Design of the biosensor-dependent coupling system stabilizes the high-synthesis phenotype of cell factory

Domain: chemistry
Published: 2026-04-13
Editorial outcome: major_revision

Abstract
Microbial cell factories offer a sustainable route to plant-derived natural products, but yield drift and strain degeneration persist. Growth-coupled biosynthesis can continuously enrich high producers, yet specific product-responsive biosensors remain scarce and their population-level effects are unclear. Here, we describe a rapid transcriptome-mining workflow that, as proof-of-concept, delivers yeast biosensors for glycyrrhetinic acid and medicarpin. By fine-tuning PDR5 promoter, we expand the dynamic range of the glycyrrhetinic acid sensor and wire it to an essential gene, establishing a growth-addiction circuit that increases titer by 46.8 % after subculture. Single-cell transcriptome reveals that the evolved strain population exhibits a completely different division of labor compared to the initial strain. Coupling does not eliminate phenotypic heterogeneity; instead, it amplifies a dedicated sub-population marked by discrete transcriptional signatures. Deletion of genes highly expressed in non-producing cells or enrichment of high-producing cell clusters can further boost population-level production. This study provides both a generalizable biosensor-discovery platform and single-cell-guided strategies for stabilizing and optimizing natural-product cell factories.

Introduction
The utilization of microbial cell factories for the synthesis of heterologous natural products confers numerous advantages in terms of sustainability, cost-effectiveness, and controllability1,2. Despite the substantial enhancement of synthetic capacity that can be achieved through classical metabolic engineering strategies, the incomplete understanding of complex intracellular networks often results in interference from unknown factors within the system, thereby hindering further breakthroughs in product yield and even leading to strain degeneration. Coupling product synthesis with microbial cell growth, termed the "metabolic addiction" strategy, has emerged as a pivotal pathway for achieving stable, efficient biosynthesis and driving synthesis-directed evolution3-10. Among these approaches, strategies leveraging biosensors that specifically respond to target products and drive the expression of essential genes for cell growth (such as antibiotic genes or nutrient-deficiency genes) represent a primary means of implementing such coupled systems9-11. Consequently, the development of efficient and specific biosensors is fundamental to the construction of growth-synthesis coupling systems. Furthermore, biosensors demonstrate broad application value in high-throughput screening, dynamic pathway regulation, and metabolic flux monitoring12,13.

Nevertheless, the identification or rational design of biosensors continues to confront substantial challenges. Biosensors with gene regulatory functions are obtained by screening naturally occurring regulatory systems, such as elements that rely on the recognition of target molecules, including transcriptional activators, repressors, or riboswitches14. Hanko et al. developed a genome-wide approach to identifying transcription factor-based biosensors, inferring transcription regulators by identifying substrate-enzyme-gene operon systems in prokaryotes15. However, such methods are constrained by intracellular endogenous metabolite-related sensing systems, whose functionality is often limited by the genetic background and regulatory environment of the host species, presenting significant species compatibility issues. To date, only a small number of biosensors targeting specific compounds have been successfully developed and applied in synthetic biology practices16-18. Consequently, there remains a critical shortage of biosensors capable of efficiently and specifically responding to target substances, necessitating the urgent establishment of a high-throughput, versatile biosensor development platform.

Furthermore, in elucidating the mechanism behind enhanced synthetic capacity in growth-synthesis coupled strains, conventional wisdom posits that this strategy confers a growth advantage to high-yielding cells by suppressing the proliferation of deceptive cells, thereby reducing metabolic phenotype heterogeneity within the population and enabling autonomous enrichment of high-yielding strains3. However, this theory remains lacking in robust evidence at the microscopic level. In recent years, single-cell transcriptomics sequencing has emerged as an effective tool for resolving gene expression heterogeneity among individual microbial cells and dynamically revealing cellular functional differentiation19-23. Analyzing expression differences in metabolic pathways and regulatory networks at single-cell resolution not only helps elucidate the intrinsic mechanisms stabilizing high-yield phenotypes within strain growth-synthesis coupling systems but also reveals finely tuned regulatory information obscured at the population level, providing a basis for discovering novel metabolic regulatory targets. Previous studies indicate that when facing environmental stress or resource limitations, microbial populations tend to differentiate into functionally specialized subpopulations: one accelerates division and proliferation to maintain population size, while the other reduces division and extends lifespan to produce specific bioactive molecules22. However, no studies have yet demonstrated whether similar division-of-labor patterns exist during microbial heterogenous product synthesis, nor how such functions are regulated and optimized.

In this study, we develop a universal workflow for the in-situ discovery of target-responsive biosensors in chassis cells. Then, we integrate this workflow into strategies for stabilizing and directing the evolution of cell factory phenotypes. To validate this strategy, we successfully identify novel biosensor elements as being capable of specifically responding to glycyrrhetinic acid (GA) and medicarpin based on the yeast transcriptome. The construction of a growth-synthesis coupling system is facilitated by the utilization of these biosensors. We demonstrate the evolution dependent on biosensors is an effective means of suppressing the instability of high-yield traits during passage, thereby yielding engineered strains with significantly enhanced GA production. Single-cell transcriptomic sequencing reveals that high-yielding yeast populations still exhibited distinct labor division and that growth coupling only drove high yields in a subset of cells. Simultaneously, single-cell transcriptomics unveils a series of pivotal differentially expressed marker genes that had been obscured by averaged signals in bulk transcriptomics. The elimination of highly expressed marker genes in non-synthesizing populations results in a substantial enhancement of the overall GA production capacity of the microbial community. These findings provide insights and inspiration for strain stabilization, evolutionary strategy design and population structure optimization in microbial cell factories synthesizing natural products.

Results
Mining and characterization of product responsive elements

To develop biosensors for the in-situ detection of products within yeast cell factories, a rapid screening platform was established (Fig. 1a). The synthesis of glycyrrhetinic acid (GA) by Saccharomyces cerevisiae was used as an exemplary case. The presence of GA, a heterologous product in yeast (Fig. 1b), can potentially trigger a stress response within the cell, and result in the induction of certain gene expressions. To identify genes that respond to GA and enable the development of a GA biosensor, we compared the transcriptomes of two yeast strains: GA180, which can synthesize GA, and SynV, the parent strain that lacks the ability to produce GA (Fig. 1c). There were 215 genes with significantly upregulated expression and 936 genes with significantly downregulated expression. Because GA is a lipophilic exogenous component, it may cause the expression of stress-related yeast drug transporters and other similar proteins. Therefore, the genes encoding transporters or hypothetical proteins with upregulated expression were selected to identify genes that respond to GA (Supplementary Table 1). The inducibility of these gene promoters is characterized using red fluorescent protein mcherry by constructing expression cassettes into GA180 and SynV strains, respectively. As a result, the PDR5 promoter (P_PDR5) displays the most significant response enhancement to GA (Fig. 1d). There is a strong positive correlation between the fluorescence response of P_PDR5 and the production of intracellular GA (Fig. 1e and f, Supplementary Fig. 1a). To validate the capability and dynamic range of P_PDR5 in response to GA, GA was added to control strain SynV with varying concentrations (Fig. 1g), and almost GA was detected within the cell (Supplementary Fig. 1b). When GA concentration ranges between 0-50 mg/L, the fluorescence intensity shows a positive correlation with the amount of added GA (Fig. 1h). When the concentration of GA exceeds 50 mg/L, the response intensity of P_PDR5 becomes saturated.

Pdr5 is a member of ATP-binding cassette (ABC) superfamily of transporter24. Pdr5 can transport diverse substances such as ion carriers, azoles, antibiotics, and xenobiotics25. Furthermore, P_PDR5 responds to some alkaloids and flavonoids, etc9. Here we investigated if certain metabolites and environmental variables can disrupt the response of P_PDR5. Some factors can increase the response of P_PDR5 (e.g. addition of urea), while some stress environments can inhibit the response (e.g. high concentrations of ethanol, high temperature, and hydrogen peroxide) (Supplementary Fig. 2a). To avoid the influence of GA precursors on the response of P_PDR5, the strains that produce GA precursors (compounds 2, 3, and 4) or their analogues (compounds 1 and 6) were used. The result showed that the P_PDR5 biosensor does not respond to the analogues and is specific to GA in the GA biosynthesis system (Supplementary Fig. 2b).

In addition, the universality of the transcriptome-based method for rapidly identifying target product biosensors was demonstrated using the typical flavonoid medicarpin26 as another example to identify the promoters of yeast response using the same method (Fig. 1i). In the case of medicarpin, it was determined that the promoters of YOR1 and PDR16 are responsive to medicarpin (Fig. 1j). The correlation between the response signals and concentrations indicates the credibility of their responses (Fig. 1k).

