Risk-Based Bioengineering Strategies for Reliable Bacterial Vaccine Production

Design of a reliable process for bacterial antigen production requires understanding of and control over critical process parameters. Current methods for process design use extensive screening experiments for determining ranges of critical process parameters yet fail to give clear insights into how they influence antigen potency. To address this gap, we propose to apply constraint-based, genome-scale metabolic models to reduce the need of experimental screening for strain selection and to optimize strains based on model driven iterative Design–Build–Test–Learn (DBTL) cycles. Application of these systematic methods has not only increased the understanding of how metabolic network properties influence antigen potency, but also allows identification of novel critical process parameters that need to be controlled to achieve high process reliability.</p

Systems analysis of Mycoplasma hyopneumoniae to improve vaccine production

Mycoplasma hyopneumoniae (M. hyopneumoniae) is a bacterial pathogen that has evolved from a gram-positive ancestor and specifically colonizes the lower respiratory tract of pigs where it causes enzootic pneumonia and plays a major role in the development of respiratory disease in pigs. Whole-cell inactivated vaccines are available that lower the severity of disease and are widely applied in pig industry to prevent clinical signs and improve pig herd health. However, production of these vaccines is challenging because it is not known which bacterial components are needed for protection and complex cultivation media are needed because growth requirements are not completely understood. The aim of this thesis was to understand growth and survival strategies of M. hyopneumoniae during infection, to integrate this knowledge with metabolic modeling under conditions used for vaccine production and apply this knowledge to improve the current production process for M. hyopneumoniae vaccines. Chapter 1 provides a general introduction into the disease, treatment and prevention methods with a focus on vaccines. I then introduce the characteristics of the M. hyopneumoniae genome, transcriptome and review the current knowledge on infectious mechanisms and the response of the pig to infection and vaccination. Finally, I discuss the challenges related to vaccine production and introduce systems biology tools that will be applied in the thesis. In chapter 2 we define a strategy for risk-based process development of bacterial vaccines which provided the framework for future studies performed during this thesis. We propose to integrate the academic workflow for rational strain design with the industry standard for process design. Systems biology tools, especially genome-scale metabolic models, play an essential role in this strategy because application of these tools reduces process risks and increases process understanding. Therefore, in line with this strategy, we created a manually curated genome-scale metabolic model of M. hyopneumoniae which we applied to dynamically model the cultivation step in the vaccine production process (chapter 3). We found that only 16% of cellular energy in a standard fermentation was used for growth and 84% was used for non-growth associated maintenance. By model-driven experimentation we were able to increase the fraction of cellular energy used for growth by addition of pyruvate to the production medium, and showed in dedicated fermentor experiments that the improved process reached a 2.3 times higher biomass yield. Although the metabolic model helped to increase process yield, it did not allow prediction of a defined cultivation medium without components from porcine origin. Therefore, to better understand the dependency of M. hyopneumoniae on host derived components, we performed a functional comparison of 80 mycoplasma genomes and used multivariate and machine-learning algorithms to relate functional capability to the specific host and niche of mycoplasma species (chapter 4). This analysis allowed us to identify protein domains possibly needed for growth and survival in the pig lung. In addition, we found that protein domains expected to be essential for bacterial growth were not persistently present in mycoplasma genomes suggesting that alternative domain configurations exist that bypass their essentiality. To better understand whether the proteins we identified as possibly important for survival in pigs actually play a role during M. hyopneumoniae infection, we sequenced the bacterial mRNA during infection in chapter 5 and compared the in vivo transcriptome to that of broth grown mycoplasma. We found 22 up-regulated and 30 down-regulated genes during infection (FDR2LOG2) and identified differentially expressed ncRNAs. In chapter 6 we build upon our mycoplasma basis to further analyse the role of ncRNAs in bacterial genomes. We identified an exponential relationship between the AT content of genomes and the number of ncRNAs and propose that this relation is the result of spurious transcription, which is more likely to occur in AT rich genomes. This hypothesis is further substantiated by showing that spurious transcription demands minimal cellular energy and that overexpression of cis-binding ncRNAs in M. pneumoniae did not influence the level of proteins translated from their overlapping mRNAs. Finally, in chapter 7 I discuss four system strategies, identified in this thesis and derived from recent literature, and discuss how these strategies could be integrated in the metabolic model of M. hyopneumoniae. Lastly, I provide an outlook on the next steps needed for improvement of the production process for M. hyopneumoniae vaccines. In conclusion, this work provided novel insight in the metabolic capability of M. hyopneumoniae based on the proteome domain content, captured in a genome-scale metabolic model and studied under in vitro and in vivo conditions. Biomass yield of the cultivation step for vaccine production was increased and the basis was laid to further improve the production process for M. hyopneumoniae vaccines using model-based experimentation.</p

