Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products

Enhancing rufomycin production by CRISPR/Cas9-based genome editing and promoter engineering in Streptomyces sp. MJM3502

Streptomyces sp. MJM3502 is a promising producer of rufomycins, which are a class of potent anti-tuberculosis lead compounds. Although the structure, activity, and mechanism of the main rufomycin 4/6 and its analogs have been extensively studied, a significant gap remains in our understanding of the genome sequence and biosynthetic pathway of Streptomyces sp. MJM3502, and its metabolic engineering has not yet been reported. This study established the genetic manipulation platform for the strain. Using CRISPR/Cas9-based technology to in-frame insert the strong kasO∗p promoter upstream of the rufB and rufS genes of the rufomycin BGC, we increased rufomycin 4/6 production by 4.1-fold and 2.8-fold, respectively. Furthermore, designing recombinant strains by inserting the kasO∗p promoter upstream of the biosynthetic genes encoding cytochrome P450 enzymes led to new rufomycin derivatives. These findings provide the basis for enhancing the production of valuable natural compounds in Streptomyces and offer insights into the generation of novel active natural products via synthetic biology and metabolic engineering.

Efficient Biosynthesis of 8-Prenylkaempferol from Kaempferol by Using Flavonoid 8-Dimethylallyltransferase Derived from Epimedium koreanum

The genus Epimedium includes popular Chinese medicinal plants, and icariin and its precursor icaritin are the key bioactive components of Epimedium. Here, we identified flavonoid 8-dimethylallyltransferase (F8DT) in the authentic medicinal material of Epimedium koreanum, which is a key gene in the icariin biosynthesis pathway. This enzyme can catalyze the synthesis of 8-prenylkaempferol (8PK) from kaempferol. The catalytic ability of the rate-limiting enzyme EkF8DT was significantly improved by truncating its N-terminal intrinsically disordered regions (IDRs) and enhancing the flux of the mevalonate pathway. Icaritin was also successfully synthesized by introducing flavonoid 4'-O-methyltransferase into the Saccharomyces cerevisiae strain. Finally, the highest production of 8PK and icaritin (3.6 g/L and 172.0 mg/L, respectively) was obtained in the S. cerevisiae strain.

Synthetic Biology in Natural Product Biosynthesis

Synthetic biology has played an important role in the renaissance of natural products research during the post-genomics era. The development and integration of new tools have transformed the workflow of natural product discovery and engineering, generating multidisciplinary interest in the field. In this review, we summarize recent developments in natural product biosynthesis from three different aspects. First, advances in bioinformatics, experimental, and analytical tools to identify natural products associated with predicted biosynthetic gene clusters (BGCs) will be covered. This will be followed by an extensive review on the heterologous expression of natural products in bacterial, fungal and plant organisms. The native host-independent paradigm to natural product identification, pathway characterization, and enzyme discovery is where synthetic biology has played the most prominent role. Lastly, strategies to engineer biosynthetic pathways for structural diversification and complexity generation will be discussed, including recent advances in assembly-line megasynthase engineering, precursor-directed structural modification, and combinatorial biosynthesis.

Advances in the biological production of sugar alcohols from biomass-derived xylose

Sugar alcohols are a common class of low-calorie sweeteners. The advancement of technologies utilizing renewable resources has heightened interest in synthesizing sugar alcohols from biomass-derived xylose for cost down of process and sustainability. This review focuses on the potential of biomass-derived xylose and its effective conversion into sugar alcohols, underscoring the significance of this process in sustainable industrial applications. The two main approaches for producing sugar alcohols which include enzyme catalysis and microbial fermentation are thoroughly discussed. The microbial fermentation pathway relies on genetically engineered strains, which are modified to efficiently convert xylose into target sugar alcohols. Enzyme catalysis, on the other hand, directly converts xylose to sugar alcohols through specific reactions. In addition, strategies to improve product selectivity and reduce by-products are discussed in the paper, which are crucial for improving the economic viability and environmental sustainability of sugar alcohol production. Overall, utilizing xylose from biomass to produce sugar alcohols manifests environmental and economic benefits, indicating its substantial potential in the shift towards a low-carbon economy. Future studies may further explore cutting edge technologies to maximize the utilization of biomass-derived xylose and the sustainable production of sugar alcohols.

