Discovery, characterization, and engineering of an advantageous Streptomyces host for heterologous expression of natural product biosynthetic gene clusters.

BACKGROUND: Streptomyces is renowned for its robust biosynthetic capacity in producing medically relevant natural products. However, the majority of natural products biosynthetic gene clusters (BGCs) either yield low amounts of natural products or remain cryptic under standard laboratory conditions. Various heterologous production hosts have been engineered to address these challenges, and yet the successful activation of BGCs has still been limited. In our search for a valuable addition to the heterologous host panel, we identified the strain Streptomyces sp. A4420, which exhibited rapid initial growth and a high metabolic capacity, prompting further exploration of its potential. RESULTS: We engineered a polyketide-focused chassis strain based on Streptomyces sp. A4420 (CH strain) by deleting 9 native polyketide BGCs. The resulting metabolically simplified organism exhibited consistent sporulation and growth, surpassing the performance of most existing Streptomyces based chassis strains in standard liquid growth media. Four distinct polyketide BGCs were chosen and expressed in various heterologous hosts, including the Streptomyces sp. A4420 wild-type and CH strains, alongside Streptomyces coelicolor M1152, Streptomyces lividans TK24, Streptomyces albus J1074, and Streptomyces venezuelae NRRL B-65442. Remarkably, only the Streptomyces sp. A4420 CH strain demonstrated the capability to produce all metabolites under every condition outperforming its parental strain and other tested organisms. To enhance visualization and comparison of the tested strains, we developed a matrix-like analysis involving 15 parameters. This comprehensive analysis unequivocally illustrated the significant potential of the new strain to become a popular heterologous host. CONCLUSION: Our engineered Streptomyces sp. A4420 CH strain exhibits promising attributes for the heterologous expression of natural products with a focus on polyketides, offering an alternative choice in the arsenal of heterologous production strains. As genomics and cloning strategies progress, establishment of a diverse panel of heterologous production hosts will be crucial for expediting the discovery and production of medically relevant natural products derived from Streptomyces.

Reversing the directionality of reactions between non-oxidative pentose phosphate pathway and glycolytic pathway boosts mycosporine-like amino acid production in Saccharomyces cerevisiae.

BACKGROUND: Mycosporine-like amino acids (MAAs) are a class of strongly UV-absorbing compounds produced by cyanobacteria, algae and corals and are promising candidates for natural sunscreen components. Low MAA yields from natural sources, coupled with difficulties in culturing its native producers, have catalyzed synthetic biology-guided approaches to produce MAAs in tractable microbial hosts like Escherichia coli, Saccharomyces cerevisiae and Corynebacterium glutamicum. However, the MAA titres obtained in these hosts are still low, necessitating a thorough understanding of cellular factors regulating MAA production. RESULTS: To delineate factors that regulate MAA production, we constructed a shinorine (mycosporine-glycine-serine) producing yeast strain by expressing the four MAA biosynthetic enzymes from Nostoc punctiforme in Saccharomyces cerevisiae. We show that shinorine is produced from the pentose phosphate pathway intermediate sedoheptulose 7-phosphate (S7P), and not from the shikimate pathway intermediate 3-dehydroquinate (3DHQ) as previously suggested. Deletions of transaldolase (TAL1) and phosphofructokinase (PFK1/PFK2) genes boosted S7P/shinorine production via independent mechanisms. Unexpectedly, the enhanced S7P/shinorine production in the PFK mutants was not entirely due to increased flux towards the pentose phosphate pathway. We provide multiple lines of evidence in support of a reversed pathway between glycolysis and the non-oxidative pentose phosphate pathway (NOPPP) that boosts S7P/shinorine production in the phosphofructokinase mutant cells. CONCLUSION: Reversing the direction of flux between glycolysis and the NOPPP offers a novel metabolic engineering strategy in Saccharomyces cerevisiae.

Application of Cas12j for Streptomyces Editing.

