Itaconic acid production by co-feeding of Ustilago maydis: A combined approach of experimental data, design of experiments, and metabolic modeling.

Itaconic acid is a platform chemical with a range of applications in polymer synthesis and is also discussed for biofuel production. While produced in industry from glucose or sucrose, co-feeding of glucose and acetate was recently discussed to increase itaconic acid production by the smut fungus Ustilago maydis. In this study, we investigate the optimal co-feeding conditions by interlocking experimental and computational methods. Flux balance analysis indicates that acetate improves the itaconic acid yield up to a share of 40% acetate on a carbon molar basis. A design of experiment results in the maximum yield of 0.14 itaconic acid per carbon source from 100 g L - 1 $\,\text{g L}{}^{-1}$ glucose and 12 g L - 1 $\,\text{g L}{}^{-1}$ acetate. The yield is improved by around 22% when compared to feeding of glucose as sole carbon source. To further improve the yield, gene deletion targets are discussed that were identified using the metabolic optimization tool OptKnock. The study contributes ideas to reduce land use for biotechnology by incorporating acetate as co-substrate, a C2-carbon source that is potentially derived from carbon dioxide.

Cross-Species Synthetic Promoter Library: Finding Common Ground between Pseudomonas taiwanensis VLB120 and Escherichia coli.

The potential of nonmodel organisms for industrial biotechnology is increasingly becoming evident since advances in systems and synthetic biology have made it possible to explore their unique traits. However, the lack of adequately characterized genetic elements that drive gene expression impedes benchmarking nonmodel with model organisms. Promoters are one of the genetic elements that contribute significantly to gene expression, but information about their performance in different organisms is limited. This work addresses this bottleneck by characterizing libraries of synthetic σ70-dependent promoters controlling the expression of msfGFP, a monomeric, superfolder green fluorescent protein, in both Escherichia coli TOP10 and Pseudomonas taiwanensis VLB120, a less explored microbe with industrially attractive attributes. We adopted a standardized method for comparing gene promoter strength across species and laboratories. Our approach uses fluorescein calibration and adjusts for cell growth variation, enabling accurate cross-species comparisons. The quantitative description of promoter strength is a valuable expansion of P. taiwanensis VLB120's genetic toolbox, while the comparison with the performance in E. coli facilitates the evaluation of P. taiwanensis VLB120's potential as a chassis for biotechnology applications.

Biotechnology Data Analysis Training with Jupyter Notebooks.

Biotechnology has experienced innovations in analytics and data processing. As the volume of data and its complexity grow, new computational procedures for extracting information are being developed. However, the rate of change outpaces the adaptation of biotechnology curricula, necessitating new teaching methodologies to equip biotechnologists with data analysis abilities. To simulate experimental data, we created a virtual organism simulator (silvio) by combining diverse cellular and subcellular microbial models. With the silvio Python package, we constructed a computer-based instructional workflow to teach growth curve data analysis, promoter sequence design, and expression rate measurement. The instructional workflow is a Jupyter Notebook with background explanations and Python-based experiment simulations combined. The data analysis is conducted either within the Notebook in Python or externally with Excel. This instructional workflow was separately implemented in two distance courses for Master's students in biology and biotechnology with assessment of the pedagogic efficiency. The concept of using virtual organism simulations that generate coherent results across different experiments can be used to construct consistent and motivating case studies for biotechnological data literacy.

Renewable carbon sources to biochemicals and -fuels: contributions of the smut fungi Ustilaginaceae.

The global demand for food, fuels, and chemicals increases annually. Using renewable C-sources (i.e. biomass, CO2, and organic waste) is a prerequisite for a future free of fossil carbon. The smut fungi Ustilaginaceae naturally produce a versatile spectrum of valuable products, such as organic acids, polyols, and glycolipids, applicable in the food, energy, chemistry, and pharmaceutical sector. Combined with the use of alternative (co-)substrates (e.g. acetate, butanediol, formate, and glycerol), these microorganisms offer excellent potential for industrial biotechnology, thereby overcoming central challenges humankind faces, including CO2 release and land use. Here, we provide insight into fundamental production capacities, present genetic modifications that improve the biotechnical application, and review recent high-performance engineering of Ustilaginaceae toward relevant platform chemicals.

Ustilago maydis Metabolic Characterization and Growth Quantification with a Genome-Scale Metabolic Model.

