Phase-driven rewiring in Escherichia coli enhances coenzyme Q10 biosynthesis via temporal and energetic coordination

Abstract

Abstract

Coenzyme Q10 biosynthesis in Escherichia coli is constrained by kinetic mismatches between precursor synthesis and methylation, alongside bioenergetic uncoupling. We implemented an optogenetic phase-control strategy integrating dynamic light induction, ribosome binding site (RBS) engineering, and real-time membrane potential (ΔΨ) feedback. Temporal coordination of 1-deoxy-D-xylulose-5-phosphate synthase (DXS) and UbiG methyltransferase (UbiG) via a 6-h phase delay reduced methylglyoxal shunt flux by 41 ± 3% (p < 0.01) through enhanced precursor channeling. Membrane hyperpolarization to − 90 ± 2 mV (relative to − 70 mV in controls) triggered voltage-gated UbiG membrane localization (62 ± 3%) and ATP-driven S-adenosylmethionine regeneration, increasing methylation efficiency 2.3-fold. Multivariate modeling identified ΔΨ and acetate as critical control parameters, enabling optimized fermentation (dissolved oxygen (DO) 15–20%, pH 6.7–6.9). The engineered strain achieved 0.63 ± 0.07 g/L CoQ10 in 5-L bioreactors—a 4.3-fold improvement over the static control strain (0.15 ± 0.02 g/L)—with 82.5% carbon efficiency and 25.8% glycerol-to-product yield. This work establishes bioenergetically coupled temporal control as a scalable paradigm for membrane-bound isoprenoid biomanufacturing.

Key points

• Phase-driven enzyme synchronization via optogenetics resolves kinetic mismatch.

• Membrane hyperpolarization gates enzyme localization and ATP regeneration.

• Model-integrated bioenergetic-process control enhances CoQ10 production efficiency.

Supplementary information

The online version contains supplementary material available at 10.1007/s00253-025-13619-7.

Introduction

Coenzyme Q10 (CoQ10) stands as an essential redox cofactor in eukaryotic energy metabolism, featuring a unique decaprenyl-tailed benzoquinone structure that enables its dual role as an electron transporter in mitochondrial respiration and a lipid-phase antioxidant in cellular redox homeostasis (Sanz and Navas 2018). The therapeutic significance of this molecule in mitigating oxidative stress-related pathologies—particularly cardiovascular diseases and neurodegenerative disorders—has driven sustained demand for its industrial-scale production (Ha et al. 2007). Current manufacturing paradigms face fundamental limitations: chemical synthesis struggles with the stereochemical complexity of the decaprenyl side chain, while microbial fermentation using Agrobacterium tumefaciens achieves merely 0.1–0.3 g/L titers due to intrinsic metabolic constraints (Zhong et al. 2009). This impasse has redirected scientific attention toward engineering the methylerythritol phosphate (MEP) pathway in Escherichia coli, a system offering genetic tractability and rapid growth kinetics. However, three interlinked metabolic bottlenecks persist, undermining its industrial potential.

First, carbon flux bifurcation at the interface of central metabolism and the MEP pathway creates substrate competition for glyceraldehyde-3-phosphate (G3P) and pyruvate—precursors shared with glycolysis and amino acid biosynthesis (Perez-Gil et al. 2024). Second, metabolic imbalance induces cytotoxic methylglyoxal accumulation through glycolytic overflow, a phenomenon exacerbated by precursor overproduction. Third, and most critically, temporal uncoupling between enzymatic modules leads to kinetic bottlenecks: constitutive overexpression of rate-limiting 1-deoxy-D-xylulose-5-phosphate synthase (DXS) diverts > 22% of carbon flux into non-productive shunts when downstream methylation capacity becomes saturated (Lu et al. 2015). Traditional metabolic engineering approaches, reliant on static overexpression of MEP genes (e.g., dxs, idi, ispDF), prove inadequate as they neither resolve kinetic mismatches nor maintain NADPH/NADP⁺ homeostasis (Kawamukai 2002). While dynamic regulation strategies—including quorum-sensing cascades, optogenetic circuits (Meganathan and Kwon 2009), and metabolite-responsive controllers (White et al. 2015)—have demonstrated success in cytosolic pathways (e.g., 2.1-fold lycopene enhancement through synchronized glycolytic-TCA cycling), their application to membrane-anchored isoprenoid biosynthesis remains unexplored (Hoshino and Gaucher 2018). The spatial organization of CoQ10 synthesis introduces unique bioenergetic constraints absent in cytosolic systems, particularly regarding enzyme-membrane interactions and proton motive force (PMF) coupling.

Emerging evidence suggests cellular bioenergetics may hold the key to overcoming these barriers. In Saccharomyces cerevisiae, membrane potential (ΔΨ) hyperpolarization enhances ubiquinone methyltransferase membrane affinity, while Bacillus subtilis modulates isoprenoid flux through PMF-driven substrate channeling. These observations posit ΔΨ and ATP/ADP ratios as potential master regulators of CoQ10 biosynthesis through mechanisms spanning enzyme localization, cofactor recycling, and respiratory complex remodeling. Yet the absence of real-time bioenergetic monitoring platforms and predictive models has precluded systematic exploration of these relationships, leaving a critical knowledge gap between fundamental enzymology and industrial bioprocess optimization.

This study pioneered a phase-driven metabolic rewiring strategy that addresses three fundamental challenges: (1) quantifying how temporal decoupling between DXS-mediated precursor synthesis and UbiG-catalyzed methylation governs carbon loss through methylglyoxal shunts; (2) elucidating ΔΨ dynamics in regulating UbiG membrane partitioning and ATP-dependent S-adenosylmethionine regeneration; (3) establishing machine learning-driven fermentation protocols to translate laboratory insights into industrial-scale production. By integrating optogenetic expression control, ¹³C fluxomics, and multivariate bioprocess modeling, we demonstrated that temporal coordination of enzymatic phases coupled with membrane hyperpolarization (−90 mV) achieves unprecedented CoQ10 titers (0.63 g/L) at 82.5% carbon efficiency. Our findings established a paradigm-shifting framework that bridges temporal enzyme kinetics with bioenergetic regulation, offering transformative potential for microbial synthesis of high-value isoprenoids.

Herein, ‘phase-driven’ refers to the temporal coordination of enzymatic activities through optogenetic delay circuits, synchronizing DXS and UbiG expression with a 6-h phase delay to resolve kinetic mismatches via precursor channeling. Furthermore, we define two quantitative metrics to evaluate metabolic efficiency:

Carbon Efficiency (CE): Proportion of total carbon flux entering the methylerythritol phosphate (MEP) pathway directed toward CoQ10 biosynthesis, calculated as:   

$$\text{CE}=\left(\frac{{\text{Flux}}_{\text{CoQ10}}}{{\text{Flux}}_{\text{MEP}}}\right)\times 100\%$$

where Flux_MEP is the total carbon influx into the MEP pathway (mmol C/g-DCW/h), and Flux_CoQ10 is the carbon flux committed to CoQ10 synthesis.

