Microbial production of multiple short-chain primary amines via retrobiosynthesis

Expanded genome and proteome reallocation in a novel, robust Bacillus coagulans strain capable of utilizing pentose and hexose sugars

Bacillus coagulans, a Gram-positive thermophilic bacterium, is recognized for its probiotic properties and recent development as a microbial cell factory. Despite its importance for biotechnological applications, the current understanding of B. coagulans' robustness is limited, especially for undomesticated strains. To fill this knowledge gap, we characterized the metabolic capability and performed functional genomics and systems analysis of a novel, robust strain, B. coagulans B-768. Genome sequencing revealed that B-768 has the largest B. coagulans genome known to date (3.94 Mbp), about 0.63 Mbp larger than the average genome of sequenced B. coagulans strains, with expanded carbohydrate metabolism and mobilome. Functional genomics identified a well-equipped genetic portfolio for utilizing a wide range of C5 (xylose, arabinose), C6 (glucose, mannose, galactose), and C12 (cellobiose) sugars present in biomass hydrolysates, which was validated experimentally. For growth on individual xylose and glucose, the dominant sugars in biomass hydrolysates, B-768 exhibited distinct phenotypes and proteome profiles. Faster growth and glucose uptake rates resulted in lactate overflow metabolism, which makes B. coagulans a lactate overproducer; however, slower growth and xylose uptake diminished overflow metabolism due to the high energy demand for sugar assimilation. Carbohydrate Transport and Metabolism (COG-G), Translation (COG-J), and Energy Conversion and Production (COG-C) made up 60%-65% of the measured proteomes but were allocated differently when growing on xylose and glucose. The trade-off in proteome reallocation, with high investment in COG-C over COG-G, explains the xylose growth phenotype with significant upregulation of xylose metabolism, pyruvate metabolism, and tricarboxylic acid (TCA) cycle. Strain B-768 tolerates and effectively utilizes inhibitory biomass hydrolysates containing mixed sugars and exhibits hierarchical sugar utilization with glucose as the preferential substrate.IMPORTANCEThe robustness of B. coagulans makes it a valuable microorganism for biotechnology applications; yet, this phenotype is not well understood at the cellular level. Through phenotypic characterization and systems analysis, this study elucidates the functional genomics and robustness of a novel, undomesticated strain, B. coagulans B-768, capable of utilizing inhibitory switchgrass biomass hydrolysates. The genome of B-768, enriched with carbohydrate metabolism genes, demonstrates high regulatory capacity. The coordination of proteome reallocation in Carbohydrate Transport and Metabolism (COG-G), Translation (COG-J), and Energy Conversion and Production (COG-C) is critical for effective cell growth, sugar utilization, and lactate production via overflow metabolism. Overall, B-768 is a novel, robust, and promising B. coagulans strain that can be harnessed as a microbial biomanufacturing platform to produce chemicals and fuels from biomass hydrolysates.

Modulating bacterial function utilizing A knowledge base of transcriptional regulatory modules

Synthetic biology enables the reprogramming of cellular functions for various applications. However, challenges in scalability and predictability persist due to context-dependent performance and complex circuit-host interactions. This study introduces an iModulon-based engineering approach, utilizing machine learning-defined co-regulated gene groups (iModulons) as design parts containing essential genes for specific functions. This approach identifies the necessary components for genetic circuits across different contexts, enhancing genome engineering by improving target selection and predicting module behavior. We demonstrate several distinct uses of iModulons: (i) discovery of unknown iModulons to increase protein productivity, heat tolerance and fructose utilization; (ii) an iModulon boosting approach, which amplifies the activity of specific iModulons, improved cell growth under osmotic stress with minimal host regulation disruption; (iii) an iModulon rebalancing strategy, which adjusts the activity levels of iModulons to balance cellular functions, significantly increased oxidative stress tolerance while minimizing trade-offs and (iv) iModulon-based gene annotation enabled natural competence activation by predictably rewiring iModulons. Comparative experiments with traditional methods showed our approach offers advantages in efficiency and predictability of strain engineering. This study demonstrates the potential of iModulon-based strategies to systematically and predictably reprogram cellular functions, offering refined and adaptable control over complex regulatory networks.

