Optimizing the strain engineering process for industrial-scale production of bio-based molecules.

Biomanufacturing could contribute as much as ${\$}$30 trillion to the global economy by 2030. However, the success of the growing bioeconomy depends on our ability to manufacture high-performing strains in a time- and cost-effective manner. The Design-Build-Test-Learn (DBTL) framework has proven to be an effective strain engineering approach. Significant improvements have been made in genome engineering, genotyping, and phenotyping throughput over the last couple of decades that have greatly accelerated the DBTL cycles. However, to achieve a radical reduction in strain development time and cost, we need to look at the strain engineering process through a lens of optimizing the whole cycle, as opposed to simply increasing throughput at each stage. We propose an approach that integrates all 4 stages of the DBTL cycle and takes advantage of the advances in computational design, high-throughput genome engineering, and phenotyping methods, as well as machine learning tools for making predictions about strain scale-up performance. In this perspective, we discuss the challenges of industrial strain engineering, outline the best approaches to overcoming these challenges, and showcase examples of successful strain engineering projects for production of heterologous proteins, amino acids, and small molecules, as well as improving tolerance, fitness, and de-risking the scale-up of industrial strains.

Web of microbes (WoM): a curated microbial exometabolomics database for linking chemistry and microbes.

BACKGROUND: As microbiome research becomes increasingly prevalent in the fields of human health, agriculture and biotechnology, there exists a need for a resource to better link organisms and environmental chemistries. Exometabolomics experiments now provide assertions of the metabolites present within specific environments and how the production and depletion of metabolites is linked to specific microbes. This information could be broadly useful, from comparing metabolites across environments, to predicting competition and exchange of metabolites between microbes, and to designing stable microbial consortia. Here, we introduce Web of Microbes (WoM; freely available at: http://webofmicrobes.org ), the first exometabolomics data repository and visualization tool. DESCRIPTION: WoM provides manually curated, direct biochemical observations on the changes to metabolites in an environment after exposure to microorganisms. The web interface displays a number of key features: (1) the metabolites present in a control environment prior to inoculation or microbial activation, (2) heatmap-like displays showing metabolite increases or decreases resulting from microbial activities, (3) a metabolic web displaying the actions of multiple organisms on a specified metabolite pool, (4) metabolite interaction scores indicating an organism's interaction level with its environment, potential for metabolite exchange with other organisms and potential for competition with other organisms, and (5) downloadable datasets for integration with other types of -omics datasets. CONCLUSION: We anticipate that Web of Microbes will be a useful tool for the greater research community by making available manually curated exometabolomics results that can be used to improve genome annotations and aid in the interpretation and construction of microbial communities.

Dynamic substrate preferences predict metabolic properties of a simple microbial consortium.

BACKGROUND: Mixed cultures of different microbial species are increasingly being used to carry out a specific biochemical function in lieu of engineering a single microbe to do the same task. However, knowing how different species' metabolisms will integrate to reach a desired outcome is a difficult problem that has been studied in great detail using steady-state models. However, many biotechnological processes, as well as natural habitats, represent a more dynamic system. Examining how individual species use resources in their growth medium or environment (exometabolomics) over time in batch culture conditions can provide rich phenotypic data that encompasses regulation and transporters, creating an opportunity to integrate the data into a predictive model of resource use by a mixed community. RESULTS: Here we use exometabolomic profiling to examine the time-varying substrate depletion from a mixture of 19 amino acids and glucose by two Pseudomonas and one Bacillus species isolated from ground water. Contrary to studies in model organisms, we found surprisingly few correlations between resource preferences and maximal growth rate or biomass composition. We then modeled patterns of substrate depletion, and used these models to examine if substrate usage preferences and substrate depletion kinetics of individual isolates can be used to predict the metabolism of a co-culture of the isolates. We found that most of the substrates fit the model predictions, except for glucose and histidine, which were depleted more slowly than predicted, and proline, glycine, glutamate, lysine and arginine, which were all consumed significantly faster. CONCLUSIONS: Our results indicate that a significant portion of a model community's overall metabolism can be predicted based on the metabolism of the individuals. Based on the nature of our model, the resources that significantly deviate from the prediction highlight potential metabolic pathways affected by species-species interactions, which when further studied can potentially be used to modulate microbial community structure and/or function.

Construction of Viable Soil Defined Media Using Quantitative Metabolomics Analysis of Soil Metabolites.

