Understanding regulation of biosynthetic gene clusters to facilitate production of secondary metabolites

微生物产生的次级代谢产物种类繁多，在医学和工业上有着广泛的应用.生物合成基因簇（BGC）上携带着负责次级代谢产物合成的基因，通常与参与BGC调控的转录因子一起。BGC在其天然宿主中的利用有时是不可能的，需要在另一宿主中异源表达，例如E. coli中进行发酵，获得较高的次生代谢产物产量。通常，转移的BGC的表达是次优的或偶尔根本不表达。这需要对簇内现有的启动子进行修饰，这些启动子必须被修饰为（i）被新宿主的RNA聚合酶识别;（ii）使BGC上的蛋白质与宿主中的蛋白质之间的相互作用最小化;（iii）确保BGC上携带的TF保持其调节功能。由于这些原因，异源表达目前涉及昂贵且耗时的重构过程-将BGC的每个基因置于其自身的启动子下，然后以半随机方式单独修饰每个启动子以达到最佳表达水平。另一种可能提高效率的策略是保留BGC的结构和监管环境，但使其适应新主机的监管网络。这种合理的方法需要改变天然BGC启动子，以达到在新宿主中的最佳表达。本研究的目的是建立一个实验和计算相结合的框架，用于准确预测和优化在大肠杆菌中异源表达的BGC内基因的表达水平。杆菌这将使优化转录表达水平ofeeach基因在BGC更迅速和更少的试错比目前可能的。

Microbes produce a wide range of secondary metabolites with diverse medical and industrialapplications. Genes responsible for the synthesis of secondary metabolites are carried onbiosynthetic gene clusters (BGCs), often alongside transcription factors that are involved in theregulation of the BGC. The exploitation of BGCs is sometimes impossible in their native host,necessitating heterologous expression in another host, such as E. coli, to obtain good yields ofsecondary metabolites. Often, the expression of a transferred BGC is sub-optimal or occasionally itis not expressed at all. This requires modification of existing promoters within the cluster thatmust be modified to (i) be recognized by the new host's RNA polymerase; (ii) minimize interactionsbetween proteins on the BGC and those in the host; (iii) ensure the TF(s) carried on the BGCmaintain their regulatory function. For these reasons, heterologous expression currently involvesan expensive and time-consuming process of refactoring - placing each gene of the BGC under itsown promoter and then individually modifying each promoter in a semi-random manner toachieve optimal expression levels. An alternative strategy with potential for greater efficiency is topreserve the structure and regulatory context of the BGC, but to adapt it to regulatory networks ofthe new host. This rational approach requires alterations of native BGC promoters in order toachieve optimal expression in the new host.The aim of this studentship is to develop a combined experimental and computationalframework for accurate prediction and optimization of expression levels of genes within BGCsheterologously expressed in E. coli. This will enable optimizing transcriptional expression levels ofeach gene in the BGC more rapidly and with less trial-and-error than currently possible.