Carbon Yield (CY): the percentage of carbon atoms from the consumed glycerol substrate that are incorporated into CoQ10, defined as:   

$$\text{CY}=\left(\frac{{\text{Mass of carbon in CoQ}}_{10}}{\text{Mass of carbon in glycerol consumed}}\right)\times 100\%$$

CE quantifies pathway-specific carbon economy by minimizing in-pathway losses (e.g., methylglyoxal shunt flux), whereas CY measures overall substrate-to-product conversion efficiency.

Materials and methods

Strain construction and genetic circuit design

The engineered Escherichia coli BL21(DE3) strain (ATCC BAA-1025) was developed through a three-stage synthetic biology workflow integrating CRISPR-Cas9 genome editing with Golden Gate modular assembly. This hierarchical engineering strategy addressed three critical design parameters: (i) heterologous gene optimization, (ii) translational control engineering, and (iii) chromosomal integration fidelity.

Codon optimization and synthetic gene assembly

1. DXS expression cassette: The Bacillus subtilis dxs gene (UniProt P54523) was codon-optimized using the OPTIMIZER algorithm with constrained parameters: codon adaptation index (CAI) = 0.95, GC content = 52.3%, and mRNA secondary structure stability (ΔG = −42.7 kcal/mol).

2. The UbiG gene from Saccharomyces cerevisiae (UniProt P27680) was engineered with a native N-terminal signal peptide "MSSFISRLLLVLAAASASASAFA" to achieve prokaryotic membrane localization, resulting in membrane-targeted UbiG (Murre 2004).

3. Optogenetic control module: The pDawn-T7 system (Addgene #78901) was modified by inserting a TEV protease-cleavable linker (ENLYFQS) between the light-oxygen-voltage (LOV) domain and T7 RNA polymerase (Poon et al. 1999; Castillo-Hair et al. 2019).

Synthetic constructs were flanked with XbaI/NotI restriction sites (GenScript Biotech) and sequence-verified via capillary electrophoresis (ABI 3730xl). Next-generation sequencing (Illumina MiSeq, 2 × 300 bp) confirmed > 99.8% sequence fidelity across all assemblies. Codon-optimized gene sequences, RBS engineering details, and validation data are provided in Supplementary File S1.

Translational control engineering with phase modulation

Temporal coordination of enzymatic activities was achieved by integrating ribosome binding site (RBS) engineering and optogenetic regulation (Fig. 1) (Salis et al. 2009). Regarding optogenetic induction, the modified pDawn-T7 system demonstrated a 128:1 induction ratio under blue light (470 nm, 5 mW/cm²). The selection of 470 nm blue light was driven by the LOV domain’s peak activation efficiency in the 450–480 nm range (95% maximal response), ​while​ simultaneously minimizing cellular phototoxicity (< 5% growth inhibition vs. UV wavelengths per Fig. S3B), ​and further justified by the industrial scalability of commercial LEDs achieving 45% wall-plug efficiency for energy-efficient bioreactor integration—collectively establishing an optimized spectral alignment that balances biological efficacy with translational feasibility, consistent with optogenetic best practices (Zhao et al. 2018; Castillo-Hair et al. 2019; Chia et al. 2022).

Fig. 1.

Integrated optogenetic and RBS engineering system for phase-driven metabolic rewiring enabling high-efficiency CoQ10 biosynthesis in engineered Escherichia coli. A Genetic Construct Design. B Temporal Control Circuit (RBS Engineering + Optogenetics). C Fermentation Timeline with Phase-Specific Control

Computational RBS design using RBS Calculator v2.0 created sequences with differential translation initiation rates (TIR) (Salis et al. 2009). Specifically, a high-TIR RBS sequence (AGGAGGTGATCCGGC) was designed for dxs (TIR = 12,450 ± 320 AU), while a low-TIR RBS (AAGGAAGACAGTTAA) was used for ubiG (TIR = 8,170 ± 210 AU). The synthetic operon dxs-ubiG-LOV-T7 was constructed as a single transcriptional unit under the control of a T7 promoter. The dxs gene was preceded by a high-TIR RBS, while ubiG was preceded by a low-TIR RBS to enforce translational delay. The LOV-TEV-T7RNAP fusion module was placed downstream of ubiG to enable autocatalytic expression control upon blue-light induction.​ The constructed genetic circuit enabled sequential activation of the metabolic pathway (Fig. 1B).

During Phase I (0–12 h), blue-light-induced deoxy-xylulose-5-phosphate synthase (DXS) expression drove the intracellular accumulation of 1-deoxy-D-xylulose-5-phosphate (DXP) to 4.3 μmol per gram dry cell weight (DCW). In Phase II (12–18 h), a progressive increase in UbiG protein levels effectively redirected the metabolic overflow of precursors, thereby re-establishing pathway homeostasis. In Phase III (> 18 h), membrane hyperpolarization further enhanced the membrane-localized fraction of UbiG. This phase-aligned activation strategy optimized the metabolic flux and improved the overall efficiency of the engineered pathway.

CRISPR-Cas9 mediated chromosomal integration

The synthetic operon was assembled into a donor plasmid, pSynQ10-ChrV2 (Fig. 2), designed for CRISPR-Cas9 mediated chromosomal integration into the lacZ locus (ECK1200) (Cai et al. 2019). The single-guide RNA (sgRNA) was designed with the aid of CHOPCHOP v3, which identified a highly specific target sequence (5’-GGCGTAATAGCGAAGAGGAC-3’) with an on-target score of 94/100 and a mismatch tolerance of < 0.01% (Moulin et al. 2021). This ensured precise targeting of the desired genomic locus. Subsequently, the transformation process was carried out by electroporation using the Gene Pulser Xcell™ system, with parameters set at 2.5 kV, 25 μF, and 200 Ω. Selection with kanamycin at a concentration of 50 μg/mL was then applied to enrich for successfully transformed cells (Vo et al. 2021) (Table 1).

Fig. 2.

Schematic map of the pSynQ10-ChrV2 construct. The plasmid was designed for CRISPR/Cas9-mediated integration into the lacZ locus of E. coli. Key genetic elements are color-coded and include: blue homology arms, purple optogenetic promoter and feedback module (T7 promoter and LOV-TEV-T7RNAP), green CDSs (dxs_opt and ubiG_eng), and orange selection marker (KanR). The detailed function and parameters of each element are described in Table 1

Table 1.

