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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. Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. In this Perspective, the authors propose an integrated solution combining growth-coupled selection with machine learning and automated workflows to accelerate development pipelines.

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.

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.

Bacterial growth rate (µ) depends on the protein synthesis capacity of the cell and thus on the number of active ribosomes and their translation elongation rate. The relationship between these fundamental growth parameters have only been described for few bacterial species, in particular Escherichia coli . Here, we analyse the growth-rate dependency of ribosome abundance and translation elongation rate for Corynebacterium glutamicum , a gram-positive model species differing from E. coli by a lower growth temperature optimum and a lower maximal growth rate. We show that, unlike in E. coli , there is little change in ribosome abundance for µ <0.4 h −1 in C. glutamicum and the fraction of active ribosomes is kept above 70% while the translation elongation rate declines 5-fold. Mathematical modelling indicates that the decrease in the translation elongation rate can be explained by a depletion of translation precursors. Bacterial growth rate depends on the number of active ribosomes and translation elongation rate. Matamouros et al. show that Corynebacterium glutamicum , a gram-positive model species, uses a different strategy than Escherichia coli during slow growth by strongly reducing the translation elongation rate while keeping a high number of active ribosomes.

Background The establishment of synthetic microbial communities comprising complementary auxotrophic strains requires efficient transport processes for common goods. With external supplementation of the required metabolite, most auxotrophic strains reach wild-type level growth. One exception was the l -trypton auxotrophic strain pha Corynebacterium glutamicum ΔTRP Δ trpP , which grew 35% slower than the wild type in supplemented defined media. C. glutamicum ΔTRP Δ trpP lacks the whole l -tryptophan biosynthesis cluster (TRP, cg3359-cg3364) as well as the putative l -tryptophan transporter TrpP (Cg3357). We wanted to explore the role of TrpP in l -tryptophan transport, metabolism or regulation and to elucidate the cause of growth limitation despite supplementation. Results Mutants lacking either TRP or trpP revealed that the growth defect was caused solely by trpP deletion, whereas l -tryptophan auxotrophy was caused only by TRP deletion. Notably, not only the deletion but also the overexpression of trpP in an l -tryptophan producer increased the final l -tryptophan titer, arguing against a transport function of TrpP. A transcriptome comparison of C. glutamicum Δ trpP with the wild type showed alterations in the regulon of WhcA, that contains an [Fe-S] cluster. Through evolution-guided metabolic engineering, we discovered that inactivation of SufR (Cg1765) partially complemented the growth defect caused by Δ trpP . SufR is the transcriptional repressor of the suf operon (cg1764-cg1759), which encodes the only system of C. glutamicum for iron‒sulfur cluster formation and repair. Finally, we discovered that the combined deletion of trpP and sufR increased l -tryptophan production by almost 3-fold in comparison with the parental strain without the deletions. Conclusions On the basis of our results, we exclude the possibility that TrpP is an l -tryptophan transporter. TrpP presence influences [Fe-S] cluster formation or repair, presumably through a regulatory function via direct interaction with another protein. [Fe-S] cluster availability influences not only certain enzymes but also targets of the WhiB-family regulator WhcA, which is involved in oxidative stress response. The reduced growth of WT Δ trpP is likely caused by the reduced activity of [Fe-S]-cluster-containing enzymes involved in central metabolism, such as aconitase or succinate: menaquinone oxidoreductase. In summary, we identified a very interesting link between l -tryptophan biosynthesis and iron sulfur cluster formation that is relevant for l -tryptophan production. Clinical trial number Not applicable.

Background Adaptive laboratory evolution (ALE) is known as a powerful tool for untargeted engineering of microbial strains and genomics research. It is particularly well suited for the adaptation of microorganisms to new environmental conditions, such as alternative substrate sources. Since the probability of generating beneficial mutations increases with the frequency of DNA replication, ALE experiments are ideally free of constraints on the required duration of cell proliferation. Results Here, we present an extended robotic workflow for performing long-term evolution experiments based on fully automated repetitive batch cultures (rbALE) in a well-controlled microbioreactor environment. Using a microtiter plate recycling approach, the number of batches and thus cell generations is technically unlimited. By applying the validated workflow in three parallel rbALE runs, ethanol utilization by Corynebacterium glutamicum ATCC 13032 (WT) was significantly improved. The evolved mutant strain WT_EtOH-Evo showed a specific ethanol uptake rate of 8.45  ±  0.12 mmol EtOH g CDW −1  h −1 and a growth rate of 0.15 ± 0.01 h −1 in lab-scale bioreactors. Genome sequencing of this strain revealed a striking single nucleotide variation (SNV) upstream of the ald gene (NCgl2698, cg3096) encoding acetaldehyde dehydrogenase (ALDH). The mutated basepair was previously predicted to be part of the binding site for the global transcriptional regulator GlxR, and re-engineering demonstrated that the identified SNV is key for enhanced ethanol assimilation. Decreased binding of GlxR leads to increased synthesis of the rate-limiting enzyme ALDH, which was confirmed by proteomics measurements. Conclusions The established rbALE technology is generally applicable to any microbial strain and selection pressure that fits the small-scale cultivation format. In addition, our specific results will enable improved production processes with C. glutamicum from ethanol, which is of particular interest for acetyl-CoA-derived products.

