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.

Long-Term Effects of a Web-Based Low-FODMAP Diet Versus Probiotic Treatment for Irritable Bowel Syndrome, Including Shotgun Analyses of Microbiota: Randomized, Double-Crossover Clinical Trial.

BACKGROUND: The long-term management of irritable bowel syndrome (IBS) poses many challenges. In short-term studies, eHealth interventions have been demonstrated to be safe and practical for at-home monitoring of the effects of probiotic treatments and a diet low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs). IBS has been linked to alterations in the microbiota. OBJECTIVE: The aim of this study was to determine whether a web-based low-FODMAP diet (LFD) intervention and probiotic treatment were equally good at reducing IBS symptoms, and whether the response to treatments could be explained by patients' microbiota. METHODS: Adult IBS patients were enrolled in an open-label, randomized crossover trial (for nonresponders) with 1 year of follow-up using the web application IBS Constant Care (IBS CC). Patients were recruited from the outpatient clinic at the Department of Gastroenterology, North Zealand University Hospital, Denmark. Patients received either VSL#3 for 4 weeks (2 × 450 billion colony-forming units per day) or were placed on an LFD for 4 weeks. Patients responding to the LFD were reintroduced to foods high in FODMAPs, and probiotic responders received treatments whenever they experienced a flare-up of symptoms. Treatment response and symptom flare-ups were defined as a reduction or increase, respectively, of at least 50 points on the IBS Severity Scoring System (IBS-SSS). Web-based ward rounds were performed daily by the study investigator. Fecal microbiota were analyzed by shotgun metagenomic sequencing (at least 10 million 2 × 100 bp paired-end sequencing reads per sample). RESULTS: A total of 34 IBS patients without comorbidities and 6 healthy controls were enrolled in the study. Taken from participating subjects, 180 fecal samples were analyzed for their microbiota composition. Out of 21 IBS patients, 12 (57%) responded to the LFD and 8 (38%) completed the reintroduction of FODMAPs. Out of 21 patients, 13 (62%) responded to their first treatment of VSL#3 and 7 (33%) responded to multiple VSL#3 treatments. A median of 3 (IQR 2.25-3.75) probiotic treatments were needed for sustained symptom control. LFD responders were reintroduced to a median of 14.50 (IQR 7.25-21.75) high-FODMAP items. No significant difference in the median reduction of IBS-SSS for LFD versus probiotic responders was observed, where for LFD it was -126.50 (IQR -196.75 to -76.75) and for VSL#3 it was -130.00 (IQR -211.00 to -70.50; P>.99). Responses to either of the two treatments were not able to be predicted using patients' microbiota. CONCLUSIONS: The web-based LFD intervention and probiotic treatment were equally efficacious in managing IBS symptoms. The response to treatments could not be explained by the composition of the microbiota. The IBS CC web application was shown to be practical, safe, and useful for clinical decision making in the long-term management of IBS. Although this study was underpowered, findings from this study warrant further research in a larger sample of patients with IBS to confirm these long-term outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03586622; https://clinicaltrials.gov/ct2/show/NCT03586622.

Author Correction: A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Corrigendum: Multi-Omics Analysis of Fatty Alcohol Production in Engineered Yeasts Saccharomyces cerevisiae and Yarrowia lipolytica.

[This corrects the article DOI: 10.3389/fgene.2019.00747.].

Multi-Omics Analysis of Fatty Alcohol Production in Engineered Yeasts Saccharomyces cerevisiae and Yarrowia lipolytica.

