Designing Microbial Cell Factories for the Production of Chemicals

Abstract

The sustainable production of chemicals from renewable, nonedible biomass has emerged as an essential alternative to address pressing environmental issues arising from our heavy dependence on fossil resources. Microbial cell factories are engineered microorganisms harboring biosynthetic pathways streamlined to produce chemicals of interests from renewable carbon sources. The biosynthetic pathways for the production of chemicals can be defined into three categories with reference to the microbial host selected for engineering: native-existing pathways, nonnative-existing pathways, and nonnative-created pathways. Recent trends in leveraging native-existing pathways, discovering nonnative-existing pathways, and designing de novo pathways (as nonnative-created pathways) are discussed in this Perspective. We highlight key approaches and successful case studies that exemplify these concepts. Once these pathways are designed and constructed in the microbial cell factory, systems metabolic engineering strategies can be used to improve the performance of the strain to meet industrial production standards. In the second part of the Perspective, current trends in design tools and strategies for systems metabolic engineering are discussed with an eye toward the future. Finally, we survey current and future challenges that need to be addressed to advance microbial cell factories for the sustainable production of chemicals.

Figure 1

Overall design process to construct a microbial cell factory for the production of a target chemical. First, an appropriate microorganism is selected as the microbial host. Next, biosynthetic pathways toward target chemical production are examined, and the optimal pathway is introduced to the microbial host, accordingly. The microbial host harboring the biosynthetic pathway is subject to systems metabolic engineering to improve strain performance.

Figure 2

Flowchart illustrating the guiding principles in designing three categories of biosynthetic pathways. Blue and red colored boxes indicate steps for constructing nonnative-existing pathways and nonnative-created pathways, respectively.

Figure 3

Two reported routes toward methyl anthranilate production from anthranilate. The first route is a two-step catalytic pathway that involves the use of methanol as the methyl donor, and the second route uses SAM as the methyl donor toward methyl anthranilate production. The abbreviations are as follows: CoA, coenzyme A; MeOH, methanol; SAH, S-adenosyl-l-homocysteine; SAM, S-adenosyl methionine.

Figure 4

Transcriptome and genome mining approaches for the discovery of novel gene and enzyme candidates. Metabolic reactions previously unknown can be elucidated by introducing microbial hosts with various gene and enzyme candidates newly discovered from the transcriptomes of plants or from bacterial gene clusters using various computational tools.

Figure 5

Use of promiscuous enzymes for constructing nonnative, created pathways. (A) A schematic illustration of enzyme promiscuity. (B) Use of promiscuous enzymes that are functional in the 1,4-butanediol biosynthetic pathway and employed for use in the 1,5-pentanediol biosynthetic pathway.

Figure 6

Template-based approaches for retrobiosynthesis. (A) An example of applying a known reaction rule to a target chemical, dopamine, to find a reactant, here in this case, L-DOPA. (B) Filtering the predicted pathways using several criteria including enzyme availability, thermodynamics, toxicity of intermediates, and yields of a pathway, among others.

Figure 7

Template-free approaches for retrobiosynthesis using transformer-based models. A SMILES string of a target molecule is translated to a SMILES string of substrate by encoders and decoders of a transformer-based model. The translation iteratively generates a next token (a SMILES character) of the substrate by taking tokens of the target molecules and the previously generated tokens of the substrate.

Figure 8

Design tools and strategies for the construction of microbial cell factories. (A) Molecular tools for the introduction of biosynthetic pathways to host cells. (B) Engineering enzymes using rational design, directed evolution and computational de novo design approaches. (C) Substrate channeling strategies toward product formation. (D) Genome-scale metabolic models driven by omics data and artificial intelligence. (E) Chassis random mutagenesis using ARTP and genome shuffling methods. (F) Increasing tolerance to target chemicals using ALE and process engineering. (G) Transporter engineering for the export and import of metabolites. (H) Engineering storage capacities for metabolites and energy. (I) Increasing membrane area by morphology engineering. (J) Antibiotics-free systems. The abbreviations are as follows: AI, artificial intelligence; ALE, adaptive laboratory evolution; ARTP, atmospheric and room-temperature plasma; FAs, fatty acids; IMV, inner membrane vesicles; OMV, outer membrane vesicles; PHB, polyhydroxybutyrate; RBS, ribosome binding site; TAGs, triacylglycerols; β-OX, β-oxidation pathway.