bioRxiv Science⌕ Search

Biology subjects

Sannino, S.

Publications and source records attributed to Sannino, S..

2 recordsLinked to original sources

Role of the NuRD complex and altered proteostasis in cancer cell quiescence

Cytotoxic chemotherapy remains the primary treatment for ovarian cancer (OvCa). Development of chemoresistance typically results in patient death within two years. As such, understanding chemoresistance is critical. One underexplored mechanism of chemotherapy resistance is quiescence. Quiescent cells, which have reversibly exited the cell cycle, are refractory to most chemotherapies which primarily target rapidly proliferating cells. Here, we report that CHD4 and MBD3, components of the nucleosome remodeling and deacetylase (NuRD) complex, are downregulated in quiescent OvCa cells (qOvCa). Indicating a direct role for NuRD complex downregulation in the induction of quiescence, either CHD4 or MBD3 knockdown or histone deacetylase inhibitors (HDACi), such as vorinostat, induce quiescence in OvCa cells. RNA-Seq analysis of HDACi-treated cells confirmed expression changes consistent with induction of quiescence. We also find that both primary qOvCa and vorinostat-induced qOvCa demonstrate altered proteostasis, including increased proteasome activity and autophagy, and combination therapy of HDACi and proteasome inhibitors or autophagy inhibitors demonstrated profound synergistic death of OvCa cells. Finally, we overlapped RNA-Seq signatures from quiescent ovarian cancer cells with genes essential for quiescence in yeast to identify a "quiescent cell core signature." This core quiescent cell signature appeared to be conserved across multiple cancer types, suggesting new therapeutic targets.

cancer biology↗

Visibility graphs for fMRI data: multiplex temporal graphs and their modulations across resting state networks

Visibility graphs have proved a convenient tool to describe the dynamics of time series, finding applications across several disciplines. Recently an approach has been proposed to extend this framework to multivariate time series, allowing a novel way to describe collective dynamics. Here we test their application to fMRI time series and give some explanation on why we think that its an interesting thing to do.

neuroscience↗