【目的】MADS-box转录因子是植物最大的转录因子家族之一,在植物生长发育和胁迫响应中均发挥重要功能。前期通过转录组数据获得一个辣椒热响应基因,该基因编码MADS-box转录因子家族成员CaAGL61(Agamous-like MADS-box protein AGL61)。...【目的】MADS-box转录因子是植物最大的转录因子家族之一,在植物生长发育和胁迫响应中均发挥重要功能。前期通过转录组数据获得一个辣椒热响应基因,该基因编码MADS-box转录因子家族成员CaAGL61(Agamous-like MADS-box protein AGL61)。在此基础上,进一步研究CaAGL61在辣椒耐热性调控中的功能,为深入了解辣椒耐热分子机制提供理论参考,为辣椒耐热性的遗传改良提供基因位点。【方法】通过SMART在线工具预测CaAGL61保守结构域,使用MEGA7构建辣椒和其他植物物种AGL61蛋白的系统发育树,利用实时荧光定量PCR技术探究CaAGL61在辣椒中的表达模式,运用烟草亚细胞定位技术和酵母双杂交自激活系统检测CaAGL61的转录因子特性,利用病毒诱导的基因沉默技术和基因瞬时过表达技术探究CaAGL61表达对辣椒耐热性的影响。【结果】CaAGL61编码179个氨基酸,包含一个MADS结构域,在系统进化方面高度保守。CaAGL61在辣椒花器官中的表达量最高、其次是茎和果实,根中的表达量最低;进一步分析发现CaAGL61的表达量随着花器官的成熟而增加,尤其在授粉坐果期的花药中表达量最高;45℃高温处理显著上调了CaAGL61的表达水平。亚细胞定位显示,CaAGL61定位于细胞核中;酵母转录激活分析表明,CaAGL61具有转录激活活性。CaAGL61沉默植株的耐热性显著增强,热胁迫处理后,与对照相比,CaAGL61沉默植株生长点萎蔫程度减轻,叶片相对电导率降低,丙二醛含量、死细胞和活性氧积累减少,而叶绿素含量升高。相反,CaAGL61瞬时过表达降低了辣椒的耐热性,与对照相比,表现为植株受热胁迫损伤程度更严重,叶片相对电导率升高,丙二醛含量、死细胞和活性氧积累增多,叶绿素含量下降。【结论】鉴定了一个辣椒热响应MADS-box转录因子基因CaAGL61,该基因通过加剧氧化胁迫而负调控辣椒耐热性。展开更多
The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedic...The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedicated to optimizing box wing configurations using low-fidelity data driven machine learning approach.This technique showcases its practicality through the utilization of a tailored low-fidelity machine learning technique,specifically designed for early-stage wing configuration.By employing surrogate model trained on small dataset derived from low-fidelity simulations,our method aims to predict outputs within an acceptable range.This strategy significantly mitigates computational costs and expedites the design exploration process.The methodology's validation relies on its successful application in optimizing the box wing of PARSIFAL,serving as a benchmark,while the primary focus remains on optimizing the newly designed box wing by Bionica.Applying this method to the Bionica configuration led to a notable 14%improvement in overall aerodynamic effciency.Furthermore,all the optimized results obtained from machine learning model undergo rigorous assessments through the high-fidelity RANS analysis for confirmation.This methodology introduces innovative approach that aims to streamline computational processes,potentially reducing the time and resources required compared to traditional optimization methods.展开更多
文摘【目的】MADS-box转录因子是植物最大的转录因子家族之一,在植物生长发育和胁迫响应中均发挥重要功能。前期通过转录组数据获得一个辣椒热响应基因,该基因编码MADS-box转录因子家族成员CaAGL61(Agamous-like MADS-box protein AGL61)。在此基础上,进一步研究CaAGL61在辣椒耐热性调控中的功能,为深入了解辣椒耐热分子机制提供理论参考,为辣椒耐热性的遗传改良提供基因位点。【方法】通过SMART在线工具预测CaAGL61保守结构域,使用MEGA7构建辣椒和其他植物物种AGL61蛋白的系统发育树,利用实时荧光定量PCR技术探究CaAGL61在辣椒中的表达模式,运用烟草亚细胞定位技术和酵母双杂交自激活系统检测CaAGL61的转录因子特性,利用病毒诱导的基因沉默技术和基因瞬时过表达技术探究CaAGL61表达对辣椒耐热性的影响。【结果】CaAGL61编码179个氨基酸,包含一个MADS结构域,在系统进化方面高度保守。CaAGL61在辣椒花器官中的表达量最高、其次是茎和果实,根中的表达量最低;进一步分析发现CaAGL61的表达量随着花器官的成熟而增加,尤其在授粉坐果期的花药中表达量最高;45℃高温处理显著上调了CaAGL61的表达水平。亚细胞定位显示,CaAGL61定位于细胞核中;酵母转录激活分析表明,CaAGL61具有转录激活活性。CaAGL61沉默植株的耐热性显著增强,热胁迫处理后,与对照相比,CaAGL61沉默植株生长点萎蔫程度减轻,叶片相对电导率降低,丙二醛含量、死细胞和活性氧积累减少,而叶绿素含量升高。相反,CaAGL61瞬时过表达降低了辣椒的耐热性,与对照相比,表现为植株受热胁迫损伤程度更严重,叶片相对电导率升高,丙二醛含量、死细胞和活性氧积累增多,叶绿素含量下降。【结论】鉴定了一个辣椒热响应MADS-box转录因子基因CaAGL61,该基因通过加剧氧化胁迫而负调控辣椒耐热性。
基金The funding for this publication was provided by Johannes Kepler University(JKU),Linz.Special thanks to Prof.Zongmin DENG from Beihang University for his invaluable guidance,insightful feedback,and constructive criticism,which greatly enhanced the quality of this manuscript.We extend our heartfelt gratitude to the PARSIFAL team for providing the supporting materials,which inspired this study.
文摘The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedicated to optimizing box wing configurations using low-fidelity data driven machine learning approach.This technique showcases its practicality through the utilization of a tailored low-fidelity machine learning technique,specifically designed for early-stage wing configuration.By employing surrogate model trained on small dataset derived from low-fidelity simulations,our method aims to predict outputs within an acceptable range.This strategy significantly mitigates computational costs and expedites the design exploration process.The methodology's validation relies on its successful application in optimizing the box wing of PARSIFAL,serving as a benchmark,while the primary focus remains on optimizing the newly designed box wing by Bionica.Applying this method to the Bionica configuration led to a notable 14%improvement in overall aerodynamic effciency.Furthermore,all the optimized results obtained from machine learning model undergo rigorous assessments through the high-fidelity RANS analysis for confirmation.This methodology introduces innovative approach that aims to streamline computational processes,potentially reducing the time and resources required compared to traditional optimization methods.
