Synthetic Biology Advanced Natural Product Discovery

Multiplexed activity metabolomics for isolation of filipin macrolides from a hypogean actinomycete

Chemical and biological stimulus screening in a hypogean actinomycete was used to elicit secondary metabolism. Optimal biosynthesis of bioactive natural products was identified using Multiplexed Activity Profiling for determining dose-dependent activity via six single-cell biological readouts. Bioactive extracts were fractioned to establish candidate compounds for isolation using Multiplexed Activity Metabolomics by correlating microtiter well-isolated phenotypes and extracted ion current peaks. This guided the isolation of four filipin polyene macrolides including a new metabolite filipin XV, an alkyl side-chain hydroxylated congener of the filipin chainin, with substantially attenuated cytotoxicity. Filipin-specific cytotoxicity was confirmed using flow cytometry and fluorescence microscopy.

Bacterial metabolomics: current applications for human welfare and future aspects

An imbalanced microbiome is linked to several diseases, such as cancer, inflammatory bowel disease, obesity, and even neurological disorders. Bacteria and their by-products are used for various industrial and clinical purposes. The metabolites under discussion were chosen based on their biological impacts on host and gut microbiota interactions as established by metabolome research. The separation of bacterial metabolites by using statistics and machine learning analysis creates new opportunities for applications of bacteria and their metabolites in the environmental and medical sciences. Thus, the metabolite production strategies, methodologies, and importance of bacterial metabolites for human well-being are discussed in this review.

Therapeutic effects and molecular mechanisms of natural products in thrombosis

Thrombotic disorders, such as myocardial infarction and stroke, are the leading cause of death in the global population and have become a health problem worldwide. Drug therapy is one of the main antithrombotic strategies, but antithrombotic drugs are not completely safe, especially the risk of bleeding at therapeutic doses. Recently, natural products have received widespread interest due to their significant efficacy and high safety, and an increasing number of studies have demonstrated their antithrombotic activity. In this review, articles from databases, such as Web of Science, PubMed, and China National Knowledge Infrastructure, were filtered and the relevant information was extracted according to predefined criteria. As a result, more than 100 natural products with significant antithrombotic activity were identified, including flavonoids, phenylpropanoids, quinones, terpenoids, steroids, and alkaloids. These compounds exert antithrombotic effects by inhibiting platelet activation, suppressing the coagulation cascade, and promoting fibrinolysis. In addition, several natural products also inhibit thrombosis by regulating miRNA expression, anti-inflammatory, and other pathways. This review systematically summarizes the natural products with antithrombotic activity, including their therapeutic effects, mechanisms, and clinical applications, aiming to provide a reference for the development of new antithrombotic drugs.

RespectM revealed metabolic heterogeneity powers deep learning for reshaping the DBTL cycle

Synthetic biology, relying on Design-Build-Test-Learn (DBTL) cycle, aims to solve medicine, manufacturing, and agriculture problems. However, the DBTL cycle's Learn (L) step lacks predictive power for the behavior of biological systems, resulting from the incompatibility between sparse testing data and chaotic metabolic networks. Herein, we develop a method, "RespectM," based on mass spectrometry imaging, which is able to detect metabolites at a rate of 500 cells per hour with high efficiency. In this study, 4,321 single cell level metabolomics data were acquired, representing metabolic heterogeneity. An optimizable deep neural network was applied to learn from metabolic heterogeneity and a "heterogeneity-powered learning (HPL)" based model was trained as well. By testing the HPL based model, we suggest minimal operations to achieve high triglyceride production for engineering. The HPL strategy could revolutionize rational design and reshape the DBTL cycle.

Recent advances in machine learning applications in metabolic engineering

Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.

High Titer of (S)-Equol Synthesis from Daidzein in Escherichia coli

(S)-Equol is the terminal metabolite of daidzein and plays important roles in human health. However, due to anaerobic inefficiency, limited productivity in (S)-equol-producing strains often hinders (S)-equol mass production. Here, a multi-enzyme cascade system was designed to generate a higher (S)-equol titer. First, full reversibility of the (S)-equol synthesis pathway was found and a blocking reverse conversion strategy was established. As biosynthetic genes are present in the microbial genome, an effective daidzein reductase was chosen using evolutionary principles. And our analyses showed that NADPH was crucial for the pathway. In response to this, a novel NADPH pool was redesigned after analyzing a cofactor metabolism model. By adjusting synthesis pathway genes at the right expression level, the entire synthesis pathway can take place smoothly. Thus, the cascade system was optimized by regulating the gene expression intensity. Finally, after optimizing fermentation conditions, a 5 L bioreactor was used to generate a high (S)-equol production titer (3418.5 mg/L), with a conversion rate of approximately 85.9%. This study shows a feasible green process route for the production of (S)-equol.

