RoyChowdhury Taniya, Bramer Lisa M, Brown Joseph, Kim Young-Mo, Zink Erika, Metz Thomas O, McCue Lee Ann, Diefenderfer Heida L, Bailey Vanessa
Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA.
Energy and Environment Directorate, Pacific Northwest National Laboratory, Sequim, WA 98382, USA.
Microorganisms. 2022 Aug 16;10(8):1653. doi: 10.3390/microorganisms10081653.
We present observations from a laboratory-controlled study on the impacts of extreme wetting and drying on a wetland soil microbiome. Our approach was to experimentally challenge the soil microbiome to understand impacts on anaerobic carbon cycling processes as the system transitions from dryness to saturation and vice-versa. Specifically, we tested for impacts on stress responses related to shifts from wet to drought conditions. We used a combination of high-resolution data for small organic chemical compounds (metabolites) and biological (community structure based on 16S rRNA gene sequencing) features. Using a robust correlation-independent data approach, we further tested the predictive power of soil metabolites for the presence or absence of taxa. Here, we demonstrate that taking an untargeted, multidimensional data approach to the interpretation of metabolomics has the potential to indicate the causative pathways selecting for the observed bacterial community structure in soils.
我们展示了一项实验室对照研究的观察结果，该研究旨在探讨极端干湿交替对湿地土壤微生物群落的影响。我们的方法是通过实验对土壤微生物群落进行挑战，以了解在系统从干燥状态转变为饱和状态以及反之亦然的过程中，对厌氧碳循环过程的影响。具体而言，我们测试了从湿润到干旱条件转变所引发的应激反应的影响。我们结合了针对小分子有机化合物（代谢物）和生物学特征（基于16S rRNA基因测序的群落结构）的高分辨率数据。使用一种强大的独立于相关性的数据方法，我们进一步测试了土壤代谢物对特定分类群存在与否的预测能力。在此，我们证明采用非靶向的多维数据方法来解释代谢组学，有可能揭示选择土壤中观察到的细菌群落结构的因果途径。
