Verhagen K J A, Eerden S A, Sikkema B J, Wahl S A
Department of Biotechnology, Delft University of Technology, Delft, Netherlands.
Lehrstuhl für Bioverfahrenstechnik, FAU Erlangen-Nürnberg, Erlangen, Germany.
Front Mol Biosci. 2022 May 16;9:863470. doi: 10.3389/fmolb.2022.863470. eCollection 2022.
Exposed to changes in their environment, microorganisms will adapt their phenotype, including metabolism, to ensure survival. To understand the adaptation principles, resource allocation-based approaches were successfully applied to predict an optimal proteome allocation under (quasi) steady-state conditions. Nevertheless, for a general, dynamic environment, enzyme kinetics will have to be taken into account which was not included in the linear resource allocation models. To this end, a resource-dependent kinetic model was developed and applied to the model organism by combining published kinetic models and calibrating the model parameters to published proteomics and fluxomics datasets. Using this approach, we were able to predict specific proteomes at different dilution rates under chemostat conditions. Interestingly, the approach suggests that the occurrence of aerobic fermentation (Crabtree effect) in is not caused by space limitation in the total proteome but rather an effect of constraints on the mitochondria. When exposing the approach to repetitive, dynamic substrate conditions, the proteome space was allocated differently. Less space was predicted to be available for non-essential enzymes (reserve space). This could indicate that the perceived "overcapacity" present in experimentally measured proteomes may very likely serve a purpose in increasing the robustness of a cell to dynamic conditions, especially an increase of proteome space for the growth reaction as well as of the trehalose cycle that was shown to be essential in providing robustness upon stronger substrate perturbations. The model predictions of proteome adaptation to dynamic conditions were additionally evaluated against respective experimentally measured proteomes, which highlighted the model's ability to accurately predict major proteome adaptation trends. This proof of principle for the approach can be extended to production organisms and applied for both understanding metabolic adaptation and improving industrial process design.
暴露于环境变化中时，微生物会调整其表型，包括新陈代谢，以确保生存。为了理解适应原理，基于资源分配的方法已成功应用于预测（准）稳态条件下的最佳蛋白质组分配。然而，对于一般的动态环境，必须考虑酶动力学，而这在线性资源分配模型中并未包含。为此，通过结合已发表的动力学模型并将模型参数校准到已发表的蛋白质组学和通量组学数据集，开发了一种依赖资源的动力学模型并将其应用于模式生物。使用这种方法，我们能够预测恒化器条件下不同稀释率下的特定蛋白质组。有趣的是，该方法表明，[具体生物]中需氧发酵（Crabtree效应）的发生不是由总蛋白质组中的空间限制引起的，而是线粒体受到限制的结果。当将该方法应用于重复的动态底物条件时，蛋白质组空间的分配方式有所不同。预计非必需酶的可用空间较少（储备空间）。这可能表明，实验测量的蛋白质组中存在的“产能过剩”很可能是为了提高细胞对动态条件的稳健性，特别是增加用于生长反应的蛋白质组空间以及海藻糖循环的空间，海藻糖循环已被证明在更强的底物扰动下提供稳健性方面至关重要。蛋白质组对动态条件适应的模型预测还与各自实验测量的蛋白质组进行了评估，这突出了该模型准确预测主要蛋白质组适应趋势的能力。该方法的原理证明可以扩展到生产生物，并应用于理解代谢适应和改进工业过程设计。
