Promoter tools for further development of Aspergillus oryzae as a platform for fungal secondary metabolite production

Edible mycelium bioengineered for enhanced nutritional value and sensory appeal using a modular synthetic biology toolkit

Filamentous fungi are critical in the transition to a more sustainable food system. While genetic modification of these organisms has promise for enhancing the nutritional value, sensory appeal, and scalability of fungal foods, genetic tools and demonstrated use cases for bioengineered food production by edible strains are lacking. Here, we develop a modular synthetic biology toolkit for Aspergillus oryzae, an edible fungus used in fermented foods, protein production, and meat alternatives. Our toolkit includes a CRISPR-Cas9 method for gene integration, neutral loci, and tunable promoters. We use these tools to elevate intracellular levels of the nutraceutical ergothioneine and the flavor-and color molecule heme in the edible biomass. The strain overproducing heme is red in color and is readily formulated into imitation meat patties with minimal processing. These findings highlight the promise of synthetic biology to enhance fungal foods and provide useful genetic tools for applications in food production and beyond.

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.

Functional role of a novel zinc finger protein, AoZFA, in growth and kojic acid synthesis in Aspergillus oryzae

Kojic acid (KA) is a valuable secondary metabolite that is regulated by zinc finger proteins in Aspergillus oryzae. However, only two such proteins have been characterized to function in kojic acid production of A. oryzae to date. In this study, we identified a novel zinc finger protein, AoZFA, required for kojic acid biosynthesis in A. oryzae. Our results showed that disruption of AozfA led to increased expression of kojA and kojR involved in kojic acid synthesis, resulting in enhanced kojic acid production, while overexpression of AozfA had the opposite effect. Furthermore, deletion of kojR in the AozfA disruption strain abolished kojic acid production, whereas overexpression of kojR enhanced it, indicating that AoZFA regulates kojic acid production by affecting kojR. Transcriptional activation assay revealed that AoZFA is a transcriptional activator. Interestingly, when kojR was overexpressed in the AozfA overexpression strain, the production of kojic acid failed to be rescued, suggesting that AozfA plays a distinct role from kojR in kojic acid biosynthesis. Moreover, we found that AozfA was highly induced by zinc during early growth stages, and its overexpression inhibited the growth promoted by zinc, whereas its deletion had no effect, suggesting that AoZFA is non-essential but has a role in the response of A. oryzae to zinc. Overall, these findings provide new insights into the roles of zinc finger proteins in the growth and kojic acid production of A. oryzae.IMPORTANCEKojic acid (KA) is an economically valuable secondary metabolite produced by Aspergillus oryzae due to its vast biological activities. Genetic modification of A. oryzae has emerged as an efficient strategy for enhancing kojic acid production, which is dependent on the mining of genes involved in kojic acid synthesis. In this study, we have characterized a novel zinc-finger protein, AoZFA, as a negative regulator of kojic acid production by affecting kojR. AozfA is an excellent target for improving kojic acid production without any effects on the growth of A. oryzae. Furthermore, the simultaneous modification of AozfA and kojR exerts a more significant promotional effect on kojic acid production than the modification of single genes. This study provides new insights for the regulatory mechanism of zinc finger proteins in the growth and kojic acid production of A. oryzae.

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

Fungal secondary metabolites (SMs) play a significant role in the diversity of ecological communities, niches, and lifestyles in the fungal kingdom. Many fungal SMs have medically and industrially important properties including antifungal, antibacterial, and antitumor activity, and a single metabolite can display multiple types of bioactivities. The genes necessary for fungal SM biosynthesis are typically found in a single genomic region forming 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 previously used machine learning models for predicting SM bioactivity from bacterial BGC data to fungal BGC data. We trained our models to predict antibacterial, antifungal, and cytotoxic/antitumor bioactivity on two datasets: 1) fungal BGCs (dataset comprised of 314 BGCs), and 2) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs); the second dataset was our control since a previous study using just the bacterial BGC data yielded prediction accuracies as high as 80%. We found that the models trained only on fungal BGCs had balanced accuracies between 51-68%, whereas training on bacterial and fungal BGCs yielded balanced accuracies between 61-74%. The lower accuracy of the predictions from fungal data likely stems from the small number of BGCs and SMs with known bioactivity; this lack of data currently limits the application of machine learning approaches in studying fungal secondary metabolism. However, our data also suggest that machine learning approaches trained on bacterial and fungal data can predict SM bioactivity with good accuracy. With more than 15,000 characterized fungal SMs, millions of putative BGCs present in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.

