Diana Széliová, Stefan Müller, and Jürgen Zanghellini. 2023. bioRxiv. doi.org/10.1101/2023.07.07.548114

Abstract: Ribosomes are protein synthesis machines that are central to cellular self-fabrication, and the synthesis time of a ribosome places an upper bound on growth rate. While most cellular enzymes are proteins, ribosomes consist of 1/3 protein and 2/3 RNA (in E. coli). Recent research suggests that ribosome composition arises from a trade-off between two "autocatalytic loops", ribosomal protein and RNA polymerase synthesis, respectively. In this work, we develop a (coarse-grained) mechanistic model of a self-fabricating cell, validate it under different growth conditions, and use resource balance analysis (RBA) to study maximum growth rate as a function of ribosome composition. Our model highlights the importance of RNA instability. If we neglect it, RNA synthesis is always "cheaper" than protein synthesis, leading to an RNA-only ribosome at maximum growth rate. To account for RNA turnover, we explore two scenarios regarding the activity of RNases. In (a) degradation is proportional to RNA content, whereas in (b) ribosomal proteins cooperatively mitigate RNA instability by protecting it from misfolding and subsequent degradation. In both cases, an increase in ribosomal protein content raises protein synthesis costs while reducing RNA turnover costs. This leads to a mixed ribosome composed of RNA and proteins. However, only in scenario (b), where we consider the cooperative protection of ribosomal RNA by proteins, our model predictions are in qualitative agreement with experimental data under different growth conditions. Our research offers new mechanistic insights into ribosome biogenesis and evolution. Furthermore, it paves the way for understanding the protein-rich ribosome composition found in archaea and mitochondria.

Mathias Gotsmy, Florian Strobl, Florian Weiß, Petra Gruber, Barbara Kraus, Jürgen Mairhofer, and Jürgen Zanghellini. 2023. bioRxiv. doi.org/10.1101/2023.02.09.527815

Abstract: Plasmid DNA (pDNA) is a key biotechnological product whose importance became apparent in the last years due to its role as a raw material in the messenger ribonucleic acid (mRNA) vaccine manufacturing process. In pharmaceutical production processes, cells need to grow in the defined medium in order to guarantee the highest standards of quality and repeatability. However, often these requirements result in low product titer, productivity, and yield.

In this study, we used constraint-based metabolic modeling to optimize the average volumetric productivity of pDNA production in a fed-batch process. We identified a set of 13 nutrients in the growth medium that are essential for cell growth but not for pDNA replication. When these nutrients are depleted in the medium, cell growth is stalled and pDNA production is increased, raising the specific and volumetric yield and productivity. To exploit this effect we designed a three-stage process (1. batch, 2. fed-batch with cell growth, 3. fed-batch without cell growth). The transition between stage 2 and 3 is induced by sulfate starvation. Its onset can be easily controlled via the initial concentration of sulfate in the medium.

We validated the decoupling behavior of sulfate and assessed pDNA quality attributes (supercoiled pDNA content) in E. coli with lab-scale bioreactor cultivations. The results showed an increase in supercoiled pDNA to biomass yield by 29% upon limitation of sulfate.

In conclusion, even for routinely manufactured biotechnological products such as pDNA, simple changes in the growth medium can significantly improve the yield and quality.

Highlights

• Genome-scale metabolic models predict growth decoupling strategies
• Sulfate limitation decouples cell growth from pDNA production
• Sulfate limitation increases the specific supercoiled pDNA yield by 33% and the volumetric productivity by 13%.
• We propose that sulfate limitation improves the biosynthesis of over 25% of naturally secreted products in E. coli.

Leopold Zehetner, Diana Széliová, Barbara Kraus, Michael Graninger, Jürgen Zanghellini, and Juan A. Hernandez Bort. 2023. Biotechnology J. doi.org/10.1002/biot.202200636

Abstract: Over the past decades, virus-like particle (VLP)-based gene therapy (GT) evolved as a promising approach to cure inherited diseases or cancer. Tremendous costs due to inefficient production processes remain one of the key challenges despite considerable efforts to improve titers. This review aims to link genome-scale metabolic models (GSMMs) to cell lines used for VLP synthesis for the first time. We summarize recent advances and challenges of GSMMs for Chinese hamster ovary (CHO) cells and provide an overview of potential cell lines used in GT. Although GSMMs in CHO cells led to significant improvements in growth rates and recombinant protein (RP)-production, no GSMM has been established for VLP production so far. To facilitate the generation of GSMM for these cell lines we further provide an overview of existing omics data and the highest production titers so far reported.