Norrin/Frizzled4 signalling in the preneoplastic niche blocks medulloblastoma initiation.

The tumor microenvironment is a critical modulator of carcinogenesis; however, in many tumor types, the influence of the stroma during preneoplastic stages is unknown. Here we explored the relationship between pre-tumor cells and their surrounding stroma in malignant progression of the cerebellar tumor medulloblastoma (MB). We show that activation of the vascular regulatory signalling axis mediated by Norrin (an atypical Wnt)/Frizzled4 (Fzd4) inhibits MB initiation in the Ptch+/- mouse model. Loss of Norrin/Fzd4-mediated signalling in endothelial cells, either genetically or by short-term blockade, increases the frequency of pre-tumor lesions and creates a tumor-permissive microenvironment at the earliest, preneoplastic stages of MB. This pro-tumor stroma, characterized by angiogenic remodelling, is associated with an accelerated transition from preneoplasia to malignancy. These data expose a stromal component that regulates the earliest stages of tumorigenesis in the cerebellum, and a novel role for the Norrin/Fzd4 axis as an endogenous anti-tumor signal in the preneoplastic niche.

SirT1 catalytic activity is required for male fertility and metabolic homeostasis in mice.

The protein encoded by the sirt1 gene is an enzyme, SirT1, that couples the hydrolysis of NAD(+) to the deacetylation of acetyl-lysine residues in substrate proteins. Mutations of the sirt1 gene that fail to encode protein have been introduced into the mouse germ line, and the animals homozygous for these null mutations have various physiological abnormalities. To determine which of the characteristics of these sirt1(-/-) mice are a consequence of the absence of the catalytic activity of the SirT1 protein, we created a mouse strain carrying a point mutation (H355Y) that ablates the catalytic activity but does not affect the amount of the SirT1 protein. Mice carrying point mutations in both sirt1 genes, sirt1(Y/Y), have a phenotype that is overlapping but not identical to that of the sirt1-null animals. The sirt1(Y/Y) phenotype is significantly milder than that seen in the sirt1(-/-) animals. For example, female sirt1(Y/Y) animals are fertile, while sirt1(-/-) females are sterile. On the other hand, both sirt1(-/-) and sirt1(Y/Y) male mice are sterile and hypermetabolic. We report that sirt1(Y/Y) mice respond aberrantly to caloric restriction, although the effects are more subtle than seen in sirt1(-/-) mice. Thus, the SirT1 protein has functions that can be attributed to the catalytic activity of the protein, as well as other functions that are conferred by the protein itself.

Quantitative epistasis analysis and pathway inference from genetic interaction data.

Inferring regulatory and metabolic network models from quantitative genetic interaction data remains a major challenge in systems biology. Here, we present a novel quantitative model for interpreting epistasis within pathways responding to an external signal. The model provides the basis of an experimental method to determine the architecture of such pathways, and establishes a new set of rules to infer the order of genes within them. The method also allows the extraction of quantitative parameters enabling a new level of information to be added to genetic network models. It is applicable to any system where the impact of combinatorial loss-of-function mutations can be quantified with sufficient accuracy. We test the method by conducting a systematic analysis of a thoroughly characterized eukaryotic gene network, the galactose utilization pathway in Saccharomyces cerevisiae. For this purpose, we quantify the effects of single and double gene deletions on two phenotypic traits, fitness and reporter gene expression. We show that applying our method to fitness traits reveals the order of metabolic enzymes and the effects of accumulating metabolic intermediates. Conversely, the analysis of expression traits reveals the order of transcriptional regulatory genes, secondary regulatory signals and their relative strength. Strikingly, when the analyses of the two traits are combined, the method correctly infers ~80% of the known relationships without any false positives.