Ustilago maydis is an important plant pathogen that causes corn smut disease and serves as an effective biotechnological production host. The lack of a comprehensive metabolic overview hinders a full understanding of the organism's environmental adaptation and a full use of its metabolic potential. Here, we report the first genome-scale metabolic model (GSMM) of Ustilago maydis (iUma22) for the simulation of metabolic activities. iUma22 was reconstructed from sequencing and annotation using PathwayTools, and the biomass equation was derived from literature values and from the codon composition. The final model contains over 25% annotated genes (6909) in the sequenced genome. Substrate utilization was corrected by BIOLOG phenotype arrays, and exponential batch cultivations were used to test growth predictions. The growth data revealed a decrease in glucose uptake rate with rising glucose concentration. A pangenome of four different U. maydis strains highlighted missing metabolic pathways in iUma22. The new model allows for studies of metabolic adaptations to different environmental niches as well as for biotechnological applications.

Genome-scale model reconstruction of the methylotrophic yeast Ogataea polymorpha.

BACKGROUND: Ogataea polymorpha is a thermotolerant, methylotrophic yeast with significant industrial applications. While previously mainly used for protein synthesis, it also holds promise for producing platform chemicals. O. polymorpha has the distinct advantage of using methanol as a substrate, which could be potentially derived from carbon capture and utilization streams. Full development of the organism into a production strain and estimation of the metabolic capabilities require additional strain design, guided by metabolic modeling with a genome-scale metabolic model. However, to date, no genome-scale metabolic model is available for O. polymorpha. RESULTS: To overcome this limitation, we used a published reconstruction of the closely related yeast Komagataella phaffii as a reference and corrected reactions based on KEGG and MGOB annotation. Additionally, we conducted phenotype microarray experiments to test the suitability of 190 substrates as carbon sources. Over three-quarter of the substrate use was correctly reproduced by the model and 27 new substrates were added, that were not present in the K. phaffii reference model. CONCLUSION: The developed genome-scale metabolic model of O. polymorpha will support the engineering of synthetic metabolic capabilities and enable the optimization of production processes, thereby supporting a sustainable future methanol economy.

Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb.

Metabolic engineering relies on modifying gene expression to regulate protein concentrations and reaction activities. The gene expression is controlled by the promoter sequence, and sequence libraries are used to scan expression activities and to identify correlations between sequence and activity. We introduce a computational workflow called Exp2Ipynb to analyze promoter libraries maximizing information retrieval and promoter design with desired activity. We applied Exp2Ipynb to seven prokaryotic expression libraries to identify optimal experimental design principles. The workflow is open source, available as Jupyter Notebooks and covers the steps to 1) generate a statistical overview to sequence and activity, 2) train machine-learning algorithms, such as random forest, gradient boosting trees and support vector machines, for prediction and extraction of feature importance, 3) evaluate the performance of the estimator, and 4) to design new sequences with a desired activity using numerical optimization. The workflow can perform regression or classification on multiple promoter libraries, across species or reporter proteins. The most accurate predictions in the sample libraries were achieved when the promoters in the library were recognized by a single sigma factor and a unique reporter system. The prediction confidence mostly depends on sample size and sequence diversity, and we present a relationship to estimate their respective effects. The workflow can be adapted to process sequence libraries from other expression-related problems and increase insight to the growing application of high-throughput experiments, providing support for efficient strain engineering.

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.

CO2 to succinic acid - Estimating the potential of biocatalytic routes.

Microbial carbon dioxide assimilation and conversion to chemical platform molecules has the potential to be developed as economic, sustainable processes. The carbon dioxide assimilation can proceed by a variety of natural pathways and recently even synthetic CO2 fixation routes have been designed. Early assessment of the performance of the different carbon fixation alternatives within biotechnological processes is desirable to evaluate their potential. Here we applied stoichiometric metabolic modeling based on physiological and process data to evaluate different process variants for the conversion of C1 carbon compounds to the industrial relevant platform chemical succinic acid. We computationally analyzed the performance of cyanobacteria, acetogens, methylotrophs, and synthetic CO2 fixation pathways in Saccharomyces cerevisiae in terms of production rates, product yields, and the optimization potential. This analysis provided insight into the economic feasibility and allowed to estimate the future industrial applicability by estimating overall production costs. With reported, or estimated data of engineered or wild type strains, none of the simulated microbial succinate production processes showed a performance allowing competitive production. The main limiting factors were identified as gas and photon transfer and metabolic activities whereas metabolic network structure was not restricting. In simulations with optimized parameters most process alternatives reached economically interesting values, hence, represent promising alternatives to sugar-based fermentations.

Systems biology approaches in aging research.