Deep learning in template-free de novo biosynthetic pathway design of natural products

Natural products (NPs) are indispensable in drug development, particularly in combating infections, cancer, and neurodegenerative diseases. However, their limited availability poses significant challenges. Template-free de novo biosynthetic pathway design provides a strategic solution for NP production, with deep learning standing out as a powerful tool in this domain. This review delves into state-of-the-art deep learning algorithms in NP biosynthesis pathway design. It provides an in-depth discussion of databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and UniProt, which are essential for model training, along with chemical databases such as Reaxys, SciFinder, and PubChem for transfer learning to expand models' understanding of the broader chemical space. It evaluates the potential and challenges of sequence-to-sequence and graph-to-graph translation models for accurate single-step prediction. Additionally, it discusses search algorithms for multistep prediction and deep learning algorithms for predicting enzyme function. The review also highlights the pivotal role of deep learning in improving catalytic efficiency through enzyme engineering, which is essential for enhancing NP production. Moreover, it examines the application of large language models in pathway design, enzyme discovery, and enzyme engineering. Finally, it addresses the challenges and prospects associated with template-free approaches, offering insights into potential advancements in NP biosynthesis pathway design.

Engineering microbial metabolic homeostasis for chemicals production

Microbial-based bio-refining promotes the development of a biotechnology revolution to encounter and tackle the enormous challenges in petroleum-based chemical production by biomanufacturing, biocomputing, and biosensing. Nevertheless, microbial metabolic homeostasis is often incompatible with the efficient synthesis of bioproducts mainly due to: inefficient metabolic flow, robust central metabolism, sophisticated metabolic network, and inevitable environmental perturbation. Therefore, this review systematically summarizes how to optimize microbial metabolic homeostasis by strengthening metabolic flux for improving biotransformation turnover, redirecting metabolic direction for rewiring bypass pathway, and reprogramming metabolic network for boosting substrate utilization. Future directions are also proposed for providing constructive guidance on the development of industrial biotechnology.

Enabling pathway design by multiplex experimentation and machine learning

The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell. Moreover, the conventional trial and error methods utilized in pathway optimization struggle to fully grasp the intricacies of installed pathways, leading to time-consuming and labor-intensive experiments, ultimately resulting in suboptimal yields. Considering these obstacles, there is a pressing need to explore the enzyme expression landscape and identify the optimal pathway configuration for enhanced production of molecules. This review delves into recent advancements in pathway engineering, with a focus on multiplex experimentation and machine learning techniques. These approaches play a pivotal role in overcoming the limitations of traditional methods, enabling exploration of a broader design space and increasing the likelihood of discovering optimal pathway configurations for enhanced production of molecules. We discuss several tools and strategies for pathway design, construction, and optimization for sustainable and cost-effective microbial production of molecules ranging from bulk to fine chemicals. We also highlight major successes in academia and industry through compelling case studies.

Design and engineering of artificial biosynthetic pathways-where do we stand and where do we go?

The production of commodity and specialty chemicals relies heavily on fossil fuels. The negative impact of this dependency on our environment and climate has spurred a rising demand for more sustainable methods to obtain such chemicals from renewable resources. Herein, biotransformations of these renewable resources facilitated by enzymes or (micro)organisms have gained significant attention, since they can occur under mild conditions and reduce waste. These biotransformations typically leverage natural metabolic processes, which limits the scope and production capacity of such processes. In this mini-review, we provide an overview of advancements made in the past 5 years to expand the repertoire of biotransformations in engineered microorganisms. This ranges from redesign of existing pathways driven by retrobiosynthesis and computational design to directed evolution of enzymes and de novo pathway design to unlock novel routes for the synthesis of desired chemicals. We highlight notable examples of pathway designs for the production of commodity and specialty chemicals, showcasing the potential of these approaches. Lastly, we provide an outlook on future pathway design approaches.

Deep learning for metabolic pathway design

The establishment of a bio-based circular economy is imperative in tackling the climate crisis and advancing sustainable development. In this realm, the creation of microbial cell factories is central to generating a variety of chemicals and materials. The design of metabolic pathways is crucial in shaping these microbial cell factories, especially when it comes to producing chemicals with yet-to-be-discovered biosynthetic routes. To aid in navigating the complexities of chemical and metabolic domains, computer-supported tools for metabolic pathway design have emerged. In this paper, we evaluate how digital strategies can be employed for pathway prediction and enzyme discovery. Additionally, we touch upon the recent strides made in using deep learning techniques for metabolic pathway prediction. These computational tools and strategies streamline the design of metabolic pathways, facilitating the development of microbial cell factories. Leveraging the capabilities of deep learning in metabolic pathway design is profoundly promising, potentially hastening the advent of a bio-based circular economy.