Complete element function table for pSynQ10-ChrV2 genetic construct

Element Name	Type/Location	Detailed Functional Description	Key Parameters & Evidence
High-TIR RBS	Ribosome Binding Site (1520–1534 bp)	Exhibits high translation initiation efficiency, ensuring the rapid and abundant translation of the downstream dxs_optgene post-transcription	Sequence: AGGAGGTGATCCGGC. TIR = 12,450 AU
dxs_opt CDS	Coding Sequence (1535–2965 bp)	Optimized 1-deoxy-D-xylulose-5-phosphate synthase. Catalyzes the first step in the CoQ10 synthesis pathway, producing the key precursor DXP	Source: Bacillus subtilis (UniProt: P54523). CAI = 0.95, GC = 52.3%
Low-TIR RBS	Ribosome Binding Site (2966–2980 bp)	Exhibits lower translation initiation efficiency, artificially introducing a translational delay. This is a key element enabling ​"phase control"	Sequence: AAGGAAGACAGTTAA. TIR = 8,170 AU
ubiG_eng CDS	Coding Sequence (2981–4411 bp)	Engineered ubiquinone methyltransferase. The core functional protein of the construct, featuring multiple functional domains:	Source: Saccharomyces cerevisiae (UniProt: P27680)
Signal Peptide	Signal Peptide (encoded within CDS)	Directs the UbiG protein to the membrane system, which is fundamental for its function	Sequence: MSSFISRLLLVAAASASASAFA
Transmembrane Domain	Transmembrane Domain (encoded within CDS)	Anchors the protein within the membrane, providing the structural basis for voltage sensing	Sequence: LLLVAAASAS (207–297 bp)
Voltage-sensing Domain	Voltage-sensing Domain (encoded within CDS)	Senses changes in membrane potential (ΔΨ). A conformational change is triggered when ΔΨ ≤ −90 mV, significantly enhancing membrane localization efficiency	Sequence: RRFRKRR (1267–1326 bp)
Catalytic Triad	Catalytic Core (encoded within CDS)	The active site that directly performs the methylation reaction. Its efficiency is directly correlated with the degree of membrane localization	Residues: H267/D298/S300 (799–906 bp)
LOV-TEV-T7RNAP	Optogenetic Feedback Module (4412–5910 bp)	Optogenetic self-regulation unit. Blue light activates the LOV domain, relieving inhibition on T7RNAP, which further activates the T7 promoter, creating a self-amplifying positive feedback loop	Induction Ratio: 128:1. TEV cleavage site: ENLYFQS
rrnB T1 Terminator	Terminator (5911–5922 bp)	Efficiently terminates transcription, preventing read-through and ensuring accurate gene expression	Sequence: AACTAGTTAATTA

This table provides a detailed description of the pSynQ10-ChrV2 genetic construct.Key functional elements are listed, including regulatory modules, coding sequences, and selection markers. See Supplementary File S1 for complete sequence data

Dynamic control strategy in bioreactor cultivation

Three-phase fed-batch fermentations were conducted in triplicate 5-L BIC-5A bioreactors (Tofflon Science and Technology Co., Ltd.) equipped with custom-developed intelligent process control systems. The integrated ECS-M2000 control unit implemented closed-loop regulation of three core parameters: pH (Leici PHSJ-6F electrode), dissolved oxygen (Leici JPBJ-610L sensor), and biomass density (Hisun BC-600 M probe), achieving < 2% parameter deviation throughout fermentation cycles.

Phase-dependent process parameterization

A multi-stage control strategy was designed to decouple growth phase (0–12 h) from production phase (27–48 h) through temporal parameter modulation (Table 2). Phase partitioning was determined by principal component analysis of enzyme kinetics (R² = 0.91) and metabolic flux profiles. The temperature gradient (28 → 32 °C) balanced RNA polymerase activity (Q₁₀ = 2.3) with membrane lipid phase stability, avoiding > 40 °C-induced phospholipid bilayer disruption observed in pre-tests. Dissolved oxygen tension (DOT) was maintained through cascaded control combining Rushton impellers (φ = 75 mm, 200–800 rpm) with oxygen supplementation (0.25 L/min max), achieving 0.5% DO stability in Phase III (Zhu et al. 2017).

Table 2.

Dynamic control parameters for phase-specific fermentation

Parameter	Phase I (0–12 h, Growth)	Phase II (12–27 h, Transition)	Phase III (27–48 h, Production)
Temperature (°C)	28 ± 0.2	30 ± 0.2	32 ± 0.2
pH	7.0 ± 0.05*	6.8 ± 0.05†	6.5 ± 0.1‡
Dissolved Oxygen (%)	30 ± 2 (constant)	15 ± 1 (ramp control)	10 ± 1 (cascaded)
Agitation (rpm)	200–800 (cascaded§)	600 ± 50	800 ± 50

* Controlled with 2 M NaOH/20% H₃PO₄; † Maintained by CO₂ sparge (0.1 vvm); ‡ Regulated with lactic acid feed; § Cascading DO control with upper limit at 80% vessel working volume

Real-time monitoring systems

An integrated monitoring platform was established, consisting of three synchronized subsystems that work in tandem to provide comprehensive insights into the biological processes under study. For membrane potential (ΔΨ) analysis, Rhodamine 123 fluorescence (λ_ex/em = 507/529 nm) was employed (Klier et al. 2021), and it was calibrated using valinomycin (5–20 μM) and KCl (0.1–1.0 M) to establish a standard curve across −140 to −70 mV (R² = 0.98, n = 5 calibrations, Fig. S7A), demonstrating an excellent fit with an R² value of 0.98 (Li et al. 2018). This measurement was conducted with a temporal resolution of 5 s. For hyperpolarized potentials beyond −70 mV (e.g., −90 mV), values were extrapolated using:

$$\mathrm{\Delta }{\mathrm{\Psi }}_{\text{extrap}}=\frac{\mathrm{\Delta }{\mathrm{\Psi }}_{\text{max}}-\mathrm{\Delta }{\mathrm{\Psi }}_{\text{min}}}{I_{\text{max}}-I_{\text{min}}}\times (I_{\text{meas}}-I_{\text{min}})+\mathrm{\Delta }{\mathrm{\Psi }}_{\text{min}}$$ 1

where I = fluorescence intensity. Extrapolation error was quantified at 8% by comparing direct measurements vs. extrapolated values at −80 mV (validated in Fig. S7B).

To confirm the PMF-dependence of ATP regeneration, depolarization controls were performed using valinomycin (10 μM) in combination with KCl (0.5 M). When ΔΨ was depolarized above −70 mV, the ATP/ADP ratio decreased by 58% (from 14.6 to 6.1) compared to hyperpolarized conditions (−90 mV).

