Host-Associated Quantitative Abundance Profiling Reveals the Microbial Load Variation of Root Microbiome

• Main Authors: Xiaoxuan Guo, Xiaoning Zhang, Yuan Qin, Yong-Xin Liu, Jingying Zhang, Na Zhang, Kun Wu, Baoyuan Qu, Zishan He, Xin Wang, Xinjian Zhang, Stéphane Hacquard, Xiangdong Fu, Yang Bai
• Format: Article
• Language: English
• Published: Elsevier 2020-01-01
• Series: Plant Communications
• Subjects: microbial load; host-associated quantitative abundance profiling; root microbiome
• Online Access: http://www.sciencedirect.com/science/article/pii/S2590346219300033

Plant-associated microbes are critical for plant growth and survival under natural environmental conditions. To date, most plant microbiome studies involving high-throughput amplicon sequencing have focused on the relative abundance of microbial taxa. However, this technique does not assess the total microbial load and the abundance of individual microbes relative to the amount of host plant tissues. Here, we report the development of a host-associated quantitative abundance profiling (HA-QAP) method that can accurately examine total microbial load and colonization of individual root microbiome members relative to host plants by the copy-number ratio of microbial marker gene to plant genome. We validate the HA-QAP method using mock experiments, perturbation experiments, and metagenomic sequencing. The HA-QAP method eliminates the generation of spurious outputs in the classical method based on microbial relative abundance, and reveals the load of root microbiome to host plants. Using the HA-QAP method, we found that the copy-number ratios of microbial marker genes to plant genome range from 1.07 to 6.61 for bacterial 16S rRNA genes and from 0.40 to 2.26 for fungal internal transcribed spacers in the root microbiome samples from healthy rice and wheat. Furthermore, using HA-QAP we found that an increase in total microbial load represents a key feature of changes in root microbiome of rice plants exposed to drought stress and of wheat plants with root rot disease, which significantly influences patterns of differential taxa and species interaction networks. Given its accuracy and technical feasibility, HA-QAP would facilitate our understanding of genuine interactions between root microbiome and plants.