Pareto Optimal Metabolic Engineering for the Growth-coupled Overproduction of Sustainable Chemicals

Upcycling CO2 into succinic acid via electrochemical and engineered Escherichia coli

Converting CO2 into value-added chemicals still remains a grand challenge. Succinic acid has long been considered as one of the top building block chemicals. This study reported efficiently upcycling CO2 into succinic acid by combining between electrochemical and engineered Escherichia coli. In this process, the Cu-organic framework catalyst was synthesized for electrocatalytic CO2-to-ethanol conversion with high Faradaic efficiency (FE, 84.7 %) and relative purity (RP, 95 wt%). Subsequently, an engineered E. coli with efficiently assimilating CO2-derived ethanol to produce succinic acid was constructed by combining computational design and metabolic engineering, and the succinic acid titer reached 53.8 mM with the yield of 0.41 mol/mol, which is 82 % of the theoretical yield. This study effort to link the two processes of efficient ethanol synthesis by electrocatalytic CO2 and succinic acid production from CO2-derived ethanol, paving a way for the production of succinic acid and other value-added chemicals by converting CO2 into ethanol.

A method for the systematic selection of enzyme panel candidates by solving the maximum diversity problem

Enzymes are being increasingly exploited for their potential as industrial biocatalysts. Establishing a portfolio of useful biocatalysts from large and diverse protein family is challenging and a systematic method for candidate selection promises to aid in this task. Moreover, accurate enzyme functional annotation can only be confidently guaranteed through experimental characterisation in the laboratory. The selection of catalytically diverse enzyme panels for experimental characterisation is also an important step for shedding light on the currently unannotated proteins in enzyme families. Current selection methods often lack efficiency and scalability, and are usually non-systematic. We present a novel algorithm for the automatic selection of subsets from enzyme families. A tabu search algorithm solving the maximum diversity problem for sequence identity was designed and implemented, and applied to three diverse enzyme families. We show that this approach automatically selects panels of enzymes that contain high richness and relative abundance of the known catalytic functions, and outperforms other methods such as k-medoids.

Computational assessment of lipid production in Rhodosporidium toruloides in two-stage and one-stage batch bioprocesses

Oleaginous yeasts are promising platforms for microbial lipids production as a renewable and sustainable alternative to vegetable oils in biodiesel production. In this paper, a thorough in silico assessment of lipid production in batch cultivation by Rhodosporidium toruloides was developed. By means of dynamic flux balance analysis, the traditional two-stage bioprocess (TSB) performed by the native strain was contrasted with one-stage bioprocess (OSB) using four designed strains obtained by gene knockout strategies. Lipid titer, yield, content, and productivity were analyzed at different initial C/N ratios as relevant performance indicators used in bioprocesses. By weighting these indicators, a global lipid efficiency metric (GLEM) was defined to consider different scenarios. Under simulated conditions, designed strains for lipid overproduction in OSB outperformed the TSB in terms of lipid title (up to threefold), lipid yield (up to 2.4-fold), lipid content (up to 2.8-fold, with a maximum of 76%), and productivity (up to 1.3-fold), depending on C/N ratios. Using these efficiency parameters and the proposed GLEM, the process of selecting the most suitable candidates for lipid production could be carried out before experimental assays. This methodology holds the potential to be extended to other oleaginous microorganisms and diverse strain design techniques.

Comparative Transcriptome Analysis Revealing the Enhanced Volatiles of Cofermentation of Yeast and Lactic Acid Bacteria on Whole Wheat Steamed Bread Dough

To reveal the underlying mechanism of enhanced volatiles of whole wheat steamed bread, the current study screened Saccharomyces cerevisiae Y5 and Lactiplantibacillus plantarum L7 from sourdough and studied the synergetic effect of cofermentation on the volatiles of steamed bread and fermented dough by comparative transcriptome analysis. Cofermentation significantly improved the types and concentration of volatiles in addition to the improved specific volume and texture. Genes involved in galactose, starch, and glucose metabolism and genes encoding pyruvate oxidase and β-galactosidase were significantly upregulated in S. cerevisiae and L. plantarum, respectively. Expression of the OPT2 encoding oligopeptide transporter in S. cerevisiae was upregulated, which facilitated the transmembrane transport of oligopeptide and amino acid into yeast cells. Genes involved in the synthesis and metabolism of amino acids, lipids, and ester compounds in L. plantarum changed significantly, and gene encoding acetic acid kinase was upregulated. Moreover, the quorum sensing-related genes in S. cerevisiae and L. plantarum were upregulated.

The Pareto principle: To what extent does it apply to resource acquisition in stable microbial communities and thereby steer their geno-/ecotype compositions and interactions between their members?

The Pareto principle, or 20:80 rule, describes resource distribution in stable communities whereby 20% of community members acquire 80% of a key resource. In this Burning Question, we ask to what extent the Pareto principle applies to the acquisition of limiting resources in stable microbial communities; how it may contribute to our understanding of microbial interactions, microbial community exploration of evolutionary space, and microbial community dysbiosis; and whether it can serve as a benchmark of microbial community stability and functional optimality?