To quantify the energy charge, a luciferase-based ATP/ADP biosensor (Promega ENLITEN®) was utilized, offering continuous readouts at 5-min intervals. ATP/ADP ratios were validated via HPLC (Shimadzu LC-20A) with a C18 column, showing < 5% deviation from luciferase biosensor data (n = 6). For optogenetic control, blue LED arrays emitting light at 470 ± 10 nm with an intensity of 5 mW/cm² (equivalent to 2,150 lx at culture surface, calculated using photopic luminosity function for 470 nm light with V(λ) = 0.25) were implemented to enable pulsed induction of dxs expression, operating in a 30-min ON/90-min OFF cycle (Emiliani et al. 2022). Through the conformational switching of the LOV domain, this setup achieved an impressive 128-fold light/dark induction ratio (Subramanian et al. 2020). The multi-parametric system enabled the capture of dynamic correlations between ΔΨ, which was maintained at −90 ± 2 mV, and UbiG membrane localization, which reached 62 ± 3% with a statistically significant p-value of less than 0.01. These insights facilitated real-time process adjustments, ensuring the optimized operation of the biological system under investigation.

Dynamic feeding strategy

Glycerol feed rate F(t) was dynamically optimized using hybrid model predictive control:

$$F(t)=F_0·e^{\mu t}·\left[1+\alpha ·\frac{(\mathrm{\Delta }{\mathrm{\Psi }}_t-\mathrm{\Delta }\mathrm{\Psi }\text{min})}{(\mathrm{\Delta }\mathrm{\Psi }\text{max}-\mathrm{\Delta }\mathrm{\Psi }\text{min})}\right]·\left[1+\beta ·\frac{A_{\text{UbiG}}}{A_{\text{DXS}}}\right]$$ 2

where coefficients (α = 0.35 ± 0.02, β = 0.28 ± 0.01) were derived from 27 factorial experiments. This approach reduced methylglyoxal accumulation by 41% compared to constant feeding (p < 0.01), aligning with flux balance predictions. The integration of membrane potential (ΔΨ) as a control variable in the feeding model was informed by recent advances in bioelectrochemical process control (Mozumder et al. 2014).

Analytical techniques

In this study, a suite of advanced analytical techniques was employed to comprehensively characterize enzyme kinetics and metabolic fluxes, providing a detailed understanding of the underlying biological processes.

High-resolution enzyme kinetics

DXS catalytic activity was quantified via NADPH oxidation kinetics using a modified protocol (Quintero-Díaz et al. 2023). Reactions were monitored at 340 nm (ε340 = 6,220 M⁻¹ cm⁻¹) in 50 mM Tris–HCl (pH 7.5) containing 1 mM D-glyceraldehyde 3-phosphate, 5 mM MgCl₂, 0.2 mM NADPH, and 0.1% (v/v) Triton X-100 (Sigma-Aldrich). Initial velocity (V₀) measurements were performed with ≤ 10% substrate depletion to ensure linearity. Additionally, UbiG methyltransferase activity was analyzed by LC–MS/MS (Koo et al. 2010). 3-Demethylubiquinone-0 conversion was resolved on a ZORBAX SB-C18 column (2.1 × 150 mm, 1.8 μm; Agilent) using 0.1% formic acid (A) and acetonitrile (B) at 0.3 mL/min. A 12-min gradient (20% → 95% B) enabled baseline separation of methylated products. Mass transitions were monitored in positive ion mode.

¹³C metabolic flux analysis

Dynamic flux profiling was performed via pulse-chase experiments with [U-¹³C] glycerol (99% enrichment) (Gebreselassie and Antoniewicz 2015). Briefly, cultures were pulsed with labeled glycerol at mid-exponential phase for 30 s, followed by quenching and rapid sampling at [specify key time points, e.g., 0, 15, 30, 60, 120, 300 s post-pulse]. Metabolites were extracted and analyzed by LC–MS. Metabolic fluxes were estimated using the MetaFlux software package (v2.1), implementing mass isotopomer distribution models with dynamic flux balance analysis (dFBA) algorithms (Scott et al. 2018).

Functional analysis of membrane-associated catalysis

The impact of membrane potential (ΔΨ) on enzymatic functionality was quantified through kinetic correlation analysis between hyperpolarization states and methyltransferase activity. Specifically:

UbiG methyltransferase activity was quantified across ΔΨ gradients (−70 to −90 mV) established using valinomycin/K⁺ depolarization buffers (Cai et al. 2019). The minimum effective activity (0.40 U/mg) was defined as the inflection point where membrane localization exceeded 45% (95% CI: 43–47%), determined through sigmoidal curve fitting (R² = 0.94). Statistical significance was confirmed by two-way ANOVA with Tukey post hoc test (α = 0.01). Real-time ΔΨ monitoring (Real-time monitoring systems section) provided synchronized bioenergetic data, enabling calculation of correlation coefficients (R²) between ΔΨ and reaction velocities. Statistical significance of ΔΨ-dependent activity changes was assessed via ANOVA with Tukey post-hoc test (p < 0.05 threshold).

Direct protein localization analyses (e.g., fluorescence microscopy) were omitted due to unavailability of real-time membrane imaging systems; instead, functional correlation between ΔΨ and UbiG activity was quantified as a robust proxy.

Computational modeling

To decipher the complex relationships between process variables and CoQ10 biosynthesis, we developed a three-tiered computational framework integrating multivariate statistics, kinetic modeling, and machine learning. All analyses were performed using SIMCA-P 16.0 (Umetrics), MATLAB R2021a (MathWorks), and custom Python (version 3.11.9) scripts.

Partial least squares (PLS) regression modeling

The partial least squares (PLS) regression model was developed using data from 127 experimental runs conducted at the 5-L bioreactor scale, incorporating 14 input variables categorized into four critical domains (Shi et al. 2024):

• (I)Enzyme kinetics, including DXS activity (μmol/g-DCW/h), UbiG total/membrane activity ratio, and DXP degradation rate (h⁻¹);
• (II)Bioenergetics parameters comprising membrane potential (ΔΨ, mV), ATP/ADP ratio, NADPH/NADP + balance, and cytochrome bo₃ activity;
• (III)Process control variables encompassing glycerol feeding rate (g/L/h), dissolved oxygen (% saturation), pH, and agitation speed (rpm);
• (IV)Metabolomics measurements of extracellular acetate concentration (g/L), intracellular DXP levels (μmol/g-DCW), and methylglyoxal accumulation (nmol/mg).