Molecular insights fast-tracked: AI in biosynthetic pathway research

Covering: 2000 to 2025This review explores the potential of artificial intelligence (AI) in addressing challenges and accelerating molecular insights in biosynthetic pathway research, which is crucial for developing bioactive natural products with applications in pharmacology, agriculture, and biotechnology. It provides an overview of various AI techniques relevant to this research field, including machine learning (ML), deep learning (DL), natural language processing, network analysis, and data mining. AI-powered applications across three main areas, namely, pathway discovery and mining, pathway design, and pathway optimization, are discussed, and the benefits and challenges of integrating omics data and AI for enhanced pathway research are also elucidated. This review also addresses the current limitations, future directions, and the importance of synergy between AI and experimental approaches in unlocking rapid advancements in biosynthetic pathway research. The review concludes with an evaluation of AI's current capabilities and future outlook, emphasizing the transformative impact of AI on biosynthetic pathway research and the potential for new opportunities in the discovery and optimization of bioactive natural products.

Self-resistance-gene-guided, high-throughput automated genome mining of bioactive natural products from Streptomyces

Natural products (NPs) from bacteria, fungi, and plants are a vital source of drug leads, with Streptomyces species being particularly significant due to their capability of producing diverse bioactive compounds. Here, we present a fully automated, scalable, high-throughput platform for discovering bioactive NPs in Streptomyces (FAST-NPS). This platform integrates computational biosynthetic gene cluster (BGC) prediction and prioritization guided by self-resistance genes, automated cloning and heterologous expression, high-throughput fermentation, and product extraction. As a proof of concept, we cloned 105 BGCs (10-100 kb) from 11 Streptomyces strains with a 95% success rate. Heterologous expression in Streptomyces lividans TK24 led to the detection of 23 NPs, including 8 with antibacterial or antitumor bioactivities from 5 BGCs. This work highlights the potential of FAST-NPS to accelerate bioactive NP discovery for biomedical and biotechnological applications. A record of this paper's transparent peer review process is included in the supplemental information.

Computational advances in biosynthetic gene cluster discovery and prediction

Biosynthetic gene clusters (BGCs) are groups of clustered genes found in bacteria, fungi, and some plants and animals that are crucial for synthesizing secondary metabolites. In recent years, genome mining of BGCs has emerged as a prominent research focus, particularly in natural product discovery and drug development. Compared to traditional experimental methods, applying computational techniques has significantly enhanced the efficiency of BGC identification and annotation, thereby facilitating the discovery of novel metabolites. The advent of artificial intelligence, particularly machine learning models and more advanced deep learning algorithms, has significantly enhanced both the speed and precision of BGC mining. This review offers a comprehensive introduction to currently developed BGC databases and prediction tools, highlighting the potential of machine learning technologies in BGC mining. Additionally, it summarizes the challenges computational methods face in this area and discusses future research directions.

Microbial Technologies Enhanced by Artificial Intelligence for Healthcare Applications

The combination of artificial intelligence (AI) with microbial technology marks the start of a major transformation, improving applications throughout biotechnology, especially in healthcare. With the capability of AI to process vast amounts of biological big data, advanced microbial technology allows for a comprehensive understanding of complex biological systems, advancing disease diagnosis, treatment and the development of microbial therapeutics. This mini review explores the impact of AI-integrated microbial technologies in healthcare, highlighting advancements in microbial biomarker-based diagnosis, the development of microbial therapeutics and the microbial production of therapeutic compounds. This exploration promises significant improvements in the design and implementation of health-related solutions, steering a new era in biotechnological applications.