Biosynthesis pathways of expanding carbon chains for producing advanced biofuels

Because the thermodynamic property is closer to gasoline, advanced biofuels (C ≥ 6) are appealing for replacing non-renewable fossil fuels using biosynthesis method that has presented a promising approach. Synthesizing advanced biofuels (C ≥ 6), in general, requires the expansion of carbon chains from three carbon atoms to more than six carbon atoms. Despite some specific biosynthesis pathways that have been developed in recent years, adequate summary is still lacking on how to obtain an effective metabolic pathway. Review of biosynthesis pathways for expanding carbon chains will be conducive to selecting, optimizing and discovering novel synthetic route to obtain new advanced biofuels. Herein, we first highlighted challenges on expanding carbon chains, followed by presentation of two biosynthesis strategies and review of three different types of biosynthesis pathways of carbon chain expansion for synthesizing advanced biofuels. Finally, we provided an outlook for the introduction of gene-editing technology in the development of new biosynthesis pathways of carbon chain expansion.

Research progress of engineering microbial cell factories for pigment production

Pigments are widely used in people's daily life, such as food additives, cosmetics, pharmaceuticals, textiles, etc. In recent years, the natural pigments produced by microorganisms have attracted increased attention because these processes cannot be affected by seasons like the plant extraction methods, and can also avoid the environmental pollution problems caused by chemical synthesis. Synthetic biology and metabolic engineering have been used to construct and optimize metabolic pathways for production of natural pigments in cellular factories. Building microbial cell factories for synthesis of natural pigments has many advantages, including well-defined genetic background of the strains, high-density and rapid culture of cells, etc. Until now, the technical means about engineering microbial cell factories for pigment production and metabolic regulation processes have not been systematically analyzed and summarized. Therefore, the studies about construction, modification and regulation of synthetic pathways for microbial synthesis of pigments in recent years have been reviewed, aiming to provide an up-to-date summary of engineering strategies for microbial synthesis of natural pigments including carotenoids, melanins, riboflavins, azomycetes and quinones. This review should provide new ideas for further improving microbial production of natural pigments in the future.

Metabolic engineering for sustainability and health

Bio-based production of chemicals and materials has attracted much attention due to the urgent need to establish sustainability and enhance human health. Metabolic engineering (ME) allows purposeful modification of cellular metabolic, regulatory, and signaling networks to achieve enhanced production of desired chemicals and degradation of environmentally harmful chemicals. ME has significantly progressed over the past 30 years through further integration of the strategies of synthetic biology, systems biology, evolutionary engineering, and data science aided by artificial intelligence. Here we review the field of ME from its emergence to the current state-of-the-art, highlighting its contribution to sustainable production of chemicals, health, and the environment through representative examples. Future challenges of ME and perspectives are also discussed.

Metabolic Engineering: Methodologies and Applications

Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.

Changes in the membrane lipid composition of a Sulfurimonas species depend on the electron acceptor used for sulfur oxidation

Sulfurimonas species are among the most abundant sulfur-oxidizing bacteria in the marine environment. They are capable of using different electron acceptors, this metabolic flexibility is favorable for their niche adaptation in redoxclines. When oxygen is depleted, most Sulfurimonas spp. (e.g., Sulfurimonas gotlandica) use nitrate ([Formula: see text]) as an electron acceptor to oxidize sulfur, including sulfide (HS-), S0 and thiosulfate, for energy production. Candidatus Sulfurimonas marisnigri SoZ1 and Candidatus Sulfurimonas baltica GD2, recently isolated from the redoxclines of the Black Sea and Baltic Sea respectively, have been shown to use manganese dioxide (MnO2) rather than [Formula: see text] for sulfur oxidation. The use of different electron acceptors is also dependent on differences in the electron transport chains embedded in the cellular membrane, therefore changes in the membrane, including its lipid composition, are expected but are so far unexplored. Here, we used untargeted lipidomic analysis to reveal changes in the composition of the lipidomes of three representative Sulfurimonas species grown using either [Formula: see text] and MnO2. We found that all Sulfurimonas spp. produce a series of novel phosphatidyldiazoalkyl-diacylglycerol lipids. Ca. Sulfurimonas baltica GD2 adapts its membrane lipid composition depending on the electron acceptors it utilizes for growth and survival. When carrying out MnO2-dependent sulfur oxidation, the novel phosphatidyldiazoalkyl-diacylglycerol headgroup comprises shorter alkyl moieties than when sulfur oxidation is [Formula: see text]-dependent. This is the first report of membrane lipid adaptation when an organism is grown with different electron acceptors. We suggest novel diazoalkyl lipids have the potential to be used as a biomarker for different conditions in redox-stratified systems.