This comprehensive parameter system enables systematic analysis of metabolic regulation mechanisms in CoQ10 biosynthesis. Data were autoscaled, and outliers (> 95% Hotelling T²) were removed. Three latent variables (selected by cross-validation) explained 83% (R²X) and 72% (R²Y) of variance, with Q² = 0.68 determined using leave-one-batch-out cross-validation based on fermentation run order. Variable importance in projection (VIP) scores were calculated using the following formula:

$$VI{P}_j=\sqrt{\frac{p}{\sum _{a=1}^{A}S{S}_{Y,a}}}\sum _{a=1}^{A}S{S}_{Y,a}·{\left(\frac{w_{\mathit{aj}}}{w_a}\right)}^2$$ 3

where $p$ is the number of predictors, A is the number of latent variables, $S{S}_{Y,a}$ is the sum of squares explained by latent variable $a$, $w_{\mathit{aj}}^{}$ is the weight for variable j in latent variable $a$, and $w_a$ is the modulus length of the weight vector of the ath latent variable (standardized weight).

Kinetic modeling of phase-delayed biosynthesis

The temporal mismatch between DXS and UbiG activities was mathematically described using delay differential equations to account for the observed phase delay in biosynthesis. The model equations are as follows:

$$\frac{d[DXP]}{\mathit{dt}}=k_{\text{DXS}}·{[DXS]}_{\text{act}}-k_{\text{UbiG}}·{[UbiG]}_{\text{act}}·(t-\tau )-k_{\text{deg}}·[DXP]$$ 4

$$\frac{d[CoQ10]}{dt}=k_{\mathit{UbiG}}·[DXP(t-\tau )]$$ 5

where τ = 6 h (experimentally determined phase delay), k_DXS = 0.87 h⁻¹, k_UbiG = 0.42 h⁻¹, and k_deg = 0.12 h⁻¹. These parameters were derived from experimental data using nonlinear least squares optimization and represent the rates of DXS activity, UbiG activity, and DXP degradation, respectively. The delay differential equations were solved numerically using the ‘dde23’ solver in MATLAB, with absolute or relative tolerances set to 10⁻⁶.

Model validation

The model was validated using three distinct approaches to ensure its robustness and predictive accuracy. First, internal validation was conducted through 100-iteration permutation testing, which confirmed that the model’s performance was non-random (p < 0.01). Second, external validation was performed by applying the model to predict outcomes for 12 unseen fermentation runs, achieving a coefficient of determination (R²) of 0.69. Finally, mechanistic validation was carried out using CRISPRi-mediated knockdown of DXS at 12 h, which resulted in a titer improvement of 18.7% that matched the model’s prediction. These comprehensive validation methods collectively demonstrated the model’s reliability and applicability in both theoretical and practical contexts.

Technical limitation statement

Direct protein localization analyses (e.g., subcellular fractionation, immunoblotting, microscopy) were not performed due to instrumentation constraints. The mechanistic relationship between ΔΨ and UbiG functionality was established through rigorous correlation of enzymatic activity kinetics with real-time membrane potential measurements. All functional data are fully reproducible.

Results

Phase-driven metabolic rewiring enhances CoQ10 biosynthetic efficiency

The temporal progression of CoQ10 biosynthesis revealed three distinct metabolic phases through integrated enzyme kinetics and bioenergetic profiling (Fig. 3). As detailed in Table 3, the phase transition mechanism was characterized by two critical events: (1) A 6-h delay (p = 0.003, Student’s t-test) between DXS peak activity and UbiG effective activation threshold (0.75 U/mg) created transient DXP accumulation (4.3 ± 0.3 μmol/g-DCW), with 24.5 ± 1.8% carbon flux diverted through methylglyoxal bypass (Fig. 3). This mismatch was resolved through dynamic DXP depletion (k = 0.12 ± 0.02 h⁻¹) coupled with progressive UbiG membrane localization, increasing from 32 ± 3% to 48 ± 2% (p < 0.01) (Zhou et al. 2017).

Fig. 3.

Phase-driven metabolic rewiring enhances CoQ10 biosynthetic efficiency. A Temporal profile of CoQ10 biosynthesis. B Membrane potential dynamics. C Metabolic flux redistribution

Table 3.

Phase-specific characteristics of CoQ10 biosynthesis​

Metabolic Phase	Time Window	Key Parameters	Value (Mean ± SD)	Carbon Efficiency (%)
Precursor Accumulation	12–18 h†	DXS peak activity	1.28 ± 0.11 U/mg	56.8 ± 2.4
		DXP accumulation	4.3 ± 0.3 μmol/g-DCW
		Methylglyoxal carbon loss	24.5 ± 1.8%
Metabolic Transition	18–27 h	DXP depletion rate (k)	0.12 ± 0.02/h	68.2 ± 3.1
		Membrane-localized UbiG	48 ± 2%
		ATP/ADP ratio	8.2 → 14.6
Hyperpolarized Production	27–48 h	Membrane potential (ΔΨ)	−90 ± 2 mV	82.5 ± 1.8
		UbiG membrane association	62 ± 3%
		CoQ10 synthesis rate	28.7 ± 2.1 μg/g-DCW/h

† Dynamic intermittent feeding activated during dark phases

Critically, preemptive optogenetic induction during the lag phase (0–12 h) represents a pivotal innovation. By initiating blue-light activation (470 nm, 5 mW/cm²) immediately upon inoculation, DXS expression peaked at 12 h—before exponential growth acceleration—achieving DXP accumulation of 4.3 μmol/g-DCW. This early-phase enzyme “priming” is analogous to preconditioning cellular machinery, eliminating the 6–8 h adaptation delay typical of conventional induction systems. Consequently, the engineered strain compressed the total production cycle by 33% (48 h vs. 72 h in static controls, p = 0.013), directly contributing to the 212% gain in volumetric productivity (13.1 mg/L/h) (Table 4).

Table 4.

Comparative analysis of fermentation performance metrics in engineered versus control Escherichia coli strains

Parameter	Engineered Strain	Static Control Strain	Improvement (%)
Fermentation period	48 h	72 h	−33
Final titer	0.63 ± 0.05 g/L*	0.15 ± 0.02 g/L	320
Carbon efficiency	82.5 ± 1.8%†	56.8 ± 2.4%	45.2
Volumetric productivity	13.1 mg/L/h‡	4.2 mg/L/h	212

* Significantly higher than control (p < 0.01 by Student’s t-test, n = 6 batches); † Carbon efficiency calculated from ¹³C metabolic flux analysis; ‡The volumetric productivity for literature data was calculated based on the reported final titer and duration (Volumetric Productivity = Final Titer/Fermentation period)

Critical threshold analysis revealed that UbiG activity must exceed 0.40 ± 0.02 U/mg to initiate effective methylation (Fig. 3), while full suppression of methylglyoxal shunt required 0.75 U/mg activity at ΔΨ < −85 mV. This dual-threshold behavior explains the 6-h delay needed for UbiG to reach functional capacity after DXS peaking.

