Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf, Forschungszentrum Jülich, 52425, Jülich, Germany.
Institute of Biotechnology, RWTH Aachen University, 52074, Aachen, Germany.
Microb Cell Fact. 2023 Oct 7;22(1):203. doi: 10.1186/s12934-023-02199-8.
Bacillus subtilis is one of the workhorses in industrial biotechnology and well known for its secretion potential. Efficient secretion of recombinant proteins still requires extensive optimization campaigns and screening with activity-based methods. However, not every protein can be detected by activity-based screening. We therefore developed a combined online monitoring system, consisting of an in vivo split GFP assay for activity-independent target detection and an mCherry-based secretion stress biosensor. The split GFP assay is based on the fusion of a target protein to the eleventh β-sheet of sfGFP, which can complement a truncated sfGFP that lacks this β-sheet named GFP1-10. The secretion stress biosensor makes use of the CssRS two component quality control system, which upregulates expression of mCherry in the htrA locus thereby allowing a fluorescence readout of secretion stress.
The biosensor strain B. subtilis PAL5 was successfully constructed by exchanging the protease encoding gene htrA with mCherry via CRISPR/Cas9. The Fusarium solani pisi cutinase Cut fused to the GFP11 tag (Cut11) was used as a model enzyme to determine the stress response upon secretion mediated by signal peptides SP, SP and SP obtained from naturally secreted proteins of B. subtilis. An in vivo split GFP assay was developed, where purified GFP1-10 is added to the culture broth. By combining both methods, an activity-independent high-throughput method was created, that allowed optimization of Cut11 secretion. Using the split GFP-based detection assay, we demonstrated a good correlation between the amount of secreted cutinase and the enzymatic activity. Additionally, we screened a signal peptide library and identified new signal peptide variants that led to improved secretion while maintaining low stress levels.
Our results demonstrate that the combination of a split GFP-based detection assay for secreted proteins with a secretion stress biosensor strain enables both, online detection of extracellular target proteins and identification of bottlenecks during protein secretion in B. subtilis. In general, the system described here will also enable to monitor the secretion stress response provoked by using inducible promoters governing the expression of different enzymes.
枯草芽孢杆菌是工业生物技术的得力助手，以其分泌潜能而闻名。然而，高效分泌重组蛋白仍需要广泛的优化和基于活性的筛选。但是，并非每种蛋白质都可以通过基于活性的筛选检测到。因此，我们开发了一种组合在线监测系统，由用于非活性靶标检测的体内分裂 GFP 测定法和基于 mCherry 的分泌应激生物传感器组成。分裂 GFP 测定法基于将靶蛋白融合到 sfGFP 的第十一个 β 片层中，该融合蛋白可以互补缺乏该 β 片层的截断 sfGFP，称为 GFP1-10。分泌应激生物传感器利用 CssRS 双组分质量控制系统，该系统上调 htrA 基因座中 mCherry 的表达，从而可以对分泌应激进行荧光读数。
通过 CRISPR/Cas9 将蛋白酶编码基因 htrA 替换为 mCherry，成功构建了枯草芽孢杆菌 PAL5 生物传感器菌株。将与 GFP11 标签融合的豌豆镰刀菌角质酶 Cut（Cut11）用作模型酶，以确定由枯草芽孢杆菌天然分泌蛋白的信号肽 SP、SP 和 SP 介导的分泌过程中的应激反应。开发了一种体内分裂 GFP 测定法，其中将纯化的 GFP1-10 添加到培养液中。通过结合这两种方法，创建了一种无需活性的高通量方法，该方法可以优化 Cut11 的分泌。使用基于分裂 GFP 的检测测定法，我们证明了分泌的角质酶的量与酶活性之间存在良好的相关性。此外，我们筛选了信号肽文库，并鉴定了新的信号肽变体，这些变体可改善分泌而又保持低应激水平。
我们的结果表明，将用于分泌蛋白的基于分裂 GFP 的检测测定法与分泌应激生物传感器菌株相结合，可实现枯草芽孢杆菌中外源靶蛋白的在线检测，并确定蛋白分泌过程中的瓶颈。通常，这里描述的系统还将能够监测使用不同酶的诱导型启动子控制表达时引起的分泌应激反应。