In recent years, CRISPR-Cas toolboxes for Streptomyces editing have rapidly accelerated natural product discovery and engineering. However, Cas efficiencies are oftentimes strain-dependent, and the commonly used Streptococcus pyogenes Cas9 (SpCas9) is notorious for having high levels of off-target toxicity effects. Thus, a variety of Cas proteins is required for greater flexibility of genetic manipulation within a wider range of Streptomyces strains. This study explored the first use of Acidaminococcus sp. Cas12j, a hypercompact Cas12 subfamily, for genome editing in Streptomyces and its potential in activating silent biosynthetic gene clusters (BGCs) to enhance natural product synthesis. While the editing efficiencies of Cas12j were not as high as previously reported efficiencies of Cas12a and Cas9, Cas12j exhibited higher transformation efficiencies compared to SpCas9. Furthermore, Cas12j demonstrated significantly improved editing efficiencies compared to Cas12a in activating BGCs in Streptomyces sp. A34053, a strain wherein both SpCas9 and Cas12a faced limitations in accessing the genome. Overall, this study expanded the repertoire of Cas proteins for genome editing in actinomycetes and highlighted not only the potential of recently characterized Cas12j in Streptomyces but also the importance of having an extensive genetic toolbox for improving the editing success of these beneficial microbes.

Exploring a general multi-pronged activation strategy for natural product discovery in Actinomycetes.

Natural products possess significant therapeutic potential but remain underutilized despite advances in genomics and bioinformatics. While there are approaches to activate and upregulate natural product biosynthesis in both native and heterologous microbial strains, a comprehensive strategy to elicit production of natural products as well as a generalizable and efficient method to interrogate diverse native strains collection, remains lacking. Here, we explore a flexible and robust integrase-mediated multi-pronged activation approach to reliably perturb and globally trigger antibiotics production in actinobacteria. Across 54 actinobacterial strains, our approach yielded 124 distinct activator-strain combinations which consistently outperform wild type. Our approach expands accessible metabolite space by nearly two-fold and increases selected metabolite yields by up to >200-fold, enabling discovery of Gram-negative bioactivity in tetramic acid analogs. We envision these findings as a gateway towards a more streamlined, accelerated, and scalable strategy to unlock the full potential of Nature's chemical repertoire.

Expression and engineering of PET-degrading enzymes from Microbispora, Nonomuraea, and Micromonospora.

IMPORTANCE: Mismanagement of PET plastic waste significantly threatens human and environmental health. Together with the relentless increase in plastic production, plastic pollution is an issue of rising concern. In response to this challenge, scientists are investigating eco-friendly approaches, such as bioprocessing and microbial factories, to sustainably manage the growing quantity of plastic waste in our ecosystem. Industrial applicability of enzymes capable of degrading PET is limited by numerous factors, including their scarcity in nature. The objective of this study is to enhance our understanding of this group of enzymes by identifying and characterizing novel enzymes that can facilitate the breakdown of PET waste. This data will expand the enzymatic repertoire and provide valuable insights into the prerequisites for successful PET degradation.

Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations.

The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations.

Cost-effective hybrid long-short read assembly delineates alternative GC-rich Streptomyces hosts for natural product discovery.

With the advent of rapid automated in silico identification of biosynthetic gene clusters (BGCs), genomics presents vast opportunities to accelerate natural product (NP) discovery. However, prolific NP producers, Streptomyces, are exceptionally GC-rich (>80%) and highly repetitive within BGCs. These pose challenges in sequencing and high-quality genome assembly which are currently circumvented via intensive sequencing. Here, we outline a more cost-effective workflow using multiplex Illumina and Oxford Nanopore sequencing with hybrid long-short read assembly algorithms to generate high quality genomes. Our protocol involves subjecting long read-derived assemblies to up to 4 rounds of polishing with short reads to yield accurate BGC predictions. We successfully sequenced and assembled 8 GC-rich Streptomyces genomes whose lengths range from 7.1 to 12.1 Mb with a median N50 of 8.2 Mb. Taxonomic analysis revealed previous misrepresentation among these strains and allowed us to propose a potentially new species, Streptomyces sydneybrenneri. Further comprehensive characterization of their biosynthetic, pan-genomic and antibiotic resistance features especially for molecules derived from type I polyketide synthase (PKS) BGCs reflected their potential as alternative NP hosts. Thus, the genome assemblies and insights presented here are envisioned to serve as gateway for the scientific community to expand their avenues in NP discovery.

Systematic evaluation of genome-wide metabolic landscapes in lactic acid bacteria reveals diet- and strain-specific probiotic idiosyncrasies.