Recent advances in genome mining and synthetic biology for discovery and biosynthesis of natural products

Natural products have long served as critical raw materials in chemical and pharmaceutical manufacturing, primarily which can provide superior scaffolds or intermediates for drug discovery and development. Over the last century, natural products have contributed to more than a third of therapeutic drug production. However, traditional methods of producing drugs from natural products have become less efficient and more expensive over the past few decades. The combined utilization of genome mining and synthetic biology based on genome sequencing, bioinformatics tools, big data analytics, genetic engineering, metabolic engineering, and systems biology promises to counter this trend. Here, we reviewed recent (2020-2023) examples of genome mining and synthetic biology used to resolve challenges in the production of natural products, such as less variety, poor efficiency, and low yield. Additionally, the emerging efficient tools, design principles, and building strategies of synthetic biology and its application prospects in NPs synthesis have also been discussed.

Developing filamentous fungal chassis for natural product production

The growing demand for green and sustainable production of high-value chemicals has driven the interest in microbial chassis. Recent advances in synthetic biology and metabolic engineering have reinforced filamentous fungi as promising chassis cells to produce bioactive natural products. Compared to the most used model organisms, Escherichia coli and Saccharomyces cerevisiae, most filamentous fungi are natural producers of secondary metabolites and possess an inherent pre-mRNA splicing system and abundant biosynthetic precursors. In this review, we summarize recent advances in the application of filamentous fungi as chassis cells. Emphasis is placed on strategies for developing a filamentous fungal chassis, including the establishment of mature genetic manipulation and efficient genetic tools, the catalogue of regulatory elements, and the optimization of endogenous metabolism. Furthermore, we provide an outlook on the advanced techniques for further engineering and application of filamentous fungal chassis.

Exploring the secrets of marine microorganisms: Unveiling secondary metabolites through metagenomics

Marine microorganisms are increasingly recognized as primary producers of marine secondary metabolites, drawing growing research interest. Many of these organisms are unculturable, posing challenges for study. Metagenomic techniques enable research on these unculturable microorganisms, identifying various biosynthetic gene clusters (BGCs) related to marine microbial secondary metabolites, thereby unveiling their secrets. This review comprehensively analyses metagenomic methods used in discovering marine microbial secondary metabolites, highlighting tools commonly employed in BGC identification, and discussing the potential and challenges in this field. It emphasizes the key role of metagenomics in unveiling secondary metabolites, particularly in marine sponges and tunicates. The review also explores current limitations in studying these metabolites through metagenomics, noting how long-read sequencing technologies and the evolution of computational biology tools offer more possibilities for BGC discovery. Furthermore, the development of synthetic biology allows experimental validation of computationally identified BGCs, showcasing the vast potential of metagenomics in mining marine microbial secondary metabolites.

Cutting-edge plant natural product pathway elucidation

Plant natural products (PNPs) play important roles in plant physiology and have been applied across diverse fields of human society. Understanding their biosynthetic pathways informs plant evolution and meanwhile enables sustainable production through metabolic engineering. However, the discovery of PNP biosynthetic pathways remains challenging due to the diversity of enzymes involved and limitations in traditional gene mining approaches. In this review, we will summarize state-of-the-art strategies and recent examples for predicting and characterizing PNP biosynthetic pathways, respectively, with multiomics-guided tools and heterologous host systems and share our perspectives on the systematic pipelines integrating these various bioinformatic and biochemical approaches.

Combinatorial biosynthesis for the engineering of novel fungal natural products

Natural products are small molecules synthesized by fungi, bacteria and plants, which historically have had a profound effect on human health and quality of life. These natural products have evolved over millions of years resulting in specific biological functions that may be of interest for pharmaceutical, agricultural, or nutraceutical use. Often natural products need to be structurally modified to make them suitable for specific applications. Combinatorial biosynthesis is a method to alter the composition of enzymes needed to synthesize a specific natural product resulting in structurally diversified molecules. In this review we discuss different approaches for combinatorial biosynthesis of natural products via engineering fungal enzymes and biosynthetic pathways. We highlight the biosynthetic knowledge gained from these studies and provide examples of new-to-nature bioactive molecules, including molecules synthesized using combinations of fungal and non-fungal enzymes.

