Faculty Bibliography

MOTIVATION/BACKGROUND:A phenotype mechanism is classically derived through the study of a set of mutants and comparison of their biochemical capabilities. One method of comparing mutant capabilities is to characterize producible and knocked out metabolites. However such an effect is difficult to manually assess, especially for a large biochemical network and a complex media. Current algorithmic approaches towards analyzing metabolic networks either do not address this specific property or are computationally infeasible on the genome-scale. RESULTS:We have developed a novel genome-scale computational approach that identifies the full set of biochemical species that are knocked out from the metabolome following a gene deletion. Results from this approach are combined with data from in vivo mutant screens to examine the essentiality of metabolite production for a phenotype. This approach can also be a useful tool for metabolic network annotation validation and refinement in newly sequenced organisms. Combining an in silico genome-scale model of Escherichia coli metabolism with in vivo survival data, we uncover possible essential roles for several cell membranes, cell walls, and quinone species. We also identify specific biomass components whose production appears to be non-essential for survival, contrary to the assumptions of previous models. AVAILABILITY/BACKGROUND:Programs are available upon request from the authors in the form of Matlab script files. SUPPLEMENTARY INFORMATION/BACKGROUND:http://www.cis.upenn.edu/biocomp/manuscripts/bioinformatics_bti245/supp-info.html.

Epidemiological studies have repeatedly demonstrated that women who undergo an early first full-term pregnancy have a significantly reduced lifetime risk of breast cancer. Similarly, rodents that have previously undergone a full-term pregnancy are highly resistant to carcinogen-induced breast cancer compared with age-matched nulliparous controls. Little progress has been made, however, toward understanding the biological basis of this phenomenon. We have used DNA microarrays to identify a panel of 38 differentially expressed genes that reproducibly distinguishes, in a blinded manner, between the nulliparous and parous states of the mammary gland in multiple strains of mice and rats. We find that parity results in the persistent down-regulation of multiple genes encoding growth factors, such as amphiregulin, pleiotrophin, and IGF-1, as well as the persistent up-regulation of the growth-inhibitory molecule, TGF-beta3, and several of its transcriptional targets. Our studies further indicate that parity results in a persistent increase in the differentiated state of the mammary gland as well as lifelong changes in the hematopoietic cell types resident within the gland. These findings define a developmental state of the mammary gland that is refractory to carcinogenesis and suggest novel hypotheses for the mechanisms by which parity may modulate breast cancer risk.