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1.  Deep Sequencing Reveals New Aspects of Progesterone Receptor Signaling in Breast Cancer Cells 
PLoS ONE  2014;9(6):e98404.
Despite the pleiotropic effects of the progesterone receptor in breast cancer, the molecular mechanisms in play remain largely unknown. To gain a global view of the PR-orchestrated networks, we used next-generation sequencing to determine the progestin-regulated transcriptome in T47D breast cancer cells. We identify a large number of PR target genes involved in critical cellular programs, such as regulation of transcription, apoptosis, cell motion and angiogenesis. Integration of the transcriptomic data with the PR-binding profiling of hormonally treated cells identifies numerous components of the small-GTPases signaling pathways as direct PR targets. Progestin-induced deregulation of the small GTPases may contribute to the PR's role in mammary tumorigenesis. Transcript expression analysis reveals significant expression changes of specific transcript variants in response to the extracellular hormonal stimulus. Using the NET1 gene as an example, we show that the PR can dictate alternative promoter usage leading to the upregulation of an isoform that may play a role in metastatic breast cancer. Future studies should aim to characterize these selectively regulated variants and evaluate their clinical utility in prognosis and targeted therapy of hormonally responsive breast tumors.
PMCID: PMC4045674  PMID: 24897521
2.  Global similarity and local divergence in human and mouse gene co-expression networks 
A genome-wide comparative analysis of human and mouse gene expression patterns was performed in order to evaluate the evolutionary divergence of mammalian gene expression. Tissue-specific expression profiles were analyzed for 9,105 human-mouse orthologous gene pairs across 28 tissues. Expression profiles were resolved into species-specific coexpression networks, and the topological properties of the networks were compared between species.
At the global level, the topological properties of the human and mouse gene coexpression networks are, essentially, identical. For instance, both networks have topologies with small-world and scale-free properties as well as closely similar average node degrees, clustering coefficients, and path lengths. However, the human and mouse coexpression networks are highly divergent at the local level: only a small fraction (<10%) of coexpressed gene pair relationships are conserved between the two species. A series of controls for experimental and biological variance show that most of this divergence does not result from experimental noise. We further show that, while the expression divergence between species is genuinely rapid, expression does not evolve free from selective (functional) constraint. Indeed, the coexpression networks analyzed here are demonstrably functionally coherent as indicated by the functional similarity of coexpressed gene pairs, and this pattern is most pronounced in the conserved human-mouse intersection network. Numerous dense network clusters show evidence of dedicated functions, such as spermatogenesis and immune response, that are clearly consistent with the coherence of the expression patterns of their constituent gene members.
The dissonance between global versus local network divergence suggests that the interspecies similarity of the global network properties is of limited biological significance, at best, and that the biologically relevant aspects of the architectures of gene coexpression are specific and particular, rather than universal. Nevertheless, there is substantial evolutionary conservation of the local network structure which is compatible with the notion that gene coexpression networks are subject to purifying selection.
PMCID: PMC1601971  PMID: 16968540

Results 1-2 (2)