Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China.
Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.
NPJ Biofilms Microbiomes. 2021 Sep 7;7(1):72. doi: 10.1038/s41522-021-00241-4.
Understanding how plants interact with their colonizing microbiota to determine plant phenotypes is a fundamental question in modern plant science. Existing approaches for genome-wide association studies (GWAS) are often focused on the association analysis between host genes and the abundance of individual microbes, failing to characterize the genetic bases of microbial interactions that are thought to be important for microbiota structure, organization, and function. Here, we implement a behavioral model to quantify various patterns of microbe-microbe interactions, i.e., mutualism, antagonism, aggression, and altruism, and map host genes that modulate microbial networks constituted by these interaction types. We reanalyze a root-microbiome data involving 179 accessions of Arabidopsis thaliana and find that the four networks differ structurally in the pattern of bacterial-fungal interactions and microbiome complexity. We identify several fungus and bacterial hubs that play a central role in mediating microbial community assembly surrounding A. thaliana root systems. We detect 1142 significant host genetic variants throughout the plant genome and then implement Bayesian networks (BN) to reconstruct epistatic networks involving all significant SNPs, of which 91 are identified as hub QTLs. Results from gene annotation analysis suggest that most of the hub QTLs detected are in proximity to candidate genes, executing a variety of biological functions in plant growth and development, resilience against pathogens, root development, and abiotic stress resistance. This study provides a new gateway to understand how genetic variation in host plants influences microbial communities and our results could help improve crops by harnessing soil microbes.
了解植物如何与其定殖的微生物群落相互作用以确定植物表型是现代植物科学的一个基本问题。现有的全基因组关联研究 (GWAS) 方法通常侧重于宿主基因与单个微生物丰度之间的关联分析,而未能描述被认为对微生物群落结构、组织和功能很重要的微生物相互作用的遗传基础。在这里,我们实施了一种行为模型来量化各种微生物相互作用模式,即互利共生、拮抗作用、攻击和利他主义,并绘制调节由这些相互作用类型构成的微生物网络的宿主基因。我们重新分析了一个涉及 179 个拟南芥品系的根系微生物组数据,发现这四个网络在细菌-真菌相互作用模式和微生物组复杂性方面结构上存在差异。我们鉴定了几个在介导拟南芥根系周围微生物群落组装方面起核心作用的真菌和细菌枢纽。我们在整个植物基因组中检测到 1142 个显著的宿主遗传变异体,然后实施贝叶斯网络 (BN) 来重建涉及所有显著 SNP 的上位网络,其中 91 个被鉴定为枢纽 QTL。基因注释分析的结果表明,检测到的大多数枢纽 QTL 都靠近候选基因,在植物生长和发育、对病原体的抗性、根系发育和非生物胁迫抗性等方面执行各种生物学功能。这项研究为理解宿主植物中的遗传变异如何影响微生物群落提供了一个新的途径,我们的研究结果可以通过利用土壤微生物来帮助改善作物。