Growth hormone overexpression in the central nervous system results in hyperphagia-induced obesity associated with insulin resistance and dyslipidemia.

It is well known that peripherally administered growth hormone (GH) results in decreased body fat mass. However, GH-deficient patients increase their food intake when substituted with GH, suggesting that GH also has an appetite stimulating effect. Transgenic mice with an overexpression of bovine GH in the central nervous system (CNS) were created to investigate the role of GH in CNS. This study shows that overexpression of GH in the CNS differentiates the effect of GH on body fat mass from that on appetite. The transgenic mice were not GH-deficient but were obese and showed increased food intake as well as increased hypothalamic expression of agouti-related protein and neuropeptide Y. GH also had an acute effect on food intake following intracerebroventricular injection of C57BL/6 mice. The transgenic mice were severely hyperinsulinemic and showed a marked hyperplasia of the islets of Langerhans. In addition, the transgenic mice displayed alterations in serum lipid and lipoprotein levels and hepatic gene expression. In conclusion, GH overexpression in the CNS results in hyperphagia-induced obesity indicating a dual effect of GH with a central stimulation of appetite and a peripheral lipolytic effect.

Computational Approaches to Accelerating Novel Medicine and Better Patient Care from Bedside to Benchtop.

Some successes have been achieved in the war on cancer over the past 30 years with recent efforts on protein kinase inhibitors. Nonetheless, we are still facing challenges due to cancer evolution. Cancers are complex and heterogeneous due to primary and secondary mutations, with phenotypic and molecular heterogeneity manifested among patients of a cancer, and within an individual patient throughout the disease course. Our understanding of cancer genomes has been facilitated by advances in omics and in bioinformatics technologies; major areas in cancer research are advancing in parallel on many fronts. Computational methods have been developed to decipher the molecular complexity of cancer and to identify driver mutations in cancers. Utilizing the identified driver mutations to develop effective therapy would require biological linkages from cellular context to clinical implication; for this purpose, computational mining of biomedical literature facilitates utilization of a huge volume of biomedical research data and knowledge. In addition, frontier technologies, such as genome editing technologies, are facilitating investigation of cancer mutations, and opening the door for developing novel treatments to treat diseases. We will review and highlight the challenges of treating cancers, which behave like moving targets due to mutation and evolution, and the current state-of-the-art research in the areas mentioned above.

Murine models for the study of congestive heart failure: Implications for understanding molecular mechanisms and for drug discovery.

Congestive heart failure (CHF) is a complex illness of diverse aetiology. Despite the current multiple therapies, the prognosis for CHF patients remains poor, and new therapeutic targets need to be identified. With the advent of the genetic era, the mouse has become an increasingly valuable animal species in experimental CHF research. A large number of murine models of cardiac hypertrophy and CHF have been created by genetic engineering. Meanwhile, traditional CHF models created by coronary artery ligation, cardiac pressure, or volume overload have been adapted to mice. The present review categorizes and highlights the value of these murine models of cardiac hypertrophy and CHF. These models, combined with sophisticated physiological measurements of cardiac haemodynamics, are expected to yield more and valuable information regarding the molecular mechanisms of CHF and aid in the discovery of novel therapeutic targets.