Overflow metabolism is well known for yeast, bacteria and mammalian cells. It typically occurs under glucose excess conditions and is characterized by excretions of by-products such as ethanol, ...acetate or lactate. This phenomenon, also denoted the short-term Crabtree effect, has been extensively studied over the past few decades, however, its basic regulatory mechanism and functional role in metabolism is still unknown. Here we present a comprehensive quantitative and time-dependent analysis of the exometabolome of Escherichia coli, Corynebacterium glutamicum, Bacillus licheniformis, and Saccharomyces cerevisiae during well-controlled bioreactor cultivations. Most surprisingly, in all cases a great diversity of central metabolic intermediates and amino acids is found in the culture medium with extracellular concentrations varying in the micromolar range. Different hypotheses for these observations are formulated and experimentally tested. As a result, the intermediates in the culture medium during batch growth must originate from passive or active transportation due to a new phenomenon termed "extended" overflow metabolism. Moreover, we provide broad evidence that this could be a common feature of all microorganism species when cultivated under conditions of carbon excess and non-inhibited carbon uptake. In turn, this finding has consequences for metabolite balancing and, particularly, for intracellular metabolite quantification and (13)C-metabolic flux analysis.
Phenylpropanoids as abundant, lignin-derived compounds represent sustainable feedstocks for biotechnological production processes. We found that the biotechnologically important soil bacterium ...Corynebacterium glutamicum is able to grow on phenylpropanoids such as p-coumaric acid, ferulic acid, caffeic acid, and 3-(4-hydroxyphenyl)propionic acid as sole carbon and energy sources. Global gene expression analyses identified a gene cluster (cg0340-cg0341 and cg0344-cg0347), which showed increased transcription levels in response to phenylpropanoids. The gene cg0340 (designated phdT) encodes for a putative transporter protein, whereas cg0341 and cg0344-cg0347 (phdA-E) encode enzymes involved in the β-oxidation of phenylpropanoids. The phd gene cluster is transcriptionally controlled by a MarR-type repressor encoded by cg0343 (phdR). Cultivation experiments conducted with C. glutamicum strains carrying single-gene deletions showed that loss of phdA, phdB, phdC, or phdE abolished growth of C. glutamicum with all phenylpropanoid substrates tested. The deletion of phdD (encoding for putative acyl-CoA dehydrogenase) additionally abolished growth with the α,β-saturated phenylpropanoid 3-(4-hydroxyphenyl)propionic acid. However, the observed growth defect of all constructed single-gene deletion strains could be abolished through plasmid-borne expression of the respective genes. These results and the intracellular accumulation of pathway intermediates determined via LC-ESI-MS/MS in single-gene deletion mutants showed that the phd gene cluster encodes for a CoA-dependent, β-oxidative deacetylation pathway, which is essential for the utilization of phenylpropanoids in C. glutamicum.
Interspecies interactions inside microbial communities bear a tremendous diversity of complex chemical processes that are by far not understood. Even for simplified, often synthetic systems, the ...interactions between two microbes are barely revealed in detail. Here, we present a microfluidic co-cultivation platform for the analysis of growth and interactions inside microbial consortia with single-cell resolution. Our device allows the spatial separation of two different microbial organisms inside adjacent microchambers facilitating sufficient exchange of metabolites via connecting nanochannels. Inside the cultivation chambers cell growth can be observed with high spatio-temporal resolution by live-cell imaging. In contrast to conventional approaches, in which single-cell activity is typically fully masked by the average bulk behavior, the small dimensions of the microfluidic cultivation chambers enable accurate environmental control and observation of cellular interactions with full spatio-temporal resolution. Our method enables one to study phenomena in microbial interactions, such as gene transfer or metabolic cross-feeding. We chose two different microbial model systems to demonstrate the wide applicability of the technology. First, we investigated commensalistic interactions between an industrially relevant l-lysine-producing Corynebacterium glutamicum strain and an l-lysine auxotrophic variant of the same species. Spatially separated co-cultivation of both strains resulted in growth of the auxotrophic strain due to secreted l-lysine supplied by the producer strain. As a second example we investigated bacterial conjugation between Escherichia coli S17-1 and Pseudomonas putida KT2440 cells. We could show that direct cell contact is essential for the successful gene transfer via conjugation and was hindered when cells were spatially separated. The presented device lays the foundation for further studies on contactless and contact-based interactions of natural and synthetic microbial communities.