Lactic acid bacteria (LAB) are well known to elicit health benefits in humans, but their functional metabolic landscapes remain unexplored. Here, we analyze differences in growth, intestinal persistence, and postbiotic biosynthesis of six representative LAB and their interactions with 15 gut bacteria under 11 dietary regimes by combining multi-omics and in silico modeling. We confirmed predictions on short-term persistence of LAB and their interactions with commensals using cecal microbiome abundance and spent-medium experiments. Our analyses indicate that probiotic attributes are both diet and species specific and cannot be solely explained using genomics. For example, although both Lacticaseibacillus casei and Lactiplantibacillus plantarum encode similarly sized genomes with diverse capabilities, L. casei exhibits a more desirable phenotype. In addition, "high-fat/low-carb" diets more likely lead to detrimental outcomes for most LAB. Collectively, our results highlight that probiotics are not "one size fits all" health supplements and lay the foundation for personalized probiotic design.

Systems Biology of Gut Microbiota-Human Receptor Interactions: Toward Anti-inflammatory Probiotics.

The incidence and prevalence of inflammatory disorders have increased globally, and is projected to double in the next decade. Gut microbiome-based therapeutics have shown promise in ameliorating chronic inflammation. However, they are largely experimental, context- or strain-dependent and lack a clear mechanistic basis. This hinders precision probiotics and poses significant risk, especially to individuals with pre-existing conditions. Molecules secreted by gut microbiota act as ligands to several health-relevant receptors expressed in human gut, such as the G-protein coupled receptors (GPCRs), Toll-like receptor 4 (TLR4), pregnane X receptor (PXR), and aryl hydrocarbon receptor (AhR). Among these, the human AhR expressed in different tissues exhibits anti-inflammatory effects and shows activity against a wide range of ligands produced by gut bacteria. However, different AhR ligands induce varying host responses and signaling in a tissue/organ-specific manner, which remain mostly unknown. The emerging systems biology paradigm, with its powerful in silico tool repertoire, provides opportunities for comprehensive and high-throughput strain characterization. In particular, combining metabolic models with machine learning tools can be useful to delineate tissue and ligand-specific signaling and thus their causal mechanisms in disease and health. The knowledge of such a mechanistic basis is indispensable to account for strain heterogeneity and actualize precision probiotics.

Genome-scale metabolic reconstruction and in silico analysis of the rice leaf blight pathogen, Xanthomonas oryzae.

Xanthomonas oryzae pv. oryzae (Xoo) is a vascular pathogen that causes leaf blight in rice, leading to severe yield losses. Since the usage of chemical control methods has not been very promising for the future disease management, it is of high importance to systematically gain new insights about Xoo virulence and pathogenesis, and devise effective strategies to combat the rice disease. To do this, we reconstructed a genome-scale metabolic model of Xoo (iXOO673) and validated the model predictions using culture experiments. Comparison of the metabolic architecture of Xoo and other plant pathogens indicated that the Entner-Doudoroff pathway is a more common feature in these bacteria than previously thought, while suggesting some of the unique virulence mechanisms related to Xoo metabolism. Subsequent constraint-based flux analysis allowed us to show that Xoo modulates fluxes through gluconeogenesis, glycogen biosynthesis, and degradation pathways, thereby exacerbating the leaf blight in rice exposed to nitrogenous fertilizers, which is remarkably consistent with published experimental literature. Moreover, model-based interrogation of transcriptomic data revealed the metabolic components under the diffusible signal factor regulon that are crucial for virulence and survival in Xoo. Finally, we identified promising antibacterial targets for the control of leaf blight in rice by using gene essentiality analysis.

In silico model-guided identification of transcriptional regulator targets for efficient strain design.

BACKGROUND: Cellular metabolism is tightly regulated by hard-wired multiple layers of biological processes to achieve robust and homeostatic states given the limited resources. As a result, even the most intuitive enzyme-centric metabolic engineering endeavours through the up-/down-regulation of multiple genes in biochemical pathways often deliver insignificant improvements in the product yield. In this regard, targeted engineering of transcriptional regulators (TRs) that control several metabolic functions in modular patterns is an interesting strategy. However, only a handful of in silico model-added techniques are available for identifying the TR manipulation candidates, thus limiting its strain design application. RESULTS: We developed hierarchical-Beneficial Regulatory Targeting (h-BeReTa) which employs a genome-scale metabolic model and transcriptional regulatory network (TRN) to identify the relevant TR targets suitable for strain improvement. We then applied this method to industrially relevant metabolites and cell factory hosts, Escherichia coli and Corynebacterium glutamicum. h-BeReTa suggested several promising TR targets, many of which have been validated through literature evidences. h-BeReTa considers the hierarchy of TRs in the TRN and also accounts for alternative metabolic pathways which may divert flux away from the product while identifying suitable metabolic fluxes, thereby performing superior in terms of global TR target identification. CONCLUSIONS: In silico model-guided strain design framework, h-BeReTa, was presented for identifying transcriptional regulator targets. Its efficacy and applicability to microbial cell factories were successfully demonstrated via case studies involving two cell factory hosts, as such suggesting several intuitive targets for overproducing various value-added compounds.