Metabolic modeling of energy balances in Mycoplasma hyopneumoniae shows that pyruvate addition increases growth rate

Mycoplasma hyopneumoniae is cultured on large-scale to produce antigen for inactivated whole-cell vaccines against respiratory disease in pigs. However, the fastidious nutrient requirements of this minimal bacterium and the low growth rate make it challenging to reach sufficient biomass yield for antigen production. In this study, we sequenced the genome of M. hyopneumoniae strain 11 and constructed a high quality constraint-based genome-scale metabolic model of 284 chemical reactions and 298 metabolites. We validated the model with time-series data of duplicate fermentation cultures to aim for an integrated model describing the dynamic profiles measured in fermentations. The model predicted that 84% of cellular energy in a standard M. hyopneumoniae cultivation was used for non-growth associated maintenance and only 16% of cellular energy was used for growth and growth associated maintenance. Following a cycle of model-driven experimentation in dedicated fermentation experiments, we were able to increase the fraction of cellular energy used for growth through pyruvate addition to the medium. This increase in turn led to an increase in growth rate and a 2.3 times increase in the total biomass concentration reached after 3-4 days of fermentation, enhancing the productivity of the overall process. The model presented provides a solid basis to understand and further improve M. hyopneumoniae fermentation processes.</p

Persistence of functional protein domains in mycoplasma species and their role in host specificity and synthetic minimal life

Mycoplasmas are the smallest self-replicating organisms and obligate parasites of a specific vertebrate host. An in-depth analysis of the functional capabilities of mycoplasma species is fundamental to understand how some of simplest forms of life on Earth succeeded in subverting complex hosts with highly sophisticated immune systems. In this study we present a genome-scale comparison, focused on identification of functional protein domains, of 80 publically available mycoplasma genomes which were consistently re-annotated using a standardized annotation pipeline embedded in a semantic framework to keep track of the data provenance. We examined the pan- and core-domainome and studied predicted functional capability in relation to host specificity and phylogenetic distance. We show that the pan- and core-domainome of mycoplasma species is closed. A comparison with the proteome of the "minimal" synthetic bacterium JCVI-Syn3.0 allowed us to classify domains and proteins essential for minimal life. Many of those essential protein domains, essential Domains of Unknown Function (DUFs) and essential hypothetical proteins are not persistent across mycoplasma genomes suggesting that mycoplasma species support alternative domain configurations that bypass their essentiality. Based on the protein domain composition, we could separate mycoplasma species infecting blood and tissue. For selected genomes of tissue infecting mycoplasmas, we could also predict whether the host is ruminant, pig or human. Functionally closely related mycoplasma species, which have a highly similar protein domain repertoire, but different hosts could not be separated. This study provides a concise overview of the functional capabilities of mycoplasma species, which can be used as a basis to further understand host-pathogen interaction or to design synthetic minimal life

Bacterial antisense RNAs are mainly the product of transcriptional noise

cis-Encoded antisense RNAs (asRNAs) are widespread along bacterial transcriptomes. However, the role of most of these RNAs remains unknown, and there is an ongoing discussion as to what extent these transcripts are the result of transcriptional noise. We show, by comparative transcriptomics of 20 bacterial species and one chloroplast, that the number of asRNAs is exponentially dependent on the genomic AT content and that expression of asRNA at low levels exerts little impact in terms of energy consumption. A transcription model simulating mRNA and asRNA production indicates that the asRNA regulatory effect is only observed above certain expression thresholds, substantially higher than physiological transcript levels. These predictions were verified experimentally by overexpressing nine different asRNAs in Mycoplasma pneumoniae. Our results suggest that most of the antisense transcripts found in bacteria are the consequence of transcriptional noise, arising at spurious promoters throughout the genome.This work was supported by the European Union Seventh Framework Programme (FP7/2007–2013), through the European Research Council (232913); Fundación Botín, the Spanish Ministry of Economy and Competitiveness (BIO2007-61762); National Plan of R + D + i; ISCIII—Subdirección General de Evaluación y Fomento de la Investigación (PI10/01702); European Regional Development Fund (to the Institució Catalana de Recerca i Estudis Avançats research professor L.S.); and Spanish Ministry of Economy and Competitiveness, “Centro de Excelencia Severo Ochoa 2013–2017” (SEV-2012-0208). A.L. received grant BFU2012-39816-C02-01 from the Spanish Ministry of Economy and Competitivity cofinanced by FEDER (Fondo Europeo de Desarrollo Regional) funds