Commensal microbiota from patients with inflammatory bowel disease produce genotoxic metabolites

Microbiota-derived metabolites that elicit DNA damage can contribute to colorectal cancer (CRC). However, the full spectrum of genotoxic chemicals produced by indigenous gut microbes remains to be defined. We established a pipeline to systematically evaluate the genotoxicity of an extensive collection of gut commensals from inflammatory bowel disease patients. We identified isolates from divergent phylogenies whose metabolites caused DNA damage and discovered a distinctive family of genotoxins-termed the indolimines-produced by the CRC-associated species Morganella morganii. A non-indolimine-producing M. morganii mutant lacked genotoxicity and failed to exacerbate colon tumorigenesis in mice. These studies reveal the existence of a previously unexplored universe of genotoxic small molecules from the microbiome that may affect host biology in homeostasis and disease.

Recent advances in high-throughput metabolic engineering: Generation of oligonucleotide-mediated genetic libraries

The preparation of genetic libraries is an essential step to evolve microorganisms and study genotype-phenotype relationships by high-throughput screening/selection. As the large-scale synthesis of oligonucleotides becomes easy, cheap, and high-throughput, numerous novel strategies have been developed in recent years to construct high-quality oligo-mediated libraries, leveraging state-of-art molecular biology tools for genome editing and gene regulation. This review presents an overview of recent advances in creating and characterizing in vitro and in vivo genetic libraries, based on CRISPR/Cas, regulatory RNAs, and recombineering, primarily for Escherichia coli and Saccharomyces cerevisiae. These libraries' applications in high-throughput metabolic engineering, strain evolution and protein engineering are also discussed.

Metabolic engineering of Escherichia coli for de novo production of 3-phenylpropanol via retrobiosynthesis approach

Background

3-Phenylpropanol with a pleasant odor is widely used in foods, beverages and cosmetics as a fragrance ingredient. It also acts as the precursor and reactant in pharmaceutical and chemical industries. Currently, petroleum-based manufacturing processes of 3-phenypropanol is environmentally unfriendly and unsustainable. In this study, we aim to engineer Escherichia coli as microbial cell factory for de novo production of 3-phenypropanol via retrobiosynthesis approach.

Results

Aided by in silico retrobiosynthesis analysis, we designed a novel 3-phenylpropanol biosynthetic pathway extending from l-phenylalanine and comprising the phenylalanine ammonia lyase (PAL), enoate reductase (ER), aryl carboxylic acid reductase (CAR) and phosphopantetheinyl transferase (PPTase). We screened the enzymes from plants and microorganisms and reconstructed the artificial pathway for conversion of 3-phenylpropanol from l-phenylalanine. Then we conducted chromosome engineering to increase the supply of precursor l-phenylalanine and combined the upstream l-phenylalanine pathway and downstream 3-phenylpropanol pathway. Finally, we regulated the metabolic pathway strength and optimized fermentation conditions. As a consequence, metabolically engineered E. coli strain produced 847.97 mg/L of 3-phenypropanol at 24 h using glucose-glycerol mixture as co-carbon source.

Conclusions

We successfully developed an artificial 3-phenylpropanol pathway based on retrobiosynthesis approach, and highest titer of 3-phenylpropanol was achieved in E. coli via systems metabolic engineering strategies including enzyme sources variety, chromosome engineering, metabolic strength balancing and fermentation optimization. This work provides an engineered strain with industrial potential for production of 3-phenylpropanol, and the strategies applied here could be practical for bioengineers to design and reconstruct the microbial cell factory for high valuable chemicals.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01615-1.

Escherichia coli as a platform microbial host for systems metabolic engineering

Bio-based production of industrially important chemicals and materials from non-edible and renewable biomass has become increasingly important to resolve the urgent worldwide issues including climate change. Also, bio-based production, instead of chemical synthesis, of food ingredients and natural products has gained ever increasing interest for health benefits. Systems metabolic engineering allows more efficient development of microbial cell factories capable of sustainable, green, and human-friendly production of diverse chemicals and materials. Escherichia coli is unarguably the most widely employed host strain for the bio-based production of chemicals and materials. In the present paper, we review the tools and strategies employed for systems metabolic engineering of E. coli. Next, representative examples and strategies for the production of chemicals including biofuels, bulk and specialty chemicals, and natural products are discussed, followed by discussion on materials including polyhydroxyalkanoates (PHAs), proteins, and nanomaterials. Lastly, future perspectives and challenges remaining for systems metabolic engineering of E. coli are discussed.