藻菌关系的生态网络研究方法:回顾与展望

The ecological network approach to algal-bacterial relationships: Review and prospects

藻类与细菌是海洋生态系统中重要的组成部分,两者间复杂的互作行为在维持共生关系、物质代谢以及协同进化中具有重要的生态意义.以往的研究证实了藻菌关系中多样化的表现,包括互利、共生、拮抗以及互害等.然而,由于藻菌关系的复杂性,传统工具无法充分挖掘暗藏在藻菌之间的深度信息.鉴于全球变化对海洋生态系统的影响和微生物组学的发展,藻菌关系的研究视角也逐渐上升到系统生态学层面.得益于高通量测序技术的快速发展和大数据分析能力的建立,藻菌关系的研究迎来了新机遇,其中微生物生态网络就是核心的一环,它影响着共生关系的建立、交互行为的发生以及生态事件的走向.鉴于此,本文以大数据背景下的信息为基础,尝试回顾与总结藻菌生态网络的研究进展,包括网络的属性、网络的构建方法、网络的功能,以及藻菌关系中共发生模式(分子网络、代谢网络)与生态意义.最后,提出了建立藻菌互作知识网络数据库、藻菌生物互作图形数据库的构想,以期充分发挥网络的鲁棒性来挖掘藻菌关系背后的全局性信息,为深入揭示藻际微生态的分子机制及其在海洋生态系统中的多维作用提供借鉴.

Algae and bacteria numerically dominate oceanic and freshwater planktonic communities. They play a central biogeochemical role in the marine ecosystem, including producing biomass to support aquatic food webs, cycling nutrients and trace elements, and decomposing organic matter. Algae and bacteria synergistically affect each other’s physiology and metabolism and have coexisted ever since the early stages of evolution that revolutionized many aspects of life on Earth.The relationship between algae and bacteria influences ecosystems and represents all conceivable modes of interaction from mutualism to parasitism. The interactions between these two groups of plankton, and the influence of their interaction on each other and on a global scale have, therefore, become the focus of recent research interest. Previous studies have summarized a variety of algal-bacterial interactions and aimed to reveal the essence of those interactions. However, it is clear that their interactions are diverse and not static, and can be initiated and broken in response to environmental and developmental cues. Traditional research methods have been unable to reveal the complexity behind algal-bacterial symbiosis. As microbial omics technology has developed and the influence of global change on marine ecosystems has become apparent, the research perspective on algal-bacterial relationships has gradually risen to the level of systemic ecology. The rapid-blooming of high-throughput sequencing technology and the ability to analyze big data offer new opportunities for a thorough study of algal-bacterial interaction. Ecological networks are prominent among these new methods: They can integrate multiple types of information and might represent systems-level behavior. Network theory methods are powerful approaches for quantifying relationships between biological components and their relevance to phenotypes, environmental conditions or other external variables within a system. In this paper, we review the research advances in the algal-bacterial ecological network based on big data. We begin by introducing the network parameters and attributes commonly used in ecological network analysis and review the existing network modeling methods in microbial studies and algorithm features and application scenarios. By comparing some common algorithms or software, researchers can choose appropriate network construction methods when confronted with different data characteristics. Next, the ecological network analysis used in the study of algal-bacterial relationships is presented at multiple layers from taxonomy to function(e.g., molecular and metabolic networks). The problems and experiences encountered during research into algal-bacterial relationships are then discussed. For example, when focusing on the interaction between host and microorganism, the time/space shift correlations among the variables will lead to wrong conclusions, including falsepositive or false-negative results. We also attempt to introduce metabolic and dynamic biological networks into the study of the algal-bacterial relationship to reveal further the mechanism of their interaction from the perspectives of function and causality. Finally, our novel proposal to establish a knowledge network database of algal-bacterial interactions and a graphic database of photosphere biological profiles is discussed. We highlight the need for microbial ecological network inference and suggest strategies to infer networks more reliably. Our study aimed to explore the holistic information behind algal-bacterial relationships and provide new cues for further revealing the molecular mechanisms of algal-bacterial interactions.