Thommes Meghan, Wang Taiyao, Zhao Qi, Paschalidis Ioannis C, Segrè Daniel
Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.
Biological Design Center, Boston University, Boston, Massachusetts, USA.
mSystems. 2019 Apr 9;4(2). doi: 10.1128/mSystems.00263-18. eCollection 2019 Mar-Apr.
Microbes face a trade-off between being metabolically independent and relying on neighboring organisms for the supply of some essential metabolites. This balance of conflicting strategies affects microbial community structure and dynamics, with important implications for microbiome research and synthetic ecology. A "gedanken" (thought) experiment to investigate this trade-off would involve monitoring the rise of mutual dependence as the number of metabolic reactions allowed in an organism is increasingly constrained. The expectation is that below a certain number of reactions, no individual organism would be able to grow in isolation and cross-feeding partnerships and division of labor would emerge. We implemented this idealized experiment using genome-scale models. In particular, we used mixed-integer linear programming to identify trade-off solutions in communities of Escherichia coli strains. The strategies that we found revealed a large space of opportunities in nuanced and nonintuitive metabolic division of labor, including, for example, splitting the tricarboxylic acid (TCA) cycle into two separate halves. The systematic computation of possible solutions in division of labor for 1-, 2-, and 3-strain consortia resulted in a rich and complex landscape. This landscape displayed a nonlinear boundary, indicating that the loss of an intracellular reaction was not necessarily compensated for by a single imported metabolite. Different regions in this landscape were associated with specific solutions and patterns of exchanged metabolites. Our approach also predicts the existence of regions in this landscape where independent bacteria are viable but are outcompeted by cross-feeding pairs, providing a possible incentive for the rise of division of labor. Understanding how microbes assemble into communities is a fundamental open issue in biology, relevant to human health, metabolic engineering, and environmental sustainability. A possible mechanism for interactions of microbes is through cross-feeding, i.e., the exchange of small molecules. These metabolic exchanges may allow different microbes to specialize in distinct tasks and evolve division of labor. To systematically explore the space of possible strategies for division of labor, we applied advanced optimization algorithms to computational models of cellular metabolism. Specifically, we searched for communities able to survive under constraints (such as a limited number of reactions) that would not be sustainable by individual species. We found that predicted consortia partition metabolic pathways in ways that would be difficult to identify manually, possibly providing a competitive advantage over individual organisms. In addition to helping understand diversity in natural microbial communities, our approach could assist in the design of synthetic consortia.
微生物在代谢独立与依赖邻近生物体供应某些必需代谢物之间面临权衡。这种相互冲突策略的平衡影响着微生物群落的结构和动态，对微生物组研究和合成生态学具有重要意义。一项用于研究这种权衡的“思想”实验将涉及监测随着生物体中允许的代谢反应数量越来越受到限制，相互依赖关系的增加情况。预期是，在一定数量的反应以下，没有单个生物体能够单独生长，交叉喂养伙伴关系和分工将会出现。我们使用基因组规模模型实施了这个理想化实验。具体而言，我们使用混合整数线性规划来确定大肠杆菌菌株群落中的权衡解决方案。我们发现的策略揭示了在细微且非直观的代谢分工中存在大量机会空间，例如，将三羧酸（TCA）循环分成两个独立的部分。对1菌株、2菌株和3菌株联合体分工中可能解决方案的系统计算产生了丰富而复杂的格局。这个格局显示出一条非线性边界，表明细胞内一个反应的丧失不一定由单一进口代谢物来补偿。这个格局中的不同区域与特定的解决方案和交换代谢物的模式相关联。我们的方法还预测了这个格局中存在一些区域，在这些区域中独立的细菌能够存活，但会被交叉喂养的配对细菌竞争淘汰，这为分工的出现提供了一种可能的诱因。理解微生物如何组装成群落是生物学中一个基本的开放性问题，与人类健康、代谢工程和环境可持续性相关。微生物相互作用的一种可能机制是通过交叉喂养，即小分子的交换。这些代谢交换可能使不同的微生物专门从事不同的任务并进化出分工。为了系统地探索分工可能策略的空间，我们将先进的优化算法应用于细胞代谢的计算模型。具体来说，我们寻找能够在单个物种无法维持的约束条件（如有限数量的反应）下生存的群落。我们发现预测的联合体以难以手动识别的方式划分代谢途径，这可能为其提供相对于单个生物体的竞争优势。除了有助于理解自然微生物群落的多样性外，我们的方法还可以协助合成联合体的设计。
