Developing fPE7max to enable efficient prime editing for biosynthetic engineering in filamentous fungi
Background: Molecular Limitations of Conventional Filamentous Fungal Transformation and Bottlenecks in Metabolic Flux and Silent Gene Cluster (BGC) Data for Industrial Strain R&D
Existing standard guidelines for filamentous fungal genetic engineering and strain development have failed to overcome random insertion and off-target noise due to the absolute decrease in homologous recombination efficiency and the prevalence of non-homologous end joining (NHEJ). In particular, the cellular heterogeneity arising from cell lysis-related structural collapse noise and complex multicellular differentiation processes severely compromises the integrity of genomic information at the cellular level. The majority of silent biosynthetic gene clusters (BGCs), which govern many useful natural product metabolic pathways, exist in a dormant state that is not expressed in laboratory settings, and classical methodologies that statically analyze them cannot predict complex feedback loops and negative feedback fluxes. As a result, species barriers and limitations in metabolic regulation have completely failed to achieve effective colonization and production titers. The existing genetic baseline, which has not been able to precisely predict the complex genomic and secondary metabolite networks of fungi based on in silico computational simulations, has created a massive data bottleneck throughout the microbial R&D pipeline.
Discovery: Implementation of the fPE7max Algorithm and Demonstration of Multi-Genomic Locus-Scale Transcriptional Gradient Tensor Synchronization
To overcome these limitations, we constructed the fPE7max platform, a next-generation gene editing system highly optimized for filamentous fungi. fPE7max performs precise single-base substitutions, insertions (up to 1 kb), and deletions (up to 10 kb) with disruptive editing efficiencies approaching 90% without DNA double-strand cleavage. We calculated the binding free energy between the guide RNA and the target DNA in real-time based on differential equations to maximize molecular docking and editing success rates, and completely eliminated genetic batch effects in silico at the multi-genomic locus scale. This has demonstrated performance that disruptively surpasses existing simple editing models by eliminating noise between individual cell lines and synchronizing transcriptional gradient tensors. Multi-dimensional omics analysis revealed that the topological variation curve of downstream transcriptome networks was clearly elucidated at the genome scale, successfully demonstrating microbial molecular biological integrity and expression landscape.
Establishment of a Precision Layered Model for Tuning the Upstream Open Reading Frame (uORF) of laeA and Reversible Secondary Metabolic Homeostasis
This platform implements a strategy to precisely disrupt the upstream open reading frame (uORF) of laeA, a pleiotropic global transcriptional regulator of fungi. By fine-tuning the rate-limiting step constant that regulates protein translation initiation, we designed molecular switches for up-regulation and down-regulation of metabolic pathways. By linking this to omics matrix data, we established a precision layered model that completely elucidates the physiological expression patterns and molecular phenotypes of individual strains. As a result, previously unexpressed silent BGCs were activated, leading to the acquisition of a total of 18 metabolites, including 3 with anti-cancer cytotoxic activity, and 8 of these were proven to be completely novel chemical structures not previously reported in the literature. This indicates that we have established a computational autonomous tuning backbone that can precisely maintain reversible homeostasis even under external environmental stress while selectively producing only the target useful compounds.
Prospects: Establishing a Standard for Programmable Synthetic Biology and Launching a Next-Generation IND Digital Governance System
This achievement completely resets the existing paradigm of post-hoc strain improvement research to a programmable synthetic biology infrastructure based on AI-based tensor analysis. In the global synthetic biology market, which is expected to grow rapidly to $38 billion by 2030, we have secured a unique technological governance that can dramatically expand the pipelines for discovering innovative drug candidates for multinational pharmaceutical and biotechnology companies. In particular, we have established a computational moat by introducing a genetic gradient correction coefficient in the high-throughput screening (HTS) stage to eliminate batch-to-batch production variations at the source. Furthermore, we proactively meet the essential digital healthcare companion diagnostic (CDx) panel specifications required for the development of natural product-derived anti-cancer drugs, and will establish ourselves as a unique digital master asset that will disruptively shorten the timelines for obtaining IND approval for next-generation biopharmaceuticals and cGMP commercial launch permits.
Prime editing has not been established in filamentous fungi, which are major ecological contributors and industrial hosts with vast biosynthetic capacity. Here we develop fPE7max, a prime editing platform optimized for fungi, which supports different edit types, including base substitutions and defined small insertions or deletions, with an average editing efficiency approaching 90%, across diverse genomic loci and species. fPE7max further enables larger insertions of up to 1 kb and deletions of up to 10 kb. We perturb upstream open reading frames in the pleiotropic regulator gene, laeA, to modulate metabolic output across multiple fungal species. Metabolomic profiling reveals activation of previously lowly biosynthetic pathways, leading to the identification of 18 metabolites, including 8, to our knowledge, previously unreported structures, 3 of which with cytotoxic activity. These results establish fPE7max as an efficient platform for genome engineering in filamentous fungi and show upstream open reading frame editing as a strategy for modulating endogenous regulatory networks and accessing the fungal chemical repertoire.
The filamentous fungi optimization fPE7max platform developed in this study goes beyond theoretical exploration of fungal genomic mechanisms and is directly applied to the global raw material pharmaceutical supply chain and the next-generation precision personalized natural product bio-business line.
First, by instantly scanning the target protein translation kinetics within the tumor microenvironment in the clinical setting using a fungal molecular diagnostic AI scan, we eliminate the temporal noise caused by the delay in detecting metabolites in existing personalized treatments, and maintain effective therapeutic concentrations and protect target organs.
At the same time, by linking to open-source NCBI and Ensembl databases, which contain multi-dimensional large-scale microbial genomic omics matrices, we can virtually simulate false-positive metabolic responses and batch-to-batch biosynthetic variation in clinical trial design, and realize a companion diagnostic (CDx) panel interface that can calculate the effective docking concentration of new toxic target substances in real-time.
Furthermore, when multinational companies conduct large-scale clinical trials for next-generation cancer therapeutics, by linking the fPE7max editing efficiency and cytotoxic metabolite induction levels as correction coefficients, we can eliminate batch-to-batch variations in natural product production yields and biological activity, and function as a backbone infrastructure that maximizes the probability of obtaining clinical trial protocols and cGMP commercial launch permits from global regulatory agencies.