Expression of proinflammatory molecules by glial cells is involved in the pathophysiological changes associated with chronic neurological diseases. Under pathological conditions, astrocytes release a number of proinflammatory molecules, such as interleukin-6 (IL-6) and interferon-gamma-inducible protein-10 (IP-10). The ovarian hormone estradiol exerts protective effects in the central nervous system that, at least in part, may be mediated by a reduction of local inflammation. This study was designed to assess whether estradiol affects the production of IL-6 and IP-10 by primary cultures of newborn mice astrocytes exposed to lipopolysaccharide (LPS), a bacterial endotoxin known to cause neuroinflammation. In addition, the possible anti-inflammatory effect of several selective estrogen receptor modulators (SERMs) was also assessed. LPS induced an increase in the expression of IL-6 and IP-10 mRNA levels in astrocytes and an increase in IL-6 and IP-10 protein levels in the culture medium. These effects of LPS were impaired by estradiol and by the four SERMs tested in our study: tamoxifen, raloxifene, ospemifene, and bazedoxifene. All SERMs tested showed a similar effect on IL-6 and IP-10 mRNA levels, but raloxifene and ospemifene were more effective than tamoxifen and bazedoxifene in reducing protein levels in LPS-treated cultures. Finally, we report that news SERMs, ospemifene and bazedoxifene, exert anti-inflammatory actions by a mechanism involving classical estrogen receptors and by the inhibition of LPS-induced NFkappaB p65 transactivation. The results suggest that estrogenic compounds may be candidates to counteract brain inflammation under neurodegenerative conditions by targeting the production and release of proinflammatory molecules by astrocytes.

Despite the fact that the cell cycle is a fundamental process of life, a detailed quantitative understanding of gene regulation dynamics throughout the cell cycle is far from complete. Single-cell RNA-sequencing (scRNA-seq) technology gives access to these dynamics without externally perturbing the cell. Here, by generating scRNA-seq libraries in different cell systems, we observe cycling patterns in the unspliced-spliced RNA space of cell cycle-related genes. Since existing methods to analyze scRNA-seq are not efficient to measure cycling gene dynamics, we propose a deep learning approach (DeepCycle) to fit these patterns and build a high-resolution map of the entire cell cycle transcriptome. Characterizing the cell cycle in embryonic and somatic cells, we identify major waves of transcription during the G1 phase and systematically study the stages of the cell cycle. Our work will facilitate the study of the cell cycle in multiple cellular models and different biological contexts.