Exometabolomics enables analysis of metabolite utilization of low molecular weight organic substances by soil bacteria. Environmentally-based defined media are needed to examine ecologically relevant patterns of substrate utilization. Here, we describe an approach for the construction of defined media using untargeted characterization of water soluble soil microbial metabolites from a saprolite soil collected from the Oak Ridge Field Research Center (ORFRC). To broadly characterize metabolites, both liquid chromatography mass spectrometry (LC/MS) and gas chromatography mass spectrometry (GC/MS) were used. With this approach, 96 metabolites were identified, including amino acids, amino acid derivatives, sugars, sugar alcohols, mono- and di-carboxylic acids, nucleobases, and nucleosides. From this pool of metabolites, 25 were quantified. Molecular weight cut-off filtration determined the fraction of carbon accounted for by the quantified metabolites and revealed that these soil metabolites have an uneven quantitative distribution (e.g., trehalose accounted for 9.9% of the <1 kDa fraction). This quantitative information was used to formulate two soil defined media (SDM), one containing 23 metabolites (SDM1) and one containing 46 (SDM2). To evaluate the viability of the SDM, we examined the growth of 30 phylogenetically diverse soil bacterial isolates from the ORFRC field site. The simpler SDM1 supported the growth of 13 isolates while the more complex SDM2 supported 15 isolates. To investigate SDM1 substrate preferences, one isolate, Pseudomonas corrugata strain FW300-N2E2 was selected for a time-series exometabolomics analysis. Interestingly, it was found that this organism preferred lower-abundance substrates such as guanine, glycine, proline and arginine and glucose and did not utilize the more abundant substrates maltose, mannitol, trehalose and uridine. These results demonstrate the viability and utility of using exometabolomics to construct a tractable environmentally relevant media. We anticipate that this approach can be expanded to other environments to enhance isolation and characterization of diverse microbial communities.

Gut microbiota mediate caffeine detoxification in the primary insect pest of coffee.

The coffee berry borer (Hypothenemus hampei) is the most devastating insect pest of coffee worldwide with its infestations decreasing crop yield by up to 80%. Caffeine is an alkaloid that can be toxic to insects and is hypothesized to act as a defence mechanism to inhibit herbivory. Here we show that caffeine is degraded in the gut of H. hampei, and that experimental inactivation of the gut microbiota eliminates this activity. We demonstrate that gut microbiota in H. hampei specimens from seven major coffee-producing countries and laboratory-reared colonies share a core of microorganisms. Globally ubiquitous members of the gut microbiota, including prominent Pseudomonas species, subsist on caffeine as a sole source of carbon and nitrogen. Pseudomonas caffeine demethylase genes are expressed in vivo in the gut of H. hampei, and re-inoculation of antibiotic-treated insects with an isolated Pseudomonas strain reinstates caffeine-degradation ability confirming their key role.

Global LC/MS Metabolomics Profiling of Calcium Stressed and Immunosuppressant Drug Treated Saccharomyces cerevisiae.

Previous studies have shown that calcium stressed Saccharomyces cerevisiae, challenged with immunosuppressant drugs FK506 and Cyclosporin A, responds with comprehensive gene expression changes and attenuation of the generalized calcium stress response. Here, we describe a global metabolomics workflow for investigating the utility of tracking corresponding phenotypic changes. This was achieved by efficiently analyzing relative abundance differences between intracellular metabolite pools from wild-type and calcium stressed cultures, with and without prior immunosuppressant drugs exposure. We used pathway database content from WikiPathways and YeastCyc to facilitate the projection of our metabolomics profiling results onto biological pathways. A key challenge was to increase the coverage of the detected metabolites. This was achieved by applying both reverse phase (RP) and aqueous normal phase (ANP) chromatographic separations, as well as electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) sources for detection in both ion polarities. Unsupervised principle component analysis (PCA) and ANOVA results revealed differentiation between wild-type controls, calcium stressed and immunosuppressant/calcium challenged cells. Untargeted data mining resulted in 247 differentially expressed, annotated metabolites, across at least one pair of conditions. A separate, targeted data mining strategy identified 187 differential, annotated metabolites. All annotated metabolites were subsequently mapped onto curated pathways from YeastCyc and WikiPathways for interactive pathway analysis and visualization. Dozens of pathways showed differential responses to stress conditions based on one or more matches to the list of annotated metabolites or to metabolites that had been identified further by MS/MS. The purine salvage, pantothenate and sulfur amino acid pathways were flagged as being enriched, which is consistent with previously published literature for transcriptomics analysis. Thus, broad discovery-based data mining combined with targeted pathway projections can be an important asset for rapidly distilling, testing and evaluating a large amount of information for further investigation.