Fatty alcohols are widely used in various applications within a diverse set of industries, such as the soap and detergent industry, the personal care, and cosmetics industry, as well as the food industry. The total world production of fatty alcohols is over 2 million tons with approximately equal parts derived from fossil oil and from plant oils or animal fats. Due to the environmental impact of these production methods, there is an interest in alternative methods for fatty alcohol production via microbial fermentation using cheap renewable feedstocks. In this study, we aimed to obtain a better understanding of how fatty alcohol biosynthesis impacts the host organism, baker's yeast Saccharomyces cerevisiae or oleaginous yeast Yarrowia lipolytica. Producing and non-producing strains were compared in growth and nitrogen-depletion cultivation phases. The multi-omics analysis included physiological characterization, transcriptome analysis by RNAseq, 13Cmetabolic flux analysis, and intracellular metabolomics. Both species accumulated fatty alcohols under nitrogen-depletion conditions but not during growth. The fatty alcohol-producing Y. lipolytica strain had a higher fatty alcohol production rate than an analogous S. cerevisiae strain. Nitrogen-depletion phase was associated with lower glucose uptake rates and a decrease in the intracellular concentration of acetyl-CoA in both yeast species, as well as increased organic acid secretion rates in Y. lipolytica. Expression of the fatty alcohol-producing enzyme fatty acyl-CoA reductase alleviated the growth defect caused by deletion of hexadecenal dehydrogenase encoding genes (HFD1 and HFD4) in Y. lipolytica. RNAseq analysis showed that fatty alcohol production triggered a cell wall stress response in S. cerevisiae. RNAseq analysis also showed that both nitrogen-depletion and fatty alcohol production have substantial effects on the expression of transporter encoding genes in Y. lipolytica. In conclusion, through this multi-omics study, we uncovered some effects of fatty alcohol production on the host metabolism. This knowledge can be used as guidance for further strain improvement towards the production of fatty alcohols.

A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism.

Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae--an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.

Dynamic investigation and modeling of the nitrogen cometabolism in Methylococcus capsulatus ( Bath).

The methanotrophic bacterium Methylococcus capsulatus is capable of assimilating methane and oxygen into protein-rich biomass, however, the diverse metabolism of the microorganism also allows for several undesired cometabolic side-reactions to occur. In this study, the ammonia cometabolism in Methylococcus capsulatus is investigated using pulse experiments. Surprisingly Methylococcus capsulatus oxidizes ammonia to nitrate through a yet unknown mechanism and fixes molecular nitrogen even at a high dissolved oxygen tension. The observed phenomena can be modeled using 14 ordinary differential equations and 18 kinetic parameters, of which 6 were revealed by Morris screening to be identifiable from the experimental data. Monte Carlo simulations showed that the model was robust and accurate even with uncertainty in the parameter values as confirmed by statistical error analysis.

A Genome-Scale Metabolic Model for Methylococcus capsulatus (Bath) Suggests Reduced Efficiency Electron Transfer to the Particulate Methane Monooxygenase.

Background: Genome-scale metabolic models allow researchers to calculate yields, to predict consumption and production rates, and to study the effect of genetic modifications in silico, without running resource-intensive experiments. While these models have become an invaluable tool for optimizing industrial production hosts like Escherichia coli and S. cerevisiae, few such models exist for one-carbon (C1) metabolizers. Results: Here, we present a genome-scale metabolic model for Methylococcus capsulatus (Bath), a well-studied obligate methanotroph, which has been used as a production strain of single cell protein (SCP). The model was manually curated, and spans a total of 879 metabolites connected via 913 reactions. The inclusion of 730 genes and comprehensive annotations, make this model not only a useful tool for modeling metabolic physiology, but also a centralized knowledge base for M. capsulatus (Bath). With it, we determined that oxidation of methane by the particulate methane monooxygenase could be driven both through direct coupling or uphill electron transfer, both operating at reduced efficiency, as either scenario matches well with experimental data and observations from literature. Conclusion: The metabolic model will serve the ongoing fundamental research of C1 metabolism, and pave the way for rational strain design strategies toward improved SCP production processes in M. capsulatus.

Microbial Methylotrophic Metabolism: Recent Metabolic Modeling Efforts and Their Applications In Industrial Biotechnology.

Developing methylotrophic bacteria into cell factories that meet the chemical demand of the future could be both economical and environmentally friendly. Methane is not only an abundant, low-cost resource but also a potent greenhouse gas, the capture of which could help to reduce greenhouse gas emissions. Rational strain design workflows rely on the availability of carefully combined knowledge often in the form of genome-scale metabolic models to construct high-producer organisms. In this review, the authors present the most recent genome-scale metabolic models in aerobic methylotrophy and their applications. Further, the authors present models for the study of anaerobic methanotrophy through reverse methanogenesis and suggest organisms that may be of interest for expanding one-carbon industrial biotechnology. Metabolic models of methylotrophs are scarce, yet they are important first steps toward rational strain-design in these organisms.

Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories.

Computational systems biology methods enable rational design of cell factories on a genome-scale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, the majority of these methods' implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knockout, knock-in, overexpression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated Web site including documentation, examples, and installation instructions can be found at http://cameo.bio . Users can also give cameo a try at http://try.cameo.bio .