Conditional deletion of hypothalamic Y2 receptors reverts gonadectomy-induced bone loss in adult mice

Reduction in levels of sex hormones at menopause in women is associated with two common, major outcomes, the accumulation of white adipose tissue, and the progressive loss of bone because of excess osteoclastic bone resorption exceeding osteoblastic bone formation. Current antiresorptive therapies can reduce osteoclastic activity but have only limited capacity to stimulate osteoblastic bone formation and restore lost skeletal mass. Likewise, the availability of effective pharmacological weight loss treatments is currently limited. Here we demonstrate that conditional deletion of hypothalamic neuropeptide Y2 receptors can prevent ongoing bone loss in sex hormone-deficient adult male and female mice. This benefit is attributable solely to activation of an anabolic osteoblastic bone formation response that counterbalances persistent elevation of bone resorption, suggesting the Y2-mediated anabolic pathway to be independent of sex hormones. Furthermore, the increase in fat mass that typically occurs after ovariectomy is prevented by germ line deletion of Y2 receptors, whereas in male mice body weight and fat mass were consistently lower than wild-type regardless of sex hormone status. Therefore, this study indicates a role for Y2 receptors in the accumulation of adipose tissue in the hypogonadal state and demonstrates that hypothalamic Y2 receptors constitutively restrain osteoblastic activity even in the absence of sex hormones. The increase in bone formation after release of this tonic inhibition suggests a promising new avenue for osteoporosis treatment.

Multilevel modelling for inference of genetic regulatory networks

Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.

Modelling conditional probability distributions for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce two novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Modelling conditional probability distributions for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we apply two novel techniques to the problem of extracting the distribution of wind vector directions from radar catterometer data gathered by a remote-sensing satellite.

Modelling conditional probability distributions for periodic variables

Most conventional techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three related techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Estimating conditional probability densities for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.