PDF(Pubmed)
Abstract：
BACKGROUND: Filamentous fungi are used as industrial cell factories to produce a diverse portfolio of proteins, organic acids, and secondary metabolites in submerged fermentation. Generating optimized strains for maximum product titres relies on a complex interplay of molecular, cellular, morphological, and macromorphological factors that are not yet fully understood.
RESULTS: In this study, we generate six conditional expression mutants in the protein producing ascomycete Aspergillus niger and use them as tools to reverse engineer factors which impact total secreted protein during submerged growth. By harnessing gene coexpression network data, we bioinformatically predicted six morphology and productivity associated \'morphogenes\', and placed them under control of a conditional Tet-on gene switch using CRISPR-Cas genome editing. Strains were phenotypically screened on solid and liquid media following titration of morphogene expression, generating quantitative measurements of growth rate, filamentous morphology, response to various abiotic perturbations, Euclidean parameters of submerged macromorphologies, and total secreted protein. These data were built into a multiple linear regression model, which identified radial growth rate and fitness under heat stress as positively correlated with protein titres. In contrast, diameter of submerged pellets and cell wall integrity were negatively associated with productivity. Remarkably, our model predicts over 60% of variation in A. niger secreted protein titres is dependent on these four variables, suggesting that they play crucial roles in productivity and are high priority processes to be targeted in future engineering programs. Additionally, this study suggests A. niger dlpA and crzA genes are promising new leads for enhancing protein titres during fermentation.
CONCLUSIONS: Taken together this study has identified several potential genetic leads for maximizing protein titres, delivered a suite of chassis strains with user controllable macromorphologies during pilot fermentation studies, and has quantified four crucial factors which impact secreted protein titres in A. niger.
RESULTS: In this study, we generate six conditional expression mutants in the protein producing ascomycete Aspergillus niger and use them as tools to reverse engineer factors which impact total secreted protein during submerged growth. By harnessing gene coexpression network data, we bioinformatically predicted six morphology and productivity associated \'morphogenes\', and placed them under control of a conditional Tet-on gene switch using CRISPR-Cas genome editing. Strains were phenotypically screened on solid and liquid media following titration of morphogene expression, generating quantitative measurements of growth rate, filamentous morphology, response to various abiotic perturbations, Euclidean parameters of submerged macromorphologies, and total secreted protein. These data were built into a multiple linear regression model, which identified radial growth rate and fitness under heat stress as positively correlated with protein titres. In contrast, diameter of submerged pellets and cell wall integrity were negatively associated with productivity. Remarkably, our model predicts over 60% of variation in A. niger secreted protein titres is dependent on these four variables, suggesting that they play crucial roles in productivity and are high priority processes to be targeted in future engineering programs. Additionally, this study suggests A. niger dlpA and crzA genes are promising new leads for enhancing protein titres during fermentation.
CONCLUSIONS: Taken together this study has identified several potential genetic leads for maximizing protein titres, delivered a suite of chassis strains with user controllable macromorphologies during pilot fermentation studies, and has quantified four crucial factors which impact secreted protein titres in A. niger.
摘要：
背景：丝状真菌被用作工业细胞工厂，以生产多种蛋白质组合，有机酸,和深层发酵中的次生代谢产物。产生优化的菌株，以获得最大的产品滴度依赖于复杂的相互作用的分子，细胞，形态学,和尚未完全理解的宏观形态因素。
结果：在这项研究中，我们在产生蛋白的子囊菌黑曲霉中产生了六个条件表达突变体，并将它们用作工具来逆向工程影响淹没生长过程中总分泌蛋白的因子。通过利用基因共表达网络数据，我们生物信息学预测了六种形态和生产力相关的形态和生产力，并使用CRISPR-Cas基因组编辑将它们置于有条件的Tet-on基因开关的控制下。在滴定形态发生基因表达后，在固体和液体培养基上对菌株进行表型筛选。生成增长率的定量测量，丝状形态,对各种非生物扰动的响应，淹没宏观形态的欧几里得参数，和总分泌蛋白。这些数据被构建成多元线性回归模型，确定热应激下的径向生长速率和适应度与蛋白质滴度呈正相关。相比之下,浸没颗粒的直径和细胞壁完整性与生产率呈负相关。值得注意的是,我们的模型预测超过60%的黑曲霉分泌蛋白滴度的变异取决于这四个变量，这表明它们在生产力中起着至关重要的作用，并且是未来工程计划中的高优先级过程。此外,这项研究表明，黑曲霉dlpA和crzA基因是提高发酵过程中蛋白质滴度的新线索。
结论：综合起来，这项研究已经确定了几种潜在的遗传线索，以最大化蛋白质滴度，在中试发酵研究期间交付了一套具有用户可控宏观形态的底盘菌株，并量化了影响黑曲霉分泌蛋白滴度的四个关键因素。
结果：在这项研究中，我们在产生蛋白的子囊菌黑曲霉中产生了六个条件表达突变体，并将它们用作工具来逆向工程影响淹没生长过程中总分泌蛋白的因子。通过利用基因共表达网络数据，我们生物信息学预测了六种形态和生产力相关的形态和生产力，并使用CRISPR-Cas基因组编辑将它们置于有条件的Tet-on基因开关的控制下。在滴定形态发生基因表达后，在固体和液体培养基上对菌株进行表型筛选。生成增长率的定量测量，丝状形态,对各种非生物扰动的响应，淹没宏观形态的欧几里得参数，和总分泌蛋白。这些数据被构建成多元线性回归模型，确定热应激下的径向生长速率和适应度与蛋白质滴度呈正相关。相比之下,浸没颗粒的直径和细胞壁完整性与生产率呈负相关。值得注意的是,我们的模型预测超过60%的黑曲霉分泌蛋白滴度的变异取决于这四个变量，这表明它们在生产力中起着至关重要的作用，并且是未来工程计划中的高优先级过程。此外,这项研究表明，黑曲霉dlpA和crzA基因是提高发酵过程中蛋白质滴度的新线索。
结论：综合起来，这项研究已经确定了几种潜在的遗传线索，以最大化蛋白质滴度，在中试发酵研究期间交付了一套具有用户可控宏观形态的底盘菌株，并量化了影响黑曲霉分泌蛋白滴度的四个关键因素。
