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Han J, Miller EP, Li S. Cutting-edge plant natural product pathway elucidation. Curr Opin Biotechnol 2024; 87:103137. [PMID: 38677219 PMCID: PMC11192039 DOI: 10.1016/j.copbio.2024.103137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 04/12/2024] [Indexed: 04/29/2024]
Abstract
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.
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Affiliation(s)
- Jianing Han
- Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Emma Parker Miller
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Sijin Li
- Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA.
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Skellam E, Rajendran S, Li L. Combinatorial biosynthesis for the engineering of novel fungal natural products. Commun Chem 2024; 7:89. [PMID: 38637654 PMCID: PMC11026467 DOI: 10.1038/s42004-024-01172-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 04/08/2024] [Indexed: 04/20/2024] Open
Abstract
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.
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Affiliation(s)
- Elizabeth Skellam
- Department of Chemistry, University of North Texas, 1155 Union Circle, Denton, TX, 76203, USA.
- BioDiscovery Institute, University of North Texas, 1155 Union Circle, Denton, TX, 76203, USA.
- Department of Biological Sciences, University of North Texas, 1155 Union Circle, Denton, TX, 76203, USA.
| | - Sanjeevan Rajendran
- Department of Chemistry, University of North Texas, 1155 Union Circle, Denton, TX, 76203, USA
- BioDiscovery Institute, University of North Texas, 1155 Union Circle, Denton, TX, 76203, USA
| | - Lei Li
- Department of Chemistry, University of North Texas, 1155 Union Circle, Denton, TX, 76203, USA
- BioDiscovery Institute, University of North Texas, 1155 Union Circle, Denton, TX, 76203, USA
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Wang H, Sheng Y, Ou Y, Xu M, Tao M, Lin S, Deng Z, Bai L, Ding W, Kang Q. Streptomyces-based whole-cell biosensors for detecting diverse cell envelope-targeting antibiotics. Biosens Bioelectron 2024; 249:116004. [PMID: 38199083 DOI: 10.1016/j.bios.2024.116004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 12/25/2023] [Accepted: 01/04/2024] [Indexed: 01/12/2024]
Abstract
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.
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Affiliation(s)
- Hengyu Wang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yong Sheng
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yixin Ou
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; Haihe Laboratory of Synthetic Biology, Tianjin, 300308, China
| | - Min Xu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, West 7th Avenue No. 32, 300308, Tianjin, China
| | - Meifeng Tao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; Haihe Laboratory of Synthetic Biology, Tianjin, 300308, China
| | - Shuangjun Lin
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; Haihe Laboratory of Synthetic Biology, Tianjin, 300308, China
| | - Zixin Deng
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; Haihe Laboratory of Synthetic Biology, Tianjin, 300308, China
| | - Linquan Bai
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Wei Ding
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Qianjin Kang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; Haihe Laboratory of Synthetic Biology, Tianjin, 300308, China.
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Santos‐Beneit F. What is the role of microbial biotechnology and genetic engineering in medicine? Microbiologyopen 2024; 13:e1406. [PMID: 38556942 PMCID: PMC10982607 DOI: 10.1002/mbo3.1406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/26/2024] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
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.
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Affiliation(s)
- Fernando Santos‐Beneit
- Institute of Sustainable ProcessesValladolidSpain
- Department of Chemical Engineering and Environmental Technology, School of Industrial EngineeringUniversity of ValladolidValladolidSpain
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Riedling O, Walker AS, Rokas A. Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning. Microbiol Spectr 2024; 12:e0340023. [PMID: 38193680 PMCID: PMC10846162 DOI: 10.1128/spectrum.03400-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024] Open
Abstract
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.
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Affiliation(s)
- Olivia Riedling
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee, USA
| | - Allison S. Walker
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee, USA
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA
| | - Antonis Rokas
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee, USA
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