Erbilgin Onur, Bowen Benjamin P, Kosina Suzanne M, Jenkins Stefan, Lau Rebecca K, Northen Trent R
Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA.
Joint Genome Institute, 2800 Mitchell Dr, Walnut Creek, CA, 94598, USA.
BMC Bioinformatics. 2017 Jan 23;18(1):57. doi: 10.1186/s12859-017-1478-2.
Mixed cultures of different microbial species are increasingly being used to carry out a specific biochemical function in lieu of engineering a single microbe to do the same task. However, knowing how different species' metabolisms will integrate to reach a desired outcome is a difficult problem that has been studied in great detail using steady-state models. However, many biotechnological processes, as well as natural habitats, represent a more dynamic system. Examining how individual species use resources in their growth medium or environment (exometabolomics) over time in batch culture conditions can provide rich phenotypic data that encompasses regulation and transporters, creating an opportunity to integrate the data into a predictive model of resource use by a mixed community.
Here we use exometabolomic profiling to examine the time-varying substrate depletion from a mixture of 19 amino acids and glucose by two Pseudomonas and one Bacillus species isolated from ground water. Contrary to studies in model organisms, we found surprisingly few correlations between resource preferences and maximal growth rate or biomass composition. We then modeled patterns of substrate depletion, and used these models to examine if substrate usage preferences and substrate depletion kinetics of individual isolates can be used to predict the metabolism of a co-culture of the isolates. We found that most of the substrates fit the model predictions, except for glucose and histidine, which were depleted more slowly than predicted, and proline, glycine, glutamate, lysine and arginine, which were all consumed significantly faster.
Our results indicate that a significant portion of a model community's overall metabolism can be predicted based on the metabolism of the individuals. Based on the nature of our model, the resources that significantly deviate from the prediction highlight potential metabolic pathways affected by species-species interactions, which when further studied can potentially be used to modulate microbial community structure and/or function.
不同微生物物种的混合培养物越来越多地被用于执行特定的生化功能，以替代改造单一微生物来完成相同任务。然而，了解不同物种的代谢如何整合以达到预期结果是一个难题，人们已使用稳态模型对此进行了详细研究。然而，许多生物技术过程以及自然栖息地代表着一个更具动态性的系统。在分批培养条件下，研究单个物种如何随时间利用其生长培养基或环境中的资源（胞外代谢组学），可以提供丰富的表型数据，这些数据涵盖调控和转运蛋白，从而创造了一个将数据整合到混合群落资源利用预测模型中的机会。
在这里，我们使用胞外代谢组学分析来研究从地下水中分离出的两种假单胞菌属和一种芽孢杆菌属细菌对19种氨基酸和葡萄糖混合物中随时间变化的底物消耗情况。与对模式生物的研究相反，我们发现资源偏好与最大生长速率或生物量组成之间的相关性出奇地少。然后，我们对底物消耗模式进行建模，并使用这些模型来检验单个分离菌株的底物使用偏好和底物消耗动力学是否可用于预测这些分离菌株共培养物的代谢情况。我们发现，除了葡萄糖和组氨酸消耗得比预测慢，以及脯氨酸、甘氨酸、谷氨酸、赖氨酸和精氨酸消耗得明显更快外，大多数底物符合模型预测。
我们的结果表明，基于个体的代谢情况可以预测模型群落整体代谢的很大一部分。根据我们模型的性质，与预测有显著偏差的资源突出了受物种间相互作用影响的潜在代谢途径，如果进一步研究，这些途径可能用于调节微生物群落结构和/或功能。
