Transcription factor EB(TFEB)-mediated autophagy protects bovine mammary epithelial cells against H_(2)O_(2)-induced oxidative damage in vitro

Background:Bovine mammary epithelial cells after calving undergo serious metabolic challenges and oxidative stress both of which could compromise autophagy.Transcription factor EB(TFEB)-mediated autophagy is an important cytoprotective mechanism against oxidative stress.However,effects of TFEB-mediated autophagy on the oxidative stress of bovine mammary epithelial cells remain unknown.Therefore,the main aim of the study was to investigate the role of TFEB-mediated autophagy in bovine mammary epithelial cells experiencing oxidative stress.Results:H_(2)O_(2) challenge of the bovine mammary epithelial cell MAC-T increased protein abundance of LC3-II,increased number of autophagosomes and autolysosomes while decreased protein abundance of p62.Inhibition of autophagy via bafilomycin A1 aggravated H_(2)O_(2)-induced reactive oxygen species(ROS)accumulation and apoptosis in MAC-T cells.Furthermore,H_(2)O_(2) treatment triggered the translocation of TFEB into the nucleus.Knockdown of TFEB by siRNA reversed the effect of H_(2)O_(2) on protein abundance of LC3-II and p62 as well as the number of autophagosomes and autolysosomes.Overexpression of TFEB activated autophagy and attenuated H_(2)O_(2)-induced ROS accumulation.Furthermore,TFEB overexpression attenuated H_(2)O_(2)-induced apoptosis by downregulating the caspase apoptotic pathway.Conclusions:Our results indicate that activation of TFEB mediated autophagy alleviates H_(2)O_(2)-induced oxidative damage by reducing ROS accumulation and inhibiting caspase-dependent apoptosis.

Inhibiting nuclear factor erythroid 2 related factor 2-mediated autophagy in bovine mammary epithelial cells induces oxidative stress in response to exogenous fatty acids

Background:In early lactation,bovine mammary epithelial cells undergo serious metabolic challenges and oxidative stress both of which could be alleviated by activation of autophagy.Nuclear factor erythroid 2 related factor 2(NFE2L2),a master regulator of cellular redox homeostasis,plays an important role in the regulation of autophagy and oxidative stress.Thus,the objective of this study was to investigate the role of NFE2L2-mediated autophagy on oxidative stress of bovine mammary epithelial cells in response to exogenous free fatty acids(FFA).Results:Exogenous FFA induced linear and quadratic decreases in activities of glutathione peroxidase(GSH-Px),catalase(CAT),and superoxide dismutase(SOD),and increases in the contents of reactive oxygen species(ROS)and malondialdehyde(MDA).Protein abundance of LC3-phosphatidylethanolamine conjugate(LC3-Ⅱ)and the number of autophagosomes and autolysosomes decreased in a dose-dependent manner,while protein abundance of p62 increased in cells challenged with FFA.Activation of autophagy via pre-treatment with Rap attenuated the FFAinduced ROS accumulation.Importantly,FFA inhibited protein abundance of NFE2L2 and the translocation of NFE2L2 into the nucleus.Knockdown of NFE2L2 by siRNA decreased protein abundance of LC3-Ⅱ,while it increased protein abundance of p62.Furthermore,sulforaphane(SFN)pre-treatment attenuated the FFA-induced oxidative stress by activating NFE2L2-mediated autophagy.Conclusions:The data suggested that NFE2L2-mediated autophagy is an important antioxidant mechanism in bovine mammary epithelial cells experiencing increased FFA loads.

AI辅助合成中的分子表示方法:从序列模型到图论

近些年,随着深层神经网络及其衍生算法的飞速发展,AI在各个领域大放异彩.在计算机辅助化合物合成路线设计领域,生成对抗网络,变分自编码器等人工智能算法表现十分出色.而如何处理分子的几何表示是将化学问题转化为算法问题的关键步骤.本文将从不同维度出发,系统地总结各种分子的序列模型、图论等表示方法在辅助合成化学中的应用,讨论目前分子表示中的短板与面临的挑战.

Efficient screening framework for organic solar cells with deep learning and ensemble learning

Organic photovoltaics have attracted worldwide interest due to their unique advantages in developing low-cost,lightweight,and flexible power sources.Functional molecular design and synthesis have been put forward to accelerate the discovery of ideal organic semiconductors.However,it is extremely expensive to conduct experimental screening of the wide organic compound space.Here we develop a framework by combining a deep learning model(graph neural network)and an ensemble learning model(Light Gradient Boosting Machine),which enables rapid and accurate screening of organic photovoltaic molecules.This framework establishes the relationship between molecular structure,molecular properties,and device efficiency.Our framework evaluates the chemical structure of the organic photovoltaic molecules directly and accurately.Since it does not involve density functional theory calculations,it makes fast predictions.The reliability of our framework is verified with data from previous reports and our newly synthesized organic molecules.Our work provides an efficient method for developing new organic optoelectronic materials.