Kojic Acid Gene Clusters and the Transcriptional Activation Mechanism of Aspergillus flavus KojR on Expression of Clustered Genes

Kojic acid (KA) is a fungal metabolite and has a variety of applications in the cosmetics and food industries. Aspergillus oryzae is a well-known producer of KA, and its KA biosynthesis gene cluster has been identified. In this study, we showed that nearly all section Flavi aspergilli except for A. avenaceus had complete KA gene clusters, and only one Penicillium species, P. nordicum, contained a partial KA gene cluster. Phylogenetic inference based on KA gene cluster sequences consistently grouped section Flavi aspergilli into clades as prior studies. The Zn(II)₂Cys₆ zinc cluster regulator KojR transcriptionally activated clustered genes of kojA and kojT in Aspergillus flavus. This was evidenced by the time-course expression of both genes in kojR-overexpressing strains whose kojR expression was driven by a heterologous Aspergillus nidulans gpdA promoter or a homologous A. flavus gpiA promoter. Using sequences from the kojA and kojT promoter regions of section Flavi aspergilli for motif analyses, we identified a consensus KojR-binding motif to be an 11-bp palindromic sequence of 5'-CGRCTWAGYCG-3' (R = A/G, W = A/T, Y = C/T). A CRISPR/Cas9-mediated gene-targeting technique showed that the motif sequence, 5'-CGACTTTGCCG-3', in the kojA promoter was critical for KA biosynthesis in A. flavus. Our findings may facilitate strain improvement and benefit future kojic acid production.

Quantitative characterization of filamentous fungal promoters on a single-cell resolution to discover cryptic natural products

Characterization of filamentous fungal regulatory elements remains challenging because of time-consuming transformation technologies and limited quantitative methods. Here we established a method for quantitative assessment of filamentous fungal promoters based on flow cytometry detection of the superfolder green fluorescent protein at single-cell resolution. Using this quantitative method, we acquired a library of 93 native promoter elements from Aspergillus nidulans in a high-throughput format. The strengths of identified promoters covered a 37-fold range by flow cytometry. P_zipA and P_sltA were identified as the strongest promoters, which were 2.9- and 1.5-fold higher than that of the commonly used constitutive promoter P_gpdA. Thus, we applied P_zipA and P_sltA to activate the silent nonribosomal peptide synthetase gene Afpes1 from Aspergillus fumigatus in its native host and the heterologous host A. nidulans. The metabolic products of Afpes1 were identified as new cyclic tetrapeptide derivatives, namely, fumiganins A and B. Our method provides an innovative strategy for natural product discovery in fungi.

Microbial chassis engineering drives heterologous production of complex secondary metabolites

The cryptic secondary metabolite biosynthetic gene clusters (BGCs) far outnumber currently known secondary metabolites. Heterologous production of secondary metabolite BGCs in suitable chassis facilitates yield improvement and discovery of new-to-nature compounds. The two juxtaposed conventional model microorganisms, Escherichia coli, Saccharomyces cerevisiae, have been harnessed as microbial chassis to produce a bounty of secondary metabolites with the help of certain host engineering. In last decade, engineering non-model microbes to efficiently biosynthesize secondary metabolites has received increasing attention due to their peculiar advantages in metabolic networks and/or biosynthesis. The state-of-the-art synthetic biology tools lead the way in operating genetic manipulation in non-model microorganisms for phenotypic optimization or yields improvement of desired secondary metabolites. In this review, we firstly discuss the pros and cons of several model and non-model microbial chassis, as well as the importance of developing broader non-model microorganisms as alternative programmable heterologous hosts to satisfy the desperate needs of biosynthesis study and industrial production. Then we highlight the lately advances in the synthetic biology tools and engineering strategies for optimization of non-model microbial chassis, in particular, the successful applications for efficient heterologous production of multifarious complex secondary metabolites, e.g., polyketides, nonribosomal peptides, as well as ribosomally synthesized and post-translationally modified peptides. Lastly, emphasis is on the perspectives of chassis cells development to access the ideal cell factory in the artificial intelligence-driven genome era.

Increasing NADPH impairs fungal H2O2 resistance by perturbing transcriptional regulation of peroxiredoxin

NADPH provides the reducing power for decomposition of reactive oxygen species (ROS), making it an indispensable part during ROS defense. It remains uncertain, however, if living cells respond to the ROS challenge with an elevated intracellular NADPH level or a more complex NADPH-mediated manner. Herein, we employed a model fungus Aspergillus nidulans to probe this issue. A conditional expression of glucose-6-phosphate dehydrogenase (G6PD)-strain was constructed to manipulate intracellular NADPH levels. As expected, turning down the cellular NADPH concentration drastically lowered the ROS response of the strain; it was interesting to note that increasing NADPH levels also impaired fungal H2O2 resistance. Further analysis showed that excess NADPH promoted the assembly of the CCAAT-binding factor AnCF, which in turn suppressed NapA, a transcriptional activator of PrxA (the key NADPH-dependent ROS scavenger), leading to low antioxidant ability. In natural cell response to oxidative stress, we noticed that the intracellular NADPH level fluctuated "down then up" in the presence of H2O2. This might be the result of a co-action of the PrxA-dependent NADPH consumption and NADPH-dependent feedback of G6PD. The fluctuation of NADPH is well correlated to the formation of AnCF assembly and expression of NapA, thus modulating the ROS defense. Our research elucidated how A. nidulans precisely controls NADPH levels for ROS defense.