Design of the GA biosensor to modify the dynamic ranges

To enhance the applicability of GA biosensor for strain screening and evolution, it is imperative to be able to adjust its dynamic range, i.e., the ratio of maximum response signal to basic signal. The rational modification of promoters presents an effective approach for designing transcription factor-based biosensors. Increasing the type and quantity of DNA binding motifs for transcription activation in the promoter can recruit more transcription-activating factors and improve promoter response intensity27-29. The regulation of the PDR5 gene is controlled by transcription activating factors Pdr1, Pdr3, Yap1, and Stb5, alongside the transcription inhibiting factor Rdr1 (Fig. 2a)30-34. Pdr1 and Pdr3 can form both homo- and hetero-dimers, and degenerately occupy the DNA motif called pleiotropic drug resistance elements (PDREs)30,31. Within the promoter sequence P_PDR5, these motifs are concentrated in the region between -200 and -600 bp (Fig. 2b). To determine which specific region within the promoter affects its response, promoter truncation experiments were performed. It was found that truncating the promoter from 600 to 400 bp resulted in a significant decrease of its response (Fig. 2c), suggesting that this region contains more critical motifs. The knockout of these transcription factors also confirmed that they are essential components for P_PDR5 to respond to GA (Fig. 2d). Pdr1 was reported as a nuclear receptor that directly interacts with several drugs to stimulate expression of PDR534. The simulation of the complex demonstrates that there is a possibility of Pdr1 binding to GA (Supplementary Fig. 3a). This finding provides an explanation for the mechanism by which PDR5 can respond to GA.

To enhance the response of the promoter, various motifs (Pdr1/3: TCCGCGGA, Yap1: TTACTAA, Stb5: CGGAACGA) were incorporated into the interval spacer regions (-220, -407, -513 bp) to assess the response intensity and dynamic range (Supplementary Fig. 4). The type, quantity, and position of inserted motifs all have impacts on the dynamic range. On this basis, multiple combination motifs were designed, resulting in the dynamic range of most promoters being higher than that of wild-type promoters. Of all the promoters tested, P_PDR5-PE23 (containing two Stb5 motifs and one Pdr1/3 motif) showed the highest dynamic range, reaching 5.16, and was deemed suitable for subsequent applications (Fig. 2e).

To further expand the dynamic range of GA biosensor, the effects of other regulatory factors were investigated. Ssz1 (also known as Pdr13) is reported to act as a positive regulator of Pdr132,33. Yeast two-hybrid validated that Ssz1 only interacts with transcription factor Pdr1 (Supplementary Fig. 3b). Upon knocking out Ssz1 and Rdr1, it was observed that deletion of Ssz1 had a noteworthy effect on the dynamic range, leading to an increase to 32.2 (Fig. 2f). Therefore, we can fine-tune and coarse-tune the GA biosensor by modifying the promoter sequence and knocking down SSZ1 gene, respectively.

GA biosensor as a tool for stabilizing and evolving strain phenotype

A significant application of biosensors is in the construction of growth-synthesis coupling system, a method which has been demonstrated to expedite synthesis performance. The engineered P_PDR5 (PE23) was utilized to regulate the expression of gene HIS3 to enhance the ability of GA synthesis. Hence, cell growth exhibiting an efficient GA synthesis ability can be exploited. Furthermore, the presence of 3-amino-1,2,4-triazole (3-AT, an inhibitor of His3 protein) necessitated that yeasts produce more GA for growth maintenance (Fig. 3a)9. To verify the efficacy of this strategy, the impact of introducing P_PDR5-HIS3 and 3-AT on GA synthesis was examined. After the incorporation of both P_PDR5-HIS3 and 1 mM 3-AT, there was a significantly enhancement in the synthesis capacity of GA per unit cell in the media lacking histidine (Fig. 3b). The positive correlation between GA concentration and cell growth was further confirmed by supplementing exogenous GA (Supplementary Fig. 5). These results demonstrate the feasibility of the growth-production coupling strategy. Moreover, both increasing 3-AT concentration and decreasing dynamic range can enhance the constraint of biosensor on cell growth, and it is speculated that adjusting the catalytic validity of His3 will lead to an optimal value that maximizes GA production (Fig. 3c).

The next is to evolve the strain to improve the cell adaption in histidine lacking media with or without 3-AT. We used the high GA production strain GA184, which contains the biosensing plasmid pRS415-PE23 and is named GA184-PE23, as the parent strain here. It was found that adding at least 10 mM 3-AT significantly inhibited cell growth (Supplementary Fig. 5). Therefore, 10 mM 3-AT was chosen in the initial adaptive evolution. During the evolution process, as the yeasts become adaptive, the concentration of 3-AT is progressively increased to 50 mM. The strain was also evolved in YPD media as a control. During 18 days of evolution, the growth of the evolved strains was detected, and the cells were withdrawn at 6, 12 and 18 days and inoculated into GM1 media to determine the production of GA (Fig. 3d). Consequently, the strains cultured in YPD media exhibited a dramatic reduction in triterpenoid production (Fig. 3e), indicating that the GA-producing trait is not steadily inherited as the production process of GA may exerting a burden on cell growth. The heterologous genes were almost lost as the strain continuous cultivation without the production-dependent selective pressure in YPD media (Fig. 3f and Supplementary Fig. 6), which may be due to unstable gene integration or overgrowth of the wild-type yeast. This phenomenon of strain deterioration also occurs in industrial production. In contrast, the evolution in SD-His media made the producing phenotype and genotype stable, due to the coupling of growth with biosynthesis. Furthermore, the addition of 3-AT led to an increased correlation between growth and GA production. After evolution in 3-AT for 6 days, the production of GA increased by 9.8% and the production of total triterpenoids (GA, glycyrrhetaldehyde and 11-oxo-β-amyrin) increased by 7.9% (Fig. 3e). To further reinforce the correlation between production and growth, we selected other engineered biosensors with reduced dynamic ranges (PE25 and PE31, which regulatory features are shown in Fig. 2e) to reduce the synthesis amount of histidine (Fig. 3c). As anticipated, the strains employing PE25 and PE31 exhibited substantial increases in GA production. The strains with PE31 evolved for 6 days achieved the highest production level, in which the production of GA increased by 17.5% and the production of total triterpenoids increased by 11.4% (Fig. 3e). To identify the high-production cells, single colonies were then randomly picked from the plates of the evolved strain with PE31 for 6 days (Fig. 3g). The highest production strain A8 showed a GA production of 207.3 mg/L and a total triterpenoid production of 886.5 mg/L, representing a 46.8% and 59.1% increase than the initial strain, respectively (Fig. 3h).

Reasons for high GA production of the evolutionary strain at the bulk transcriptional level

To investigate the role of biosensor in driving the evolution, the transcriptomes of the evolved strain A8 and the control strain GA184Y (obtained from the strain GA184-PE23 evolved in YPD medium for 6 days) were analyzed. The expression levels of heterologous genes involved in synthesizing GA in evolved strain were found to be significantly higher than the control (Supplementary Fig. 7a and b), which agrees with the result of the gene loss rate (Fig. 3e). Moreover, the expression of HIS3 gene demonstrated elevated, thereby validating the efficacy of the biosensor-dependent growth-production coupling strategy. Furthermore, the INO1 gene, encoding inositol-3-phosphate synthase and involving phospholipids synthesis, was found to be significantly upregulated (Supplementary Fig. 7b). Previous studies have shown that the expression of INO1 is regulated by the transcription factor Ino2, which promotes the expression of phospholipid synthesis genes, increases the capacity of the endoplasmic reticulum, and provides more space for the expression of P450 and terpenoid synthesis35. In this study, INO2 was also upregulated in the evolved strain. Consequently, the upregulation of INO1 and INO2 suggests that the phospholipid content and endoplasmic reticulum capacity of the evolved strain may have increased to promote the synthesis process of GA. As demonstrated in our previous research, the expression of INO2 has been shown to increase the production of glycyrrhetinic acid by 50.2%36. Concurrently, genes implicated in the competition pathway for GA synthesis, namely the ergosterol synthesis pathway, exhibited significant downregulation (Supplementary Fig. 7a). Ergosterol is an essential component for yeast cell growth. The trade-off between ergosterol synthesis and heterologous terpenoid synthesis has always been an important means of regulating high terpenoid production. Excessive inhibition of ergosterol synthesis has been demonstrated to limit cell growth and result in a reduction in target terpenoids. The identification of a suitable balance point represents a considerable challenge37,38. The high production strain A8 indeed exhibits lower biomass (Fig. 3i), which is consistent with the metabolic changes of ergosterol synthesis pathway. It is evident that, through biosensor-dependent evolution, the biosynthesis pathway has become more adapted to the chassis metabolic network.

Single-cell RNA-seq revealed the sub-population differences of the evolved strain

The single colonies showed that there were differences in phenotype between individual cells after evolution (Fig. 3g). Previous studies have shown that there may be division of labor and cooperation within a single microbial species20-23. To investigate the potential different evolutionary directions of populations caused by different evolutionary pressures, single-cell RNA-seq were used for analysis. The single-cell transcriptome exhibited gene expression patterns analogous to those of the bulk transcriptome (Supplementary Fig. 7c), yet the former is capable of more profoundly reflecting differences between individual cells within a microbial culture.