Aging is a systemic process which progressively manifests itself at multiple levels of structural and functional organization from molecular reactions and cell-cell interactions in tissues to the physiology of an entire organ. There is ever increasing data on biomedical relevant network interactions for the aging process at different scales of time and space. To connect the aging process at different structural, temporal and spatial scales, extensive systems biological approaches need to be deployed. Systems biological approaches can not only systematically handle the large-scale datasets (like high-throughput data) and the complexity of interactions (feedback loops, cross talk), but also can delve into nonlinear behaviors exhibited by several biological processes which are beyond intuitive reasoning. Several public-funded agencies have identified the synergistic role of systems biology in aging research. Using one of the notable public-funded programs (GERONTOSYS), we discuss how systems biological approaches are helping the scientists to find new frontiers in aging research. We elaborate on some systems biological approaches deployed in one of the projects of the consortium (ROSage). The systems biology field in aging research is at its infancy. It is open to adapt existing systems biological methodologies from other research fields and devise new aging-specific systems biological methodologies.

Simulations of stressosome activation emphasize allosteric interactions between RsbR and RsbT.

BACKGROUND: The stressosome is a bacterial signalling complex that responds to environmental changes by initiating a protein partner switching cascade, which leads to the release of the alternative sigma factor, σB. Stress perception increases the phosphorylation of the stressosome sensor protein, RsbR, and the scaffold protein, RsbS, by the protein kinase, RsbT. Subsequent dissociation of RsbT from the stressosome activates the σB cascade. However, the sequence of physical events that occur in the stressosome during signal transduction is insufficiently understood. RESULTS: Here, we use computational modelling to correlate the structure of the stressosome with the efficiency of the phosphorylation reactions that occur upon activation by stress. In our model, the phosphorylation of any stressosome protein is dependent upon its nearest neighbours and their phosphorylation status. We compare different hypotheses about stressosome activation and find that only the model representing the allosteric activation of the kinase RsbT, by phosphorylated RsbR, qualitatively reproduces the experimental data. CONCLUSIONS: Our simulations and the associated analysis of published data support the following hypotheses: (i) a simple Boolean model is capable of reproducing stressosome dynamics, (ii) different stressors induce identical stressosome activation patterns, and we also confirm that (i) phosphorylated RsbR activates RsbT, and (ii) the main purpose of RsbX is to dephosphorylate RsbS-P.

Proteolysis of beta-galactosidase following SigmaB activation in Bacillus subtilis.

In Bacillus subtilis the σ(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of σ(B) activity. In the mutant strain BSA115, σ(B) transcription is inducible by the addition of IPTG and negative control of σ(B) activity by the anti-sigma factor RsbW is absent. In contrast to our expectations of a continuous ß-galactosidase activity from a ctc::lacZ fusion, we observed a transient activity in the mutant. To explain this experimental finding, we constructed mathematical models reflecting different hypotheses regarding the regulation of σ(B) and ß-galactosidase dynamics. Only the model assuming instability of either ctc::lacZ mRNA or ß-galactosidase protein is able to reproduce the experiments in silico. Subsequent Northern blot experiments revealed stable high-level ctc::lacZ mRNA concentrations after the induction of the σ(B) response. Therefore, we conclude that protein instability following σ(B) activation is the most likely explanation for the experimental observations. Our results thus support the idea that B. subtilis increases the cytoplasmic proteolytic degradation to adapt the proteome in face of environmental challenges following activation of the general stress response. The findings also have practical implications for the analysis of stress response dynamics using lacZ reporter gene fusions, a frequently used strategy for the σ(B) response.

How mathematical modelling elucidates signalling in Bacillus subtilis.

Appropriate stimulus perception, signal processing and transduction ensure optimal adaptation of bacteria to environmental challenges. In the Gram-positive model bacterium Bacillus subtilis signalling networks and molecular interactions therein are well-studied, making this species a suitable candidate for the application of mathematical modelling. Here, we review systems biology approaches, focusing on chemotaxis, sporulation, σ(B) -dependent general stress response and competence. Processes like chemotaxis and Z-ring assembly depend critically on the subcellular localization of proteins. Environmental response strategies, including sporulation and competence, are characterized by phenotypic heterogeneity in isogenic cultures. The examples of mathematical modelling also include investigations that have demonstrated how operon structure and signalling dynamics are intricately interwoven to establish optimal responses. Our review illustrates that these interdisciplinary approaches offer new insights into the response of B. subtilis to environmental challenges. These case studies reveal modelling as a tool to increase the understanding of complex systems, to help formulating hypotheses and to guide the design of more directed experiments that test predictions.