New Promoters for Metabolic Engineering of Ashbya gossypii

Ashbya gossypii is a filamentous fungus that is currently exploited for the industrial production of riboflavin. In addition, metabolically engineered strains of A. gossypii have also been described as valuable biocatalysts for the production of different metabolites such as folic acid, nucleosides, and biolipids. Hence, bioproduction in A. gossypii relies on the availability of well-performing gene expression systems both for endogenous and heterologous genes. In this regard, the identification of novel promoters, which are critical elements for gene expression, decisively helps to expand the A. gossypii molecular toolbox. In this work, we present an adaptation of the Dual Luciferase Reporter (DLR) Assay for promoter analysis in A. gossypii using integrative cassettes. We demonstrate the efficiency of the analysis through the identification of 10 new promoters with different features, including carbon source-regulatable abilities, that will highly improve the gene expression platforms used in A. gossypii. Three novel strong promoters (P_CCW12, P_SED1, and P_TSA1) and seven medium/weak promoters (P_HSP26, P_AGL366C, P_TMA10, P_CWP1, P_AFR038W, P_PFS1, and P_CDA2) are presented. The functionality of the promoters was further evaluated both for the overexpression and for the underexpression of the A. gossypii MSN2 gene, which induced significant changes in the sporulation ability of the mutant strains.

A Computational Framework for Identifying Promoter Sequences in Nonmodel Organisms Using RNA-seq Data Sets

Engineering microorganisms into biological factories that convert renewable feedstocks into valuable materials is a major goal of synthetic biology; however, for many nonmodel organisms, we do not yet have the genetic tools, such as suites of strong promoters, necessary to effectively engineer them. In this work, we developed a computational framework that can leverage standard RNA-seq data sets to identify sets of constitutive, strongly expressed genes and predict strong promoter signals within their upstream regions. The framework was applied to a diverse collection of RNA-seq data measured for the methanotroph Methylotuvimicrobium buryatense 5GB1 and identified 25 genes that were constitutively, strongly expressed across 12 experimental conditions. For each gene, the framework predicted short (27-30 nucleotide) sequences as candidate promoters and derived -35 and -10 consensus promoter motifs (TTGACA and TATAAT, respectively) for strong expression in M. buryatense. This consensus closely matches the canonical E. coli sigma-70 motif and was found to be enriched in promoter regions of the genome. A subset of promoter predictions was experimentally validated in a XylE reporter assay, including the consensus promoter, which showed high expression. The pmoC, pqqA, and ssrA promoter predictions were additionally screened in an experiment that scrambled the -35 and -10 signal sequences, confirming that transcription initiation was disrupted when these specific regions of the predicted sequence were altered. These results indicate that the computational framework can make biologically meaningful promoter predictions and identify key pieces of regulatory systems that can serve as foundational tools for engineering diverse microorganisms for biomolecule production.

Aspergillus oryzae Biosynthetic Platform for de Novo Iridoid Production

The iridoids and their derivatives monoterpene indole alkaloids (MIAs) are two broad classes of plant-derived natural products with valuable pharmaceutical properties. However, the poor source limited their application. Nepetalactol, a common iridoid scaffold of MIAs, was heterologously produced in Saccharomyces cerevisiae. Although the optimization of nepetalactol production in S. cerevisiae was achieved by metabolic engineering, the inherent metabolic constraints impose a restriction on the production. Herein, we developed a high nepetalactol-producing Aspergillus oryzae platform strain. First, the co-expression of 5 nepetalactol biosynthetic genes, in a high isopentenyl pyrophosphate (IPP)-producing strain A. oryzae AK2, succeeded in the biosynthesis of nepetalactol. Second, the improvement of the IPP supply and the suppression of the byproduct citronellol formation were simultaneously achieved. Finally, the highest titer of nepetalactol of 7.2 mg/L was obtained with the engineered strain, after the optimization of the carbon source. To the best of our knowledge, this is the highest reported titer of nepetalactol in microbial cells. The developed A. oryzae strain represents an attractive biosynthetic platform host for the de novo production of iridoids and MIAs.