The single-cell transcriptome data of the evolved strain A8 and the control strain GA184Y was combined for dimensionality reduction to display the differences between the two strains (Fig. 4a and b). All cells are divided into nine clusters (Fig. 4c). The evolved strain exhibited substantial separation in terms of significant main component features compared to the control strain. The 6969 cells detected in the control were predominant in clustered 3, 4, and 6, accounting for 36.9%, 23.4%, and 15.9% of the total cell count, respectively. In contrast, the 7253 cells detected in the evolved strain were primarily in cluster 0, 1, and 7, accounting for 33.9%, 32.5%, and 4.0% of the total cell count, respectively (Fig. 4d and e). Furthermore, cluster 2, 5, and 8 are shared by two strains. Furthermore, the highly variable genes and marker genes in the nine clusters were analyzed (Fig. 4f and g, Supplementary Fig. 8). There are 2000 highly variable genes among all the cells. In clusters 0, 1, and 7, which are predominantly comprised of evolved strain, these three clusters exhibit substantial differences in RNA feature quantity, signifying intercellular division of labor. Cluster 0 has the highest RNA features and enriched with a budding marker gene PIR1 (a marker for early G1 phase, Fig. 4e and h)39, indicating that this cluster of cells are metabolic and proliferative activity. Furthermore, genes involved in cell wall synthesis and remodeling, including EXG1, HSP150, CRH1, NCW2, PST1, and CWP2, also indicate that the cells in cluster 0 are in a state of budding (Fig. 4h, Supplementary Fig. 8). In cluster 0, the expression levels of heterologous genes involved in product synthesis are slightly upregulated. In cluster 1, the heterologous genes involved in product synthesis, CYP72A63, CYP88D6, and βAS, exhibited significant upregulation compared to other clusters, and the cell growth determining gene HIS3 also demonstrated significant upregulation, suggesting that cluster 1 plays the principal role in product synthesis and provides essential histidine for community growth in evolved strain. Low RNA feature of cluster 7 indicates that cells are in a physiological resting state (e.g. the G0 phase), experiencing environmental stress, or in a senescent state. The increased expression of FIT3 and heat shock proteins (e.g. HSP12 and DDR2) also indicate cell aging and the renaturation of proteins22. The expression of the heterologous genes did not obvious increase in cluster 7, indicating that it was not the primary contributor to product synthesis.

In clusters 3, 4, and 6, which are dominated by control strain, there was an observed downregulation of the heterologous genes involved in product synthesis. This finding is consistent with the results of the production levels, gene lost rates (Fig. 3e), and the bulk RNA-seq (Supplementary Fig. 7). The upregulation of gene expression of large numbers of ribosomal subunit proteins in cluster 3 indicates that these cells are mainly responsible for protein synthesis (Fig. 4h and Supplementary Fig. 8). Cluster 4 has a high abundance of RNA feature and many genes involved in the tricarboxylic acid cycle are upregulated (e.g. MLS1, ICL1, CIT2, and SFC1), indicating that the cells are in a stage of vigorous cellular metabolism. In cluster 6, gene ADH2, ADY2, ATO3, and CTA1 were significantly upregulated. These genes have been shown to regulate the adaption to metabolic stress such as ethanol, acetic acid, ammonia, and reactive oxygen species. It is evident that the expression of peroxisome genes in cluster 6 is elevated (CTA1 and ECI1). This finding indicates that these cells play a role in balancing cellular homeostasis. However, the expression levels of these genes are minimal in the evolved strain. The transcriptional characteristics of cluster 6 are most divergent from those of the evolved strain, indicating that the synthesis of the product does not require or is inhibited by these genes. Consequently, the knockout of these genes demonstrated that the deletion of ADY2 and CTA1 can enhance the production of GA, suggesting that these two genes may have a significant impact on GA synthesis (Fig. 4i).

Single-cell transcriptomics revealed the different division of labor between the evolved strain and the control strain at the single-cell resolution. It also provides detailed information regarding the regulation of the metabolism of a specific cell type within a population. We annotated the functional features of cell clusters based on the above analysis of the marker genes (Fig. 4j). The evolved strain exhibits high expression of synthetic pathway genes, yet it also demonstrates a pronounced division of labor mechanism: not all cells are highly productive. Instead, one group exhibits stronger cell regeneration characteristics (cluster 0), while another group displays distinct quiescent traits (cluster 7). In contrast, the control strain shows a more growth-biased transcriptional profile compared to the evolved strain.

To further verify the ability of each cluster to synthesize products, cells of strain A8 with high expression of marker genes were sorted out to detect GA production (Supplementary Fig. 9). The result shows that cells with high expression of marker genes in clusters 0, 1, 3, and 4 generally have higher than average levels of product synthesis ability, while clusters 6 and 7 have lower than average synthesis levels. It is noteworthy that cells expressing elevated levels of mitochondrial functional proteins in strain A8 also demonstrate high productivity. This suggests that, under conditions of elevated cellular activity (such as those associated with respiration and proliferation) cells may exhibit considerable potential for productivity.

Methods
Media and strains

Escherichia coli was used for routine cloning procedures and protein purification. E. coli was cultivated at 37°C in Luria-Bertani (LB) media containing 100 μg/mL ampicillin or kanamycin for selection. Initial strain Saccharomyces cerevisiae SynV45, and glycyrrhetinic acid (GA) producing strain S. cerevisiae GA18046 and GA18447 were cultivated in YPD media (2% peptone, 1% yeast extract, 2% glucose) or GM1 media (1% corn steep liquor, 1% yeast extract, 2% glucose). The genotypes of the engineered strains were listed in Supplementary Table 2. Primers used in this study were listed in Supplementary Data 1.

RNA-seq

To mine the product responsive genes, producing strain and initial strain were cultured in YPD media for 48 h and then the cell pellets were collected. To investigate the changes in the evolution process of the biosensor-driven strain, the evolutionary strain A8 and the control strain GA184Y (cultured in YPD medium for 6 days) were cultured in GM1 media for 120 h and then the cell pellets were collected.

RNA was extracted by the TRIzol method. 1 μg total RNA was used for following library preparation. The poly(A) mRNA isolation was performed using Oligo(dT) beads. The mRNA fragmentation was performed using divalent cations and high temperature. Priming was performed using Random Primers. First strand cDNA and the second-strand cDNA were synthesized. The purified double-stranded cDNA was then treated to repair both ends and add a dA-tailing in one reaction, followed by a T-A ligation to add adaptors to both ends. Size selection of Adaptor-ligated DNA was then performed using DNA Clean Beads. Each sample was then amplified by PCR using P5 and P7 primers and the PCR products were validated. Then libraries with different indexs were multiplexed and loaded on an Illumina HiSeq instrument for sequencing using a 2x150 paired-end (PE) configuration according to manufacturer’s instructions. The raw transcriptomic data have been deposited in the NCBI database under BioProject accession numbers PRJNA1146062, PRJNA1415461 and PRJNA1436984. The relationship between log FoldChange and -log P-value was represented using a scatter diagram (volcano plot).

Strain construction

Gene parts (promoters, terminators, ORFs, and homologous arms) were amplified by polymerase chain reactions (PCRs) using the 2×Phanta® Flash Master Mix (Vazyme, China). PCR products were separated on 1% agarose gels and purified using the GeneJET Gel Extraction Kit (Thermo Fisher). Overlap extension PCR or Gibson assembly was used to assemble the gene parts into genetic circuits. Genetic circuits flanked by homologous arms were integrated into the yeast genome by homologous recombination using electro-transformation. The transformed yeasts were cultivated on YPD agar plates at 30°C for 3 days. Positive clones were verified by diagnostic PCR.

Construction of biosensors

The transcriptomic data of the initial strain and producing strain were analyzed. Genes were selected from the upregulated differentially expressed genes (DEGs, log FoldChange>1, P-value <0.01) as product responsive candidates. The upstream 1000 bp sequence of the start codon was selected as the promoter region. The expression cassette was constructed with the promoters that regulate the expression of a red or green fluorescent protein gene. The expression cassettes were integrated into the initial strain and producing strain genomes by yeast electro-transformation.

Fermentation and products determination

The yeast strains were pre-cultured in 2 mL YPD media at 30°C, 200 rpm for 20 h. Then, the strains were inoculated into 50 mL GM1 media in flasks with an inoculated OD of 0.1. Subsequently, the strains were cultivated for 120 h for the accumulation of GA. Biomass of yeast cells were determined with a micro-spectrophotometer (Nano-300, AllSheng, China) or a microplate reader (BioTek Synergy H1). All cultivations were performed with three independent biological replicates.