Genome and evolution of the shade-requiring medicinal herb Panax ginseng.

Panax ginseng C. A. Meyer, reputed as the king of medicinal herbs, has slow growth, long generation time, low seed production and complicated genome structure that hamper its study. Here, we unveil the genomic architecture of tetraploid P. ginseng by de novo genome assembly, representing 2.98 Gbp with 59 352 annotated genes. Resequencing data indicated that diploid Panax species diverged in association with global warming in Southern Asia, and two North American species evolved via two intercontinental migrations. Two whole genome duplications (WGD) occurred in the family Araliaceae (including Panax) after divergence with the Apiaceae, the more recent one contributing to the ability of P. ginseng to overwinter, enabling it to spread broadly through the Northern Hemisphere. Functional and evolutionary analyses suggest that production of pharmacologically important dammarane-type ginsenosides originated in Panax and are produced largely in shoot tissues and transported to roots; that newly evolved P. ginseng fatty acid desaturases increase freezing tolerance; and that unprecedented retention of chlorophyll a/b binding protein genes enables efficient photosynthesis under low light. A genome-scale metabolic network provides a holistic view of Panax ginsenoside biosynthesis. This study provides valuable resources for improving medicinal values of ginseng either through genomics-assisted breeding or metabolic engineering.

Genome-scale modeling and transcriptome analysis of Leuconostoc mesenteroides unravel the redox governed metabolic states in obligate heterofermentative lactic acid bacteria.

Obligate heterofermentative lactic acid bacteria (LAB) are well-known for their beneficial health effects in humans. To delineate the incompletely characterized metabolism that currently limits their exploitation, at systems-level, we developed a genome-scale metabolic model of the representative obligate heterofermenting LAB, Leuconostoc mesenteroides (iLME620). Constraint-based flux analysis was then used to simulate several qualitative and quantitative phenotypes of L. mesenteroides, thereby evaluating the model validity. With established predictive capabilities, we subsequently employed iLME620 to elucidate unique metabolic characteristics of L. mesenteroides, such as the limited ability to utilize amino acids as energy source, and to substantiate the role of malolactic fermentation (MLF) in the reduction of pH-homeostatic burden on F0F1-ATPase. We also reported new hypothesis on the MLF mechanism that could be explained via a substrate channelling-like phenomenon mainly influenced by intracellular redox state rather than the intermediary reactions. Model simulations further revealed possible proton-symporter dependent activity of the energy efficient glucose-phosphotransferase system in obligate heterofermentative LAB. Moreover, integrated transcriptomic analysis allowed us to hypothesize transcriptional regulatory bias affecting the intracellular redox state. The insights gained here about the low ATP-yielding metabolism of L. mesenteroides, dominantly controlled by the cellular redox state, could potentially aid strain design for probiotic and cell factory applications.

In silico model-driven cofactor engineering strategies for improving the overall NADP(H) turnover in microbial cell factories.

Optimizing the overall NADPH turnover is one of the key challenges in various value-added biochemical syntheses. In this work, we first analyzed the NADPH regeneration potentials of common cell factories, including Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis, and Pichia pastoris across multiple environmental conditions and determined E. coli and glycerol as the best microbial chassis and most suitable carbon source, respectively. In addition, we identified optimal cofactor specificity engineering (CSE) enzyme targets, whose cofactors when switched from NAD(H) to NADP(H) improve the overall NADP(H) turnover. Among several enzyme targets, glyceraldehyde-3-phosphate dehydrogenase was recognized as a global candidate since its CSE improved the NADP(H) regeneration under most of the conditions examined. Finally, by analyzing the protein structures of all CSE enzyme targets via homology modeling, we established that the replacement of conserved glutamate or aspartate with serine in the loop region could change the cofactor dependence from NAD(H) to NADP(H).