Nakata Miyuki T, Takahara Masahiro, Sakamoto Shingo, Yoshida Kouki, Mitsuda Nobutaka
Plant Gene Regulation Research Group, Bioproduction Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan.
Acacia Horticulture, Adachi, Japan.
Front Plant Sci. 2018 Jun 12;9:780. doi: 10.3389/fpls.2018.00780. eCollection 2018.
Mechanical properties are rarely used as quantitative indices for the large-scale mutant screening of plants, even in the model plant . The mechanical properties of plant stems generally influence their vibrational characteristics. Here, we developed Python-based software, named AraVib, for the high-throughput analysis of free vibrations of plant stems, focusing specifically on Arabidopsis stem vibrations, and its extended version, named AraVibS, to identify mutants with altered mechanical properties. These programs can be used without knowledge of Python and require only an inexpensive handmade setting stand and an iPhone/iPad with a high-speed shooting function for data acquisition. Using our system, we identified an double-mutant lacking secondary cell walls in fiber cells and a mutant displaying ectopic formation of secondary cell wall compared with wild type by employing only two growth traits (stem height and fresh weight) in addition to videos of stem vibrations. Furthermore, we calculated the logarithmic decrement, the damping ratio, the natural frequency and the stiffness based on the spring-mass-damper model from the video data using AraVib. The stiffness was estimated to be drastically decreased in , which agreed with previous tensile test results. However, in , the stiffness was significantly increased. These results demonstrate the effectiveness of our new system. Because our method can be applied in a high-throughput manner, it can be used to screen for mutants with altered mechanical properties.
即使在模式植物中,机械性能也很少被用作植物大规模突变体筛选的定量指标。植物茎的机械性能通常会影响其振动特性。在此,我们开发了基于Python的软件AraVib,用于高通量分析植物茎的自由振动,特别关注拟南芥茎的振动,并开发了其扩展版本AraVibS,以识别机械性能发生改变的突变体。这些程序无需具备Python知识即可使用,只需要一个便宜的手工制作的固定架和一部具有高速拍摄功能的iPhone/iPad来采集数据。使用我们的系统,除了茎振动视频外,仅通过两个生长性状(茎高和鲜重),我们就鉴定出了纤维细胞中缺乏次生细胞壁的双突变体,以及与野生型相比显示次生细胞壁异位形成的突变体。此外,我们使用AraVib根据视频数据基于弹簧-质量-阻尼器模型计算了对数减量、阻尼比、固有频率和刚度。估计在[具体突变体]中刚度大幅降低,这与之前的拉伸试验结果一致。然而,在[具体突变体]中,刚度显著增加。这些结果证明了我们新系统的有效性。由于我们的方法可以高通量应用,因此可用于筛选机械性能发生改变的突变体。