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Posttranslational modifications are involved in the regulation of virtually all cellular processes, including immune response, nevertheless, they are also targets manipulated by invading pathogens. The first investigated example is protein citrullination which is an important posttranslational modification that acts on a multitude of processes like supervision of cell pluripotency and rheumatoid arthritis. Citrullination of targeted arginine residues is performed by the Peptidylarginine deiminase. Within the first published manuscript, being part of this thesis, it was possible to show the use of this posttranslational modification by the human pathogen Porphyromonas gingivalis to facilitate innate immune evasion at three distinct level. P. gingivalis was demonstrated to citrullinate proteins by Porphyromonas peptidylarginine deiminase resulting in diminished phagocytosis and subsequent killing by neutrophils. Furthermore, it was shown that citrullination of histone H3 enables P. gingivalis to survive in neutrophil extracellular traps and incapacitate the lysozyme-derived peptide LP9.
The second investigated posttranslational modification is ubiquitination and its role in respiratory tract infections. Ubiquitination is the covalent attachment of a small protein that consisting of only 76 amino acids to the ε-amino group of lysine residues to posttranslational modify proteins. Acute infections of the lower respiratory tract such as viral and bacterial co-infections are among the most prevalent reasons of fatal casualties worldwide. Therefore, the interactions between host and pathogens resulting in the impairment of the hosts immune response and immune evasion of the pathogens, need to be elucidated. To get new insights in the infection driven changes in protein polyubiquitination and alterations in the abundance of ubiquitin E3 ligases involved in ubiquitination, cellular proteomes were monitored in detail by high resolution mass spectrometry. Therefore, the epithelial cell lines 16HBE14o- (Manuscript II) and A549 (Manuscript III) were co-infected with influenza A virus H1N1 and Streptococcus pyogenes or Staphylococcus aureus or with influenza A virus H1N1 and Streptococcus pneumoniae, respectively. Here, it could be shown in 16HBE14o- cells that co-infection of epithelial cells is not characterized by decreased cell survival and that observable effects on the proteome and ubiquitinome are mostly additive rather than synergistic. S. pyogenes infection affected the mitochondrial function, cell-cell adhesion, endocytosis and actin organization. Viral infection affected mRNA processing and Rho signaling. Viral and bacterial co-infection was detected to affect processes that were already affected by both of the corresponding single infections. No further pathways were strongly affected by the co-infection. A similar result has been observed in A549 cells co-infected IAV and S. pneumoniae. Overrepresented gene ontology terms depict the sum of those observed in the viral and bacterial single infection. Moreover, no significant change in cell survival upon co-infection compared to single bacterial infection was noticed for A549 cells either. This led to the suggestion that co-infection of investigated epithelial cells under examined conditions possesses additive rather than synergistic effect and thus, may not worsen the outcome of the infection within the studied conditions. Infections in other systems, may provide varying results and thus should be examined in future studies.
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.