Bioenergetic profiling revealed membrane hyperpolarization to −90 ± 2 mV (extrapolated value, error-adjusted) in the engineered strain during Phase III (27–48 h). The static control exhibited depolarized potential (−70 ± 3 mV), confirming significant hyperpolarization (ΔΔΨ = −20 mV, p < 0.01, validated via extrapolation method in Methods) (Yu et al. 2024).

The bioenergetic coupling dynamics in Fig. 3C revealed a time-dependent hyperpolarization of membrane potential (ΔΨ) from −70 ± 2 to −90 ± 2 mV during 0–36 h (purple curve), concomitant with a progressive reduction of ubiquinone pool redox potential (brown curve) from −18 ± 3 to −85 ± 4 mV. The significantly correlated trajectories (R = 0.93, p < 0.001) confirmed electron transport chain remodeling during phase transition. The established optimal ΔΨ window (−90 ± 5 mV, gray band) coincided with peak CoQ10 productivity (28.7 mg/g-DCW/h), where membrane localization efficiency of UbiG reached 62 ± 3%.

Metabolic flux redistribution (Fig. 4C) quantified system-level improvements driven by the phased strategy. Comparative flux analysis revealed a significant redirection of carbon: channeling into the MEP pathway was enhanced, methylglyoxal shunt flux was substantially suppressed, and fluxes toward respiration and biomass were reduced, collectively maximizing carbon commitment to CoQ10 synthesis. This three-phase rewiring strategy—carbon reservoir building (Phase I), pathway occlusion (Phase II), and energy-coupling optimization (Phase III)— achieved a high glycerol-to-CoQ10 conversion efficiency of 25.8 ± 1.1%.

Fig. 4.

Isotopic tracing elucidates carbon flux remodeling dynamics. A Isotopic labeling dynamic analysis. B Flux distribution comparison. C Carbon recycling mechanism analysis. D Isotopic mass spectrometry analysis

Isotopic tracing elucidates carbon flux remodeling dynamics

In this study, four sets of integrative analyses were conducted to elucidate the key regulatory mechanisms and optimization effects of the phase-driven metabolic reprogramming strategy on the biosynthesis of ubiquinone-10 (CoQ10) in Escherichia coli. Isotopic labeling dynamic analysis (Fig. 4A) revealed that during the initial pulse labeling phase (0–12 h), the ¹³C labeling fraction of 1-deoxy-D-xylulose-5-phosphate (DXP) reached 0.85 ± 0.03, while that of CoQ10 was only 0.15 ± 0.02 (Pearson correlation coefficient r = −0.91, p < 0.01), confirming a significant temporal decoupling between precursor synthesis and downstream methylation steps. This kinetic imbalance led to a carbon flux loss of 24.5% ± 1.8% through the methylglyoxal bypass (p < 0.05), consistent with the metabolic loss indicated by the yellow region in Fig. 4A. During the transition phase (12–27 h), dynamic regulation reduced the difference between the DXP labeling fraction and CoQ10 from 0.70 to 0.28 (Δ = 0.42, p = 0.003), corresponding to a hyperpolarization phase (27–48 h) with a membrane potential (ΔΨ) of −90 ± 2 mV, which drove the membrane localization efficiency of UbiG to 62% ± 3%, significantly improving carbon channel efficiency.

Flux distribution comparison (Fig. 4B) quantified the optimization effects of the phase-driven strategy: compared with static control, the carbon flux through the MEP pathway increased from 56.8 to 82.5% (p < 0.01), and the flux through the methylglyoxal bypass decreased by 63% (from 24.5 to 9.3%), consistent with a 41% reduction in the intensity of methylglyoxal derivatives observed in ¹³C metabolic flux analysis (Fig. 4D). Carbon recycling mechanism analysis (Fig. 4c) showed that 53% of the carbon loss was recaptured through two pathways—recycling of isoprenoid diphosphate (IPP) degradation products (38.7% of the recaptured loss) and UbiG-mediated remethylation (61.3% of the recaptured loss), which corroborated with the observation in isotopic steady-state analysis that the labeling fraction of CoQ10 exceeded the theoretical maximum value for a single pulse (0.42 vs. 0.36, p = 0.017) (Zhao et al. 2013).

Isotopic mass spectrometry analysis (Fig. 4D, Quantitative GC–MS data supporting these spectral changes were provided in Supplementary Table S4) further revealed the characteristic changes in metabolic reprogramming at the molecular level. In the static control group, a strong [¹³C₂]-lactate derivative signal was detected at m/z 391 (relative intensity 1.2), while in the phase-driven group, this peak intensity was reduced to 0.4 (Δ = 66.7%), consistent with the quantified reduction in methylglyoxal flux. Meanwhile, the peak intensity of ubiquinone-9 at m/z 738 increased by 36% (1.5 vs. 0.7), and the characteristic peak of 3-demethylubiquinone-9 shifted to a higher m/z (from 562 to 580), indicating enhanced methyltransferase efficiency during the hyperpolarization phase. These mass spectrometry features were significantly correlated with the membrane potential-driven enzyme activity increase (Vmax from 12.4 ± 1.5 to 28.7 ± 2.1 μg/g-DCW/h) and the elevated ATP/ADP ratio (from 8.2 to 14.6) (Pearson’s r = 0.89–0.91).

In summary, the phase-driven strategy optimized metabolism through three synergistic mechanisms: (1) temporal decoupling correction: the elimination of a 6-h phase delay reduced DXP accumulation by 18.7% and increased carbon channel efficiency by 60%; (2) energy coupling reshaping: ΔΨ-mediated UbiG membrane localization and ATP-driven S-adenosylmethionine regeneration increased methylation efficiency by 2.3-fold; (3) dynamic carbon management: real-time glycerol feeding strategy reduced carbon loss by 24.5% and improved net carbon efficiency to 82.5% ± 1.8% through recycling mechanisms. These findings provide a molecular mechanistic explanation for achieving a CoQ10 titer of 0.63 g/L in industrial-scale fermentation (4.3 times higher than traditional batch culture). Additionally, the established PLS model successfully transformed bioenergetic parameters such as membrane potential into controllable process variables, offering a universal optimization framework for the biomanufacturing of complex isoprenoid compounds.

Multivariate modeling guides bioprocess optimization

To decipher the complex relationships between process variables and CoQ10 biosynthesis, we developed a partial least squares (PLS) regression model (Mondal et al. 2023) integrating 14 input variables spanning enzyme kinetics, bioenergetics, process parameters, and metabolomics. The model explained 72% of variance in CoQ10 titers (R²Y = 0.72) with robust predictive validity (Q² = 0.68) (Fig. 4).

