Unlocking the potentials of Ustilago trichophora for up-cycling polyurethane-derived monomer 1,4-butanediol.

Plastic usage by microbes as a carbon source is a promising strategy to increase the recycling quota. 1,4-butanediol (BDO) is a common monomer derived from polyesters and polyurethanes. In this study, Ustilago trichophora was found to be an efficient cell-factory to valorize BDO. To investigate product formation by U. trichophora, we refined the traditional ion exclusion liquid chromatography method by examining eluent, eluent concentrations, oven temperatures, and organic modifiers to make the chromatography compatible with mass spectrometry. An LC-UV/RI-MS2 method is presented here to identify and quantify extracellular metabolites in the cell cultures. With this method, we successfully identified that U. trichophora secreted malic acid, succinic acid, erythritol, and mannitol into the culture medium. Adaptive laboratory evolution followed by medium optimization significantly improved U. trichophora growth on BDO and especially malic acid production. Overall, the carbon yield on the BDO substrate was approximately 33% malic acid. This study marks the first report of a Ustilaginaceae fungus capable of converting BDO into versatile chemical building blocks. Since U. trichophora is not genetically engineered, it is a promising microbial host to produce malic acid from BDO, thereby contributing to the development of the envisaged sustainable bioeconomy.

High-quality physiology of Alcanivorax borkumensis SK2 producing glycolipids enables efficient stirred-tank bioreactor cultivation.

Glycine-glucolipid, a glycolipid, is natively synthesized by the marine bacterium Alcanivorax borkumensis SK2. A. borkumensis is a Gram-negative, non-motile, aerobic, halophilic, rod-shaped γ-proteobacterium, classified as an obligate hydrocarbonoclastic bacterium. Naturally, this bacterium exists in low cell numbers in unpolluted marine environments, but during oil spills, the cell number significantly increases and can account for up to 90% of the microbial community responsible for oil degradation. This growth surge is attributed to two remarkable abilities: hydrocarbon degradation and membrane-associated biosurfactant production. This study aimed to characterize and enhance the growth and biosurfactant production of A. borkumensis, which initially exhibited poor growth in the previously published ONR7a, a defined salt medium. Various online analytic tools for monitoring growth were employed to optimize the published medium, leading to improved growth rates and elongated growth on pyruvate as a carbon source. The modified medium was supplemented with different carbon sources to stimulate glycine-glucolipid production. Pyruvate, acetate, and various hydrophobic carbon sources were utilized for glycolipid production. Growth was monitored via online determined oxygen transfer rate in shake flasks, while a recently published hyphenated HPLC-MS method was used for glycine-glucolipid analytics. To transfer into 3 L stirred-tank bioreactor, aerated batch fermentations were conducted using n-tetradecane and acetate as carbon sources. The challenge of foam formation was overcome using bubble-free membrane aeration with acetate as the carbon source. In conclusion, the growth kinetics of A. borkumensis and glycine-glucolipid production were significantly improved, while reaching product titers relevant for applications remains a challenge.

Ustilaginaceae Biocatalyst for Co-Metabolism of CO2-Derived Substrates toward Carbon-Neutral Itaconate Production.

The family Ustilaginaceae (belonging to the smut fungi) are known for their plant pathogenicity. Despite the fact that these plant diseases cause agricultural yield reduction, smut fungi attracted special attention in the field of industrial biotechnology. Ustilaginaceae show a versatile product spectrum such as organic acids (e.g., itaconate, malate, succinate), polyols (e.g., erythritol, mannitol), and extracellular glycolipids, which are considered value-added chemicals with potential applications in the pharmaceutical, food, and chemical industries. This study focused on itaconate as a platform chemical for the production of resins, plastics, adhesives, and biofuels. During this work, 72 different Ustilaginaceae strains from 36 species were investigated for their ability to (co-) consume the CO2-derived substrates acetate and formate, potentially contributing toward a carbon-neutral itaconate production. The fungal growth and product spectrum with special interest in itaconate was characterized. Ustilago maydis MB215 and Ustilago rabenhorstiana NBRC 8995 were identified as promising candidates for acetate metabolization whereas Ustilago cynodontis NBRC 7530 was identified as a potential production host using formate as a co-substrate enhancing the itaconate production. Selected strains with the best itaconate production were characterized in more detail in controlled-batch bioreactor experiments confirming the co-substrate utilization. Thus, a proof-of-principle study was performed resulting in the identification and characterization of three promising Ustilaginaceae biocatalyst candidates for carbon-neutral itaconate production contributing to the biotechnological relevance of Ustilaginaceae.

Special Issue "Metabolic Engineering and Synthetic Biology Volume 2".

In times of ever-increasing demand for chemicals and the subsequent increase in CO2 in the atmosphere, we have to intensify our efforts to establish a circular (bio) economy [...].

GC-MS-Based Metabolomics for the Smut Fungus Ustilago maydis: A Comprehensive Method Optimization to Quantify Intracellular Metabolites.