After cultivation, 2 mL of yeast cell culture was treated by a sample freezing grinder, and then were extracted using ethyl acetate. For each sample, equal volumes of organic phase were collected for subsequent analysis. The resulting extracts and aqueous-phase were respectively pooled, dried, and trimethylsilylated with pyridine and N,O-Bis (trimethylsilyl) trifluoroacetamide (BSTFA) for analysis by GC-MS. The GC-MS analysis was performed using Agilent 8890-5977B GC/MSD Ultra (Agilent) equipped with Agilent 19091S-433. J&W HP-5ms GC Column (30 m × 0.25 mm × 0.25 µm, Agilent) with a carrier gas helium flow of 1.0 mL/min, using a split mode; split ratio was 5:1. The compound separation procedure was as follows: injector temperature 250°C; column temperature start with 80°C for 1 min, 290°C for 38 min at a 20°C/min increase; ion source temperature 200°C and interface temperature set to 290°C, mass spectra were acquired in electron ionization (EI) mode at 70 eV, using scan mode and monitoring the m/z range from 50 to 700 with a solvent delay of 15 min. GA standard (Sigma-Aldrich) was used for quantification of both GA, glycyrrhetaldehyde and 11-oxo-β-amyrin due to their structural similarity. Compound identification was performed by comparison of mass spectra with the NIST Mass Spectral Library and by comparison of retention time with authentic standards.

Measurement of cell fluorescence

Fermentation samples were centrifuged at 2348×g for 2 min and supernatant was disposed. 0.01 M PBS buffer (pH 7.4) was used to resuspend and wash the cells. Appropriate volume of PBS buffer was used to resuspend cells to an OD range of 0.2 to 0.8. 100 μL of the samples was added in the 96-well plate and detected by the microplate reader. Cell fluorescence was determined with excitation/emission wavelengths to 580/610 nm. The fluorescence intensity of unit cell is ultimately used for evaluation.

Cell fluorescence was also determined using a flow cytometer (Bio-Rad S3eTM Cell Sorter). Cells were diluted in PBS to a final concentration of 10^6 cells/mL and screened through a 300-mesh cell sieve. After quality control, the filtered sample was placed on the sample stage and pushed to the run position. The gain for the FSC, SSC and FL3 channels was set to 210, 280 and 850, respectively. A total of 50000 cells were counted per sample to determine the percentage of fluorescent cells. SynV and SynV-P_TDH3-mcherry cells were used to establish the negative and positive boundary, respectively.

Molecular docking and hydrogen bond analysis between GA and Pdr1

In this study, AlphaFold3 was employed to predict the structure of the Saccharomyces cerevisiae Pdr1 homodimer, which served as the receptor for molecular docking with the GA ligand48. The Pdr1 sequence was obtained from NCBI, while the two-dimensional structure of the GA ligand was sourced from PubChem. Molecular docking, scoring, and ranking were performed using AutoDock Vina software to simulate the interaction between GA and Pdr1. During docking, Pdr1 served as the receptor and GA as the ligand. The docking region center coordinates for the design box were set to center_x = 0.561, center_y = -0.452, center_z = 0.258. The docking box dimensions were set to size_x = 71.75, size_y = 70.61, size_z = 71.75. The binding affinity and hydrogen bond interactions between Pdr1 and GA were analyzed.

Adaptive evolution of the GA producing yeast

The P_PDR5-HIS3 expression cassette was constructed on the pRS415 plasmid and transformed into the high GA producing strain S. cerevisiae GA184. Positive clones were selected on the plate of SD-His-Leu media (2% glucose, 0.67% yeast nitrogen base without amino acids, 0.13% amino acid drop-out without histone and leucine, adjusting pH to 6.2). The engineered yeasts were cultured continuously in SD-His media (2% glucose, 0.67% yeast nitrogen base without amino acids, 0.13% amino acid drop-out without histone, adjusting pH to 6.2) with or without 3-AT (Sigma-Aldrich), as well as a control cultured in YPD media. Cells were transferred to the accordingly fresh media every 48 h at an initial OD of 0.2. Cell growth was monitored by sampling and measuring using a spectrophotometer. In the 3-AT addition group, the initial concentration of 3-AT was 10 mM, and the concentration of 3-AT was increased to 30 mM after 6 days and increased to 50 mM after 12 days. The evolutionary strains were harvested, inoculated into GM1 media and cultured for 5 days to determine triterpenoid production.

Single-cell RNA sequencing

Samples of yeast cells from two biological replicates were pooled into a single trial. Yeast cells were collected by centrifuging at 3000×g for 5 min at 4℃, and resuspended in PBS for counting. Cell wall was removed using 0.2 U Zymolyase (MCE) in a resuspension buffer v2.1 (MobiDrop, China). Then, cells were loaded into Microfluidic Chip A gle-cell RNA-Single-cell Kit v2.1 (MobiDrop) with MobiNova-100 (MobiDrop) to generate droplets. Each cell was involved into a droplet which contained a gel bead linked with up to millions oligos (cell unique barcode). After encapsulation, droplets suffered light cut by MobiNovaSP-100 (MobiDrop) while oligos diffuse into reaction mix. The mRNAs were captured by cell barcodes with cDNA amplification in droplets. Following reverse transcription, cDNAs with barcodes were amplified, and a library was constructed using the High Throughput Single-Cell 3' Transcriptome Kit v2.1 (MobiDrop) and the 3' Dual Index Kit (MobiDrop)49,50. The resulting libraries were sequenced on DNBSEQ-T7 platform with PE150 mode.

The single-cell RNA-seq raw sequencing data have been deposited in the NCBI database under BioProject accession number PRJNA1415668. The raw sequencing data was analyzed by FastQC51 to check the sequencing quality. The MobiVision®-M software was used to quantify the expression levels of each gene in each cell. MobiVision®-M automatically ran following analysis: adaptor removing, low quality reads filtering, barcoding, alignment, adjustment of multi-mapped reads, filtering low quality microbes, generating of report and expression matrix. Generally, following the pipeline of normal single-cell RNA-seq, the cluster analysis was performed by Seurat. The microbes with relatively low gene count or UMI count was filtered. The list of high variable genes was selected by “FindVariableFeatures” function after normalization. Then, the expression matrix was scaled. The PCA analysis was performed based on variable genes. The cluster was separated by “FindNeighbors” and “FindClusters” functions. The results were visualized by uniform manifold approximation and projection (UMAP) algorithms.

“FindAllMarkers” and “FindMarkers” functions were used to analysis differentially expressed genes (DEGs). The enrichment analysis started by creating customized database. The pathway or term related to each gene were annotated by eggNOG-mapper52 and eggNOG53 database. Based on the annotation, the customized database was generated by R package “AnnotationForge”. The GO and KEGG enrichment analysis were performed by R package “clusterProfiler”. The enrichment results were visualized by R package “ggplot2”. The analysis was based on the gene expression matrix with UMI count. The data was believed to follow a negative binomial distribution. The genes expressed in less than 10 microbes were removed. The genes used for analysis were identified by “FindAllMarkers” function of R package Seurat with P-value lower than 0.05.

Subgroup-representative cell sorting and fermentation

Based on single-cell transcriptome sequencing analysis, distinct cellular subpopulations were identified in the evolved strain A8 and the control strain GA184Y, along with strain-specific marker genes. For each subpopulation, the top-3 marker genes with the most significant upregulation were selected based on statistical significance. The promoter region of the selected marker gene was cloned upstream of the green fluorescent protein coding sequence to construct a reporter expression cassette, which was then transformed into the evolved strain A8. After screening for positive transformants, they were cultured in GM1 fermentation medium for 5 days.

Flow cytometry analysis and cell sorting were performed using a BD FACSAria SORP flow cytometer (BD Biosciences, USA). Yeast cells were collected after cultivation, washed three times with sterile 1×PBS, and diluted to OD ≈ 0.5. The cell suspension was filtered through a 200-mesh sieve to remove clumps and large debris. For gating, the main yeast population was selected using FSC-A and SSC-A parameters (Supplementary Fig. 9b). Doublets and cell aggregates were excluded using FSC-A versus FSC-H, selecting the linear distribution of single cells. Green fluorescence intensity was measured using the 488 nm laser (FITC channel). The top 2% of cells with the highest fluorescence intensity were gated as the target subpopulation, and a control population was gated from cells with low fluorescence intensity and a concentrated distribution of events. For each sorted population, 20,000 cells were collected. Data acquisition and analysis were performed using FlowJo v10.9 software. All experiments were conducted in biological triplicates, and results are reported as mean ± SD unless otherwise indicated.

The sorted cells were transferred to a 500 μL deep-well plate fermentation system and further cultured in GM1 medium for 5 days. Cell growth was assessed by measuring OD and the yields of triterpenoid products were quantitatively analyzed.

