Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.
Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.
mSystems. 2024 May 16;9(5):e0130523. doi: 10.1128/msystems.01305-23. Epub 2024 Apr 29.
Microbial communities in nature are dynamically evolving as member species change their interactions subject to environmental variations. Accounting for such context-dependent dynamic variations in interspecies interactions is critical for predictive ecological modeling. In the absence of generalizable theoretical foundations, we lack a fundamental understanding of how microbial interactions are driven by environmental factors, significantly limiting our capability to predict and engineer community dynamics and function. To address this issue, we propose a novel theoretical framework that allows us to represent interspecies interactions as an explicit function of environmental variables (such as substrate concentrations) by combining growth kinetics and a generalized Lotka-Volterra model. A synergistic integration of these two complementary models leads to the prediction of alterations in interspecies interactions as the outcome of dynamic balances between positive and negative influences of microbial species in mixed relationships. The effectiveness of our method was experimentally demonstrated using a synthetic consortium of two mutants that are metabolically dependent (due to an inability to synthesize essential amino acids) but competitively grow on a shared substrate. The analysis of the binary consortium using our model not only showed how interactions between the two amino acid auxotrophic mutants are controlled by the dynamic shifts in limiting substrates but also enabled quantifying previously uncharacterizable complex aspects of microbial interactions, such as asymmetry in interactions. Our approach can be extended to other ecological systems to model their environment-dependent interspecies interactions from growth kinetics.IMPORTANCEModeling environment-controlled interspecies interactions through separate identification of positive and negative influences of microbes in mixed relationships is a new capability that can significantly improve our ability to understand, predict, and engineer the complex dynamics of microbial communities. Moreover, the prediction of microbial interactions as a function of environmental variables can serve as valuable benchmark data to validate modeling and network inference tools in microbial ecology, the development of which has often been impeded due to the lack of ground truth information on interactions. While demonstrated against microbial data, the theory developed in this work is readily applicable to general community ecology to predict interactions among macroorganisms, such as plants and animals, as well as microorganisms.
自然界中的微生物群落是动态演化的，其成员物种会根据环境变化改变它们的相互作用。在物种间相互作用中考虑这种依赖于上下文的动态变化对于预测生态模型至关重要。由于缺乏可推广的理论基础，我们对微生物相互作用是如何受到环境因素驱动的缺乏基本理解，这极大地限制了我们预测和设计群落动态和功能的能力。为了解决这个问题，我们提出了一个新的理论框架，通过将生长动力学和广义Lotka-Volterra 模型相结合，将物种间相互作用表示为环境变量（如底物浓度）的显式函数。这两个互补模型的协同集成导致了物种间相互作用的变化预测，作为混合关系中微生物物种的正、负影响之间动态平衡的结果。使用两个代谢依赖（由于无法合成必需氨基酸）但在共享底物上竞争生长的突变体合成的 consortium 进行实验验证，证明了我们方法的有效性。使用我们的模型对二元 consortium 进行分析不仅表明了两个氨基酸营养缺陷型突变体之间的相互作用如何受到限制底物动态变化的控制，而且还能够量化微生物相互作用以前无法描述的复杂方面，例如相互作用的不对称性。我们的方法可以扩展到其他生态系统，从生长动力学的角度来模拟它们的环境依赖的物种间相互作用。重要性通过单独识别混合关系中微生物的正、负面影响来模拟环境控制的物种间相互作用是一种新的能力，可以显著提高我们理解、预测和设计微生物群落复杂动态的能力。此外，将微生物相互作用作为环境变量的函数进行预测可以作为微生物生态学中建模和网络推断工具的有价值的基准数据，这些工具的开发通常由于缺乏关于相互作用的真实信息而受到阻碍。虽然该理论是针对微生物数据提出的，但在这项工作中开发的理论可以很容易地应用于一般社区生态学，以预测植物和动物等宏观生物以及微生物之间的相互作用。
