Sulfate limitation increases specific plasmid DNA yield and productivity in E. coli fed-batch processes.

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 33% and an increase of supercoiled pDNA volumetric productivity by 13 % 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.

Probabilistic quotient's work and pharmacokinetics' contribution: countering size effect in metabolic time series measurements.

Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, many sampling methods suffer from unknown sample sizes, commonly known as size effects. This prevents absolute quantification of biomarkers. Recently, several mathematical post acquisition normalization methods have been developed to overcome these problems either by exploiting already known pharmacokinetic information or by statistical means. Here we present an improved normalization method, MIX, that combines the advantages of both approaches. It couples two normalization terms, one based on a pharmacokinetic model (PKM) and the other representing a popular statistical approach, probabilistic quotient normalization (PQN), in a single model. To test the performance of MIX, we generated synthetic data closely resembling real finger sweat metabolome measurements. We show that MIX normalization successfully tackles key weaknesses of the individual strategies: it (i) reduces the risk of overfitting with PKM, and (ii), contrary to PQN, it allows to compute sample volumes. Finally, we validate MIX by using real finger sweat as well as blood plasma metabolome data and demonstrate that MIX allows to better and more robustly correct for size effects. In conclusion, the MIX method improves the reliability and robustness of quantitative biomarker detection in finger sweat and other biofluids, paving the way for biomarker discovery and hypothesis generation from metabolomic time course data.

Exploring the structure and dynamics of proteins in soil organic matter.

Alongside inorganic materials, water, and air, soil organic matter (SOM) is one of the major components of soil and has tremendous influence on the environment given its vital role in the carbon cycle. Many soil dwelling organisms like plants, fungi and bacteria excrete proteins, whose interaction with SOM is poorly understood on an atomistic level. In this study, molecular dynamics simulations were used to investigate selected proteins in soil models of different complexity from simple co-solvent molecules to Leonardite humic acids (LHA). We analyzed the proteins in terms of their structural stability, the nature and strength of the interactions with their surroundings, as well as their aggregation behavior. Upon insertion of proteins in complex SOM models, their structural stability decreased, although no unfolding or disruption of secondary structure was observed. The interactions of proteins and SOM were primarily governed by electrostatic forces, often in form of hydrogen bonds. However, also weaker van der Waals forces made a significant contribution to the total interaction energies. Moreover, we showed that even though the molecular structure and size of SOM molecules varied, the functional groups of SOM ordered around the protein in a similar pattern. Finally, the number of aggregates formed by proteins and SOM molecules was shown to be primarily proportional to the size of the latter. Strikingly, for varying protein net charges no changes in the formation of aggregates with the strongly negatively charged LHA were observed.

Finger sweat analysis enables short interval metabolic biomonitoring in humans.

Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues, which are impeding time-course studies. Here, we show that the metabolic profiling of the minute amounts of sweat sampled from fingertips addresses this challenge. Sweat sampling from fingertips is non-invasive, robust and can be accomplished repeatedly by untrained personnel. The sweat matrix represents a rich source for metabolic phenotyping. We confirm the feasibility of short interval sampling of sweat from the fingertips in time-course studies involving the consumption of coffee or the ingestion of a caffeine capsule after a fasting interval, in which we successfully monitor all known caffeine metabolites as well as endogenous metabolic responses. Fluctuations in the rate of sweat production are accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. To conclude, metabotyping using sweat from fingertips combined with mathematical network modelling shows promise for broad applications in precision medicine by enabling the assessment of dynamic metabolic patterns, which may overcome the limitations of purely compositional biomarkers.

An antibody with Fab-constant domains exchanged for a pair of CH3 domains.

We have designed a complete antibody-like construct where the CH1 and Cκ domains are exchanged for a pair of the CH3 domains and efficient pairing of the heavy and light variable domain is achieved using "Knobs-into-Holes" strategy. This construct, composed of only naturally occurring immunoglobulin sequences without artificial linkers, expressed at a high level in mammalian cells, however exhibited low solubility. Rational mutagenesis aimed at the amino acid residues located at the interface of the variable domains and the exchanged CH3 domains was applied to improve the biophysical properties of the molecule. The domain-exchanged construct, including variable domains of the HER2/neu specific antibody trastuzumab, was able to bind to the surface of the strongly HER2/neu positive cell line SK-BR3 4-fold weaker than trastuzumab, but could nevertheless incite a more potent response in an antibody-dependent cell cytotoxicity (ADCC) reporter assay with FcγRIIIa-overexpressing T-cells. This could be explained with a stronger binding to the FcγRIIIa. Importantly, the novel construct could mediate a specific ADCC effect with natural killer cells similar to the parental antibody.