The co‐catabolism of multiple host‐derived carbon substrates is required by Mycobacterium tuberculosis (Mtb) to successfully sustain a tuberculosis infection. However, the metabolic plasticity of this pathogen and the complexity of the metabolic networks present a major obstacle in identifying those nodes most amenable to therapeutic interventions. It is therefore critical that we define the metabolic phenotypes of Mtb in different conditions. We applied metabolic flux analysis using stable isotopes and lipid fingerprinting to investigate the metabolic network of Mtb growing slowly in our steady‐state chemostat system. We demonstrate that Mtb efficiently co‐metabolises either cholesterol or glycerol, in combination with two‐carbon generating substrates without any compartmentalisation of metabolism. We discovered that partitioning of flux between the TCA cycle and the glyoxylate shunt combined with a reversible methyl citrate cycle is the critical metabolic nodes which underlie the nutritional flexibility of Mtb. These findings provide novel insights into the metabolic architecture that affords adaptability of bacteria to divergent carbon substrates and expand our fundamental knowledge about the methyl citrate cycle and the glyoxylate shunt. Synopsis Quantitative metabolic analysis using stable isotopes, lipid fingerprinting, and mathematical modelling are applied to investigate the metabolic network of Mycobacterium tuberculosis growing slowly in a steady state chemostat system. The tubercle bacillus efficiently co‐metabolises cholesterol or glycerol, in combination with two‐carbon generating substrates without compartmentalisation of metabolism. Metabolic flux profiles of M . tuberculosis growing slowly on the dual carbon sources are described using an expanded 13C isotopomer model. Partitioning of metabolite flux between the TCA cycle and the glyoxylate shunt combined with a reversible methyl citrate cycle are critical nodes underlying the metabolic flexibility of M. tuberculosis . Graphical Abstract Quantitative metabolic analysis using stable isotopes, lipid fingerprinting, and mathematical modelling are applied to investigate the metabolic network of Mycobacterium tuberculosis growing slowly in a steady state chemostat system.

Background Currently, Aspergillus terreus is used for the industrial production of itaconic acid. Although, alternative feedstock use in fermentations is crucial for cost-efficient and sustainable itaconic acid production, their utilisation with A. terreus most often requires expensive pretreatment. Ustilaginacea are robust alternatives for itaconic acid production, evading the challenges, including the pretreatment of crude feedstocks regarding reduction of manganese concentration, that A. terreus poses. Results In this study, five different Ustilago strains were screened for their growth and production of itaconic acid on defined media. The most promising strains were then used to find a suitable alternative feedstock, based on the local food industry. U. cynodontis ITA Max pH, a highly engineered production strain, was selected to determine the biologically available nitrogen concentration in thick juice and molasses. Based on these findings, thick juice was chosen as feedstock to ensure the necessary nitrogen limitation for itaconic acid production. U. cynodontis ITA Max pH was further characterised regarding osmotolerance and product inhibition and a successful scale-up to a 2 L stirred tank reactor was accomplished. A titer of 106.4 g itaconic acid /L with a theoretical yield of 0.50 g itaconic acid /g sucrose and a space-time yield of 0.72 g itaconic acid /L/h was reached. Conclusions This study demonstrates the utilisation of alternative feedstocks to produce ITA with Ustilaginaceae, without drawbacks in either titer or yield, compared to glucose fermentations.

Anthranilate and its derivatives are important basic chemicals for the synthesis of polyurethanes as well as various dyes and food additives. Today, anthranilate is mainly chemically produced from petroleum‐derived xylene, but this shikimate pathway intermediate could be also obtained biotechnologically. In this study, Corynebacterium glutamicum was engineered for the microbial production of anthranilate from a carbon source mixture of glucose and xylose. First, a feedback‐resistant 3‐deoxy‐arabinoheptulosonate‐7‐phosphate synthase from Escherichia coli, catalysing the first step of the shikimate pathway, was functionally introduced into C. glutamicum to enable anthranilate production. Modulation of the translation efficiency of the genes for the shikimate kinase (aroK) and the anthranilate phosphoribosyltransferase (trpD) improved product formation. Deletion of two genes, one for a putative phosphatase (nagD) and one for a quinate/shikimate dehydrogenase (qsuD), abolished by‐product formation of glycerol and quinate. However, the introduction of an engineered anthranilate synthase (TrpEG) unresponsive to feedback inhibition by tryptophan had the most pronounced effect on anthranilate production. Component I of this enzyme (TrpE) was engineered using a biosensor‐based in vivo screening strategy for identifying variants with increased feedback resistance in a semi‐rational library of TrpE muteins. The final strain accumulated up to 5.9 g/L (43 mM) anthranilate in a defined CGXII medium from a mixture of glucose and xylose in bioreactor cultivations. We believe that the constructed C. glutamicum variants are not only limited to anthranilate production but could also be suitable for the synthesis of other biotechnologically interesting shikimate pathway intermediates or any other aromatic compound derived thereof. Corynebacterium glutamicum was engineered for the production of anthranilate from a mixture of glucose and xylose. In the context of strain engineering, a biosensor‐based in vivo screening strategy for identifying enzyme variants with increased feedback‐resistance in semi‐rational libraries was used. The final strain accumulated up to 5.9 g/L anthranilate in defined medium from a glucose/xylose mixture. Constructed variants are not only limited to anthranilate production but could also be suitable for the synthesis of other biotechnologically interesting aromatic compounds.