In-Silico Genomic Approaches To Understanding Lactation, Mammary Development, And Breast Cancer

Authors: HADSELL, DARRYL - Children'S Nutrition Research Center (CNRC); OLEA, WALTER - Children'S Nutrition Research Center (CNRC); HADSELL, LOUANN - Children'S Nutrition Research Center (CNRC); Grusak, Michael; RIJNKELS, MONIQUE - Children'S Nutrition Research Center (CNRC); CREIGHTON, CHAD - Baylor College Of Medicine; COX, TIMOTHY - University Of Washington; SMYTH, IAN - Monash University; SHORT, KIERAN - Monash University; WEI, JERRY - University Of Sydney; WILLIAMSON, PETER - University Of Sydney

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/22/2014
Publication Date: 10/6/2014
Citation: Hadsell, D.L., Olea, W., Hadsell, L., Grusak, M.A., Rijnkels, M., Creighton, C., Cox, T., Smyth, I., Short, K., Wei, J., Williamson, P. 2014. In-Silico Genomic Approaches To Understanding Lactation, Mammary Development, And Breast Cancer. Meeting Abstract. p.5.

Interpretive Summary:

Technical Abstract: Lactation-related traits are influenced by genetics. From a quantitative standpoint, these traits have been well studied in dairy species, but there has also been work on the genetics of lactation in humans and mice. In addition, there is evidence to support the notion that other mammary gland traits including those describing mammary ductal development as well as risk for breast cancer are also genetically regulated. Previous work in our laboratory using in-silico genome wide association (GWAS) has identified several quantitative trait loci (QTL) that could drive some of the variation in lactation performance that was observed in an inbred mouse mapping panel known as the Mouse Diversity Panel (MDP). Our additional work in the MDP has also identified variations in maternal food intake, body composition, milk macronutrient and mineral composition, and lastly post-pubertal mammary ductal morphogenesis. With regard to milk macronutrient composition we have detected a total of 60 QTL associated with variations in milk lactose, protein, and triglyceride concentrations. For milk mineral composition, we used inductively coupled plasma optical emission spectrometry to measure the concentrations of nine minerals including calcium, copper, iron, potassium, magnesium, sodium, phosphorus, sulfur and zinc. All of the minerals were significantly influenced by genetic background (P=0.03 to P=2x10(-16)). GWAS of these data detected a total of 20 milk mineral QTL (Mmq) encompassing 15 gene candidates. Among the most significant of the Mmq was an association on MMU3 for milk calcium (P=2x10(-9)) and magnesium (P=7x10(-7)). Closer inspection of this region revealed a cluster of three associated SNPs that were located in the first intron of the gene Ppm1l, which encodes for a magnesium-dependent protein phosphatase. These particular SNPs were also very close to, or within, potential binding sites for the chromatin regulator CTCF, suggesting that the expression of this candidate gene could be differentially regulated through the modulation of chromatin structure. With regard to our mammary ductal development work, a second and larger cohort of mice from the MDP was used to document strain-dependent variation in normal post-pubertal mammary ductal development and to relate this to breast cancer in both mouse models and humans. By measuring five quantitative ductal development traits in digital images of mammary whole mounts collected at two different ages, we first demonstrated that the variation in mammary ductal development was higher than previously described on the basis of commonly used breast cancer research strains such as the FVB/NJ and C57BL/6J. We also identified two strains, CZECHII/EiJ and KK/HlJ, which displayed extreme mammary ductal development differences. These differences were highlighted through 3-dimensional imaging. The work also identified correlations between ductal development and mammary tumor latency in a murine breast cancer model based on polyomavirus middle-T. GWAS analysis of this dataset identified 20 mammary development QTL (Mdq) of which five overlapped syntenic intervals found in human breast cancer studies. Within these loci were 43 genes, of which some could be linked to normal mammary gland development as well as breast cancer on the basis of their biology. These studies highlight the opportunities that exist for novel discoveries in understanding lactation, mammary development and cancer, when combining genomic approaches with genetically diverse mice.