Modeling N-Glycosylation: A Systems Biology Approach for Evaluating Changes in the Steady-State Organization of Golgi-Resident Proteins.
Modeling N-Glycosylation: A Systems Biology Approach for Evaluating Changes in the Steady-State Organization of Golgi-Resident Proteins.
复制标题
N-糖基化建模：用于评估高尔基体驻留蛋白稳态组织变化的系统生物学方法。
DOI：
10.1007/978-1-0716-2639-9_40
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Morgan R
中科院分区：
文献类型：
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作者：
Morgan R
高尔基体驻留蛋白的组织对于在分泌途径内分选分子和调节翻译后修饰至关重要。然而，评估高尔基体组织的变化可能具有挑战性，通常需要广泛的实验研究。在这里，我们提出了一个系统生物学的方法，在该方法中，高尔基体驻留的蛋白质分选和定位的变化可以推断使用cellularN-glycan配置文件作为唯一的实验输入。当N-糖基化以非模板驱动的方式进行时，N-聚糖生物合成酶在高尔基体内的分布确保了这一过程不是完全随机的。因此，N-聚糖谱的改变为细胞表型的改变如何影响高尔基体驻留蛋白的分选和定位提供了线索。在这里，我们生成一个随机模拟N-聚糖的生物合成，以产生一个模拟的聚糖的档案类似于实验获得的，然后联合收割机这与贝叶斯拟合，使推理的变化，酶的数量和本地化。通过计算当从模拟一种细胞状态的聚糖谱（例如，野生型）转变为改变的细胞状态（例如，突变体）。我们的方法说明了如何利用数学系统生物学和最小实验细胞生物学的迭代组合来最大限度地整合生物学知识，以一种单独无法获得的方式获得对潜在机制的深刻了解。
The organization of Golgi-resident proteins is crucial for sorting molecules within the secretory pathway and regulating posttranslational modifications. However, evaluating changes to Golgi organization can be challenging, often requiring extensive experimental investigations. Here, we propose a systems biology approach in which changes to Golgi-resident protein sorting and localization can be deduced using cellularN-glycan profiles as the only experimental input.The approach detailed here utilizes the influence of Golgi organization onN-glycan biosynthesis to investigate the mechanisms involved in establishing and maintaining Golgi organization. WhileN-glycosylation is carried out in a non-template-driven manner, the distribution ofN-glycan biosynthetic enzymes within the Golgi ensures this process is not completely random. Therefore, changes toN-glycan profiles provide clues into how altered cell phenotypes affect the sorting and localization of Golgi-resident proteins. Here, we generate a stochastic simulation ofN-glycan biosynthesis to produce a simulated glycan profile similar to that obtained experimentally and then combine this with Bayesian fitting to enable inference of changes in enzyme amounts and localizations. Alterations to Golgi organization are evaluated by calculating how the fitted enzyme parameters shift when moving from simulating the glycan profile of one cellular state (e.g., a wild type) to an altered cellular state (e.g., a mutant). Our approach illustrates how an iterative combination of mathematical systems biology and minimal experimental cell biology can be utilized to maximally integrate biological knowledge to gain insightful knowledge of the underlying mechanisms in a manner inaccessible to either alone.