Mitochondrial Respiratory Chain Alternative Components Activity During Different Growth Phases in Yarrowia Lipolytica

Additional file 4. Spreadsheet file containing BioLector raw data set with backscatter and DO readings for twenty-four cultivations of C. glutamicum strains, used for calculation of growth rates in the application example

Machine learning in bioprocess development: From promise to practice

Fostered by novel analytical techniques, digitalization and automation, modern bioprocess development provides high amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have a high potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. The aim of this review is to demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges and point out domains that can potentially benefit from technology transfer and further progress in the field of ML

Comprehensive clone screening and evaluation of fed-batch strategies in a microbioreactor and lab scale stirred tank bioreactor system : application on Pichia pastoris producing Rhizopus oryzae lipase

Background: In Pichia pastoris bioprocess engineering, classic approaches for clone selection and bioprocess optimization at small/micro scale using the promoter of the alcohol oxidase 1 gene (PAOX1), induced by methanol, present low reproducibility leading to high time and resource consumption. - Results: An automated microfermentation platform (RoboLector) was successfully tested to overcome the chronic problems of clone selection and optimization of fed-batch strategies. Different clones from Mut+P. pastoris phenotype strains expressing heterologous Rhizopus oryzae lipase (ROL), including a subset also overexpressing the transcription factor HAC1, were tested to select the most promising clones. The RoboLector showed high performance for the selection and optimization of cultivation media with minimal cost and time. Syn6 medium was better than conventional YNB medium in terms of production of heterologous protein. The RoboLector microbioreactor was also tested for different fed-batch strategies with three clones producing different lipase levels. Two mixed substrates fed-batch strategies were evaluated. The first strategy was the enzymatic release of glucose from a soluble glucose polymer by a glucosidase, and methanol addition every 24 hours. The second strategy used glycerol as co-substrate jointly with methanol at two different feeding rates. The implementation of these simple fed-batch strategies increased the levels of lipolytic activity 80-fold compared to classical batch strategies used in clone selection. Thus, these strategies minimize the risk of errors in the clone selection and increase the detection level of the desired product. Finally, the performance of two fed-batch strategies was compared for lipase production between the RoboLector microbioreactor and 5 liter stirred tank bioreactor for three selected clones. In both scales, the same clone ranking was achieved. - Conclusion: The RoboLector showed excellent performance in clone selection of P. pastoris Mut+ phenotype. The use of fed-batch strategies using mixed substrate feeds resulted in increased biomass and lipolytic activity. The automated processing of fed-batch strategies by the RoboLector considerably facilitates the operation of fermentation processes, while reducing error-prone clone selection by increasing product titers.The scale-up from microbioreactor to lab scale stirred tank bioreactor showed an excellent correlation, validating the use of microbioreactor as a powerful tool for evaluating fed-batch operational strategies

Signature of short-range van der Waals forces observed in Poisson spot diffraction with indium atoms

The phase of de Broglie matter waves is a sensitive probe for small forces. In particular, the attractive van der Waals force experienced by polarizable atoms in the close vicinity of neutral surfaces is of importance in nanoscale systems. It results in a phase shift that can be observed in matter-wave diffraction experiments. Here, we observe Poisson spot diffraction of indium atoms at submillimeter distances behind spherical submicron silicon dioxide particles to probe the dispersion forces between atoms and the particle surfaces. We compare the measured relative intensity of Poisson’s spot to theoretical results derived from first principles in an earlier communication and find a clear signature of the atom-surface interaction

Growth independent rhamnolipid production from glucose using the non-pathogenic Pseudomonas putida KT2440

<p>Abstract</p> <p>Background</p> <p>Rhamnolipids are potent biosurfactants with high potential for industrial applications. However, rhamnolipids are currently produced with the opportunistic pathogen <it>Pseudomonas aeruginosa </it>during growth on hydrophobic substrates such as plant oils. The heterologous production of rhamnolipids entails two essential advantages: Disconnecting the rhamnolipid biosynthesis from the complex quorum sensing regulation and the opportunity of avoiding pathogenic production strains, in particular <it>P. aeruginosa</it>. In addition, separation of rhamnolipids from fatty acids is difficult and hence costly.</p> <p>Results</p> <p>Here, the metabolic engineering of a rhamnolipid producing <it>Pseudomonas putida </it>KT2440, a strain certified as safety strain using glucose as carbon source to avoid cumbersome product purification, is reported. Notably, <it>P. putida </it>KT2440 features almost no changes in growth rate and lag-phase in the presence of high concentrations of rhamnolipids (> 90 g/L) in contrast to the industrially important bacteria <it>Bacillus subtilis, Corynebacterium glutamicum</it>, and <it>Escherichia coli. P. putida </it>KT2440 expressing the <it>rhlAB</it>-genes from <it>P. aeruginosa </it>PAO1 produces mono-rhamnolipids of <it>P. aeruginosa </it>PAO1 type (mainly C₁₀:C₁₀). The metabolic network was optimized in silico for rhamnolipid synthesis from glucose. In addition, a first genetic optimization, the removal of polyhydroxyalkanoate formation as competing pathway, was implemented. The final strain had production rates in the range of <it>P. aeruginosa </it>PAO1 at yields of about 0.15 g/g_glucosecorresponding to 32% of the theoretical optimum. What's more, rhamnolipid production was independent from biomass formation, a trait that can be exploited for high rhamnolipid production without high biomass formation.</p> <p>Conclusions</p> <p>A functional alternative to the pathogenic rhamnolipid producer <it>P. aeruginosa </it>was constructed and characterized. <it>P. putida </it>KT24C1 pVLT31_<it>rhlAB </it>featured the highest yield and titer reported from heterologous rhamnolipid producers with glucose as carbon source. Notably, rhamnolipid production was uncoupled from biomass formation, which allows optimal distribution of resources towards rhamnolipid synthesis. The results are discussed in the context of rational strain engineering by using the concepts of synthetic biology like chassis cells and orthogonality, thereby avoiding the complex regulatory programs of rhamnolipid production existing in the natural producer <it>P. aeruginosa</it>.</p

