PDF(Pubmed)
Abstract：
BACKGROUND: Microbial robustness is crucial for developing cell factories that maintain consistent performance in a challenging environment such as large-scale bioreactors. Although tools exist to assess and understand robustness at a phenotypic level, the underlying metabolic and genetic mechanisms are not well defined, which limits our ability to engineer more strains with robust functions.
RESULTS: This study encompassed four steps. (I) Fitness and robustness were analyzed from a published dataset of yeast mutants grown in multiple environments. (II) Genes and metabolic processes affecting robustness or fitness were identified, and 14 of these genes were deleted in Saccharomyces cerevisiae CEN.PK113-7D. (III) The mutants bearing gene deletions were cultivated in three perturbation spaces mimicking typical industrial processes. (IV) Fitness and robustness were determined for each mutant in each perturbation space. We report that robustness varied according to the perturbation space. We identified genes associated with increased robustness such as MET28, linked to sulfur metabolism; as well as genes associated with decreased robustness, including TIR3 and WWM1, both involved in stress response and apoptosis.
CONCLUSIONS: The present study demonstrates how phenomics datasets can be analyzed to reveal the relationship between phenotypic response and associated genes. Specifically, robustness analysis makes it possible to study the influence of single genes and metabolic processes on stable microbial performance in different perturbation spaces. Ultimately, this information can be used to enhance robustness in targeted strains.
RESULTS: This study encompassed four steps. (I) Fitness and robustness were analyzed from a published dataset of yeast mutants grown in multiple environments. (II) Genes and metabolic processes affecting robustness or fitness were identified, and 14 of these genes were deleted in Saccharomyces cerevisiae CEN.PK113-7D. (III) The mutants bearing gene deletions were cultivated in three perturbation spaces mimicking typical industrial processes. (IV) Fitness and robustness were determined for each mutant in each perturbation space. We report that robustness varied according to the perturbation space. We identified genes associated with increased robustness such as MET28, linked to sulfur metabolism; as well as genes associated with decreased robustness, including TIR3 and WWM1, both involved in stress response and apoptosis.
CONCLUSIONS: The present study demonstrates how phenomics datasets can be analyzed to reveal the relationship between phenotypic response and associated genes. Specifically, robustness analysis makes it possible to study the influence of single genes and metabolic processes on stable microbial performance in different perturbation spaces. Ultimately, this information can be used to enhance robustness in targeted strains.
摘要：
背景：微生物的鲁棒性对于开发在具有挑战性的环境（例如大规模生物反应器）中保持一致性能的细胞工厂至关重要。尽管存在在表型水平上评估和理解稳健性的工具，潜在的代谢和遗传机制还没有很好的定义，这限制了我们设计更多具有强大功能的菌株的能力。
结果：本研究包括四个步骤。(I)从在多种环境中生长的酵母突变体的公开数据集分析适应性和稳健性。(II)确定了影响稳健性或适应性的基因和代谢过程，其中14个基因在酿酒酵母CEN中缺失。PK113-7D。（III）在模拟典型工业过程的三个扰动空间中培养了带有基因缺失的突变体。(IV)在每个扰动空间中确定每个突变体的适合度和稳健性。我们报告说，鲁棒性根据扰动空间而变化。我们确定了与增加的稳健性相关的基因，如MET28，与硫代谢相关;以及与降低的稳健性相关的基因，包括TIR3和WWM1，均参与应激反应和细胞凋亡。
结论：本研究证明了如何分析表型数据集，以揭示表型反应与相关基因之间的关系。具体来说,稳健性分析使得研究单基因和代谢过程对不同扰动空间中微生物稳定性能的影响成为可能。最终,这些信息可用于增强目标菌株的稳健性。
结果：本研究包括四个步骤。(I)从在多种环境中生长的酵母突变体的公开数据集分析适应性和稳健性。(II)确定了影响稳健性或适应性的基因和代谢过程，其中14个基因在酿酒酵母CEN中缺失。PK113-7D。（III）在模拟典型工业过程的三个扰动空间中培养了带有基因缺失的突变体。(IV)在每个扰动空间中确定每个突变体的适合度和稳健性。我们报告说，鲁棒性根据扰动空间而变化。我们确定了与增加的稳健性相关的基因，如MET28，与硫代谢相关;以及与降低的稳健性相关的基因，包括TIR3和WWM1，均参与应激反应和细胞凋亡。
结论：本研究证明了如何分析表型数据集，以揭示表型反应与相关基因之间的关系。具体来说,稳健性分析使得研究单基因和代谢过程对不同扰动空间中微生物稳定性能的影响成为可能。最终,这些信息可用于增强目标菌株的稳健性。
