Engineering of cofactor preference and catalytic activity of methanol dehydrogenase by growth-coupled directed evolution

Methanol,produced from carbon dioxide,natural gas,and biomass,has drawn increasing attention as a promising green carbon feedstock for biomanufacturing due to its sustainable and energy-rich properties.Nicotinamide adenine dinucleotide(NAD^(+))-dependent methanol dehydrogenase(MDH)catalyzes the oxidation of methanol to formaldehyde via NADH generation,providing a highly active C1 intermediate and reducing power for subsequent biosynthesis.However,the unsatisfactory catalytic efficiency and cofactor bias of MDH significantly impede methanol valorization,especially in nicotinamide adenine dinucleotide phosphate(NADP^(+))-dependent biosynthesis.Herein,we employed synthetic NADH and NADPH auxotrophic Escherichia coli strains as growth-coupled selection platforms for the directed evolution of MDH from Bacillus stearothermophilus DSM 2334.NADH or NADPH generated by MDH-catalyzed methanol oxidation enabled the growth of synthetic cofactor auxotrophs,establishing a positive correlation between the cell growth rate and MDH activity.Using this principle,MDH mutants exhibiting a 20-fold improvement in catalytic efficiency(k_(cat)/K_(m))and a 90-fold cofactor specificity switch from NAD^(+)to NADP+without a decrease in specific enzyme activity,were efficiently screened from random and semi-rationally designed libraries.We envision that these mutants will advance methanol valorization and that the synthetic cofactor auxotrophs will serve as versatile selection platforms for the evolution of NAD(P)^(+)-dependent enzymes.

Automated and integrated ultrahigh throughput screening for industrial strain enabled by acoustic-droplet-ejection mass spectrometry

Synthetic biology provides unprecedented opportunities to tackle critical issues including climate change and sustainable development by constructing microbial cell factories to produce industrially valuable biochemicals,biofuels,and biomaterials using renewable biomass resources[1],where strain screening for specific metabolic traits is a critical step.Rapid,accurate,and simultaneous quantification of multiple metabolites is critical for multi-perspective strain performance evaluation and strain screening,and a generalized method or platform will also reduce method development time to speed up the screening process.

谷氨酸棒杆菌中氨基酸分泌转运蛋白及其代谢改造研究进展

氨基酸是一类在食品、医药及化工等领域具有广泛应用的重要化合物。谷氨酸棒杆菌Corynebacterium glutamicum是生物合成氨基酸最重要的微生物菌株,其年产各类氨基酸超过百万吨。谷氨酸棒杆菌高产氨基酸除具有强大的合成代谢能力外,高效的分泌转运能力也是不可忽略的分子基础。文中综述了近年来谷氨酸棒杆菌中氨基酸分泌转运蛋白及其代谢改造的研究进展,并展望了未来发展方向,为进一步改造提升其发酵生产氨基酸的能力提供了可资借鉴的资料。

Directed evolution of a neutrophilic and mesophilic methanol dehydrogenase based on high-throughput and accurate measurement of formaldehyde

Methanol is a promising one-carbon feedstock for biomanufacturing,which can be sustainably produced from carbon dioxide and natural gas.However,the efficiency of methanol bioconversion is limited by the poor catalytic properties of nicotinamide adenine dinucleotide(NAD^(+))-dependent methanol dehydrogenase(Mdh)that oxidizes methanol to formaldehyde.Herein,the neutrophilic and mesophilic NAD^(+)-dependent Mdh from Bacillus stearothermophilus DSM 2334(Mdh_(Bs))was subjected to directed evolution for enhancing the catalytic activity.The combination of formaldehyde biosensor and Nash assay allowed high-throughput and accurate measurement of formaldehyde and facilitated efficient selection of desired variants.Mdh_(Bs)variants with up to 6.5-fold higher K_(cat)/K_(M)value for methanol were screened from random mutation libraries.The T153 residue that is spatially proximal to the substrate binding pocket has significant influence on enzyme activity.The beneficial T153P mutation changes the interaction network of this residue and breaks theα-helix important for substrate binding into two shortα-helices.Reconstructing the interaction network of T153 with surrounding residues may represent a promising strategy to further improve Mdh_(Bs),and this study provides an efficient strategy for directed evolution of Mdh.

Genome-scale CRISPRi screening:A powerful tool in engineering microbiology

Deciphering gene function is fundamental to engineering of microbiology.The clustered regularly interspaced short palindromic repeats(CRISPR)system has been adapted for gene repression across a range of hosts,creating a versatile tool called CRISPR interference(CRISPRi)that enables genome-scale analysis of gene function.This approach has yielded significant advances in the design of genome-scale CRISPRi libraries,as well as in applica-tions of CRISPRi screening in medical and industrial microbiology.This review provides an overview of the recent progress made in pooled and arrayed CRISPRi screening in microorganisms and highlights representative studies that have employed this method.Additionally,the challenges associated with CRISPRi screening are discussed,and potential solutions for optimizing this strategy are proposed.

High-throughput metagenomic analysis of petroleum-contaminated soil microbiome reveals the versatility in xenobiotic aromatics metabolism

The soil with petroleum contamination is one of the most studied soil ecosystems due to its rich microorganisms for hydrocarbon degradation and broad applications in bioremediation. However, our understanding of the genomic properties and functional traits of the soil microbiome is limited. In this study, we used high-throughput metagenomic sequencing to comprehensively study the microbial community from petroleum-contaminated soils near Tianjin Dagang oilfield in eastern China. The analysis reveals that the soil metagenome is characterized by high level of community diversity and metabolic versatility. The metageome community is predominated by 7-Proteobacteria and a-Proteobacteria, which are key players for petroleum hydrocarbon degradation. The functional study demonstrates over-represented enzyme groups and pathways involved in degradation of a broad set of xenobiotic aromatic compounds, including toluene, xylene, chlorobenzoate, aminobenzoate, DDT, methylnaph- thalene, and bisphenol. A composite metabolic network is proposed for the identified pathways, thus consolidating our identification of the pathways. The overall data demon- strated the great potential of the studied soil microbiome in the xenobiotic aromatics degradation. The results not only establish a rich reservoir for novel enzyme discovery but also provide putative applications in bioremediation.

Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing

Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a widerange of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, wereview the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. Wefirst briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis fordata-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are alsopresented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factorydevelopment using examples from recent studies, including the prediction of protein function, improvement of metabolicmodels, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization.In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methodsshould be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains forindustrial biomanufacturing.