Streptomyces-based whole-cell biosensors for detecting diverse cell envelope-targeting antibiotics

Cell envelope-targeting antibiotics are potent therapeutic agents against various bacterial infections. The emergence of multiple antibiotic-resistant strains underscores the significance of identifying potent antimicrobials specifically targeting the cell envelope. However, current drug screening approaches are tedious and lack sufficient specificity and sensitivity, warranting the development of more efficient methods. Genetic circuit-based whole-cell biosensors hold great promise for targeted drug discovery from natural products. Here, we performed comparative transcriptomic analysis of Streptomyces coelicolor M1146 exposed to diverse cell envelope-targeting antibiotics, aiming to identify regulatory elements involved in perceiving and responding to these compounds. Differential gene expression analysis revealed significant activation of VanS/R two-component system in response to the glycopeptide class of cell envelope-acting antibiotics. Therefore, we engineered a pair of VanS/R-based biosensors that exhibit functional complementarity and possess exceptional sensitivity and specificity for glycopeptides detection. Additionally, through promoter screening and characterization, we expanded the biosensor's detection range to include various cell envelope-acting antibiotics beyond glycopeptides. Our genetically engineered biosensor exhibits superior performance, including a dynamic range of up to 887-fold for detecting subtle antibiotic concentration changes in a rapid 2-h response time, enabling high-throughput screening of natural product libraries for antimicrobial agents targeting the bacterial cell envelope.

What is the role of microbial biotechnology and genetic engineering in medicine?

Microbial products are essential for developing various therapeutic agents, including antibiotics, anticancer drugs, vaccines, and therapeutic enzymes. Genetic engineering techniques, functional genomics, and synthetic biology unlock previously uncharacterized natural products. This review highlights major advances in microbial biotechnology, focusing on gene-based technologies for medical applications.

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Fungal secondary metabolites (SMs) contribute to the diversity of fungal ecological communities, niches, and lifestyles. Many fungal SMs have one or more medically and industrially important activities (e.g., antifungal, antibacterial, and antitumor). The genes necessary for fungal SM biosynthesis are typically located right next to each other in the genome and are known as biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted machine learning models that predicted SM bioactivity from bacterial BGC data with accuracies as high as 80% to fungal BGC data. We trained our models to predict the antibacterial, antifungal, and cytotoxic/antitumor bioactivity of fungal SMs on two data sets: (i) fungal BGCs (data set comprised of 314 BGCs) and (ii) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs). We found that models trained on fungal BGCs had balanced accuracies between 51% and 68%, whereas training on bacterial and fungal BGCs had balanced accuracies between 56% and 68%. The low prediction accuracy of fungal SM bioactivities likely stems from the small size of the data set; this lack of data, coupled with our finding that including bacterial BGC data in the training data did not substantially change accuracies currently limits the application of machine learning approaches to fungal SM studies. With >15,000 characterized fungal SMs, millions of putative BGCs in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.IMPORTANCEFungi are key sources of natural products and iconic drugs, including penicillin and statins. DNA sequencing has revealed that there are likely millions of biosynthetic pathways in fungal genomes, but the chemical structures and bioactivities of >99% of natural products produced by these pathways remain unknown. We used artificial intelligence to predict the bioactivities of diverse fungal biosynthetic pathways. We found that the accuracies of our predictions were generally low, between 51% and 68%, likely because the natural products and bioactivities of only very few fungal pathways are known. With >15,000 characterized fungal natural products, millions of putative biosynthetic pathways present in fungal genomes, and increased demand for novel drugs, our study suggests that there is an urgent need for efforts that systematically identify fungal biosynthetic pathways, their natural products, and their bioactivities.