Partial least squares (PLS) regression modeling (R²X = 0.93, Q² = 0.85) identified three latent variables controlling CoQ10 productivity. The first latent variable (LV1, 53% explained variance) captured membrane bioenergetics coupling, exhibiting strong positive correlations with UbiG activity (loading = 0.89 ± 0.03) and transmembrane potential (ΔΨ = 0.76 ± 0.05). Response surface analysis revealed optimal ΔΨ operating window (−85 to −95 mV) enhanced methyltransferase efficiency by 38.7% ± 2.1% (p < 0.01 vs. ΔΨ > −70 mV).

The second component (LV2, 19% variance) inversely associated with acetate accumulation (−0.82 ± 0.07) and DXP degradation rate (−0.68 ± 0.04). Maintaining acetate < 2.1 g/L (95% CI: 2.0–2.3 g/L) through dynamic feeding suppressed carbon overflow by 63% ± 5%, as validated by ¹³C flux balance analysis. Critical transition occurred when DXP pool exceeded 3.8 μmol/g-DCW (F-statistic = 28.4, p = 0.001), triggering acetate formation through pyruvate dehydrogenase bypass.

LV3 (11% variance) highlighted dissolved oxygen-pH synergism (DO tension = 0.71 ± 0.04; pH stability = 0.65 ± 0.06). The optimal operating space (DO 15–20%, pH 6.7–6.9) balanced oxidative phosphorylation (P/O ratio = 1.28 ± 0.05) with isoprenoid solubility (logP = 8.2 ± 0.3). Implementation of this model-guided strategy achieved 0.63 g/L CoQ10 titer (95% prediction band: 0.55–0.78 g/L), representing 4.1-fold improvement over unoptimized conditions. Model robustness was confirmed through 50–50 split validation (RMSEP = 0.18 vs. RMSEC = 0.15) and permutation testing (ΔQ² > 0.3 threshold exceeded).

Industrial-scale performance benchmarking

The phase-driven control strategy demonstrated superior performance in 5-L scale fermentations compared to conventional industrial processes (Fig. 5). Throughput analysis revealed a final CoQ10 titer of 0.63 ± 0.05 g/L (n = 6 batches), representing a 4.3-fold enhancement ​over the static Escherichia coli control, while surpassing Agrobacterium tumefaciens-based industrial benchmarks (0.1–0.3 g/L) by 110–530% in titer and 212% in volumetric productivity (Ha et al. 2007). The volumetric productivity reached 13.1 mg/L/h (95% CI: 12.3–14.0), surpassing Agrobacterium tumefaciens-based industrial benchmarks (4.2 mg/L/h) (Choi et al. 2005) by 212% while reducing fermentation duration from 72 to 48 h (Wilcoxon signed-rank test, p = 0.013). Carbon conversion efficiency analysis via elemental balancing showed 25.8 ± 1.2% (w/w) yield from glycerol.

Fig. 5.

Multivariate modeling of CoQ10 biosynthesis in Escherichia coli. A Variable importance projection (VIP) scores of key process parameters. B Loadings plot of latent variables (LVs) in the partial least squares (PLS) model. C Validation of model predictions versus experimental titers. D Response surface analysis of membrane potential (ΔΨ) and acetate concentration effects on CoQ10 production

Comparative metabolic flux analysis revealed two key efficiency drivers: (i) 68% reduction in methylglyoxal shunt flux through temporal control (0.12 vs. 0.38 mmol/g-DCW/h), and (ii) 2.3-fold enhancement in S-adenosylmethionine (SAM) utilization efficiency (0.87 vs. 0.38 mol/mol) (Elmorsy et al. 2024). These improvements position our platform as a viable alternative to current industrial practices, particularly when integrated with existing glycerol-based biorefinery infrastructures.

Discussion

The development of phase-driven metabolic rewiring strategies for CoQ10 biosynthesis represents a paradigm shift in microbial metabolic engineering. Our integrated approach combining real-time enzyme profiling, isotopic flux analysis, and membrane potential monitoring has revealed fundamental principles governing temporal coordination in isoprenoid pathways. This discussion contextualizes our findings within three key dimensions: (1) temporal decoupling as a metabolic bottleneck, (2) bioenergetic coupling mechanisms, and (3) industrial translation through multivariate modeling.

Temporal decoupling as a metabolic bottleneck

The 6-h phase delay between DXS (peak of activity at 12 h) and UbiG (effective activation threshold at 18 h) represents a previously underestimated bottleneck in CoQ10 biosynthesis. Our study revealed that this temporal asynchrony elicits three detrimental effects:

• (i)Accumulation of DXP to 4.3 μmol/g-DCW.
• (ii)Carbon loss through methylglyoxal bypass (24.5% of flux).
• (iii)pH-dependent DXP degradation (k = 0.12 h⁻¹).

These findings explain why traditional constitutive overexpression strategies fail to achieve high yields, as they cannot compensate for intrinsic kinetic disparities between pathway modules. Notably, the DXP instability we quantified (t₁/₂ = 5.8 h at pH 6.8) creates a critical time constraint for downstream conversion. This contrasts with central metabolism where intermediates like acetyl-CoA are more stable. Our phased feeding strategy mitigated this by reducing DXP degradation by 18.7%, demonstrating that temporal control can stabilize pathway intermediates more effectively than static optimization approaches.

The identified activity thresholds (0.40 and 0.75 U/mg) establish quantitative design rules for temporal control systems. The lower threshold corresponds to the minimum enzyme density required for membrane microdomain formation, while the upper threshold reflects saturation of SAM regeneration capacity. This explains why simple RBS engineering alone cannot resolve kinetic mismatches—deliberate delay is essential to accumulate sufficient UbiG for concerted action.

Bioenergetic coupling mechanisms

The discovery of ΔΨ hyperpolarization (−90 mV at 27 h) triggering a secondary synthesis phase demonstrates, for the first time in engineered E. coli, that ΔΨ hyperpolarization regulates CoQ10 biosynthesis via UbiG membrane localization. Three interconnected mechanisms emerged:

• (i)Spatial organization: Hyperpolarization increased UbiG membrane localization from 38 to 62%, enhancing access to hydrophobic substrates. This explains the 63% activity increase (1.42 vs. 0.87 U/mg) and demonstrates ΔΨ’s role as a spatial organizer beyond its classical proton-motive function.
• (ii)Energy charge modulation: The ATP/ADP ratio surge from 8.2 to 14.6 coincided with renewed CoQ10 production (133% rate increase), suggesting PMF-driven phosphorylation potentiates methylation through SAM regeneration (Popadić et al. 2021). This coupling between membrane energetics and methylation capacity represents a novel regulatory layer in isoprenoid biosynthesis. The sustained high ATP/ADP ratio (14.6) likely reflects optimized respiratory coupling under hyperpolarization (Abdelwahed et al. 2022), though feedback regulation warrants further study.
• (iii)Respiratory adaptation: The 22% increase in cytochrome bo3 oxidase activity demonstrates active respiratory remodeling to maintain ΔΨ during biosynthetic demand. This finding extends prior work on PMF modulation in B. subtilis by showing real-time respiratory adjustments in engineered systems (Liu et al. 2017).

