Project description:Muconic acid production from engineered Corynebacterium glutamicum. Gene expression analysis in the pathway redesigned Corynebacterium glutamicum
Project description:Muconic acid (MA) is a valuable compound for adipic acid production, which is a precursor for the synthesis of various polymers such as plastics, coatings, and nylons. Although MA biosynthesis has been previously reported in several bacteria, the engineered strains were not satisfactory owing to low MA titers. Here, we generated an engineered Corynebacterium cell factory to produce a high titer of MA through 3-dehydroshikimate (DHS) conversion to MA, with heterologous expression of foreign protocatechuate (PCA) decarboxylase genes. To accumulate key intermediates in the MA biosynthetic pathway, aroE (shikimate dehydrogenase gene), pcaG/H (PCA dioxygenase alpha/beta subunit genes) and catB (chloromuconate cycloisomerase gene) were disrupted. To accomplish the conversion of PCA to catechol (CA), a step that is absent in Corynebacterium, a codon-optimized heterologous PCA decarboxylase gene was expressed as a single operon under the strong promoter in a aroE-pcaG/H-catB triple knock-out Corynebacterium strain. This redesigned Corynebacterium, grown in an optimized medium, produced about 38 g/L MA and 54 g/L MA in 7-L and 50-L fed-batch fermentations, respectively. These results show highest levels of MA production demonstrated in Corynebacterium, suggesting that the rational cell factory design of MA biosynthesis could be an alternative way to complement petrochemical-based chemical processes.
Project description:3-Dehydroshikimate (DHS) is a useful starting metabolite for the biosynthesis of muconic acid (MA) and shikimic acid (SA), which are precursors of various valuable polymers and drugs. Although DHS biosynthesis has been previously reported in several bacteria, the engineered strains were far from satisfactory, due to their low DHS titers. Here, we created an engineered Escherichia coli cell factory to produce a high titer of DHS as well as an efficient system for the conversion DHS into MA. First, the genes showing negative effects on DHS accumulation in E. coli, such as tyrR (tyrosine dependent transcriptional regulator), ptsG (glucose specific sugar: phosphoenolpyruvate phosphotransferase), and pykA (pyruvate kinase 2), were disrupted. In addition, the genes involved in DHS biosynthesis, such as aroB (DHQ synthase), aroD (DHQ dehydratase), ppsA (phosphoenolpyruvate synthase), galP (D-galactose transporter), aroG (DAHP synthase), and aroF (DAHP synthase), were overexpressed to increase the glucose uptake and flux of intermediates. The redesigned DHS-overproducing E. coli strain grown in an optimized medium produced ~117 g/L DHS in 7-L fed-batch fermentation, which is the highest level of DHS production demonstrated in E. coli. To accomplish the DHS-to-MA conversion, which is originally absent in E. coli, a codon-optimized heterologous gene cassette containing asbF, aroY, and catA was expressed as a single operon under a strong promoter in a DHS-overproducing E. coli strain. This redesigned E. coli grown in an optimized medium produced about 64.5 g/L MA in 7-L fed-batch fermentation, suggesting that the rational cell factory design of DHS and MA biosynthesis could be a feasible way to complement petrochemical-based chemical processes.
Project description:The design of large genetic circuits requires genetic regulatory devices capable of performing complex logic operations. Hybrid riboswitches, synthetically enhanced compact RNA elements (<100 nucleotides) that form a tertiary structure with the ability to specifically bind two different target molecules, can be used to design genetic regulators that emulate Boolean logic. When inserted into the 5' UTR of an mRNA, these devices can regulate translation initiation upon specific binding of one or both ligands. The goal of this study is to design hybrid riboswitches that emulate Boolean NAND logic in yeast. We propose a novel machine learning-based design framework combining high-throughput in vivo screening and deep Bayesian optimization. Through an initial screening, we discovered a hybrid riboswitch with NAND behavior. Using batch Bayesian optimization with an ensemble neural network as surrogate, we further improve the NAND functionality of our hybrid riboswitch with respect to a performance score, thereby achieving near digital NAND behavior. With its focus on model-based and score-driven design, our proposed method can complement experiment driven approaches by allowing fine grained adaptation of functionality, including constructs sensitive to single nucleotide changes.
Project description:The development of modern genome editing and DNA synthesis has enabled researchers to edit DNA sequences with high precision but has left unsolved the problem of designing these edits. We introduce Ledidi, a computational method that rephrases the discrete design task of choosing which edits to make as an easily solvable continuous optimization problem. Ledidi can use any pre-trained deep learning model to guide the optimization, yielding an edited sequence that exhibits the desired outcome while explicitly minimizing the number of edits. When applied in dozens of settings, we find that Ledidi's designs can precisely control transcription factor binding, chromatin accessibility, transcription, and enhancer activity in silico. By using several deep learning models simultaneously, we design cell type-specific enhancers and experimentally validate them in cellulo. Finally, we introduce the concept of an "affinity catalog'', where the design task is repeated multiple times across continuous variants of the design target. We demonstrate how these catalogs can be used to interpret deep learning models and the impact of starting template sequences, and also to design regulatory elements that control transcriptional dosage in a cell type-specific fashion.