目的 研究人喉表皮癌细胞系Hep-2中乳腺癌易感基因1（BRCAl）、P53结合蛋白1（53BP1）和DNA损伤检测点介质1（MDC1）的表达及临床意义。 方法 采用逆转录聚合酶链式反应检测BRCA1、53BP1、MDC1在喉癌细胞系Hep-2中mRNA的表达，同时用免疫印迹法检测蛋白的表达。 结果 在所检测的人喉癌细胞系Hep-2中BACR1、53BP1、MDC1在基因与蛋白两个水平均有表达。 结论 BRCA1、53BP1、MDC1可能在喉癌的发生发展中有一定作用。
目的 通过检测异染色质蛋白1α（HP1α）在DNA损伤后的磷酸化状况，介绍一种用磷酸化标签(phos-tag)试剂检测磷酸化蛋白质的新方法。 方法 取雄雌C57小鼠交配后孕13.5 d胚胎，分离并原代培养小鼠胚胎成纤维细胞。对照组及实验组(6个损伤时间点)各取2个100 mm培养皿的细胞进行实验，实验组细胞用喜树碱进行DNA损伤；对照组用等量的二甲基亚砜处理。用掺入phos-tag的十二烷基硫酸钠-聚丙烯酰胺凝胶电泳分离蛋白并转印，将膜用抗HP1α的抗体孵育，用偶联辣根过氧化物酶的抗体做二抗，通过成像系统检测蛋白。 结果 实验组存在一条与HP1α有明显不同迁移率的磷酸化HP1α条带，与对照组相比DNA损伤后磷酸化HP1α含量一过性增多。 结论 HP1α被DNA损伤诱导为磷酸化状态，提示其可能在DNA修复过程中扮演重要角色。 Phos-tag 蛋白质印迹法可采用普通抗体检测磷酸化的蛋白，是一种简便易行的检测未知磷酸化蛋白质的新方法。
Cardiovascular disease is a severe threat to human health and life. Among many risk factors of cardiovascular disease, genetic or gene-based ones are drawing more and more attention in recent years. Accumulated evidence has demonstrated that the loss or mutation of ataxia telangiectasia mutated (ATM) gene can result in DNA damage repair dysfunctions, telomere shortening, decreased antioxidant capacity, insulin resistance, increased lipid levels, etc., and thus can promote the occurrence of cardiovascular risk factors, such as aging, atherosclerosis and metabolic syndrome. In this review, we discusses the possible mechanisms between ATM gene and cardiovascular risk factors, which could be helpful to the related research and clinical application.
This study aims to explore the temporal pattern of DNA breaks induced by nanosecond electric pulses (nsEP) in cisplatin-sensitive and cisplatin-resistant human ovarian cancer cells. Human ovarian cancer cells A2780 (cisplatin-sensitive subline) and C30 (cisplatin-resistant subline) were exposed to nsEP. Sham exposed groups were shame exposed to nsEP. Cell viability was determined using CCK-8 assay after 0 h, 4 h, 8 h, 12 h and 24 h, respectively, and the percentage of dead cells was calculated. The DNA break was detected with the alkaline single cell gel electrophoresis (comet assay), and the 75th percentiles of TL (tail length), TM (tail moment) and OTM (Olive tail moment) were measured. Cell viability displayed an early decrease and late increase, with the valley value seen at 8 h. Percentages of cell death and comet-formed in A2780 cells were higher than those in C30 cells (P<0.05) at 8 h, respectively. TL, TM and OTM in C30 cells were less than those in A2780 cells (P<0.05). The percentage of comet-formed correlated with that of cell death in either A2780 (r=0.997, P<0.05) or C30 (r=0.998, P<0.05) cells. DNA breaks induced by nsEP in cisplatin-sensitive cells differred from that in resistant cells, and DNA break resulted in fraction of cell death.
O6-carboxymethyl guanine(O6-CMG) is a highly mutagenic alkylation product of DNA that causes gastrointestinal cancer in organisms. Existing studies used mutant Mycobacterium smegmatis porin A (MspA) nanopore assisted by Phi29 DNA polymerase to localize it. Recently, machine learning technology has been widely used in the analysis of nanopore sequencing data. But the machine learning always need a large number of data labels that have brought extra work burden to researchers, which greatly affects its practicability. Accordingly, this paper proposes a nano-Unsupervised-Deep-Learning method (nano-UDL) based on an unsupervised clustering algorithm to identify methylation events in nanopore data automatically. Specially, nano-UDL first uses the deep AutoEncoder to extract features from the nanopore dataset and then applies the MeanShift clustering algorithm to classify data. Besides, nano-UDL can extract the optimal features for clustering by joint optimizing the clustering loss and reconstruction loss. Experimental results demonstrate that nano-UDL has relatively accurate recognition accuracy on the O6-CMG dataset and can accurately identify all sequence segments containing O6-CMG. In order to further verify the robustness of nano-UDL, hyperparameter sensitivity verification and ablation experiments were carried out in this paper. Using machine learning to analyze nanopore data can effectively reduce the additional cost of manual data analysis, which is significant for many biological studies, including genome sequencing.