide information on the genetic background of the skeletal muscle phenotype. Structural muscular components that differed with increasing distance in lean growth or meat quality in mammals were found to be myofibre number, myofibre size, proportions of fibre types as well as the numbers and proportions of secondary and primary fibres. Furthermore, markers of satellite cell proliferation, metabolic enzyme activities, glycogen and fat contents, the expression of myosin heavy chain isoforms, of activated AMPKα and other proteins in skeletal muscle tissue and circulating IGF1 and IGF-binding proteins have been identified to be involved in selection responses observed in pigs, cattle and/or chicken. The use of molecular methods for selective breeding of fish has only recently been adopted in aquaculture and studies of the genetic basis of growth and flesh quality traits are scarce. Some of the molecular markers of muscle structure/metabolism in livestock have also been identified in fish, but so far no studies have linked them with selection response. Genome scans have been applied to identify genomic regions exhibiting quantitative trait loci that control traits of interest, for example, muscle structure and meat quality in pigs and growth rate in chicken. As another approach, polymorphisms in candidate genes reveal the relationship between genetic variation and target traits. Thus, in large-scale studies with pigs' associations of polymorphisms in the HMGA2, CA3, EPOR, NME1 and TTN genes with traits of carcass and meat quality were detected. Other studies revealed the significance of mutations in the IGF2 and RYR1 genes for carcass lean and muscle fibre traits in pigs. Mutations in the myostatin (MSTN) gene in fish were also examined. Advances in research of the genetic and environmental control of traits related to meat quality and growth have been made by the application of holistic 'omics' techniques that studied the whole muscle-specific genome, transcriptome and proteome in relation to muscle and meat traits, the development of new methods for muscle fibre typing and the adaptation of biophysical measures to develop parameters of muscle fibre traits as well as the application of in vitro studies. Finally, future research priorities in the field are defined.

Skeletal muscle development in vertebrates - also termed myogenesis - is a highly integrated process. Evidence to date indicates that the processes are very similar across mammals, poultry and fish, although the timings of the various steps differ considerably. Myogenesis is regulated by the myogenic regulatory factors and consists of two to three distinct phases when different fibre populations appear. The critical times when myogenesis is prone to hormonal or environmental influences depend largely on the developmental stage. One of the main mechanisms for both genetic and environmental effects on muscle fibre development is via the direct action of the growth hormone-insulin-like growth factor (GH-IGF) axis. In mammals and poultry, postnatal growth and function of muscles relate mainly to the hypertrophy of the fibres formed during myogenesis and to their fibre-type composition in terms of metabolic and contractile properties, whereas in fish hyperplasia still plays a major role. Candidate genes that are important in skeletal muscle development, for instance, encode for IGFs and IGF-binding proteins, myosin heavy chain isoforms, troponin T, myosin light chain and others have been identified. In mammals, nutritional supply in utero affects myogenesis and the GH-IGF axis may have an indirect action through the partitioning of nutrients towards the gravid uterus. Impaired myogenesis resulting in low skeletal myofibre numbers is considered one of the main reasons for negative long-term consequences of intrauterine growth retardation. Severe undernutrition in utero due to natural variation in litter or twin-bearing species or insufficient maternal nutrient supply may impair myogenesis and adversely affect carcass quality later in terms of reduced lean and increased fat deposition in the progeny. On the other hand, increases in maternal feed intake above standard requirement seem to have no beneficial effects on the growth of the progeny with myogenesis not or only slightly affected. Initial studies on low and high maternal protein feeding are published. Although there are only a few studies, first results also reveal an influence of nutrition on skeletal muscle development in fish and poultry. Finally, environmental temperature has been identified as a critical factor for growth and development of skeletal muscle in both fish and poultry.

Subfertility has negative effects for dairy farm profitability, animal welfare and sustainability of animal production. Increasing herd sizes and economic pressures restrict the amount of time that farmers can spend on counteractive management. Genetic improvement will become increasingly important to restore reproductive performance. Complementary to traditional breeding value estimation procedures, genomic selection based on genome-wide information will become more widely applied. Functional genomics, including transcriptomics (gene expression profiling), produces the information to understand the consequences of selection as it helps to unravel physiological mechanisms underlying female fertility traits. Insight into the latter is needed to develop new effective management strategies to combat subfertility. Here, the importance of functional genomics for dairy cow reproduction so far and in the near future is evaluated. Recent gene profiling studies in the field of dairy cow fertility are reviewed and new data are presented on genes that are expressed in the brains of dairy cows and that are involved in dairy cow oestrus (behaviour). Fast-developing new research areas in the field of functional genomics, such as epigenetics, RNA interference, variable copy numbers and nutrigenomics, are discussed including their promising future value for dairy cow fertility.

Leptin is a 16-kDa protein synthesized by adipose tissue and is involved in regulation of feed intake, energy balance, fertility, and immune functions. Since evidence of a genetic correlation between start of luteal activity and energy balance, milk yield, and live weights is present, we investigated the association of genetic differences in the bovine leptin gene with these traits. Between 1990 and 1997, a total of 613 Holstein-Friesian heifers of two genetic groups with known pedigree were followed from parturition until d 105 of lactation. During the first 15 wk of lactation live weight, feed intake, and milk yield were measured for 565 cows. The start of luteal activity was set at the first day with a progesterone concentration higher than 3 ng/ml. In addition to the interval between calving and start of luteal activity, analyses were performed for average milk yield, percentage fat, protein, and lactose in milk, dry matter intake, feed intake, energy balance, and live weight over the first 15 wk of lactation. All 613 cows were genotyped for two restricted fragment length polymorphisms and for the BM1500 microsatellite, all located at the leptin gene locus. Significances of the genotype effects were estimated using the approximated F-statistic provided by ASREML. Fixed effects were year-season, genetic group, and a quadratic polynomial for age at calving. Animal was fitted as a random effect including the additive relationship between animals to account for background genes. Firstly, each genotype effect was fitted in turn, and secondly the other restriction fragment length polymorphisms were fitted as a cofactor to take into account effects of linkage disequilibrium. Thirdly, sire x genotype interaction was investigated. Heifers with the RFLP1-AB genotype produce 1.32 kg/d more milk and consume 0.73 kg/d more food compared with the RFLP1-AA genotype. No effects were found for start of luteal activity. When linkage disequilibrium with the other markers was taken into account and DMI was included as fixed effect in the model a 0.96 kg/d higher milk yield was still found. Assuming that no pleiotropic effects on traits such as immunity and milk production in later lactations exist, future breeding programs favoring the RFLP1 B-allele can yield a higher milk production without negatively affecting energy balance and fertility. The prospects are good because in this study the frequency of the RFLP1 AB- and BB-genotypes were only 18.5 and 0.2%, respectively.