Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif.
Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif.
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DOI：
10.7554/elife.56517
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发表时间：
2020-08-18
期刊：
影响因子：
7.7
通讯作者：
Brewster RC
中科院分区：
文献类型：
--
作者：
Ali MZ;Parisutham V;Choubey S;Brewster RC
从DNA序列预测基因表达仍然是基因调控领域的主要目标。这一目标面临的一个挑战是网络的连通性，其在改变基因表达中的作用仍不清楚。在这里，我们研究了一个常见的自动调节网络模体，负单输入模块，探索从模体继承的调节特性。利用随机模拟和合成生物学方法，在E。在大肠杆菌中，我们发现TF基因和它的靶基因在调控中具有固有的不对称性，即使它们的启动子是相同的; TF基因比它的靶基因更受抑制。不对称性的大小取决于网络特征，如网络大小和TF结合亲和力。有趣的是，当生长速度太快或太慢时，不对称性消失，并且对于典型的生长条件最显著。这些结果强调了在基因表达的定量模型中考虑网络结构的重要性。
Predicting gene expression from DNA sequence remains a major goal in the field of gene regulation. A challenge to this goal is the connectivity of the network, whose role in altering gene expression remains unclear. Here, we study a common autoregulatory network motif, the negative single-input module, to explore the regulatory properties inherited from the motif. Using stochastic simulations and a synthetic biology approach in E. coli, we find that the TF gene and its target genes have inherent asymmetry in regulation, even when their promoters are identical; the TF gene being more repressed than its targets. The magnitude of asymmetry depends on network features such as network size and TF-binding affinities. Intriguingly, asymmetry disappears when the growth rate is too fast or too slow and is most significant for typical growth conditions. These results highlight the importance of accounting for network architecture in quantitative models of gene expression.