Quantification and statistical analysis

Data were processed in Excel or GraphPad Prism. The significant comparisons of two different groups are indicated in the graphs statistical analysis was performed using two-sided unpaired Student’s t-test (*p<0.05, **p<0.01, ***p<0.001). The graphs represented means ± SD (n=3) unless otherwise indicated, as described in the figure legends. No statistical method was used to predetermine sample size. No data were excluded from the analysis. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript describes a strategy for constructing a metabolite-based biosensor responsive to GA and applying it to improve product formation through evolution engineering. Specifically, the study begins by identifying GA-responsive gene using comparative transcriptomic analysis. Following biosensor optimization, GA production was coupled to cell growth by placing the essential gene HIS3 under the control of a GA-responsive promoter (PDR5). Through serial transfer cultivation, a single colony was identified that exhibited significantly improved production of GA and licorice triterpenoids. Furthermore, single-cell transcriptomic analysis revealed that a substantial portion of the evolved population remained high biosynthetic efficiency. While many trategies utilized have been previously reported, the study presents some new insights into PDR5 regulation and heterogeneity issue in cell factories. However, several critical issues concerning the experiment design and data interpretation require further clarification.\nMajor Concerns:\n1, The GA (or upstream intermediates)-responsive expression of PDR5 appears to rely on transcriptional regulation. The\nmanuscript suggests that transcription factors Pdr1/3 and/or Stb5 may be involved. However, additional data are required to\nconfirm whether these transcriptional factors directly respond to GA or its intermediates. Clarifying this would significantly\nenhance our understanding of the regulatory mechanisms involved.\n2, If the improved dynamic range of the biosensor corresponds to increased PDR5 expression, this may reduce the selective\npressure when HIS3 is placed under its control. Since the aim of coupling biosensor activity to essential gene expression is\nto apply selective pressure that stabilizes genotype during evolution, the rationale for increasing dynamic range needs to be\nclearly explained.\nAdditionally, if 3AT is introduced to strengthen selection via the biosensor system, how stable is the genotype of the evolved\nstrains under no selective pressure? For instance, what is the performance of the evolved strain A8 after being transferred for\n6 or 18 days without 3AT?\nOther Issues:\nL117, it stated “ a series of genes were selected”. How many genes were initially selected and tested? If only 12 genes were\nevaluated (as shown in Fig1c), what was the basis for selecting these 12 out of 215 upregulated genes? Moreover, why did\nonly PDR5 show enhanced fluorescence while the others showed decreased signal (Fig1c), especially if they were all\nselected from the same upregulated gene set?\nFig1c, the figure seemed to imply GA directly interacts with the promoter regions of the tested genes. Is this correct, or is the\ninteraction mediated through transcription factor(s)?\nFig 1d, e, and f showed intracellular GA production in relation to the fluorescence response. Was GA accumulation\nexclusively intracellular under these conditions, or was there also extracellular GA detected?\nFig1g, when GA was added into the culture, how much was taken up by the cells (i.e., what were the intracellular\nconcentrations)?\nFig2, it was not clear whether the compounds shown were produced endogenously by the yeast cell factories or externally\nsupplemented. Fig2a, in particular, needs clearer explanation, i.e., does the indicated concentration refer to the maximum\nfluorescence achieved or the maximum concentration that the engineered yeast cell factories were able to produce?\nL240, what regulatory features did PE25 and PE31 promoters contain?\nL245, “single colonies were then randomly picked” was mentioned. Could the authors clarify the source of these colonies?\nWere they picked at the final stage of the evolution experiment?\nFig4b shows normalized GA production per biomass. Could the authors provide the actual biomass concentrations used\nunder these different conditions?", "reviewer_2": "Reviewer Summary\nThe authors discovered a responsive element (PDR5 gene) to the presence of GA; by fusing its translation to fluorescent\nprotein production it formed a biosensor for GA. Firstly its responsiveness to GA concentration, specificity and its potential\ninterference by cell stress conditions was tested, then an attempt at dynamic range enhancement by promoter engineering\nwhich was initially modest (5-fold) but greatly enhanced by knocking out two genes that interact with PDR5: Ssz1 and Rdr1.\nUsing a 5-fold enhanced dynamic range PDR5 promoter, coupled to HIS3 gene expression, histidine production (hence cell\ngrowth) was regulated by PDR promoter activity. In addition, an azole histidine synthesis inhibitor was co-incubated to\nfurther impede histidine synthesis (as the coupled receptor was not impactful enough). Ultimately an engineered form of the\nPDR promoter with lower dynamic range, and in the absence of azole, was most effective at enhancing GA production by\nthe cell.\nBulk cell transcriptomic analysis was conducted comparing the highest producing isolate (A8) and GA184, which was a\ncontrol but unspecified. The basis for the GA production differences between the strains was confirmed by higher expression\nof HIS3 and major GA pathway genes, upregulated INO1 indicating phospholipid synthesis and downregulation of\nergosterol synthesis genes.\nFinally, single cell transcriptomic analysis was conducted on approx. 14000 cells divided between A8 and the control. This\nsorted the transcriptomes into patterns of upregulation/downregulation and into 9 clusters of similar gene expression\npatterns among the strains. This analysis showed clear delineation of gene expression between A8 and the control and\nconfirmed the bulk cell transcriptomic analysis and higher HIS3 and GA pathway gene expression in A8. The analysis also\nidentified several potential gene targets in the control which could influence GA production. Two\nstrains that had deletion of 1 of 2 target genes were deleted gave higher GA yields; other knockouts identified from the\nsingle cell transcriptomic analysis resulted in lower GA yields.\nReview\nThe manuscript does not universalise the concept that was promised in this work. It was claimed that the manuscript\n“developed a universal paradigm for the construction of biosensors and their subsequent application in stabilizing and\nevolving the genotype of cell factories” but the work is restricted to the example, GA biosynthesis, and no attempt to broaden\nthe concept in either the Results or Discussion was made.\nIndeed, some key aspects are missing (e.g. how 12 upregulated genes in GA exposure were selected as potential\nbiosensors, what role does biosensor dynamic range have in potentially increasing yield, what is considered a ‘control’ in\nthis kind of research). This knowledge is essential for extending the applicability of this concept. Some of these queries are\ndetailed further below.\nThe single cell transcriptomics analysis produced some interesting results as any opportunity to look deeply into a yeast\nculture and infer its performance via gene expression can do. In this case, the analysis seemed an add-on and not central to\nthe point of the paper. The bulk cell transcriptome analysis already showed the key gene expression differences between\nGA184 and A8.\nThe concept of using biosensors to indicate positive directions of genetic/metabolic engineering and high throughput\nscreening or adaptive laboratory evolution is not new. The selection of PDR was based on earlier work and others that have\ncoupled biosensor response to growth. A selection of relevant research and review articles is given below.\nLi et al (2019) quoted by the authors identified the action of PDR and used this biosensor approach (fluorescent biosensor\nbased on PDR) in their study. That is, they state “here we developed a small molecule-responsive biosensor that couples\ntranscriptional induction of PDR genes to growth rate in the yeast Saccharomyces cerevisiae “ (Li J, Kolberg K, Schlecht U,\nSt Onge RP, Aparicio AM, Horecka J, Davis RW, Hillenmeyer ME, Harvey CJB. A biosensor-based approach reveals links\nbetween efflux pump expression and cell cycle regulation in pleiotropic drug resistance of yeast. J Biol Chem. 2019 Jan\n25;294(4):1257-1266. doi: 10.1074/jbc.RA118.003904)\nKrüger, A., Göddecke, J., Osthege, M. et al. Biosensor-based growth-coupling as an evolutionary strategy to improve heme\nexport in Corynebacterium glutamicum. Microb Cell Fact 23, 276 (2024). https://doi.org/10.1186/s12934-024-02556-1\nDacquay LC, McMillen DR. Improving the design of an oxidative stress sensing biosensor in yeast. FEMS Yeast Res. 2021\nMay 1;21(4):foab025. doi: 10.1093/femsyr/foab025. PMID: 33864457; PMCID: PMC8088429.\nYongkun Lv, Shuai Qian, Guocheng Du, Jian Chen, Jingwen Zhou, Peng Xu, Coupling feedback genetic circuits with\ngrowth phenotype for dynamic population control and intelligent bioproduction, Metabolic Engineering,54,2019,109-116,\nhttps://doi.org/10.1016/j.ymben.2019.03.009.\nLv Y, Gu Y, Xu J, Zhou J, Xu P. Coupling metabolic addiction with negative autoregulation to improve strain stability and\npathway yield. Metab Eng. 2020 Sep;61:79-88. doi: 10.1016/j.ymben.2020.05.005.\nMany approaches to growth coupled chemical bioproduction have been reviewed in the following (and others)\nGazi Sakir Hossain, Mukesh Saini, Ryoma Miyake, Hua Ling, Matthew Wook Chang, Genetic Biosensor Design for Natural\nProduct Biosynthesis in Microorganisms, Trends in Biotechnology,38, 2020, 797-810,\nhttps://doi.org/10.1016/j.tibtech.2020.03.013.