Calibration of 2‐D soil zymography for correct analysis of enzyme distribution
Calibration of 2‐D soil zymography for correct analysis of enzyme distribution
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DOI：
10.1111/ejss.12744
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发表时间：
2019-03
影响因子：
4.2
通讯作者：
A. Guber;A. Kravchenko;B. Razavi;E. Blagodatskaya;Y. Kuzyakov
中科院分区：
文献类型：
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作者：
A. Guber;A. Kravchenko;B. Razavi;E. Blagodatskaya;Y. Kuzyakov
土壤酶谱是一种新技术，旨在可视化酶活性的二维分布。该方法包括在土壤样品表面孵育浸有酶特异性荧光底物的膜，然后记录荧光产品（例如 MUF：甲基伞形酮）在紫外光下生成的膜图像。尽管其相对易于使用，但进行酶谱分析涉及多个用户做出的决定，这些决定可能会影响酶活性估计的准确性。因此，需要统一酶谱方法来正确估计和比较各种研究。我们评估了酶谱法实施的以下方法学方面：（a）相机设置和图像处理，（b）蒸发的影响和（c）校准程序。相机设置（快门速度或曝光时间）会影响背景荧光的强度和信噪比 (SNR)。然而，由于它们的综合效应根据 MUF 浓度而变化，因此在酶谱分析之前需要针对预期的 MUF 浓度范围优化光线和相机设置。 MUF 溶液从膜上蒸发对荧光没有影响。校准期间 MUF 浓度和荧光强度之间的关系表现出饱和模式，并且受到最佳范围之外的图像噪声的强烈影响（例如 8–14 μm MUF Pixel−1）。我们开发了一种基于分段线性回归的新校准方法。新方法考虑了 MUF 浓度的特定范围，并使用非均匀饱和膜，反映了土壤中酶活性的真实分布。新的校准算法消除了标准校准的偏差，从而提高了预测 MUF 浓度的准确性。
Soil zymography is a new technique developed to visualize two‐dimensional distributions of enzyme activities. The method consists of incubating a membrane saturated with an enzyme‐specific fluorogenic substrate on a surface of the soil sample, followed by recording the membrane image generated by a fluorescent product (e.g. MUF: methylumbelliferone) in ultraviolet light. Despite its relative ease of use, performing zymography involves multiple user‐made decisions that might affect the accuracy of enzyme activity estimates. Therefore, unification of the zymography methodology is required for correct estimations and comparisons of various studies. We evaluated the following methodological aspects of the implementation of zymography: (a) camera settings and image processing, (b) effects of evaporation and (c) calibration procedures. Camera settings (shutter speeds or exposure time) affected the intensity of background fluorescence and signal‐to‐noise ratios (SNR). However, because their combined effects varied depending on MUF concentrations, light and camera setting need to be optimized for the expected range of MUF concentrations prior to zymography. Evaporation of MUF solution from the membrane had no effect on fluorescence. Relations between MUF concentration and intensity of fluorescence during calibrations demonstrated a saturated pattern and were strongly affected by image noise outside the optimal range (e.g. 8–14 μm MUF pixel−1). We developed a new calibration approach that is based on a piecewise linear regression. The new approach accounted for specific ranges of MUF concentration and uses nonuniformly saturated membranes, reflecting the real distribution of enzyme activities in soil. The new calibration algorithm eliminated biases of the standard calibration and resulted in greater accuracy in predicting MUF concentrations.