Ustilago maydis, a smut fungus, is an appealing model in fundamental research and an upcoming cell factory for industrial biotechnology. The genome of U. maydis has been sequenced and some synthesis pathways were biochemically described; however, the operation of the cellular metabolic network is not well-characterized. Thus, we conducted a comprehensive study to optimize the sample preparation procedure for metabolomics of U. maydis using GC-MS/MS. Due to the unique characteristics of U. maydis cell culture, two quenching solutions, different washing steps, eight extraction methods, and three derivatization conditions have been examined. The optimal method was then applied for stable isotope-assisted quantification of low molecular weight hydrophilic metabolites while U. maydis utilized different carbon sources including sucrose, glucose, and fructose. This study is the first report on a methodology for absolute quantification of intracellular metabolites in U. maydis central carbon metabolism such as sugars, sugar phosphates, organic acids, amino acids, and nucleotides. For biotechnological use, this method is crucial to exploit the full production potential of this fungus and can also be used to study other fungi of the family Ustilaginaceae.

High titer methyl ketone production with tailored Pseudomonas taiwanensis VLB120.

Methyl ketones present a group of highly reduced platform chemicals industrially produced from petroleum-derived hydrocarbons. They find applications in the fragrance, flavor, pharmacological, and agrochemical industries, and are further discussed as biodiesel blends. In recent years, intense research has been carried out to achieve sustainable production of these molecules by re-arranging the fatty acid metabolism of various microbes. One challenge in the development of a highly productive microbe is the high demand for reducing power. Here, we engineered Pseudomonas taiwanensis VLB120 for methyl ketone production as this microbe has been shown to sustain exceptionally high NAD(P)H regeneration rates. The implementation of published strategies resulted in 2.1 g Laq-1 methyl ketones in fed-batch fermentation. We further increased the production by eliminating competing reactions suggested by metabolic analyses. These efforts resulted in the production of 9.8 g Laq-1 methyl ketones (corresponding to 69.3 g Lorg-1 in the in situ extraction phase) at 53% of the maximum theoretical yield. This represents a 4-fold improvement in product titer compared to the initial production strain and the highest titer of recombinantly produced methyl ketones reported to date. Accordingly, this study underlines the high potential of P. taiwanensis VLB120 to produce methyl ketones and emphasizes model-driven metabolic engineering to rationalize and accelerate strain optimization efforts.

Electrophysiology of the Facultative Autotrophic Bacterium Desulfosporosinus orientis.

Electroautotrophy is a novel and fascinating microbial metabolism, with tremendous potential for CO2 storage and valorization into chemicals and materials made thereof. Research attention has been devoted toward the characterization of acetogenic and methanogenic electroautotrophs. In contrast, here we characterize the electrophysiology of a sulfate-reducing bacterium, Desulfosporosinus orientis, harboring the Wood-Ljungdahl pathway and, thus, capable of fixing CO2 into acetyl-CoA. For most electroautotrophs the mode of electron uptake is still not fully clarified. Our electrochemical experiments at different polarization conditions and Fe0 corrosion tests point to a H2- mediated electron uptake ability of this strain. This observation is in line with the lack of outer membrane and periplasmic multi-heme c-type cytochromes in this bacterium. Maximum planktonic biomass production and a maximum sulfate reduction rate of 2 ± 0.4 mM day-1 were obtained with an applied cathode potential of -900 mV vs. Ag/AgCl, resulting in an electron recovery in sulfate reduction of 37 ± 1.4%. Anaerobic sulfate respiration is more thermodynamically favorable than acetogenesis. Nevertheless, D. orientis strains adapted to sulfate-limiting conditions, could be tuned to electrosynthetic production of up to 8 mM of acetate, which compares well with other electroacetogens. The yield per biomass was very similar to H2/CO2 based acetogenesis. Acetate bioelectrosynthesis was confirmed through stable isotope labeling experiments with Na-H13CO3. Our results highlight a great influence of the CO2 feeding strategy and start-up H2 level in the catholyte on planktonic biomass growth and acetate production. In serum bottles experiments, D. orientis also generated butyrate, which makes D. orientis even more attractive for bioelectrosynthesis application. A further optimization of these physiological pathways is needed to obtain electrosynthetic butyrate production in D. orientis biocathodes. This study expands the diversity of facultative autotrophs able to perform H2-mediated extracellular electron uptake in Bioelectrochemical Systems (BES). We characterized a sulfate-reducing and acetogenic bacterium, D. orientis, able to naturally produce acetate and butyrate from CO2 and H2. For any future bioprocess, the exploitation of planktonic growing electroautotrophs with H2-mediated electron uptake would allow for a better use of the entire liquid volume of the cathodic reactor and, thus, higher productivities and product yields from CO2-rich waste gas streams.

Machine Learning Applications for Mass Spectrometry-Based Metabolomics.

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.