\nYoshihiro Toya, Hiroshi Shimizu, Coupling and uncoupling growth and product formation for producing chemicals, Current\nOpinion in Biotechnology, 87, 2024, 03133, https://doi.org/10.1016/j.copbio.2024.103133.\nAlter TB, Ebert BE. Determination of growth-coupling strategies and their underlying principles. BMC Bioinformatics. 2019\nAug 28;20(1):447. doi: 10.1186/s12859-019-2946-7\nFurther comments regarding suggestions on improving the manuscript, the lack of or unclear experimental details, language,\nand results interpretation are provided.\nAbstract\nIt is difficult to get any sense of the novelty of the study from this abstract. There is some strange phrasing used (see General\ncomments) which adds to the difficulty in getting value from it. There are no measures of impact in the whole abstract – no\nindication of the extent of enhancement for example is given; no gene names, no promoter name or how the biosensor\nworks, the overall impact etc.\nIntroduction\nIt may be a personal dislike but I don’t find the idea that a research area is a “hot research topic” a very compelling reason to\ndo research. Also, proponents of ALE are unlikely to consider the approach ‘irrational’.\nThe introduction would benefit from being rewritten to improve clarity and to do justice to the literature that is touched on.\nGiven the complexity (lines 71-75) of developing a biosensor and using it to evolve strains, taking this approach needs\nstronger justification. What are the specific drivers of this research? What is the novelty? Why is this a “universal paradigm”\nor “universal workflow” when only GA synthesis is described?\nThe sentences (lines 76-88) relating to within culture differentiation are a sharp direction change from the rest of the\nintroduction and look like they have been parked there to justify undertaking single cell transcriptomics. Line 84 suggests\nwithin isogenic cultures experiencing nutritional variability there is always a differentiation of labour among cells but then it is\nconfusingly concluded that “We surprisingly found that the high-production yeast population exhibits division of labor, using\nsingle-cell transcriptome sequencing.” .\nResults\nLine 115\nFig 1b shows 1109 genes were upregulated in the GA109 strain compared to control and the text reads “215 with\nsignificantly upregulated expression and 936 genes with significantly downregulated expression”\nUltimately only 12 genes are listed in Table S1 and ultimately tested. How were these selected? What was considered\n“significant”? Table S1 does not show fold upregulation for the selected genes and the criteria for selection of these 12\ngenes are not clear.\nLine 188\n“and fluorescent proteins were utilized to characterize the promoter response” Suggest restating this to more accurately\nreflect what you have shown in Fig 1c.\nPDR response in GA180 is enhanced compared with the control strain because the former is producing GA\nIn Fig 1f and 1g,\nwas GA added to control yeast strains to test the responsiveness of PDR to GA? This would seem like the logical\nexperiment to show that this is an accurate biosensor.\nGlycyrrhetinic acid is considered water insoluble. How was it delivered to yeast cultures?\nIn Fig 2,\nthe potential impact of a range of stressors and environmental agents were tested against non-GA producing yeast\nexpressing the PDR biosensor. In the absence of stressor (Fig 1a) control yeast produced up to 250 FU at 120 h. If all\nconditions were the same for Fig 2a and b, why are the FU so different? That is, control is 100 FU and GA elicits a response\nof 205 FU at 120 h.\nWhat is meant by “(b) The response of the GA biosensor to other natural products in the corresponding engineered strains.”\nWhat are the corresponding engineered strains? Werent molecules 1 – 10 added to the flasks at different concentrations?\nLine 159 onwards\nExtensive empirical promoter engineering was undertaken to attempt to increase the dynamic range of its response with little\nimpact. Was this result surprising?\nFig 3 data.\nDo all these attempted promoter engineering results need to be in the main document? Most do not make an important\ndifference.\nLine 228 :\n“Consequently, the strains evolved in YPD media exhibited a dramatic reduction in triterpenoid production indicating that the\nGA-producing trait is not steadily inherited as the production process of GA may exerting a burden on cell growth”\nThis looks to be a classic case of loss of plasmid pRS415 from the cells in rich media. The selection pressure to keep the\nplasmid was removed as histidine is drawn from the media. As cells divide there is no benefit to retaining the plasmid. Also\nthe azole is not effective.\nLine 231\n“The heterologous genes were almost lost as the strain continuous cultivation without a selective pressure in YPD media\n(Fig. 4e and Supplementary Fig. 2).”\nThis is very surprising as these heterologous genes (2CYPs and CPR) are integrated into the yeast genome. The absence\nof the genome-integrated pathway genes at 18 d in YPD suggests that either the gene insertions are unstable and prone to\nexcision or that the culture was overgrown by wild type yeast. Could you please comment.\nLine 233\nI’m struggling to understand these two seemingly contradictory points:\n“In contrast, the evolution in SD-His media made the producing phenotype and genotype more stable, due to the coupling of\ngrowth with biosynthesis. Furthermore, the addition of 3-AT led to an increased correlation between growth and GA\nproduction”\nThe more the yeast needs to produce histidine via the PDR-PE23 promoter (with 5-fold dynamic range) plus the effect of\nazole, the more GA is produced. That makes sense.\n“As anticipated, the strains employing PE25 and PE31 exhibited substantial increases in GA production. The strains with\nPE31 evolved for 6 days achieved the highest production level, in which the production of GA increased by 17.5% and the\nproduction of total triterpenoids increased by 11.4% (Fig. 4d).”\nPDR-PE25 and -PE31 have lower dynamic range (~2-3.5-fold) and no azole was added to enhance the endogenous\nhistidine requirement. Why was it anticipated that this would result in a substantial increase in GA production?\nLine 265\n“strain A8 and the control strain (cultured in YPD medium for 6 days) were analyzed”\nWhat is the “control” strain for these experiments and why was this strain selected? In Fig 5 is shown as GA184 but what\ndoes that signify?\nLine 281\nThe downregulation of ergosterol synthesis pathway genes in the A8 in comparison to GA184 was interesting and no doubt\nbeneficial for obtaining a higher yield of GA in that strain. It is stated that “It is evident that, through biosensor-dependent\nevolution, this balance can be rapidly achieved.”\nBut this was not made clear in the manuscript. Were there any negative phenotypical consequences for the ergosterol\nsynthesis downregulation evident in the A8 strain? (these effects have been extensively reported e.g. Johnston EJ, Moses T,\nRosser SJ. The wide-ranging phenotypes of ergosterol biosynthesis mutants, and implications for microbial cell factories.\nYeast. 2020 Jan;37(1):27-44. doi: 10.1002/yea.3452). There may have to be a choice between poorly growing strains/high\nGA and normal growth/moderate GA.\nLine 381\nPIR1 is a described as a “newborn marker gene”, “cluster 0 are in a state of neogenesis”\nPIR1 is a structural constituent of the cell wall involved in intracellular protein transport and cell wall organization; also\nlocalizes to the bud scar and nuclear periphery. According to ref 41 PIR1 is a marker for early G1 phase but does not\ndistinguish between mother or daughter cells (they suggest use of DSE2 which is only expressed in daughter cells).\nThe genes upregulated PIR1, EXG1, HSP150, CRH1, NCW2, PST1, and CWP2 are all involved in cell wall synthesis and\nremodelling. Together their upregulation could indicate budding or cell wall remodelling for other reasons.\nIt is not clear from the clustering description (line 381 onwards) what the authors are suggesting. Doesn’t the clustering\nrepresent major patterns of gene expression identified in individual cells. Most of the cells have patterns of gene expression\n(up-, down and no change) that locate to one of nine patterns. These are a different way of grouping the cells compared with\nthe UMAP graphs.\nLine 329:\n“The low RNA feature of cluster 7 indicates that this cluster may be undergoing the process of aging”\nwhat does the low RNA feature mean? Cluster 7 has a lower number of “features” compared to some of the clusters as does\ncluster 5. The gene expression pattern for cells in cluster 7 shows upregulation of stress response genes like HSP12, DDR2\nand down regulation of GA pathway genes. Interestingly, it is mainly A8 cells this cluster.\nLine 343\n“In cluster 6, gene ADH2, ADY2, ATO3, and CTA1 were significantly upregulated. These genes have been shown to\nregulate the treatment with ethanol, acetic acid, ammonia, and reactive oxygen species, respectively.”\nThese are either metabolism genes or transporters. I don’t think it is accurate to say they regulate the treatment with those\nsubstances.\nLine 412:\n“For the control strain, subculture results in a loss of the strain capacity for synthesizing licorice triterpenoids.”\nAgain, what strain is meant by ‘control’ is never clear. Loss of GA production seemed to occur during subculture in rich\nmedia (YPD) where the plasmid coding for HIS3 was lost during replication.\nLine 420:\n“It can therefore be inferred that their role may be to maintain the sustainability of high-production cells (cell regeneration and\nprotein renaturation) and the stability of growth environments”\nThis inference is likely projecting well beyond the data. The different gene clustering within the high performance A8 strain\ncould represent cells in a distinct life stage to cluster 0, or the gene expression pattern may result from the gradient of\nnutrient and oxygen availability across the culture.