SUMO1-dependent modulation of SERCA2a in heart failure

The calcium-transporting ATPase ATP2A2, also known as SERCA2a, is a critical ATPase responsible for Ca(2+) re-uptake during excitation-contraction coupling. Impaired Ca(2+) uptake resulting from decreased expression and reduced activity of SERCA2a is a hallmark of heart failure. Accordingly, restoration of SERCA2a expression by gene transfer has proved to be effective in improving cardiac function in heart-failure patients, as well as in animal models. The small ubiquitin-related modifier (SUMO) can be conjugated to lysine residues of target proteins, and is involved in many cellular processes. Here we show that SERCA2a is SUMOylated at lysines 480 and 585 and that this SUMOylation is essential for preserving SERCA2a ATPase activity and stability in mouse and human cells. The levels of SUMO1 and the SUMOylation of SERCA2a itself were greatly reduced in failing hearts. SUMO1 restitution by adeno-associated-virus-mediated gene delivery maintained the protein abundance of SERCA2a and markedly improved cardiac function in mice with heart failure. This effect was comparable to SERCA2A gene delivery. Moreover, SUMO1 overexpression in isolated cardiomyocytes augmented contractility and accelerated Ca(2+) decay. Transgene-mediated SUMO1 overexpression rescued cardiac dysfunction induced by pressure overload concomitantly with increased SERCA2a function. By contrast, downregulation of SUMO1 using small hairpin RNA (shRNA) accelerated pressure-overload-induced deterioration of cardiac function and was accompanied by decreased SERCA2a function. However, knockdown of SERCA2a resulted in severe contractile dysfunction both in vitro and in vivo, which was not rescued by overexpression of SUMO1. Taken together, our data show that SUMOylation is a critical post-translational modification that regulates SERCA2a function, and provide a platform for the design of novel therapeutic strategies for heart failure.     

肿瘤放疗并发症概率预测模型参数拟合方法

为了建立具有群体特异性的肿瘤放疗NTCP预测模型,提出了一种模型参数拟合方法.首先,基于NTCP模型的特点构建最大似然函数;然后,分别采用确定性优化方法和随机性优化方法对最大似然函数进行优化,分析优化过程的时间成本及优化结果,探讨用于拟合NTCP模型参数的最优方法.实验结果表明,用于拟合NTCP模型参数的最大似然函数是非凸的,存在局部最优解;遗传算法是一种最稳定的最大似然函数优化方法,其运行时间比模拟退火算法短,而且可以在每次优化结束后给出全局最优解,以作为NTCP模型参数.所提方法可以帮助肿瘤放疗工作者在临床随访数据的基础上建立具有群体特异性的放疗并发症预测模型.