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Bacillus subtilis has been extensively used as a microbial cell factory for industrial enzymes due to its excellent capacities for protein secretion and large-scale fermentation. This bacterium is also an attractive host for biopharmaceutical production. However, the secretion potential of this organism is not fully utilized yet, mostly due to a limited understanding of critical rearrangements in the membrane proteome upon high-level protein secretion. Recently, it was shown that bottlenecks in heterologous protein secretion can be resolved by genome minimization. Here, we present for the first time absolute membrane protein concentrations of a genome-reduced B. subtilis strain (“midiBacillus”) expressing the immunodominant Staphylococcus aureus antigen A (IsaA). We quantitatively characterize the membrane proteome adaptation of midiBacillus during production stress on the level of molecules per cell for more than 400 membrane proteins, including determination of protein concentrations for ∼61% of the predicted transporters. We demonstrate that ∼30% of proteins with unknown functions display a significant increase in abundance, confirming the crucial role of membrane proteins in vital biological processes. In addition, our results show an increase of proteins dedicated to translational processes in response to IsaA induction. For the first time reported, we provide accumulation rates of a heterologous protein, demonstrating that midiBacillus secretes 2.41 molecules of IsaA per minute. Despite the successful secretion of this protein, it was found that there is still some IsaA accumulation occurring in the cytosol and membrane fraction, leading to a severe secretion stress response, and a clear adjustment of the cell’s array of transporters. This quantitative dataset offers unprecedented insights into bioproduction stress responses in a synthetic microbial cell.
Recently, we engineered a tunable rhamnose promoter-based setup for the production of recombinant proteins in E. coli. This setup enabled us to show that being able to precisely set the production rate of a secretory recombinant protein is critical to enhance protein production yields in the periplasm. It is assumed that precisely setting the production rate of a secretory recombinant protein is required to harmonize its production rate with the protein translocation capacity of the cell. Here, using proteome analysis we show that enhancing periplasmic production of human Growth Hormone (hGH) using the tunable rhamnose promoter-based setup is accompanied by increased accumulation levels of at least three key players in protein translocation; the peripheral motor of the Sec-translocon (SecA), leader peptidase (LepB), and the cytoplasmic membrane protein integrase/chaperone (YidC). Thus, enhancing periplasmic hGH production leads to increased Sec-translocon capacity, increased capacity to cleave signal peptides from secretory proteins and an increased capacity of an alternative membrane protein biogenesis pathway, which frees up Sec-translocon capacity for protein secretion. When cells with enhanced periplasmic hGH production yields were harvested and subsequently cultured in the absence of inducer, SecA, LepB, and YidC levels went down again. This indicates that when using the tunable rhamnose-promoter system to enhance the production of a protein in the periplasm, E. coli can adapt its protein translocation machinery for enhanced recombinant protein production in the periplasm.
Microbial cell factories have been largely exploited for the controlled production of recombinant proteins, including industrial enzymes and biopharmaceuticals. The advent of high-throughput ‘-omics’ techniques have boosted the design of these production systems due to their valuable contribution to the field of systems metabolic engineering, a discipline integrating metabolic engineering with systems and synthetic biology. In order to thrive, the field of systems metabolic engineering needs absolute proteomics data to be generated, as proteins are the central players in the complex metabolic and adaptational networks. Due to advent of mass spectrometry-based proteomics, a substantial amount of absolute proteomic data became available in the past decade. However, membrane proteins remained inaccessible to these efforts.
Nonetheless, comparative studies targeting the membrane proteome have been quite successful in characterizing physiological processes. Hence, label-free proteomics was used in a study (Quesada-Ganuza et al, 2019 – Article I) to identify and optimize PrsA in Bacillus subtilis, for improved yield of amylase. Amylase is one of the most relevant enzymes in the biotechnological sector. By employing a label-free mass spectrometry approach targeting the membrane proteome of this bacterium, relative changes in heterologous and native levels of PrsA could be quantified. The results of this study evidenced that each PrsA shows different relative abundancies, but with no relevant impact in the yield of amylase.
Even though relative protein quantification can already provide a good visualization of the physiological changes occurring between different conditions, they are not sufficient to understand how resources are allocated in the cell under certain physiological conditions. Therefore, a global method for absolute membrane protein quantification remains the biggest requirement for systems metabolic engineering.
Hence, with this work, we successfully developed a mass spectrometry-based approach enabling the absolute quantification of membrane proteins (Antelo-Varela et al, 2019 – Article II). This study was also performed in the Gram-positive model organism Bacillus subtilis, regarded as a prolific microbial cell factory. The method developed in this work combines the comprehensiveness of shotgun proteomics with the sensitivity and accuracy of targeted mass spectrometry. Fundamental to the method is that it relies on the application of a correction and an enrichment factor to calibrate absolute membrane protein abundances derived from shotgun mass spectrometry. This has permitted, for the first time reported, the calculation of absolute membrane protein abundances in a living organism.
The newly developed approach enabled to accurately quantify ~40% of the predicted proteome of this bacterium, offering a clear visualization of the physiological rearrangements occurring upon the onset of osmotic stress. In addition, this work also provides evidence for new membrane protein stoichiometries.
Overall, this study enabled the development of a straightforward methodology long-needed in the scientific and biotechnological community and, for the first time reported, providing absolute abundances of one of the most puzzling fractions of the cell – the membrane proteome.
The next step of the work summarized here was to implement the afore described method to a biotechnological relevant strain, as absolute membrane protein abundances are essential to understand the fundamental principles of protein secretion and production stress. Hence, this work was applied in a genome-reduced B. subtilis strain, ‘midiBacillus’, expressing the major staphylococcal antigen IsaA (Antelo-Varela et al, submitted – Article III). The employed absolute membrane protein quantification methodology enabled the analysis of physiological rearrangements occurring upon the induction of heterologous protein production. This work showed that, even though IsaA was successfully secreted into the growth medium, one of the main requirements for the biotechnological sector, it was still partly accumulated in the cell membrane of this bacterium. This led to an exacerbated physiological response where membrane proteins involved in the management of secretion stress were activated. In addition, this study also showed that a rearrangement of the cell’s translocation machinery occurs upon induction of production, where a ‘game’ of in- and decrease of transporters takes place.
Anticipating the impact of genetic and environmental insults, such as the ones caused by production stress, is essential for the field of systems metabolic engineering. Thus, the highly accurate and comprehensive dataset generated during this work can be implemented in predictive mathematical models, thereby contributing in the rational design of next-generation secretion systems.