\nLine 425:\n“Whereas, within the control strain, there was a progressive decline in the capacity for product synthesis, attributable to the\nabsence of biosensor-dependent synthesis and growth coupling.”\nThere was no time based analysis in this single cell transcriptomic analysis so what is meant by a progressive decline? The\nsingle cell transcriptomes show that A8 and Control have different gene expressions which are consistent with the\ndifferences in GA production and lower HIS3 expression.\n“Consequently, the cells gradually degenerated into metabolic characteristics dominated by growth”\nThis is poor phrasing. Cell undertaking normal metabolism for growth shouldn’t be termed degenerated.\nGeneral:\nAt many points, the manuscript lacks scholarly treatment, ie sentences lack key words and details which results in them\nsounding very general or facile. I recommend a complete and thorough edit and a rewrite of the Introduction.\nSome phrasing needs to be reconsidered such as:\nLine 219 and onward “growth-production coupling strategy”? Perhaps coupled growth-production is better\n“evolutionary strains” should be evolved strains\nThese terms and phrases need to be reconsidered:\n“improving the efficiency of synthetic products”\nhigh-effective screening of cell factory products\nstrategy of coupling synthesis and growth prevents the degeneration of cell synthesis ability.\nthe biosensor-dependent evolution of synthetic chemicals in cell factories\ndifficulties of enabling strains to evolve in a synthesis-oriented manner.\nlead to strain degeneration\nan absence of methodologies capable of impeding the descent of cells towards a state of diminished synthesis ability.\ncan potentially trigger exclusionary responses in the organism", "reviewer_3": "This study focuses on designing a biosensor-dependent coupling system to stabilize the high-synthesis phenotype of yeast\ncell factories for licorice triterpenoid production, It constructs a biosensor targeting glycyrrhetinic acid (GA), a licorice\ntriterpenoid, optimizes its dynamic range through promoter engineering, and designs genetic circuits coupling product\nsynthesis with cell growth. Via adaptive evolution, it significantly enhances licorice triterpenoid yields. Single-cell\ntranscriptomics reveals a population division of labor in evolved strains, with \"highly efficient synthetic cells\" and \"viability-\nmaintaining cells,\" offering new strategies for regulating microbial cell factory stability and improving synthesis efficiency.\nHowever, the study has limitations in experimental design rigor, such as insufficient specificity verification of the biosensor,\nand lack of reproducibility in single-cell sequencing, which somewhat undermine the persuasiveness of conclusions and the\nuniversal promotion of the method.\nDetail comments below:\nFor experimental design details, regarding the biosensor, although the responsiveness of the PDR5 promoter to GA is\nconfirmed, the dynamic concentrations of compounds like naringin chalcone, which it weakly responds to, in the GA\nsynthesis system are not detected. Additionally, the molecular mechanism (e.g., interaction with Pdr1) by which Ssz1\nknockout improves the dynamic range lacks verification. In the growth-synthesis coupling system, the non-specific metabolic\neffects of 3-AT are not excluded, the independent impact of nutrient differences in different media (YPD, SD-His, SD-\nHis+3AT) on evolutionary results is not distinguished, and the passage stability of the high-producing strain A8 is not tested.\nIn transcriptome and single-cell analysis, single-cell sequencing lacks biological replicates, the ADY2 and CTA1 knockout\nexperiments lack complementary verification, and the function of population division of labor (e.g., sorting cells from different\nclusters for separate culture to verify synthesis and growth capabilities) is not confirmed through experiments.\nFor article structure, the \"unclear mechanism of microbial population division of labor\" proposed in the introduction only\ndescribes phenomena without delving into mechanisms in the single-cell analysis of results. Bulk transcriptome (Fig. 5) and\nsingle-cell data (Fig. 6) lack correlation analysis, such as the enrichment of upregulated genes in specific cell clusters. The\ndiscussion overly emphasizes the universality of the \"universal paradigm\" and insufficiently addresses limitations such as\nPPDR5's dependence on hydrophobic compounds and the off-target risk of the coupling system in complex networks.\nMoreover, it fails to compare with similar studies in Cell Rep (2021) to clarify incremental contributions. Key information is\nmissing in chart annotations (e.g., the correlation between the abscissa range and saturation points in Fig. 1g, the clusters\ncorresponding to marker genes in Fig. 6k). Supplementary materials, such as the retention rate of heterologous genes in\nSupplementary Fig. 2, are not quantified, and the selection criteria for candidate genes in Supplementary Table S1 are not\nexplained, resulting in insufficient support.\nJudging from the title, there may be a lack of more examples to verify the author's concept of \"biosensor-dependent coupling\nsystem stabilizes the high-synthesis phenotype of cell factory.\nLines 117-118 state, \"To identify GA-responsive elements, a series of genes were selected from those with significantly\nupregulated expression\". What is the basis for selecting these genes?\nLine 119 mentions, \"As a result, the PDR5 promoter (PPDR5) displays a marked fluorescent response to GA (Fig. 1c)\". From\nFig. 1c, it appears that other promoters (such as REE) also show responses to the target compound. What is the rationale for\nselecting only PPDR5?\nLines 146-148 state, \"To examine the spectrum of PPDR5, its capacity to respond to other natural products (triterpenoids:\ncompound 1-6, flavonoids: compound 7-10)\". The article focuses on the response to GA throughout, so why test these\nseemingly unrelated substances here? This seems inconsistent with the main theme of the article, and it is suggested that\nthe authors should instead consider substances related to the GA synthesis pathway."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have added additional information that addresses my concerns.", "reviewer_2": "Thank you for the comprehensive response and corresponding updates you have made to the manuscript and\nsupplementary information. This revised manuscript has been a substantial undertaking and it has been done with care and\nattention to detail. My queries have been satisfactorily addressed.\nOne minor point is your use of the terms \"strain degradation\" and \"degradation in cell factories\". I think you could be more\nspecific as to your meaning as they could be confused with cell death.", "reviewer_3": "All comments have been well replied. I suggest to accept the manuscript."}, "author_response": "We thank Reviewer for the positive evaluation of our work and their constructive suggestions,\nwhich significantly improved the manuscript. Below, we address each comment in detail.\nPDR5 responds to GA, but not its intermediates (such as β-amyrin, 11-O-β-amyrin, glycerrhetol\nand glycerrhizin). This has been tested and shown in Fig.S2, and more explanation has been added\nin manuscript (Line 171-175).\nFurthermore, Pdr1 was reported as a nuclear receptor that directly interacts with several drugs to\nstimulate expression of PDR5. To investigate the role of these TFs (Pdr1/Pdr3/Stb5) on signal\ntransduction, these TFs was knocked out (Fig. 2d). Results showed that both pdr1, pdr3 and stb5 are\nnecessary to correspond to GA. Docking result also shows that Pdr1 can directly bind to GA (Fig.S3).\n2, If the improved dynamic range of the biosensor corresponds to increased PDR5 expression, this\nmay reduce the selective pressure when HIS3 is placed under its control. Since the aim of coupling\nbiosensor activity to essential gene expression is to apply selective pressure that stabilizes genotype\nduring evolution, the rationale for increasing dynamic range needs to be clearly explained.\nResponse:\nBiosensors need to increase or decrease their dynamic range depending on the application\nrequirements. Since the aim of coupling biosensor activity to essential gene expression is to apply\nselective pressure, the dynamic range needs to be decreased (Fig. 3c). Subsequent experiments\nconfirmed this (Fig. 3e). Here we revised the statement as “To enhance the applicability of GA\nbiosensor for strain screening and evolution, it is imperative to be able to adjust its dynamic range”\n(Line 183).\nAdditionally, if 3AT is introduced to strengthen selection via the biosensor system, how stable is the\ngenotype of the evolved strains under no selective pressure? For instance, what is the performance\nof the evolved strain A8 after being transferred for 6 or 18 days without 3AT?\nResponse:\nThe selective pressure in this study mainly comes from the defects of His. And 3AT only plays a\nrole in regulating the selective pressure. Our supplementary experimental results indicate that the\nphenotype (synthetic ability) of A8 remains stable after passage in a medium with selective pressure\nfor 15 days (A8-HIS-15d) (see the figure below). While its synthetic ability still decreases after\npassage without selective pressure (A8-YPD-15d). The decrease in A8 production is not as\nsignificant as that of the starting strain GA184. This result is consistent with the design principles\nof our biosensors and reflects the important role of biosensors in constraining product synthesis.\n[...]\nOther Issues:\nL117, it stated “ a series of genes were selected”. How many genes were initially selected and tested?