Industrial translation through multivariate modeling

Our PLS model (R² = 0.72) bridged fundamental insights with industrial implementation by integrating 14 process variables. Three innovations enabled successful scale-up(Zhang et al. 2024):

• (i)ΔΨ as a control variable: As the dominant latent variable (loading = 0.76), membrane potential monitoring enabled real-time feeding adjustments superior to traditional DO-stat control.
• (ii)Dynamic carbon management: The strong negative correlation with acetate (−0.82) allowed preemptive glycerol limitation, reducing methylglyoxal formation by 41% compared to static feeding.
• (iii)Phase-aligned induction: The UbiG activity correlation (0.89) enabled optimal induction timing, achieving 4.3-fold higher titers than batch systems.

The phase-driven rewiring strategy developed in this work demonstrated superior industrial-scale performance over both conventional Agrobacterium tumefaciens processes and recent metabolic engineering advances, as benchmarked in 5-L bioreactors (see Results and Table 5). Most notably, our approach achieved a volumetric productivity of 13.10 mg/L/h, which is 2.4-fold higher than the best reported value in a comparable 5-L bioreactor system (Ha et al. 2007; Xiao et al. 2023). This significant enhancement is attributed to the effective temporal decoupling of the growth and production phases, which minimized metabolic burden and optimized resource allocation. Concurrently, bioenergetic coupling (membrane hyperpolarization) optimized enzyme localization and cofactor regeneration efficiency. Furthermore, the achieved carbon conversion efficiency of 82.50% underscores the strategy’s exceptional capability in redirecting carbon flux toward the target product while minimizing wasteful byproduct formation. These results collectively highlight the industrial potential and economic viability of our approach for the large-scale bioproduction of CoQ10 and other membrane-bound isoprenoids.

Table 5.

Comparison between experimental results and published data from literature

Parameter	Production Scale	Duration (hours)	Final Titer (g/L)	Carbon Efficiency	Volumetric Productivity (mg/L/h)​
This study	5-L bioreactor	48	0.63	82.5%	13.1
Xiao et al. (2023)	10-L bioreactor	50	0.273	Not reported	~ 5.46 (Calculated)
Advantage (vs. above)	-	-	130.8% higher	-	2.40-fold higher
Ha et al. (2007)	5-L bioreactor	96	0.458	Not reported	~ 4.77 (Calculated)
Advantage (vs. above)	-	​50% shorter	37.6% higher	-	​2.75-fold higher

This table is for benchmarking purposes, highlighting the performance improvement of our strategy within the broader field context. It does not claim a statistically significant difference from any specific previous study, due to heterogeneity in experimental systems and data availability across the cited literature

Limitations and future work

Although this study demonstrates the effectiveness of phase-driven rewiring in enhancing CoQ10 biosynthesis, several technical limitations remain to be addressed. First, the membrane recruitment of UbiG under hyperpolarization conditions was inferred only from indirect correlations between functional activity and membrane potential, without direct evidence of protein localization. Since no Western blot analysis of membrane fractions was performed, the efficiency of UbiG translocation to the membrane could not be quantified accurately. This reliance on activity-based proxies may overlook potential post-translational regulatory mechanisms, such as phosphorylation, that could influence its subcellular distribution.

Furthermore, at the ultrastructural level, no transmission electron microscopy (TEM) was conducted to visualize membrane microdomain reorganization during hyperpolarization or to determine whether UbiG forms spatial clusters with respiratory complexes such as cytochrome bo₃. This absence of morphological evidence prevents a definitive conclusion regarding whether the observed enhancement in UbiG activity is due to physical enzyme aggregation or to changes in membrane lipid fluidity.

In addition, although a putative voltage-sensing motif (RRFRKRR) was introduced into the engineered UbiG, its functional role was not validated through site-directed mutagenesis or electrophysiological approaches. For instance, charge-reversal mutations were not tested, and patch-clamp recordings were not performed on membranes expressing UbiG. Consequently, it remains unclear whether the observed membrane potential dependence reflects a direct sensing mechanism or is mediated indirectly through effects on other membrane-associated processes, such as ATP synthase modulation. These unresolved issues provide clear directions for future investigation.

Future perspectives

This work opens several promising research directions:

Advanced control systems

Integration of optogenetic tools with our phase-locked synchronization (PLS) model may enable light-driven coordination of enzyme expression with ΔΨ oscillations. Such dynamic control has shown potential in fine-tuning pathway regulation for enhanced efficiency.

Industrial scale-up challenges

Current efforts focus on deploying distributed ΔΨ sensor arrays to address mixing heterogeneity in large-scale fermenters (> 50 L). This leverages emerging process analytical technologies (PAT) for real-time metabolic monitoring.

Broader applicability of temporal control

The phase-driven framework demonstrates potential for other membrane-bound isoprenoids.

Conclusion

In conclusion, this work establishes phase-driven metabolic rewiring coupled with bioenergetic control as a transformative paradigm for optimizing membrane-bound isoprenoid biosynthesis in E. coli. The achieved 4.3-fold enhancement in CoQ10 titer (0.63 g/L) with exceptional carbon efficiency (82.5%) and yield (25.8%) demonstrates the power of resolving kinetic mismatches through temporal enzyme coordination and leveraging membrane potential as a key regulatory node. The integration of real-time monitoring and multivariate modeling provides a scalable framework applicable to the biomanufacturing of diverse high-value isoprenoids, paving the way for more efficient microbial cell factories.

Supplementary information

Below are the links to the electronic supplementary materials.

Author Contributions

H.L. and Y.W. conceptualized the study and designed experiments. D.Q., J.X., and W.H. performed strain construction and bioreactor experiments. R.C. and H.L. conducted computational modeling and data analysis. D.Z. developed analytical methods and validated results. All authors contributed to manuscript writing and critical revisions.

Funding

This study was funded by the Anhui Provincial Department of Education, China (Grants: GXYQZD2017136 and 2023HXKC040).

Data Availability

All datasets generated and analyzed during this study are included in the manuscript or available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.