Wang Qian, Liu Xinjuan, Jiang Libo, Cao Yige, Zhan Xiang, Griffin Christopher H, Wu Rongling
Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.
Department of Gastroenterology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
mSystems. 2019 Oct 29;4(5):e00550-19. doi: 10.1128/mSystems.00550-19.
Increasing evidence shows that the influence of microbiota on biogeochemical cycling, plant development, and human health is executed through a complex network of microbe-microbe interactions. However, characterizing how microbes interact and work together within closely packed and highly heterogeneous microbial ecosystems is extremely challenging. Here, we describe a rule-of-thumb framework for visualizing polymicrobial interactions and extracting general principles that underlie microbial communities. We integrate elements of metabolic ecology, behavioral ecology, and game theory to quantify the interactive strategies by which microbes at any taxonomic level compete for resources and cooperate symbiotically with each other to form and stabilize ecological communities. We show how the framework can chart an omnidirectional landscape of microbial cooperation and competition that may drive various natural processes. This framework can be implemented into genome-wide association studies to unravel the genetic mechanisms underlying microbial interaction networks and their evolutionary consequences along spatiotemporal gradients. Identifying general biological rules that underlie the complexity and heterogeneity of microbial communities has proven to be highly challenging. We present a rule-of-thumb framework for studying and characterizing how microbes interact with each other across different taxa to determine community behavior and dynamics. This framework is computationally simple but conceptually meaningful, and it can provide a starting point to generate novel biological hypotheses about microbial interactions and explore internal workings of microbial community assembly in depth.
越来越多的证据表明，微生物群对生物地球化学循环、植物发育和人类健康的影响是通过一个复杂的微生物-微生物相互作用网络来实现的。然而，描述微生物在紧密堆积且高度异质的微生物生态系统中如何相互作用和协同工作极具挑战性。在这里，我们描述了一个经验法则框架，用于可视化多微生物相互作用并提取微生物群落背后的一般原则。我们整合了代谢生态学、行为生态学和博弈论的元素，以量化任何分类水平的微生物竞争资源并相互共生合作以形成和稳定生态群落的交互策略。我们展示了该框架如何描绘可能驱动各种自然过程的微生物合作与竞争的全方位图景。这个框架可以应用于全基因组关联研究，以揭示微生物相互作用网络背后的遗传机制及其沿时空梯度的进化后果。事实证明，识别微生物群落复杂性和异质性背后的一般生物学规律极具挑战性。我们提出了一个经验法则框架，用于研究和描述微生物如何跨不同分类群相互作用以确定群落行为和动态。这个框架计算简单但概念上有意义，它可以为生成关于微生物相互作用的新生物学假设以及深入探索微生物群落组装的内部机制提供一个起点。
