Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
Genetics Department, University of Georgia, Athens, GA, USA.
Sci Rep. 2024 May 13;14(1):10866. doi: 10.1038/s41598-024-61181-5.
The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum. The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.
丛枝菌根真菌 (AMF) 存在于维管陆地植物的根部，是支持氮磷交换以进行光合作用产生的碳的最古老的共生关系之一。在这里，我们提供了一种多尺度建模方法，用于预测从高粱和甘蔗的重组近交系 (RIL) 群体中衍生的全球作物的 AMF 定殖。这里真菌结构的高通量表型方法依赖于计算机视觉中的基于掩模区域的卷积神经网络 (Mask R-CNN) 进行像素级别的真菌结构分割，以及混合线性模型来探索 AMF 定殖、根系小生境和真菌结构分配之间的关系。所提出的模型捕捉到 AMF 定殖的 95%以上的变化，作为根系小生境和每种植物中真菌结构相对丰度的函数。丛枝的分配是姊妹植物之间 AMF 定殖的重要预测因子。涉及养分交换的丛枝和根外菌丝预测在顶部根系部分的 AMF 定殖最高。我们的工作表明，深度学习可以被社区用于植物根系中 AMF 的高通量表型分析。混合线性模型为测试关于 AMF 定殖表型作为根系小生境和真菌结构分配的函数的假设提供了一个框架。
