Genomic and proteomic biases inform metabolic engineering strategies for anaerobic fungi.
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ABSTRACT:
SUBMITTER: Wilken SE
PROVIDER: S-EPMC6883316 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundMicroorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste.ResultsIn this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies-an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale.ConclusionsFixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the 'test' phase of the design-build-test-learn cycle of metabolic engineering.
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Project description:In the past few years, many studies investigated the anaerobic digestion microbiome by means of 16S rRNA amplicon sequencing. Results obtained from these studies were compared to each other without taking into consideration the followed procedure for amplicons preparation and data analysis. This negligence was mainly due to the lack of knowledge regarding the biases influencing specific steps of the microbiome investigation process. In the present study, the main technical aspects of the 16S rRNA analysis were checked giving special attention to the approach used for high throughput sequencing. More specifically, the microbial compositions of three laboratory scale biogas reactors were analyzed before and after addition of sodium oleate by sequencing the microbiome with three different approaches: 16S rRNA amplicon sequencing, shotgun DNA and shotgun RNA. This comparative analysis revealed that, in amplicon sequencing, abundance of some taxa (Euryarchaeota and Spirochaetes) was biased by the inefficiency of universal primers to hybridize all the templates. Reliability of the results obtained was also influenced by the number of hypervariable regions under investigation. Finally, amplicon sequencing and shotgun DNA underestimated the Methanoculleus genus, probably due to the low 16S rRNA gene copy number encoded in this taxon.
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Project description:MotivationThe computer-aided design of metabolic intervention strategies has become a key component of an integrated metabolic engineering approach and a broad range of methods and algorithms has been developed for this task. Many of these algorithms enforce coupling of growth with product synthesis and may return thousands of possible intervention strategies from which the most suitable strategy must then be selected.ResultsThis work focuses on how to evaluate and rank, in a meaningful way, a given pool of computed metabolic engineering strategies for growth-coupled product synthesis. Apart from straightforward criteria, such as a preferably small number of necessary interventions, a reasonable growth rate and a high product yield, we present several new criteria useful to pick the most suitable intervention strategy. Among others, we investigate the robustness of the intervention strategies by searching for metabolites that may disrupt growth coupling when accumulated or secreted and by checking whether the interventions interrupt pathways at their origin (preferable) or at downstream steps. We also assess thermodynamic properties of the pathway(s) favored by the intervention strategy. Furthermore, strategies that have a significant overlap with alternative solutions are ranked higher because they provide flexibility in implementation. We also introduce the notion of equivalence classes for grouping intervention strategies with identical solution spaces. Our ranking procedure involves in total ten criteria and we demonstrate its applicability by assessing knockout-based intervention strategies computed in a genome-scale model of E.coli for the growth-coupled synthesis of l-methionine and of the heterologous product 1,4-butanediol.Availability and implementationThe MATLAB scripts that were used to characterize and rank the example intervention strategies are available at http://www2.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:BackgroundBiofuels and value-added biochemicals derived from renewable biomass via biochemical conversion have attracted considerable attention to meet global sustainable energy and environmental goals. Isobutanol is a four-carbon alcohol with many advantages that make it attractive as a fossil-fuel alternative. Zymomonas mobilis is a highly efficient, anaerobic, ethanologenic bacterium making it a promising industrial platform for use in a biorefinery.ResultsIn this study, the effect of isobutanol on Z. mobilis was investigated, and various isobutanol-producing recombinant strains were constructed. The results showed that the Z. mobilis parental strain was able to grow in the presence of isobutanol below 12 g/L while concentrations greater than 16 g/L inhibited cell growth. Integration of the heterologous gene encoding 2-ketoisovalerate decarboxylase such as kdcA from Lactococcus lactis is required for isobutanol production in Z. mobilis. Moreover, isobutanol production increased from nearly zero to 100-150 mg/L in recombinant strains containing the kdcA gene driven by the tetracycline-inducible promoter Ptet. In addition, we determined that overexpression of a heterologous als gene and two native genes (ilvC and ilvD) involved in valine metabolism in a recombinant Z. mobilis strain expressing kdcA can divert pyruvate from ethanol production to isobutanol biosynthesis. This engineering improved isobutanol production to above 1 g/L. Finally, recombinant strains containing both a synthetic operon, als-ilvC-ilvD, driven by Ptet and the kdcA gene driven by the constitutive strong promoter, Pgap, were determined to greatly enhance isobutanol production with a maximum titer about 4.0 g/L. Finally, isobutanol production was negatively affected by aeration with more isobutanol being produced in more poorly aerated flasks.ConclusionsThis study demonstrated that overexpression of kdcA in combination with a synthetic heterologous operon, als-ilvC-ilvD, is crucial for diverting pyruvate from ethanol production for enhanced isobutanol biosynthesis. Moreover, this study also provides a strategy for harnessing the valine metabolic pathway for future production of other pyruvate-derived biochemicals in Z. mobilis.
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