Methanol is already an important carbon feedstock in the chemical industry, but it has found only limited application in biotechnological production processes. This can be mostly attributed to the ...inability of most microbial platform organisms to utilize methanol as a carbon and energy source. With the aim to turn methanol into a suitable feedstock for microbial production processes, we engineered the industrially important but nonmethylotrophic bacterium Corynebacterium glutamicum toward the utilization of methanol as an auxiliary carbon source in a sugar-based medium. Initial oxidation of methanol to formaldehyde was achieved by heterologous expression of a methanol dehydrogenase from Bacillus methanolicus, whereas assimilation of formaldehyde was realized by implementing the two key enzymes of the ribulose monophosphate pathway of Bacillus subtilis: 3-hexulose-6-phosphate synthase and 6-phospho-3-hexuloisomerase. The recombinant C. glutamicum strain showed an average methanol consumption rate of 1.7 ± 0.3 mM/h (mean ± standard deviation) in a glucose-methanol medium, and the culture grew to a higher cell density than in medium without methanol. In addition, (13)Cmethanol-labeling experiments revealed labeling fractions of 3 to 10% in the m + 1 mass isotopomers of various intracellular metabolites. In the background of a C. glutamicum Δald ΔadhE mutant being strongly impaired in its ability to oxidize formaldehyde to CO2, the m + 1 labeling of these intermediates was increased (8 to 25%), pointing toward higher formaldehyde assimilation capabilities of this strain. The engineered C. glutamicum strains represent a promising starting point for the development of sugar-based biotechnological production processes using methanol as an auxiliary substrate.
Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low ...throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.
Quantitative characterization of biotechnological production processes requires the determination of different key performance indicators (KPIs) such as titer, rate and yield. Classically, these KPIs ...can be derived by combining black‐box bioprocess modeling with non‐linear regression for model parameter estimation. The presented pyFOOMB package enables a guided and flexible implementation of bioprocess models in the form of ordinary differential equation systems (ODEs). By building on Python as powerful and multi‐purpose programing language, ODEs can be formulated in an object‐oriented manner, which facilitates their modular design, reusability, and extensibility. Once the model is implemented, seamless integration and analysis of the experimental data is supported by various Python packages that are already available. In particular, for the iterative workflow of experimental data generation and subsequent model parameter estimation we employed the concept of replicate model instances, which are linked by common sets of parameters with global or local properties. For the description of multi‐stage processes, discontinuities in the right‐hand sides of the differential equations are supported via event handling using the freely available assimulo package. Optimization problems can be solved by making use of a parallelized version of the generalized island approach provided by the pygmo package. Furthermore, pyFOOMB in combination with Jupyter notebooks also supports education in bioprocess engineering and the applied learning of Python as scientific programing language. Finally, the applicability and strengths of pyFOOMB will be demonstrated by a comprehensive collection of notebook examples.
Production of proteins and biochemicals in microbial cell factories is often limited by carbon and energy spent on excess biomass formation. To address this issue, we developed several genetic growth ...switches based on CRISPR interference technology. We demonstrate that growth of Escherichia coli can be controlled by repressing the DNA replication machinery, by targeting dnaA and oriC, or by blocking nucleotide synthesis through pyrF or thyA. This way, total GFP-protein production could be increased by up to 2.2-fold. Single-cell dynamic tracking in microfluidic systems was used to confirm functionality of the growth switches. Decoupling of growth from production of biochemicals was demonstrated for mevalonate, a precursor for isoprenoid compounds. Mass yield of mevalonate was increased by 41%, and production was maintained for more than 45h after activation of the pyrF-based growth switch. The developed methods represent a promising approach for increasing production yield and titer for proteins and biochemicals.
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•E. coli growth and protein/biochemical production both require carbon and energy.•CRISPRi inhibition of replication or nucleotide synthesis arrests E. coli growth.•Arrested biomass accumulation enhances both protein and biochemical production.•Production yield of mevalonate was increased by 41% through growth inhibition.•Cell activity was maintained for 40h after decoupling growth from production.
In the continuously growing field of industrial biotechnology the scale-up from lab to industrial scale is still a major hurdle to develop competitive bioprocesses. During scale-up the productivity ...of single cells might be affected by bioreactor inhomogeneity and population heterogeneity. Currently, these complex interactions are difficult to investigate. In this report, design, fabrication and operation of a disposable picolitre cultivation system is described, in which environmental conditions can be well controlled on a short time scale and bacterial microcolony growth experiments can be observed by time-lapse microscopy. Three exemplary investigations will be discussed emphasizing the applicability and versatility of the device. Growth and analysis of industrially relevant bacteria with single cell resolution (in particular Escherichia coli and Corynebacterium glutamicum) starting from one single mother cell to densely packed cultures is demonstrated. Applying the picolitre bioreactor, 1.5-fold increased growth rates of C. glutamicum wild type cells were observed compared to typical 1 litre lab-scale batch cultivation. Moreover, the device was used to analyse and quantify the morphological changes of an industrially relevant l-lysine producer C. glutamicum after artificially inducing starvation conditions. Instead of a one week lab-scale experiment, only 1 h was sufficient to reveal the same information. Furthermore, time lapse microscopy during 24 h picolitre cultivation of an arginine producing strain containing a genetically encoded fluorescence sensor disclosed time dependent single cell productivity and growth, which was not possible with conventional methods.
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