Helmholtz Institute for Functional Marine Biodiversity (HIFMB) at the University of Oldenburg , Oldenburg, Germany.
Helmholtz Centre for Marine and Polar Research, Alfred-Wegener-Institute , Bremerhaven, Germany.
mSystems. 2023 Jun 29;8(3):e0002823. doi: 10.1128/msystems.00028-23. Epub 2023 May 31.
Progress in molecular methods has enabled the monitoring of bacterial populations in time. Nevertheless, understanding community dynamics and its links with ecosystem functioning remains challenging due to the tremendous diversity of microorganisms. Conceptual frameworks that make sense of time series of taxonomically rich bacterial communities, regarding their potential ecological function, are needed. A key concept for organizing ecological functions is the niche, the set of strategies that enable a population to persist and define its impacts on the surroundings. Here we present a framework based on manifold learning to organize genomic information into potentially occupied bacterial metabolic niches over time. Manifold learning tries to uncover low-dimensional data structures in high-dimensional data sets that can be used to describe the data in reduced dimensions. We apply the method to re-construct the dynamics of putatively occupied metabolic niches using a long-term bacterial time series from the Baltic Sea, the Linnaeus Microbial Observatory (LMO). The results reveal a relatively low-dimensional space of occupied metabolic niches comprising groups of taxa with similar functional capabilities. Time patterns of occupied niches were strongly driven by seasonality. Some metabolic niches were dominated by one bacterial taxon, whereas others were occupied by multiple taxa, depending on the season. These results illustrate the power of manifold learning approaches to advance our understanding of the links between community composition and functioning in microbial systems. IMPORTANCE The increase in data availability of bacterial communities highlights the need for conceptual frameworks to advance our understanding of these complex and diverse communities alongside the production of such data. To understand the dynamics of these tremendously diverse communities, we need tools to identify overarching strategies and describe their role and function in the ecosystem in a comprehensive way. Here, we show that a manifold learning approach can coarse grain bacterial communities in terms of their metabolic strategies and that we can thereby quantitatively organize genomic information in terms of potentially occupied niches over time. This approach, therefore, advances our understanding of how fluctuations in bacterial abundances and species composition can relate to ecosystem functions and it can facilitate the analysis, monitoring, and future predictions of the development of microbial communities.
分子方法的进展使得能够及时监测细菌种群。然而，由于微生物的巨大多样性，理解群落动态及其与生态系统功能的联系仍然具有挑战性。需要有概念框架来理解在潜在生态功能方面具有丰富分类学的细菌群落的时间序列，使其具有意义。组织生态功能的一个关键概念是小生境，即一组使种群能够持续存在并定义其对周围环境影响的策略。在这里，我们提出了一个基于流形学习的框架，用于将基因组信息组织成随时间推移潜在占据的细菌代谢小生境。流形学习试图在高维数据集（如时间序列）中发现低维数据结构，以便可以在降维后描述数据。我们应用该方法来使用来自波罗的海（LMO）的 Linnaeus 微生物观测站（LMO）的长期细菌时间序列重新构建潜在占据代谢小生境的动态。结果揭示了一个相对低维的占据代谢小生境空间，其中包含具有相似功能能力的分类群组。占据小生境的时间模式主要受季节性驱动。一些代谢小生境由一个细菌分类群主导，而另一些则取决于季节，由多个分类群占据。这些结果说明了流形学习方法的强大功能，可以促进我们对微生物系统中群落组成与功能之间联系的理解。
重要性
细菌群落数据可用性的增加突显了需要概念框架来推进我们对这些复杂多样的群落的理解，同时也需要生成这些数据。为了理解这些极其多样化的群落的动态，我们需要工具来识别总体策略，并全面描述它们在生态系统中的作用和功能。在这里，我们表明，流形学习方法可以根据代谢策略对细菌群落进行粗粒度处理，并且我们可以根据时间推移对潜在占据的小生境进行定量的基因组信息组织。因此，这种方法可以帮助我们理解细菌丰度和物种组成的波动如何与生态系统功能相关，并且可以促进对微生物群落发展的分析、监测和未来预测。