Application of Gene Knockout and Heterologous Expression Strategy in Fungal Secondary Metabolites Biosynthesis

The in-depth study of fungal secondary metabolites (SMs) over the past few years has led to the discovery of a vast number of novel fungal SMs, some of which possess good biological activity. However, because of the limitations of the traditional natural product mining methods, the discovery of new SMs has become increasingly difficult. In recent years, with the rapid development of gene sequencing technology and bioinformatics, new breakthroughs have been made in the study of fungal SMs, and more fungal biosynthetic gene clusters of SMs have been discovered, which shows that the fungi still have a considerable potential to produce SMs. How to study these gene clusters to obtain a large number of unknown SMs has been a research hotspot. With the continuous breakthrough of molecular biology technology, gene manipulation has reached a mature stage. Methods such as gene knockout and heterologous expression techniques have been widely used in the study of fungal SM biosynthesis and have achieved good effects. In this review, the representative studies on the biosynthesis of fungal SMs by gene knockout and heterologous expression under the fungal genome mining in the last three years were summarized. The techniques and methods used in these studies were also briefly discussed. In addition, the prospect of synthetic biology in the future under this research background was proposed.

Genome mining as a biotechnological tool for the discovery of novel biosynthetic genes in lichens

Natural products (NPs) and their derivatives are a major contributor to modern medicine. Historically, microorganisms such as bacteria and fungi have been instrumental in generating drugs and lead compounds because of the ease of culturing and genetically manipulating them. However, the ever-increasing demand for novel drugs highlights the need to bioprospect previously unexplored taxa for their biosynthetic potential. Next-generation sequencing technologies have expanded the range of organisms that can be explored for their biosynthetic content, as these technologies can provide a glimpse of an organism's entire biosynthetic landscape, without the need for cultivation. The entirety of biosynthetic genes can be compared to the genes of known function to identify the gene clusters potentially coding for novel products. In this study, we mine the genomes of nine lichen-forming fungal species of the genus Umbilicaria for biosynthetic genes, and categorize the biosynthetic gene clusters (BGCs) as "associated product structurally known" or "associated product putatively novel". Although lichen-forming fungi have been suggested to be a rich source of NPs, it is not known how their biosynthetic diversity compares to that of bacteria and non-lichenized fungi. We found that 25%-30% of biosynthetic genes are divergent as compared to the global database of BGCs, which comprises 1,200,000 characterized biosynthetic genes from plants, bacteria, and fungi. Out of 217 BGCs, 43 were highly divergant suggesting that they potentially encode structurally and functionally novel NPs. Clusters encoding the putatively novel metabolic diversity comprise polyketide synthases (30), non-ribosomal peptide synthetases (12), and terpenes (1). Our study emphasizes the utility of genomic data in bioprospecting microorganisms for their biosynthetic potential and in advancing the industrial application of unexplored taxa. We highlight the untapped structural metabolic diversity encoded in the lichenized fungal genomes. To the best of our knowledge, this is the first investigation identifying genes coding for NPs with potentially novel properties in lichenized fungi.

Physiological Responses of Ribosomal Protein S12 K43 Mutants of Corynebacterium glutamicum

Bacterial resistance to streptomycin is often acquired as a consequence of mutations in rpsL, the gene encoding ribosomal protein S12. Corynebacterium glutamicum is a non-pathogenic Gram-positive soil bacterium that has been widely used in industry. In a previous study, we screened several streptomycin-resistant rpsL K43 mutants of C. glutamicum, and surprisingly found that two of them also confer chloramphenicol and/or kanamycin resistance. In order to understand whether or not a single mutation of rpsL^K43 could confer resistance to multiple antibiotics, in this study we attempted to construct saturation mutagenesis of rpsL K43 by rational genetic manipulation. Despite many efforts had been made, only nine mutants were successfully constructed. They were indeed resistant to streptomycin, but not to other antibiotics. This suggested that other mutations should be acquired, contributing to multiple antibiotics in the screened strains. The growth and enhanced green fluorescent protein (eGFP) expression of these nine mutants were then investigated. The results showed that they grew differently in CGXII minimal medium, but not in BHI medium. When cultured in the absence of streptomycin, the expression of eGFP was positively proportional to the growth, approximately, while in the presence of streptomycin, the expression of eGFP was proportional to the ability of streptomycin resistance.