Proteome-Wide Effect of 17-β-Estradiol and Lipoxin A4 in an Endometriotic Epithelial Cell Line.

Endometriosis affects approximately 10% of women of reproductive age. This chronic, gynecological inflammatory disease results in a decreased quality of life for patients, with the main symptoms including chronic pelvic pain and infertility. The steroid hormone 17-β Estradiol (E2) plays a key role in the pathology. Our previous studies showed that the anti-inflammatory lipid Lipoxin A4 (LXA4) acts as an estrogen receptor-alpha agonist in endometrial epithelial cells, inhibiting certain E2-mediated effects. LXA4 also prevents the progression of endometriosis in a mouse model via anti-proliferative mechanisms and by impacting mediators downstream of ER signaling. The aim of the present study was therefore to examine global proteomic changes evoked by E2 and LXA4 in endometriotic epithelial cells. E2 impacted a greater number of proteins in endometriotic epithelial cells than LXA4. Interestingly, the combination of E2 and LXA4 resulted in a reduced number of regulated proteins, with LXA4 mediating a suppressive effect on E2-mediated signaling. These proteins are involved in diverse pathways of relevance to endometriosis pathology and metabolism, including mRNA translation, growth, proliferation, proteolysis, and immune responses. In summary, this study sheds light on novel pathways involved in endometriosis pathology and further understanding of signaling pathways activated by estrogenic molecules in endometriotic epithelial cells

A baby steps/giant steps Monte Carlo algorithm for computing roadmaps in smooth compact real hypersurfaces

International audienceWe consider the problem of constructing roadmaps of real algebraic sets. The problem was introduced by Canny to answer connectivity questions and solve motion planning problems. Given $s$ polynomial equations with rational coefficients, of degree $D$ in $n$ variables, Canny's algorithm has a Monte Carlo cost of $s^n\log(s) D^{O(n^2)}$ operations in $\mathbb{Q}$; a deterministic version runs in time $s^n \log(s) D^{O(n^4)}$. The next improvement was due to Basu, Pollack and Roy, with an algorithm of deterministic cost $s^{d+1} D^{O(n^2)}$ for the more general problem of computing roadmaps of semi-algebraic sets ($d \le n$ is the dimension of an associated object). We give a Monte Carlo algorithm of complexity $(nD)^{O(n^{1.5})}$ for the problem of computing a roadmap of a compact hypersurface $V$ of degree $D$ in $n$ variables; we also have to assume that $V$ has a finite number of singular points. Even under these extra assumptions, no previous algorithm featured a cost better than $D^{O(n^2)}$