Studies on bioprocesses for protein secretion with Corynebacterium glutamicum

After being discovered 60 years ago, Corynebacterium glutamicum is a major industrial workhorse for the production of amino acids like L-glutamate and L-lysine at several million tons per year. Intense and still ongoing basic and applied research fueled this great biotechnological success story. Currently, C. glutamicum is increasingly getting into focus as production host for heterologous proteins of both technical and clinical interest. Consequently, research is shifting towards the use of C. glutamicum microbial cell factory for protein production.To facilitate rapid and reliable characterization of newly constructed strain variants from metabolic engineering, microbioreactor (MBR) systems emerged as versatile tools. Such systems provide an increased experimental throughput with the ability to control environmental cultivation parameters, as well as monitoring of culture dynamics like biomass formation. In this study, methods for MBR systems are developed to suite the demands of a specific screening objective, namely the quantitative phenotyping of C. glutamicum strains secreting green fluorescent protein (GFP) and cutinase as heterologous model proteins. This involves the careful selection of standard operating conditions with respect to physiological demands like oxygen-unlimited metabolization of glucose as main carbon source, as well as determination of accuracy and imprecision of online biomass monitoring. Furthermore, to obtain time-resolved data on secretory protein formation and substrate consumption, an improved method is presented and validated that does not accompany a loss in MBR cultivation throughput. The need for (semi-)automated data processing from high-throughput MBR cultivations is also discussed on the example of derived performance indicators (PIs) that represent condensed evaluation metrics for rapid evaluation of whole cultivation experiments. As application example, a method for automated growth rate determination is presented in detail, since this PI is probably the most often applied characteristic in biological fitness testing of mutant strain libraries. In addition, on the example of maximizing secreted GFP titer, it is shown how MBR systems, integration of laboratory automation and Kriging-based Design of Experiments (DoE) complement each other in a synergistic way. As a result, an iterative workflow is presented that serves as blueprint for development of further biotechnological applications. Unexpectedly, secreted GFP titer could be doubled, showing that routinely applied nutrition media designed for amino acid production with C. glutamicum need to be carefully adapted and optimized with changing screening objectives, that is here secretion of heterologous proteins. To complement the current knowledge on how to select the optimal signal peptide (SP) for different expression hosts and different target proteins of choice, the interrelation of bioprocess control strategy and choice of SP to optimize cutinase secretion with C. glutamicum is investigated in detail. Since the envisaged degree of process control could not be realized with the available MBR systems, a consistent data set was generated relying on more than 150 bench scale bioreactor runs. Furthermore, the results are discussed and interpreted in the light of changing bioprocess optimization objectives, which again highlights the need for careful definition of optimization objectives. Representing a typical application example of MBR systems, the quantitative microbial phenotyping of a library of genome reduced C. glutamicum strains for heterologous cutinase secretion was conducted. The collected data comprise growth rates and cutinase yields as extracellular phenotypes, as well as detailed analysis at the transcriptome and proteome level for a small subset of strains. Next to surprising phenotypes due to specific genomic deletions, as well as differential analysis of phenotypes from strains with overlapping genomic deletions, attempts were made to explain the metabolic perturbations from observed significantly differential regulation at the protein level. Also, by incorporating all data on extracellular phenotypes, a data-driven, phenomenological multiple regression approach was used to identify the minimum set of genomic deletions needed in terms of improved cutinase secretion. Finally, a few future aspects outreaching the scope of this work are presented as outlook. These aspects concern the application of C. glutamicum as potential alternative host for heterologous protein production, the demand for further development of microbioreactor systems and the need for smart solutions for warehousing, (re-)processing and interpretation of heterogeneous data sets to cope with the foreseeable increase of information output generated from high-throughput experimentation in combination with powerful analytical methods