273 research outputs found
Adipokines – Toward the Molecular Dissection of Interactions Between Stromal Adipocytes and Breast Cancer Cells
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Arsenite binds to the RING finger domains of RNF20-RNF40 histone E3 ubiquitin ligase and inhibits DNA double-strand break repair.
Arsenic is a widespread environmental contaminant. However, the exact molecular mechanisms underlying the carcinogenic effects of arsenic remain incompletely understood. Core histones can be ubiquitinated by RING finger E3 ubiquitin ligases, among which the RNF20-RNF40 heterodimer catalyzes the ubiquitination of histone H2B at lysine 120. This ubiquitination event is important for the formation of open and biochemically accessible chromatin fiber that is conducive for DNA repair. Herein, we found that arsenite could bind directly to the RING finger domains of RNF20 and RNF40 in vitro and in cells, and treatment with arsenite resulted in substantially impaired H2B ubiquitination in multiple cell lines. Exposure to arsenite also diminished the recruitment of BRCA1 and RAD51 to laser-induced DNA double-strand break (DSB) sites, compromised DNA DSB repair in human cells, and rendered cells sensitive toward a radiomimetic agent, neocarzinostatin. Together, the results from the present study revealed, for the first time, that arsenite may exert its carcinogenic effect by targeting cysteine residues in the RING finger domains of histone E3 ubiquitin ligase, thereby altering histone epigenetic mark and compromising DNA DSB repair. Our results also suggest arsenite as a general inhibitor for RING finger E3 ubiquitin ligases
Multi-scale Attention Flow for Probabilistic Time Series Forecasting
The probability prediction of multivariate time series is a notoriously
challenging but practical task. On the one hand, the challenge is how to
effectively capture the cross-series correlations between interacting time
series, to achieve accurate distribution modeling. On the other hand, we should
consider how to capture the contextual information within time series more
accurately to model multivariate temporal dynamics of time series. In this
work, we proposed a novel non-autoregressive deep learning model, called
Multi-scale Attention Normalizing Flow(MANF), where we integrate multi-scale
attention and relative position information and the multivariate data
distribution is represented by the conditioned normalizing flow. Additionally,
compared with autoregressive modeling methods, our model avoids the influence
of cumulative error and does not increase the time complexity. Extensive
experiments demonstrate that our model achieves state-of-the-art performance on
many popular multivariate datasets
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