\nIf only 12 genes were evaluated (as shown in Fig1c), what was the basis for selecting these 12 out\nof 215 upregulated genes? Moreover, why did only PDR5 show enhanced fluorescence while the\nothers showed decreased signal (Fig1c), especially if they were all selected from the same\nupregulated gene set?\nResponse:\nBecause GA is a lipophilic exogenous component, it may cause the expression of stress-related\nyeast drug transporters and other similar proteins. Therefore, the basis for the selection was the\ngenes encoding transporters or hypothetical proteins with upregulated expression. Based on this, 12\ngenes were selected, and we listed the protein functions and fold changes in Table S1. The related\nstatements in manuscript were revised.\nThe reason why some genes showed reduced induced expression in the experimental validation,\nexcept for PDR5, is probably due to the differences in sampling time, or the context effect of genes.\nMore detailed information was supplied in Fig.1 Caption.\nFig1c, the figure seemed to imply GA directly interacts with the promoter regions of the tested genes.\nIs this correct, or is the interaction mediated through transcription factor(s)?\nResponse:\nAccording to the experiments and the literature (Fig.2 and Fig.S4), GA plays direct binding role\non Pdr1. Pdr1 was a nuclear receptor that directly interacts with GA to stimulate expression of PDR5.\nFig 1d, e, and f showed intracellular GA production in relation to the fluorescence response. Was\nGA accumulation exclusively intracellular under these conditions, or was there also extracellular\nGA detected?\nResponse:\nGA was detected primarily within cells. Following endogenous GA synthesis and exogenous GA\naddition, the intracellular and extracellular GA concentrations were quantified, as shown in Fig.S1.\nFig1g, when GA was added into the culture, how much was taken up by the cells (i.e., what were\nthe intracellular concentrations)?\nResponse:\nThe intracellular and extracellular GA concentrations were quantified, as shown in Fig.S1.\nIn addition, we tested the GA solubility in different solvents. The solubility of GA in pure water\nis reported as 6.62 μg/L. Because GA solubility in complex growth media and fermentation broths\nhas not been systematically reported, we directly measured it under conditions relevant to our study.\nA 4 g/L GA stock solution was prepared using ethyl acetate as solvent. To test the solubility of GA\nin the cell culture, the GA stock solution was added to the culture to achieve a final concentration\nof up to 200 mg/L. The soluble fraction of GA in YPD medium without yeast was 13 mg/L, whereas\nthe cell-free fermentation broth collected after 5 days showed a higher apparent solubility of 34\nmg/L. The increase observed during fermentation may be explained by medium changes that\npromote GA dispersion. For example, shifts in pH, accumulation of metabolic by-products,\nsecretion of amphiphilic proteins or surfactants, and/or formation of colloidal or micellar phases\nthat enhance apparent solubility. Crucially, when the yeast was added into the YPD medium\n(OD=2.71), the intracellular GA (measured after cell harvest, washing and organic extraction) \nreached 160 mg per liter fermentation broth, equivalent to a GA loading of 59 mg/L per OD cell\nculture. This indicates strong partitioning and accumulation within yeast and demonstrating that the\nGA levels are sufficient to support the function of the biosensors used in this study. GA is a\nmembrane-affinity component, so it readily binds to organelles with membrane structures.\nTherefore, a GA concentration below 200 mg/L ensures complete absorption by the yeast.\n[...]\nFig2, it was not clear whether the compounds shown were produced endogenously by the yeast cell\nfactories or externally supplemented. Fig2a, in particular, needs clearer explanation, i.e., does the\nindicated concentration refer to the maximum fluorescence achieved or the maximum concentration\nthat the engineered yeast cell factories were able to produce?\nResponse:\nIn Fig.2a (current Fig.S2a), the compounds were exogenously added to the culture media in a\nreasonable amount. The concentrations were designed based on the concentrations of cell\nmetabolites under normal conditions.\nIn fig.2b (current Fig.S2b), the compounds were endogenously synthesized by the corresponding\nengineered strains. The detailed explanation has been added in manuscript and the Fig. S1 Caption\nand Table S2.\nL240, what regulatory features did PE25 and PE31 promoters contain?\nResponse:\nThe regulatory features can be found in Fig.2e. And the explanation was added in the context:\n“To further reinforce the correlation between production and growth, we selected other engineered\nbiosensors with reduced dynamic ranges (PE25 and PE31, which regulatory features are shown in\nFig. 2e) to reduce the synthesis amount of histidine (Fig. 3c).” (Line 271-274)\nL245, “single colonies were then randomly picked” was mentioned. Could the authors clarify the\nsource of these colonies? Were they picked at the final stage of the evolution experiment?\nResponse:\nThe colonies were isolated after the evolution with PE31 for 6 days. Subsequently, yeast culture\nwas dispersed onto SD-His medium plate. After the colony growth, 58 single colonies were\nrandomly selected and inoculated into GM1 medium. Fermentation was conducted for 5 days, after\nwhich the yield was measured. This explanation was added in the context (Line 278) and Fig.3\nCaption.\nFig4b shows normalized GA production per biomass. Could the authors provide the actual biomass\nconcentrations used under these different conditions?\nResponse:\nThe actual biomass concentrations have been supplemented in Fig.3b.\n[...]\nReviewer #2 (Remarks to the Author):\nReviewer Summary\n[...]\nReview\nThe manuscript does not universalise the concept that was promised in this work. It was claimed\nthat the manuscript “developed a universal paradigm for the construction of biosensors and their\nsubsequent application in stabilizing and evolving the genotype of cell factories” but the work is\nrestricted to the example, GA biosynthesis, and no attempt to broaden the concept in either the\nResults or Discussion was made.\nResponse:\nWe thank Reviewer for the positive evaluation of our work and their constructive suggestions,\nwhich significantly improved the manuscript. Below, we address each comment in detail.\nWe supplemented another experiment to demonstrate the generality and scalability of the\nconcept—medicarpin (Fig.1i-k). Through supplementary experiments, the universal workflow for\nthe in-situ discovery of target-responsive biosensors in chassis cells has been demonstrated to be\nreliable. And more discussion was added (Line 431-436).\nIndeed, some key aspects are missing (e.g. how 12 upregulated genes in GA exposure were selected\nas potential biosensors, what role does biosensor dynamic range have in potentially increasing yield,\nwhat is considered a ‘control’ in this kind of research). This knowledge is essential for extending\nthe applicability of this concept. Some of these queries are detailed further below.\nResponse:\nThe basis for the selection was the genes encoding transporters or hypothetical proteins with\nupregulated expression. Based on this, 12 genes were selected, and we listed the protein functions\nand fold changes in Table S1. The related statements in manuscript were revised (Line 131).\nThe effect of dynamic range on the production was added and discussed in manuscript (Line 246),\nand a figure (Fig. 3c) was added to illustrate this impact. Theoretically, both increasing 3-AT\nconcentration and decreasing dynamic range can enhance the constraint of biosensor on cell growth.\nAll the ‘control’ was clarified and explained in manuscript (Line 137, 147, 289, 309, 419).\nThe single cell transcriptomics analysis produced some interesting results as any opportunity to look\ndeeply into a yeast culture and infer its performance via gene expression can do. In this case, the\nanalysis seemed an add-on and not central to the point of the paper. The bulk cell transcriptome\nanalysis already showed the key gene expression differences between GA184 and A8.\nResponse:\nWe added the comparison between the bulk and single-cell RNA-seq. The two methods\ndemonstrate overall consistency (Fig. S7). However, single-cell sequencing has demonstrated\nirreplaceability. The significant differentially expressed genes discovered through it in subgroups\ndo not show any differences in bulk transcriptome, such as ADY2 and CTA1 (Fig. S10). These\nDEGs in subgroups have also been shown to be key factors of product synthesis. Moreover, the\nconclusion of cell division in this article is entirely based on single-cell sequencing. These\nconclusions cannot be obtained using bulk transcriptome. More discussion was added (Line 486-\n493).\n[...]\nReviewer 2: Thank you for the comprehensive response and corresponding updates you\nhave made to the manuscript and supplementary information. This revised manuscript\nhas been a substantial undertaking and it has been done with care and attention to detail.\nMy queries have been satisfactorily addressed.\nOne minor point is your use of the terms \"strain degradation\" and \"degradation in cell\nfactories\". I think you could be more specific as to your meaning as they could be\nconfused with cell death.\nResponse:\nWe thank Reviewer for the positive evaluation of our work and the constructive\nsuggestions. We have made modifications to the word 'degradation' based on your\nsuggestion:\nP51 'strain degradation' was revised to 'degeneration'.\nP105 'the degradation of high-yield traits during passage' was revised to ' the\ninstability of high-yield traits during passage '.\nP266 'strain degradation' was revised to 'strain deterioration'.\nP429 'the issue of degradation in cell factories' was revised to 'the issue of\ndegeneration in cell factories'."}]

Decision letter